Esper Reference

The first statement inserts all OrderEvent events into the AllOrders stream. The runtime processes the second statement before it processes the third statement since the second statement has @priority(1). For the second statement, if the order quantity is greater than 100, the runtime inserts the event into the LargeOrders stream and the runtime stop processing that AllOrders event since the second statement also has the @drop annotation. If the order quantity is 100 or less, the runtime processes the third statement and inserts the event into the SmallOrders stream.

insert into AllOrders select * from OrderEvent;
@priority(1) @drop insert into LargeOrders select * from AllOrders(orderQty > 100);
insert into SmallOrders select * from AllOrders;

5.17. Variables and Constants

A variable is a scalar, object, event or set of aggregation values that is available for use in all statements including patterns. Variables can be used in an expression anywhere in a statement as well as in the output clause for output rate limiting.

Variables must first be declared or configured before use, by defining each variable's type and name. Variables can be created via the create variable syntax or declared by runtime or static configuration. Variables can be assigned new values by using the on set syntax or via the setVariableValue methods on EPVariableService. The EPVariableService also provides method to read variable values.

A variable can be declared constant. A constant variable always has the initial value and cannot be assigned a new value. A constant variable can be used like any other variable and can be used wherever a constant is required. By declaring a variable constant you enable the runtime to optimize and perform query planning knowing that the variable value cannot change.

When declaring a class-typed, event-typed or aggregation-typed variable you may read or set individual properties within the same variable.

The runtime guarantees consistency and atomicity of variable reads and writes on the level of context partition (this is a soft guarantee, see below). Variables are optimized for fast read access and are also multithread-safe.

When you associate a context to the variable then each context partition maintains its own variable value. See Section 4.9, “Context and Variables” for more information.

Your application can only undeploy the statement that created the variable after all statements using the variables are also undeployed.

5.17.1. Creating Variables: The `Create Variable` Clause

The create variable syntax creates a new variable by defining the variable type and name. In alternative to the syntax, variables can also be declared in the configuration object.

The synopsis for creating a variable is as follows:

create [constant] variable variable_type [[]] variable_name [ = assignment_expression ]

Specify the optional constant keyword when the variable is a constant whose associated value cannot be altered. Your EPL design should prefer constant variables over non-constant variables.

The variable_type can be any of the following:

variable_type
	:  string
	|  char 
	|  character
	|  bool 
	|  boolean
	|  byte
	|  short 
	|  int 
	|  integer 
	|  long 
	|  double
	|  float
	|  object
	|  enum_class
	|  class_name
	|  event_type_name

Variable types can accept null values. The object type is for an untyped variable that can be assigned any value. You can provide a class name (use imports) or a fully-qualified class name to declare a variable of that Java class type including an enumeration class. You can also supply the name of an event type to declare a variable that holds an event of that type. Parameterized types such as java.util.Collection<String> are supported as well.

Append [] to the variable type to declare an array variable. A limitation is that if your variable type is an event type then array is not allowed (applies to variables only and not to named windows or tables). For arrays of primitives, specify [primitive], for example int[primitive].

The variable_name is an identifier that names the variable. The variable name should not already be in use by another variable.

The assignment_expression is optional. Without an assignment expression the initial value for the variable is null. If present, it supplies the initial value for the variable.

Note

When specifying a class_name or enum_class class name the class must have public visibility.

The EPStatement object of the create variable statement provides access to variable values. The pull API methods iterator and safeIterator return the current variable value. Listeners to the create variable statement subscribe to changes in variable value: the runtime posts new and old value of the variable to all listeners when the variable value is updated by an on set statement.

The example below creates a variable that provides a threshold value. The name of the variable is var_threshold and its type is long. The variable's initial value is null as no other value has been assigned:

create variable long var_threshold

This statement creates an integer-type variable named var_output_rate and initializes it to the value ten (10):

create variable integer var_output_rate = 10

The next statement declares a constant string-type variable:

create constant variable string const_filter_symbol = 'GE'

In addition to creating a variable via the create variable syntax, the configuration also allows adding variables. The next code snippet illustrates the use of the configuration API to declare a string-typed variable:

Configuration configuration = new Configuration();
configuration.getCommon()..addVariable("myVar", String.class, "init value");

The following example declares a constant that is an array of string:

create constant variable string[] const_filters = {'GE', 'MSFT'}

The next example declares a constant that is an array of enumeration values. It assumes the Color enumeration class was imported:

create constant variable Color[] const_colors = {Color.RED, Color.BLUE}

For an array of primitive-type bytes, specify the primitive keyword in square brackets, as the next example shows:

create variable byte[primitive] mybytes = SomeClass.getBytes()

Use the new keyword to initialize object instances (the example assumes the package or class was imported):

create constant variable AtomicInteger cnt = new AtomicInteger(1)

The runtime removes the variable if the deployment that created the variable is undeployed.

5.17.2. Setting Variable Values: The `On Set` Clause

The on set statement assigns a new value to one or more variables when a triggering event arrives or a triggering pattern occurs. Use the setVariableValue methods on EPVariableService to assign variable values programmatically.

The synopsis for setting variable values is:

on event_type[(filter_criteria)] [as stream_name]
  set mutation_expression  [, mutation_expression [,...]]

The event_type is the name of the type of events that trigger the variable assignments. It is optionally followed by filter_criteria which are filter expressions to apply to arriving events. The optional as keyword can be used to assign an stream name. Patterns and named windows can also be specified in the on clause.

After the set keyword follow one or more mutation expressions with variable names on the left hande side. The mutation expressions set the value of one or more variables. Subqueries may by part of expressions however aggregation functions and the prev or prior function may not be used in expressions.

The below table lists the available mutation expessions:

Table 5.8. Mutation Expressions in Update-Set for Use With Variables

Description	Syntax and Examples
Assignment	variable_name = value_expression price = 10, side = 'BUY'
Array Element Assignment	arrayvariable_name[array_index_expression] = value_expression values[position] = 10
Event Property Assignment	eventvariable_name.subproperty_name = value_expression myevent.price = 10
Any Expression, For Example: User-Defined Function Call	functionname(..., variable_name, ...) clearQuantities(variable) Requires the use of a single variable as part of each mutation expression.

All new variable values are applied atomically: the changes to variable values by the on set statement become visible to other statements all at the same time. No changes are visible to other processing threads until the on set statement completed processing, and at that time all changes become visible at once.

The EPStatement object provides access to variable values. The pull API methods iterator and safeIterator return the current variable values for each of the variables set by the statement. Listeners to the statement subscribe to changes in variable values: the runtime posts new variable values of all variables to any listeners.

In the following example, a variable by name var_output_rate has been declared previously. When a NewOutputRateEvent event arrives, the variable is updated to a new value supplied by the event property 'rate':

on NewOutputRateEvent set var_output_rate = rate

The next example shows two variables that are updated when a ThresholdUpdateEvent arrives:

on ThresholdUpdateEvent as t 
  set var_threshold_lower = t.lower,
      var_threshold_higher = t.higher

The sample statement shown next counts the number of pattern matches using a variable. The pattern looks for OrderEvent events that are followed by CancelEvent events for the same order id within 10 seconds of the OrderEvent:

on pattern[every a=OrderEvent -> (CancelEvent(orderId=a.orderId) where timer:within(10 sec))]
  set var_counter = var_counter + 1

5.17.3. Using Variables

A variable name can be used in any expression and can also occur in an output rate limiting clause. This section presents examples and discusses performance, consistency and atomicity attributes of variables.

The next statement assumes that a variable named 'var_threshold' was created to hold a total price threshold value. The statement outputs an event when the total price for a symbol is greater then the current threshold value:

select symbol, sum(price) from TickEvent 
group by symbol 
having sum(price) > var_threshold

This example uses a variable to dynamically change the output rate on-the-fly. The variable 'var_output_rate' holds the current rate at which the statement posts a current count to listeners:

select count(*) from TickEvent output every var_output_rate seconds

Variables are optimized towards high read frequency and lower write frequency. Variable reads do not incur locking overhead (99% of the time) while variable writes do incur locking overhead.

The runtime softly guarantees consistency and atomicity of variables when your statement executes in response to an event or timer invocation. Variables acquire a stable value (implemented by versioning) when your statement starts executing in response to an event or timer invocation, and variables do not change value during execution. When one or more variable values are updated via on set statements, the changes to all updated variables become visible to statements as one unit and only when the on set statement completes successfully.

The atomicity and consistency guarantee is a soft guarantee. If any of your application statements, in response to an event or timer invocation, execute for a time interval longer then 15 seconds (default interval length), then the runtime may use current variable values after 15 seconds passed, rather then then-current variable values at the time the statement started executing in response to an event or timer invocation.

The length of the time interval that variable values are held stable for the duration of execution of a given statement is by default 15 seconds, but can be configured via runtime settings.

5.17.4. Object-Type Variables

A variable of type object (or java.lang.Object via the API) can be assigned any value including null. When using an object-type variable in an expression, your statement may need to cast the value to the desired type.

The following sample EPL creates a variable by name varobj of type object:

create variable object varobj

5.17.5. Class and Event-Type Variables

The create variable syntax and the API accept a fully-qualified class name or alternatively the name of an event type. This is useful when you want a single variable to have multiple property values to read or set.

The next statement assumes that the event type PageHitEvent is declared:

create variable PageHitEvent varPageHitZero

These example statements show two ways of assigning to the variable:

// You may assign the complete event
on PageHitEvent(ip='0.0.0.0') pagehit set varPageHitZero = pagehit

// Or assign individual properties of the event
on PageHitEvent(ip='0.0.0.0') pagehit set varPageHitZero.userId = pagehit.userId

select * from FirewallEvent(userId=varPageHitZero.userId)

Instance method can also be invoked:

create variable com.example.StateCheckerService stateChecker

select * from TestEvent as e where stateChecker.checkState(e)

A variable that represents a service for calling instance methods could be initialized by calling a factory method. This example assumes the classes were added to imports:

create constant variable StateCheckerService stateChecker = StateCheckerServiceFactory.makeService()

You can add a variable via the configuration API; an example code snippet is next:

Configuration configuration = new Configuration();
configuration.getCommon().addVariable("stateChecker", StateCheckerService.class, StateCheckerServiceFactory.makeService(), true);

Application objects can also be passed via transient configuration information as described in Section 17.7, “Passing Services or Transient Objects”.

Note

When using non-constant class or event-type variables and when your EPL intends to set property values on the variable itself (i.e. set varPageHitZero.userId), please note the following requirements. In order for the runtime to assign property values, the underlying event type must allow writing property values. If using JavaBean event classes the class must have setter methods and a default constructor. The underlying event type must also be copy-able i.e. implement Serializable or configure a copy method (only for non-constant variables and when setting property values).

5.18. Declaring Global Expressions, Aliases and Scripts: Create Expression

Your application can declare an expression or script using the create expression clause. Such expressions or scripts become available globally to any statement.

The synopsis of the create expression syntax is:

create expression expression_or_script

Use the create expression keywords and append the expression or scripts.

Expression aliases are the simplest means of sharing expressions and do not accept parameters. Expression declarations limit the expression scope to the parameters that are passed.

The runtime may cache declared expression result values and reuse cache values, see Section 17.6.10.6, “Declared Expression Value Cache Size”.

5.18.1. Global Expression Aliases

The syntax and additional examples for declaring an expression is outlined in Section 5.2.8, “Expression Alias”, which discusses expression aliases that are visible within the same statement i.e. visible locally only.

When using the create expression syntax to declare an expression the runtime remembers the expression alias and expression and allows the alias to be referenced in all other statements.

The below EPL declares a globally visible expression alias for an expression that computes the total of the mid-price which is the buy and sell price divided by two:

create expression totalMidPrice alias for { sum((buy + sell) / 2) }

The next EPL returns mid-price for events for which the mid-price per symbol stays below 10:

select symbol, midPrice from MarketDataEvent group by symbol having midPrice < 10

The expression name must be unique among all other expression aliases and expression declarations.

Your application can provide an expression alias of the same name local to a given statement as well as globally using create expression. The locally-provided expression alias overrides the global expression alias.

The compiler validates global expression aliases at the time your application creates a statement that references the alias. When a statement references a global alias, the compiler uses the that statement's local expression scope to validate the expression. Expression aliases can therefore be dynamically typed and type information does not need to be the same for all statements that reference the expression alias.

5.18.2. Global Expression Declarations

The syntax and additional examples for declaring an expression is outlined in Section 5.2.9, “Expression Declaration”, which discusses declaring expressions that are visible within the same statement i.e. visible locally only.

When using the create expression syntax to declare an expression the compiler remembers the expression and allows the expression to be referenced in all other statements.

The below EPL declares a globally visible expression that computes a mid-price and that requires a single parameter:

create expression midPrice { in => (buy + sell) / 2 }

The next EPL returns mid-price for each event:

select midPrice(md) from MarketDataEvent as md

The expression name must be unique for global expressions. It is not possible to declare the same global expression twice with the same name.

Your application can declare an expression of the same name local to a given statement as well as globally using create expression. The locally-declared expression overrides the globally declared expression.

The compiler validates globally declared expressions at the time your application creates a statement that references the global expression. When a statement references a global expression, the compiler uses that statement's type information to validate the global expressions. Global expressions can therefore be dynamically typed and type information does not need to be the same for all statements that reference the global expression.

This example shows a sequence of EPL, that can be created in the order shown, and that demonstrates expression validation at time of referral:

create expression minPrice {(select min(price) from OrderWindow)}

create window OrderWindow#time(30) as OrderEvent

insert into OrderWindow select * from OrderEvent

// Validates and incorporates the declared global expression
select minPrice() as minprice from MarketData

5.18.3. Global Scripts

The syntax and additional examples for declaring scripts is outlined in Chapter 19, Script Support, which discusses declaring scripts that are visible within the same statement i.e. visible locally only.

When using the create expression syntax to declare a script the compiler remembers the script and allows the script to be referenced in all other statements.

The below EPL declares a globally visible script in the JavaScript dialect that computes a mid-price:

create expression midPrice(buy, sell) [ (buy + sell) / 2 ]

The next EPL returns mid-price for each event:

select midPrice(buy, sell) from MarketDataEvent

The compiler validates globally declared scripts at the time your application creates a statement that references the global script. When a statement references a global script, the compiler uses that statement's type information to determine parameter types. Global scripts can therefore be dynamically typed and type information does not need to be the same for all statements that reference the global script.

The script name in combination with the number of parameters must be unique for global scripts. It is not possible to declare the same global script twice with the same name and number of parameters.

Your application can declare a script of the same name and number of parameters that is local to a given statement as well as globally using create expression. The locally-declared script overrides the globally declared script.

5.19. Contained-Event Selection

Contained-event selection is for use when an event contains properties that are themselves events, or more generally when your application needs to split or explode an event into multiple events. One example is when application events are coarse-grained structures and you need to perform bulk operations on the rows of the property graph in an event.

Use the contained-event selection syntax in a filter expression such as in a pattern, from clause, subselect, on-select and on-delete. This section provides the synopsis and examples.

To review, in the from clause a contained_selection may appear after the event stream name and filter criteria, and before any data windows.

The synopsis for contained_selection is as follows:

[select select_expressions from] 
  contained_expression [@type(eventtype_name)] [as alias_name]
  [where filter_expression]

The select clause and select_expressions are optional and may be used to select specific properties of contained events.

The contained_expression is required and returns individual events. The expression can, for example, be an event property name that returns an event fragment, i.e. a property that can itself be represented as an event by the underlying event representation. The expression can also be any other expression such as a single-row function or a script that returns either an array or a java.util.Collection of events. Simple values such as integer or string are not fragments but can be used as well as described below.

Provide the @type(name) annotation after the contained expression to name the event type of events returned by the expression. The annotation is optional and not needed when the contained-expression is an event property that returns a class or other event fragment.

The alias_name can be provided to assign a name to the expression result value rows.

The where clause and filter_expression is optional and may be used to filter out properties.

As an example event, consider a media order. A media order consists of order items as well as product descriptions. A media order event can be represented as an object graph (POJO event representation), or a structure of nested Maps (Map event representation) or a XML document (XML DOM or Axiom event representation) or other custom plug-in event representation.

To illustrate, a sample media order event in XML event representation is shown below. Also, a XML event type can optionally be strongly-typed with an explicit XML XSD schema that is not shown here. Note that Map and POJO representation can be considered equivalent for the purpose of this example.

Let us now assume the event type MediaOrder as being represented by the root node <mediaorder> of such XML snip:

<mediaorder>
  <orderId>PO200901</orderId>
  <items>
    <item>
      <itemId>100001</itemId>
      <productId>B001</productId>
      <amount>10</amount>
      <price>11.95</price>
    </item>
  </items>
  <books>
    <book>
      <bookId>B001</bookId>
      <author>Heinlein</author>
      <review>
        <reviewId>1</reviewId>
        <comment>best book ever</comment>
      </review>
    </book>
    <book>
      <bookId>B002</bookId>
      <author>Isaac Asimov</author>
    </book>
  </books>
</mediaorder>

The next statement utilizes the contained-event selection syntax to return each book:

select * from MediaOrder[books.book]

The result of the above statement is one event per book. Output events contain only the book properties and not any of the mediaorder-level properties.

Note that, when using listeners, the runtime delivers multiple results in one invocation of each listener. Therefore listeners to the above statement can expect a single invocation passing all book events within one media order event as an array.

To better illustrate the position of the contained-event selection syntax in a statement, consider the next two statements:

select * from MediaOrder(orderId='PO200901')[books.book]

The above statement the returns each book only for media orders with a given order id. This statement illustrates a contained-event selection and a data window:

select count(*) from MediaOrder[books.book]#unique(bookId)

The sample above counts each book unique by book id.

Contained-event selection can be staggered. When staggering multiple contained-event selections the staggered contained-event selection is relative to its parent.

This example demonstrates staggering contained-event selections by selecting each review of each book:

select * from MediaOrder[books.book][review]

Listeners to the statement above receive a row for each review of each book. Output events contain only the review properties and not the book or media order properties.

The following is not valid:

// not valid
select * from MediaOrder[books.book.review]

The book property in an indexed property (an array or collection) and thereby requires an index in order to determine which book to use. The expression books.book[1].review is valid and means all reviews of the second (index 1) book.

The contained-event selection syntax is part of the filter expression and may therefore occur in patterns and anywhere a filter expression is valid.

A pattern example is below. The example assumes that a Cancel event type has been defined that also has an orderId property:

select * from pattern [c=Cancel -> books=MediaOrder(orderId = c.orderId)[books.book] ]

When used in a pattern, a filter with a contained-event selection returns an array of events, similar to the match-until clause in patterns. The above statement returns, in the books property, an array of book events.

5.19.1. Select-Clause in a Contained-Event Selection

The optional select clause provides control over which fields are available in output events. The expressions in the select-clause apply only to the properties available underneath the property in the from clause, and the properties of the enclosing event.

When no select is specified, only the properties underneath the selected property are available in output events.

In summary, the select clause may contain:

Any expressions, wherein properties are resolved relative to the property in the from clause.
Use the wildcard (*) to provide all properties that exist under the property in the from clause.
Use the alias_name.* syntax to provide all properties that exist under a property in the from clause.

The next statement's select clause selects each review for each book, and the order id as well as the book id of each book:

select * from MediaOrder[select orderId, bookId from books.book][select * from review]
// ... equivalent to ...
select * from MediaOrder[select orderId, bookId from books.book][review]]

Listeners to the statement above receive an event for each review of each book. Each output event has all properties of the review row, and in addition the bookId of each book and the orderId of the order. Thus bookId and orderId are found in each result event, duplicated when there are multiple reviews per book and order.

The above statement uses wildcard (*) to select all properties from reviews. As has been discussed as part of the select clause, the wildcard (*) and property_alias.* do not copy properties for performance reasons. The wildcard syntax instead specifies the underlying type, and additional properties are added onto that underlying type if required. Only one wildcard (*) and property_alias.* (unless used with a column rename) may therefore occur in the select clause list of expressions.

All the following statements produce an output event for each review of each book. The next sample statements illustrate the options available to control the fields of output events.

The output events produced by the next statement have all properties of each review and no other properties available:

select * from MediaOrder[books.book][review]

The following statement is not a valid statement, since the order id and book id are not part of the contained-event selection:

// Invalid select-clause: orderId and bookId not produced.
select orderId, bookId from MediaOrder[books.book][review]

This statement is valid. Note that output events carry only the orderId and bookId properties and no other data:

select orderId, bookId from MediaOrder[books.book][select orderId, bookId from review]
//... equivalent to ...
select * from MediaOrder[select orderId, bookId from books.book][review]

This variation produces output events that have all properties of each book and only reviewId and comment for each review:

select * from MediaOrder[select * from books.book][select reviewId, comment from review]
// ... equivalent to ...
select * from MediaOrder[books.book as book][select book.*, reviewId, comment from review]

The output events of the next EPL have all properties of the order and only bookId and reviewId for each review:

select * from MediaOrder[books.book as book]
    [select mediaOrder.*, bookId, reviewId from review] as mediaOrder

This EPL produces output events with 3 columns: a column named mediaOrder that is the order itself, a column named book for each book and a column named review that holds each review:

insert into ReviewStream select * from MediaOrder[books.book as book]
  [select mo.* as mediaOrder, book.* as book, review.* as review from review as review] as mo

// .. and a sample consumer of ReviewStream...
select mediaOrder.orderId, book.bookId, review.reviewId from ReviewStream

Please note these limitations:

Sub-selects, aggregation functions and the prev and prior operators are not available in contained-event selection.
Expressions in the select and where clause of a contained-event selection can only reference properties relative to the current event and property.

5.19.2. Where Clause in a Contained-Event Selection

The optional where clause may be used to filter out properties at the same level that the where-clause occurs.

The properties in the filter expression must be relative to the property in the from clause or the enclosing event.

This statement outputs all books with a given author:

select * from MediaOrder[books.book where author = 'Heinlein']

This statement outputs each review of each book where a review comment contains the word 'good':

select * from MediaOrder[books.book][review where comment like 'good']

5.19.3. Contained-Event Selection and Joins

This section discusses contained-event selection in joins.

When joining within the same event it is not required to specify a data window. Recall, in a join or outer join there must be a data window specified that defines the subset of events available to be joined. For self-joins, no data window is required and the join executes against the data returned by the same event.

This statement inner-joins items to books where book id matches the product id:

select book.bookId, item.itemId 
from MediaOrder[books.book] as book, 
      MediaOrder[items.item] as item 
where productId = bookId

Statement results for the above statement when sending the media order event as shown earlier are:

book.bookId	item.itemId
B001	100001

The next example statement is a left outer join. It returns all books and their items, and for books without item it returns the book and a null value:

select book.bookId, item.itemId 
from MediaOrder[books.book] as book 
  left outer join 
    MediaOrder[items.item] as item 
  on productId = bookId

Statement results for the above statement when sending the media order event as shown earlier are:

book.bookId	item.itemId
B001	100001
B002	null

A full outer join combines the results of both left and right outer joins. The joined table will contain all records from both tables, and fill in null values for missing matches on either side.

This example statement is a full outer join, returning all books as well as all items, and filling in null values for book id or item id if no match is found:

select orderId, book.bookId,item.itemId 
from MediaOrder[books.book] as book 
  full outer join 
     MediaOrder[select orderId, * from items.item] as item 
  on productId = bookId 
order by bookId, item.itemId asc

As in all other statements, aggregation results are cumulative from the time the statement was created.

The following statement counts the cumulative number of items in which the product id matches a book id:

select count(*) 
from MediaOrder[books.book] as book, 
      MediaOrder[items.item] as item 
where productId = bookId

The unidirectional keyword in a join indicates to the runtime that aggregation state is not cumulative. The next statement counts the number of items in which the product id matches a book id for each event:

select count(*) 
from MediaOrder[books.book] as book unidirectional, 
      MediaOrder[items.item] as item 
where productId = bookId

5.19.4. Sentence and Word Example

The next example splits an event representing a sentence into multiple events in which each event represents a word. It represents all events and the logic to split events into contained events as Java code. The next chapter has additional examples that use Map-type events and put contained-event logic into a separate expression or script.

The sentence event in this example is represented by a class declared as follows:

public class SentenceEvent {
  private final String sentence;

  public SentenceEvent(String sentence) {
    this.sentence = sentence;
  }

  public WordEvent[] getWords() {
    String[] split = sentence.split(" ");
    WordEvent[] words = new WordEvent[split.length];
    for (int i = 0; i < split.length; i++) {
      words[i] = new WordEvent(split[i]);
    }
    return words;
  }
}

The sentence event as above provides an event property words that returns each word event.

The declaration of word event is also a class:

public class WordEvent {
  private final String word;

  public WordEvent(String word) {
    this.word = word;
  }

  public String getWord() {
    return word;
  }
}

The statement to populate a stream of words from a sentence event is:

insert into WordStream select * from SentenceEvent[words]

Finally, the API call to send a sentence event to the runtime is shown here:

runtime.getEventService().sendEventBean(new SentenceEvent("Hello Word Contained Events"), "SentenceEvent");

5.19.5. More Examples

The examples herein are not based on the POJO events of the prior example. They are meant to demonstrate different types of contained-event expressions and the use of @type(type_name) to identify the event type of the return values of the contained-event expression.

The example first defines a few sample event types:

create schema SentenceEvent(sentence String)

create schema WordEvent(word String)

create schema CharacterEvent(char String)

The following EPL assumes that your application defined a plug-in single-row function by name splitSentence that returns an array of Map, producting output events that are WordEvent events:

insert into WordStream select * from SentenceEvent[splitSentence(sentence)@type(WordEvent)]

The example EPL shown next invokes a JavaScript function which returns some events of type WordEvent:

expression Collection js:splitSentenceJS(sentence) [
  var CollectionsClazz = Java.type('java.util.Collections');
  var words = new java.util.ArrayList();
  words.add(CollectionsClazz.singletonMap('word', 'wordOne'));
  words.add(CollectionsClazz.singletonMap('word', 'wordTwo'));
  words;
]
select * from SentenceEvent[splitSentenceJS(sentence)@type(WordEvent)]

In the next example the sentence event first gets split into words and then each word event gets split into character events via an additional splitWord single-row function, producing events of type CharacterEvent:

select * from SentenceEvent
  [splitSentence(sentence)@type(WordEvent)]
  [splitWord(word)@type(CharacterEvent)]

5.19.6. Contained Expression Returning an Array of Property Values

Your contained_expression may return an array of property values such as an array of integer or string values. In this case you must specify a @type(name) annotation and provide an event type name that declares a single column with a type that matches the array component type.

create schema IdContainer(id int)

create schema MyEvent(ids int[])

select * from MyEvent[ids@type(IdContainer)]

This example declares a named window and uses on-delete:

create window MyWindow#keepall (id int)

on MyEvent[ids@type(IdContainer)] as my_ids 
delete from MyWindow my_window 
where my_ids.id = my_window.id

5.19.7. Contained Expression Returning an Array of EventBean

Your contained_expression may return an array of EventBean instances. This is handy when the expression itself must decide the type of each event to return.

Then declare mySplitFunction returning an array of events, such as:

create schema BaseEvent();

create schema AEvent(pa string) inherits BaseEvent;

create schema BEvent(pb string) inherits BaseEvent;

create schema ValueEvent(value string);

select * from ValueEvent[mySplitFunction(value) @type(BaseEvent)]

public static EventBean[] mySplitFunction(String value, EPLMethodInvocationContext context) {
  EventBean[] events = new EventBean[1];
  if (value.startsWith("A")) {
    events[0] =  context.getEventBeanService().adapterForMap(Collections.singletonMap("pa", value), "AEvent");
  }
  else {
    events[0] =  context.getEventBeanService().adapterForMap(Collections.singletonMap("pb", value), "BEvent");
  }
  return events;
}

5.19.8. Contained Expression Returning a Collection of Underlying Event Objects

Your contained_expression may return a collection of underlying event instances.

In this example the selectFrom enumeration method formats subquery results and returns a collection of Map instances. Since Map is the underlying event object for Map-type event types, each Map with the room id can be processed as an event by the contained event syntax.

// Declare Person and Room event types, represented as java.util.Map events (the default)
@public @buseventtype create schema Person(personId string);
@public @buseventtype create schema Room(roomId string);

// Have a window that holds all rooms
create window RoomWindow#keepall as Room;
insert into RoomWindow select * from Room;

// Output all rooms ids as a collection of maps into the "rooms" column 
insert into PersonAndRooms select personId, (select roomId from RoomWindow).selectFrom(v => new {roomId = v}) as rooms from Person;

// Select each combination of person and room
@Name('s0') select personId, roomId from PersonAndRooms[select personId, roomId from rooms@type(Room)];

5.19.9. Generating Marker Events Such as a Begin and End Event

The syntax for splitting and duplicating streams can be used to generate marker events. Please see Section 5.16.1, “Generating Marker Events for Contained Events” for more information.

5.19.10. Contained-Event Limitations

The following restrictions apply to contained-event selection:

When selecting contained events from a named window in a join, the stream must be marked as unidirectional.
Selecting contained events from a named window in a correlated subquery is not allowed.

5.20. Updating an Insert Stream: The `Update IStream` Clause

The update istream statement allows declarative modification of event properties of events entering a stream. Update is a pre-processing step to each new event, modifying an event before the event applies to any statements.

The synopsis of update istream is as follows:

update istream event_type [as stream_name]
  set mutation_expression [, mutation_expression] [,...]
  [where where_expression]

The event_type is the name of the type of events that the update applies to. The optional as keyword can be used to assign a name to the event type for use with subqueries, for example.

After the set keyword follow one or more mutation expressions. The below table lists the available mutation expessions:

Table 5.9. Mutation Expressions in Update IStream

Description

Syntax and Examples

Assignment

property_name = value_expression

price = 10, side = 'BUY'

Array Element Assignment

arrayproperty_name[array_index_expression] = value_expression

values[position] = 10

Any Expression, For Example: User-Defined Function Call

functionname(alias)

clearQuantities(myevent)

The optional where clause and expression can be used to filter out events to which to apply updates.

Listeners to an update statement receive the updated event in the insert stream (new data) and the event prior to the update in the remove stream (old data). Note that if there are multiple update statements that all apply to the same event then the runtime will ensure that the output events delivered to listeners or subscribers are consistent with the then-current updated properties of the event (if necessary making event copies, as described below, in the case that listeners are attached to update statements). Iterating over an update statement returns no events.

As an example, the below statement assumes an AlertEvent event type that has properties named severity and reason:

update istream AlertEvent 
  set severity = 'High'
  where severity = 'Medium' and reason like '%withdrawal limit%'

The statement above changes the value of the severity property to "High" for AlertEvent events that have a medium severity and contain a specific reason text.

Update statements apply the changes to event properties before other statements receive the event(s) for processing, e.g. "select * from AlertEvent" receives the updated AlertEvent. This is true regardless of the order in which your application creates statements.

When multiple update statements apply to the same event, the runtime executes updates in the order in which update statements were deployed. We recommend the @Priority EPL annotation to define a deterministic order of processing updates, especially in the case where update statements get deployed and undeployed dynamically or multiple update statements update the same fields. The update statement with the highest @Priority value applies last.

The update clause can be used on streams populated via insert into, as this example utilizing a pattern demonstrates:

insert into DoubleWithdrawalStream 
select a.id, b.id, a.account as account, 0 as minimum 
from pattern [a=Withdrawal -> b=Withdrawal(id = a.id)]

update istream DoubleWithdrawalStream set minimum = 1000 where account in (10002, 10003)

When using update istream with named windows, any changes to event properties apply before an event enters the named window. The update istream is not available for tables.

Consider the next example (shown here with statement names in @Name EPL annotation, multiple statements):

@Name("CreateWindow") create window MyWindow#time(30 sec) as AlertEvent

@Name("UpdateStream") update istream MyWindow set severity = 'Low' where reason = '%out of paper%'

@Name("InsertWindow") insert into MyWindow select * from AlertEvent

@Name("SelectWindow") select * from MyWindow

The UpdateStream statement specifies an update clause that applies to all events entering the named window. Note that update does not apply to events already in the named window at the time an application creates the UpdateStream statement, it only applies to new events entering the named window (after an application created the update statement).

Therefore, in the above example listeners to the SelectWindow statement as well as the CreateWindow statement receive the updated event, while listeners to the InsertWindow statement receive the original AlertEvent event (and not the updated event).

Subqueries can also be used in all expressions including the optional where clause.

This example demonstrates a correlated subquery in an assignment expression and also demonstrates the optional as keyword. It assigns the phone property of an AlertEvent event a new value based on the lookup within all unique PhoneEvent events (according to an empid property) correlating the AlertEvent property reporter with the empid property of PhoneEvent:

update istream AlertEvent as ae
  set phone = 
    (select phone from PhoneEvent#unique(empid) where empid = ae.reporter)

When updating indexed properties use the syntax propertyName[index] = value with the index value being an integer number. When updating mapped properties use the syntax propertyName(key) = value with the key being a string value.

When using update, please note these limitations:

Expressions may not use aggregation functions.
The prev and prior functions may not be used.
For underlying event representations that are Java objects, a event object class must implement the java.io.Serializable interface as discussed below.
When using an XML underlying event type, event properties in the XML document representation are not available for update.
Nested properties are not supported for update. Variant streams may also not be updated.
update istream is a pre-processing step and updates take place before other statements see the event; Therefore, when using @priority, the priority assigned to the update istream statement only prioritizes the statement among other update istream statements.

5.20.1. Immutability and Updates

When updating event objects the runtime maintains consistency across statements. The runtime ensures that an update to an event does not impact the results of statements that look for or retain the original un-updated event. As a result the runtime may need to copy an event object to maintain consistency.

In the case your application utilizes Java objects as the underlying event representation and an update statement updates properties on an object, then in order to maintain consistency across statements it is necessary for the runtime to copy the object before changing properties (and thus not change the original object).

For Java application objects, the copy operation is implemented by serialization. Your event object must therefore implement the java.io.Serializable interface to become eligible for update. As an alternative to serialization, you may instead configure a copy method as part of the event type configuration via ConfigurationEventTypeLegacy.

5.21. Controlling Event Delivery : The `For` Clause

The runtime delivers all result events of a given statement to the statement's listeners and subscriber (if any) in a single invocation of each listener and subscriber's update method passing an array of result events. For example, a statement using a time-batch window may provide many result events after a time period passes, a pattern may provide multiple matching events or in a join the join cardinality could be multiple rows.

For statements that typically post multiple result events to listeners the for keyword controls the number of invocations of the runtime to listeners and subscribers and the subset of all result events delivered by each invocation. This can be useful when your application listener or subscriber code expects multiple invocations or expects that invocations only receive events that belong together by some additional criteria.

The for keyword is a reserved keyword. It is followed by either the grouped_delivery keyword for grouped delivery or the discrete_delivery keyword for discrete delivery. The for clause is valid after any EPL select statement.

The synopsis for grouped delivery is as follows:

... for grouped_delivery (group_expression [, group_expression] [,...])

The group_expression expression list provides one or more expressions to apply to result events. The runtime invokes listeners and subscribers once for each distinct set of values returned by group_expression expressions passing only the events for that group. Further detail on key expressions can be found at Section 5.2.13, “Composite Keys and Array Values as Keys”.

The synopsis for discrete delivery is as follows:

... for discrete_delivery

With discrete delivery the runtime invokes listeners and subscribers once for each result event passing a single result event in each invocation.

Consider the following example without for-clause. The time batch data window collects RFIDEvent events for 10 seconds and posts an array of result events:

select * from RFIDEvent#time_batch(10 sec)

Let's consider an example event sequence as follows:

Table 5.10. Sample Sequence of Events for For Keyword

Event
RFIDEvent(id:1, zone:'A')
RFIDEvent(id:2, zone:'B')
RFIDEvent(id:3, zone:'A')

Without for-clause and after the 10-second time period passes, the runtime delivers an array of 3 events in a single invocation to listeners and the subscriber.

The next example specifies the for-clause and grouped delivery by zone:

select * from RFIDEvent#time_batch(10 sec) for grouped_delivery (zone)

With grouped delivery and after the 10-second time period passes, the above statement delivers result events in two invocations to listeners and the subscriber: The first invocation delivers an array of two events that contains zone A events with id 1 and 3. The second invocation delivers an array of 1 event that contains a zone B event with id 2.

The next example specifies the for-clause and discrete delivery:

select * from RFIDEvent#time_batch(10 sec) for discrete_delivery

With discrete delivery and after the 10-second time period passes, the above statement delivers result events in three invocations to listeners and the subscriber: The first invocation delivers an array of 1 event that contains the event with id 1, the second invocation delivers an array of 1 event that contains the event with id 2 and the third invocation delivers an array of 1 event that contains the event with id 3.

Remove stream events are also delivered in multiple invocations, one for each group, if your statement selects remove stream events explicitly via irstream or rstream keywords.

The insert into for inserting events into a stream is not affected by the for-clause.

The delivery order respects the natural sort order or the explicit sort order as provided by the order by clause, if present.

The following are known limitations:

The compiler validates group_expression expressions against the output event type, therefore all properties specified in group_expression expressions must occur in the select clause.

Chapter 6. EPL Reference: Named Windows and Tables

6.1. Overview

6.1.1. Named Window Overview
6.1.2. Table Overview
6.1.3. Comparing Named Windows and Tables

6.2. Named Window Usage

6.2.1. Creating Named Windows: The Create Window clause
6.2.2. Inserting Into Named Windows
6.2.3. Selecting From Named Windows

6.3. Table Usage

6.3.1. Creating Tables: The Create Table Clause
6.3.2. Aggregating Into Table Rows: The Into Table Clause
6.3.3. Table Column Keyed-Access Expressions
6.3.4. Inserting Into Tables
6.3.5. Selecting From Tables
6.3.6. Resetting Table Columns and Aggregation State
6.3.7. Initializing Table Columns and Aggregation State

6.4. Triggered Select: The On Select Clause

6.4.1. Notes on On-Select With Named Windows
6.4.2. Notes on On-Select With Tables
6.4.3. On-Select Compared to Join

6.5. Triggered Select+Delete: The On Select Delete Clause

6.6. Updating Data: The On Update Clause

6.6.1. Notes on On-Update With Named Windows
6.6.2. Notes on On-Update With Tables

6.7. Deleting Data: The On Delete Clause

6.7.1. Using Patterns in the On-Delete Clause
6.7.2. Silent Delete With Named Windows
6.7.3. Notes on On-Delete With Named Windows
6.7.4. Notes on On-Update With Tables

6.8. Triggered Upsert Using the On-Merge Clause

6.8.1. On-Merge Insert
6.8.2. On-Merge Update, Delete, Select (Upsert)
6.8.3. Notes on On-Merge With Named Windows
6.8.4. Notes on On-Merge With Tables

6.9. Explicitly Indexing Named Windows and Tables

6.10. Using Fire-and-Forget Queries With Named Windows and Tables

6.10.1. Inserting Data
6.10.2. Updating Data
6.10.3. Deleting Data

6.11. Events as Property

6.1. Overview

A named window is a globally-visible data window. A table is a globally-visible data structure organized by primary key or keys.

Named windows and tables both offer a way to share state between statements and are stateful. Named windows and tables have differing capabilities and semantics.

To query a named window or table, simply use the named window name or table name in the from clause of your statement, including statements that contain subqueries, joins and outer-joins.

Certain clauses operate on either a named window or a table, namely the on-merge, on-update, on-delete and on-select clauses. The fire-and-forget queries also operate on both named windows and tables.

Both named windows and tables can have columns that hold events as column values, as further described in Section 6.11, “Events as Property”.

6.1.1. Named Window Overview

Named Windows are introduced in Section 2.14.1, “Named Windows”.

A named window is a global data window that can take part in many statements and that can be inserted-into and deleted-from by multiple statements. A named window holds events of the same type or supertype, unless used with a variant stream.

The create window clause declares a new named window. The named window starts up empty unless populated from an existing named window at time of deployment. Events must be inserted into the named window using the insert into clause. Events can also be deleted from a named window via the on delete clause.

Events enter the named window by means of insert into clause of a select statement. Events leave a named window either because the expiry policy of the declared data window removes events from the named window, or through statements that use the on delete clause to explicitly delete from a named window.

A named window may also decorate an event to preserve original events as described in Section 5.10.4, “Decorated Events” and Section 6.2.2.1, “Named Windows Holding Decorated Events”.

To tune subquery performance when the subquery selects from a named window, consider the hints discussed in Section 5.11.8, “Hints Related to Subqueries”.

6.1.2. Table Overview

Tables are introduced in Section 2.14.2, “Tables”.

A table is a data structure that is globally visible and that holds state.

The columns of a table can store aggregation state, allowing for co-location of event data with aggregation state. Other statements can directly create and update the shared aggregation state. Statements can also query the aggregation state conveniently. Aggregation state can include comprehensive state such as for example a large matrix of long-type values for use in a Count-min sketch approximation. Common aggregation state can be updated by multiple statements.

Use the create table clause to declare a new table.

The atomicity guarantees under multi-threaded evaluation are as follows. For a given statement, a table row or rows either exists or do not exist, consistently, for the duration of the evaluation of an event or timer against a context partition of a statement. The same is true for updates in that for a given context partition of a statement, each table row is either completely updated or not updated at all for the duration of an evaluation. Stream-level filter expressions against tables are not part of statement evaluation and the same atomicity applies to stream-level filter expressions.

6.1.3. Comparing Named Windows and Tables

As a general rule-of-thumb, if you need to share a data window between statements, the named window is the right approach. If however rows are organized by primary key or hold aggregation state, a table may be preferable. Statements allow the combined use of both.

In summary the difference comes from named windows holding immutable events, versus tables which hold update-in-place rows and columns that can hold plain values and complex aggregation state side-by-side.

6.1.3.1. Nature of Data

One important difference between named windows and tables is in the data that a row holds: While named windows hold events, tables can hold additional derived state.

For example, a table column can hold rich derived state such as a distinct values set and rich aggregation state such as the state of a Count-min sketch approximation aggregation (a large matrix of long-type values).

// Declare a table to hold a Count-min sketch approximate count per feed
create table AppoximateCountPerWord (feed string, approx countMinSketch())

6.1.3.2. Data Organization

A second difference between named windows and tables is the organization of rows. For named windows, the organization of rows follows the data window declaration. Tables, on the other hand, can be organized by a primary key or by multiple primary keys that make up a compound key.

For example, if your declaration specifies a sliding time window to hold 10 seconds of stock tick events then the rows are held in a sliding time window, i.e. a list or queue according to arrival order.

// Declare a named window to hold 10 seconds of stock tick events
create window TenSecOfTicksWindow#time(10 sec) as StockTickEvent

An iterator for a named window returns rows in the order as provided by the data window(s) declared for the named window. An iterator for a table returns rows in an unpredictable order.

6.1.3.3. Insert and Remove Stream

Only named windows provide an insert and remove stream to other statements. Tables do not provide an insert and remove stream.

For example, considering the TenSecOfTicksWindow named window declared above, the following statement outputs the current count each time events enter or leave the named window.

select count(*) from TenSecOfTicksWindow

Also for example, considering the AppoximateCountPerWord table declared above, the following EPL does not output any rows when table rows gets inserted, updated or deleted and only outputs rows when the statement is iterated:

// does not continously output for table changes
select * from AppoximateCountPerWord

6.1.3.4. Immutability and Copy-On-Write

As named windows hold events and events are immutable, when an update statement updates events held in a named window, the runtime performs a logical copy operation (copy-on-write, as configured for the type) of each updated event, and only modifies the newly created event, preserving the immutable original event.

Data in tables are updated in-place. There is no copy operation for table rows.

6.1.3.5. Removal of Rows

For named windows, the data window declared for the named window instructs the runtime to expire and remove events from the named window. Events can also be removed via on-merge, on-delete and fire-and-forget delete.

For tables, row can only be removed via on-merge, on-delete, on-select-and-delete and fire-and-forget delete.

6.2. Named Window Usage

6.2.1. Creating Named Windows: The `Create Window` clause

The create window statement creates a named window by specifying a window name and one or more data windows, as well as the type of event to hold in the named window.

There are two syntaxes for creating a named window: The first syntax allows modeling a named window after an existing event type or an existing named window. The second syntax is similar to the SQL create-table syntax and provides a list of column names and column types.

A new named window starts up empty. It must be explicitly inserted into by one or more statements, as discussed below. A named window can also be populated at time of creation from an existing named window.

Your application can only undeploy the statement that created the named window after all other statements that use the named window are also undeployed.

The create window statement posts to listeners any events that are inserted into the named window as new data. The statement posts all deleted events or events that expire out of the data window to listeners as the remove stream (old data). The named window contents can also be iterated on via the pull API to obtain the current contents of a named window.

6.2.1.1. Creation by Modeling After an Existing Type

The benefit of modeling a named window after an existing event type is that event properties can be nested, indexed, mapped or other types that your event objects may provide as properties, including the type of the underlying event itself. Also, using the wildcard (*) operator means your EPL does not need to list each individual property explicitly.

The syntax for creating a named window by modeling the named window after an existing event type, is as follows:

[context context_name] 
		create window window_name.window_spec 
		[as] [select list_of_properties from] event_type_or_windowname
		[insert [where filter_expression]]

The window_name you assign to the named window can be any identifier. The name should not already be in use as an event type or stream name or table name.

The window_spec are one or more data windows that define the expiry policy for removing events from the named window. Named windows must explicitly declare a data window. This is required to ensure that the policy for retaining events in the data window is well defined. To keep all events, use the keep-all window: It indicates that the named window should keep all events and only remove events from the named window that are deleted by on delete, on merge or fire-and-forget delete. Data windows are listed in Chapter 14, EPL Reference: Data Windows.

The select clause and list_of_properties are optional. If present, they specify the column names and, implicitly by definition of the event type, the column types of events held by the named window. Expressions other than column names are not allowed in the select list of properties. Wildcards (*) and wildcards with additional properties can also be used.

The event_type_or_windowname is required if using the model-after syntax. It provides the name of the event type of events held in the data window, unless column names and types have been explicitly selected via select. The name of an (existing) other named window is also allowed here. Please find more details in Section 6.2.1.4, “Populating a Named Window From an Existing Named Window”.

Finally, the insert clause and optional filter_expression are used if the new named window is modelled after an existing named window, and the data of the new named window is to be populated from the existing named window upon deployment. The optional filter_expression can be used to exclude events.

You may refer to a context by specifying the context keyword followed by a context name. Contexts are described in more detail at Chapter 4, Context and Context Partitions. The effect of referring to a context is that your named window operates according to the context dimensional information as declared for the context. For usage and limitations please see the respective chapter.

The next statement creates a named window OrdersNamedWindow for which the expiry policy is simply to keep all events. Assume that the event type 'OrderMapEventType' has been configured. The named window is to hold events of type 'OrderMapEventType':

create window OrdersNamedWindow#keepall as OrderMapEventType

The below sample statement demonstrates the select syntax. It defines a named window in which each row has the three properties 'symbol', 'volume' and 'price'. This named window actively removes events from the window that are older than 30 seconds.

create window OrdersTimeWindow#time(30 sec) as 
		select symbol, volume, price from OrderEvent

In an alternate form, the as keyword can be used to rename columns, and constants may occur in the select-clause as well:

create window OrdersTimeWindow#time(30 sec) as 
  select symbol as sym, volume as vol, price, 1 as alertId from OrderEvent

Note

The select-clause when used with create window provides types information only. You must still use on-merge or insert-into to insert events.

6.2.1.2. Creation By Defining Columns Names and Types

The second syntax for creating a named window is by supplying column names and types:

[context context_name] 
create window window_name.window_spec [as] (column_name column_type 
  [,column_name column_type [,...])

The column_name is an identifier providing the event property name. The column_type is also required for each column. Valid column types are listed in Section 5.17.1, “Creating Variables: The Create Variable Clause” and are the same as for variable types.

For attributes that are array-type append [] (left and right brackets).

The next statement creates a named window:

create window SecurityEvent#time(30 sec) 
(ipAddress string, userId String, numAttempts int, properties String[])

Named window columns can hold events by declaring the column type as the event type name. Array-type in combination with event-type is also supported.

The next two statements declare an event type and create a named window with a column of the defined event type:

create schema SecurityData (name String, roles String[])

create window SecurityEvent#time(30 sec) 
    (ipAddress string, userId String, secData SecurityData, historySecData SecurityData[])

Whether the named window uses a Map, Object-array, JSON or Avro event representation for the rows can be specified as follows. If the create-window statement provides the @EventRepresentation(objectarray) annotation the runtime maintains named window rows as object array, for example.If no annotation is provided, the runtime uses the configured default event representation as discussed in Section 17.4.9.1, “Default Event Representation”.

The following statement instructs the runtime to represent FooWindow rows as object arrays:

@EventRepresentation(objectarray) create window FooWindow#time(5 sec) as (string prop1)

6.2.1.3. Dropping or Removing Named Windows

There is no syntax to drop or remove a named window.

Undeploying the deployment that created the named window also removes the named window.

6.2.1.4. Populating a Named Window From an Existing Named Window

Your statement may specify the name of an existing named window when creating a new named window, and may use the insert keyword to indicate that the new named window is to be populated from the events currently held by the existing named window.

For example, and assuming the named window OrdersNamedWindow already exists, this statement creates a new named window ScratchOrders and populates all orders in OrdersNamedWindow into the new named window:

create window ScratchOrders#keepall as OrdersNamedWindow insert

The where keyword is also available to perform filtering, for example:

create window ScratchBuyOrders#time(10) as OrdersNamedWindow insert where side = 'buy'

6.2.2. Inserting Into Named Windows

The insert into clause inserts events into named windows. Your application must ensure that the column names and types match the declared column names and types of the named window to be inserted into.

For inserting into a named window and for simultaneously checking if the inserted row already exists in the named window or for atomic update-insert operation on a named window, or for targeting a specific partition, you must use on-merge as described in Section 6.8, “Triggered Upsert Using the On-Merge Clause”. On-merge is similar to the SQL merge clause and provides what is known as an "Upsert" operation: Update existing events or if no existing event(s) are found then insert a new event, all in one atomic operation provided by a single statement.

This example first creates a named window using some of the columns of an OrderEvent event type:

create window OrdersWindow#keepall as select symbol, volume, price from OrderEvent

The insert into the named window selects individual columns to be inserted:

insert into OrdersWindow(symbol, volume, price) select name, count, price from FXOrderEvent

An alternative form is shown next:

insert into OrdersWindow select name as symbol, vol as volume, price from FXOrderEvent

Following above statement, the runtime enters every FXOrderEvent arriving into the runtime into the named window 'OrdersWindow'.

The following statements create a named window for an event type backed by a Java class and insert into the window any 'OrderEvent' where the symbol value is IBM:

create window OrdersWindow#time(30) as com.mycompany.OrderEvent

insert into OrdersWindow select * from com.mycompany.OrderEvent(symbol='IBM')

The last example adds one column named 'derivedPrice' to the 'OrderEvent' type by specifying a wildcard, and uses a user-defined function to populate the column:

create window OrdersWindow#time(30) as select *, price as derivedPrice from OrderEvent

insert into OrdersWindow select *, MyFunc.func(price, percent) as derivedPrice from OrderEvent

Event representations based on Java base classes or interfaces, and subclasses or implementing classes, are compatible as these statements show:

// create a named window for the base class
create window OrdersWindow#unique(name) as select * from ProductBaseEvent

// The ServiceProductEvent class subclasses the ProductBaseEvent class
insert into OrdersWindow select * from ServiceProductEvent

// The MerchandiseProductEvent class subclasses the ProductBaseEvent class
insert into OrdersWindow select * from MerchandiseProductEvent

To avoid duplicate events inserted in a named window and atomically check if a row already exists, use on-merge as outlined in Section 6.8, “Triggered Upsert Using the On-Merge Clause”. An example:

on ServiceProductEvent as spe merge OrdersWindow as win
where win.id = spe.id when not matched then insert select *

6.2.2.1. Named Windows Holding Decorated Events

Decorated events hold an underlying event and add additional properties to the underlying event, as described further in Section 5.10.4, “Decorated Events”.

This sample statement creates a named window that decorates OrderEvent events by adding an additional property named priceTotal to each OrderEvent. A matching insert into statement is also part of the sample:

create window OrdersWindow#time(30) as select *, price as priceTotal from OrderEvent

insert into OrdersWindow select *, price * unit as priceTotal from ServiceOrderEvent

The property type of the additional priceTotal column is the property type of the existing price property of OrderEvent.

6.2.3. Selecting From Named Windows

A named window can be referred to by any statement in the from clause of the statement. Filter criteria can also be specified. Data windows cannot be specified when selecting from a named window (the special derived-value windows are allowed however).

A statement selecting all events from a named window OrdersNamedWindow is shown next. The named window must first be created via the create window clause before use.

select * from OrdersNamedWindow

The statement as above simply receives the unfiltered insert stream of the named window and reports that stream to its listeners. The iterator method returns all events in the named window, if any.

If your application desires to obtain the events removed from the named window, use the rstream keyword as this statement shows:

select rstream * from OrdersNamedWindow

The next statement derives an average price per symbol for the events held by the named window:

select symbol, avg(price) from OrdersNamedWindow group by symbol

A statement that consumes from a named window, like the one above, receives the insert and remove stream of the named window. The insert stream represents the events inserted into the named window. The remove stream represents the events expired from the named window data window and the events explicitly deleted via on-delete for fire-and-forget (on-demand) delete.

Your application may create a consuming statement such as above on an empty named window, or your application may create the above statement on an already filled named window. The runtime provides correct results in either case: At the time of deployment the runtime internally initializes the consuming statement from the current named window, also taking your declared filters into consideration. Thus, your statement deriving data from a named window does not start empty if the named window already holds one or more events. A consuming statement also sees the remove stream of an already populated named window, if any.

If you require a subset of the data in the named window, you can specify one or more filter expressions onto the named window as shown here:

select symbol, avg(price) from OrdersNamedWindow(sector='energy') group by symbol

By adding a filter to the named window, the aggregation and grouping receive a filtered insert and remove stream. The above statement thus outputs, continuously, the average price per symbol for all orders in the named window that belong to a certain sector.

A side note on variables in filters filtering events from named windows: The runtime initializes consuming statements at statement deployment time and changes aggregation state continuously as events arrive. If the filter criteria contain variables and variable values changes, then the runtime does not re-evaluate or re-build aggregation state. In such a case you may want to place variables in the having clause which evaluates on already-built aggregation state.

The following example further declares a custom derived-value window named myplugindatawindow.

select * from OrdersNamedWindow(volume>0, price>0)#myplugindatawindow()

Data windows cannot be used onto named windows since named windows post insert and remove streams for the events entering and leaving the named window, thus the expiry policy and batch behavior are well defined by the data window declared for the named window. For example, the following is not allowed and fails at time of statement compilation:

// not a valid statement
select * from OrdersNamedWindow#time(30 sec)

6.3. Table Usage

6.3.1. Creating Tables: The `Create Table` Clause

The create table statement creates a table.

A new table starts up empty. It must be explicitly aggregated-into using into table, or populated by an on-merge statement, or populated by insert into.

The syntax for creating a table provides the table name, lists column names and types and designates primary key columns:

[context context_name] 
create table table_name [as] (column_name column_type [primary key]
  [,column_name column_type [primary key] [,...]])

The table_name you assign to the table can be any identifier. The name should not already be in use as an event type or named window name.

You may refer to a context by specifying the context keyword followed by a context name. Contexts are described in more detail at Chapter 4, Context and Context Partitions. The effect of referring to a context is that your table operates according to the context dimensional information as declared for the context. For usage and limitations please see the respective chapter.

The column_name is an identifier providing the column name.

The column_type is required for each column. There are two categories of column types:

Non-aggregating column types: Valid column types are listed in Section 5.17.1, “Creating Variables: The Create Variable Clause” and are the same as for variable types. For attributes that are array-type append [] (left and right brackets). Table columns can hold events by declaring the column type as the event type name. Array-type in combination with event-type is also supported.
Aggregation column types: These instruct the runtime to retain aggregation state.

After each column type you may add the primary key keywords. This keyword designates the column as a primary key. When multiple columns are designated as primary key columns the combination of column values builds a compound primary key. The order in which the primary key columns are listed is important. Further detail on key expressions can be found at Section 5.2.13, “Composite Keys and Array Values as Keys”.

The next statement creates a table to hold a numAttempts count aggregation state and a column named active of type boolean, per ipAddress and userId:

create table SecuritySummaryTable (
  ipAddress string primary key,
  userId String primary key, 
  numAttempts count(*),
  active boolean)

The example above specifies ipAddress and userId as primary keys. This instructs the runtime that the table holds a single row for each distinct combination of ipAddress and userId. The two values make up the compound key and there is a single row per compound key value.

If you do not designate any columns of the table as a primary key column, the table holds only one row (or no rows).

The create table statement does not provide output to its listeners. The table contents can be iterated on via the pull API to obtain the current contents of a table.

6.3.1.1. Column Types for Aggregation Functions

All aggregation functions can be used as column types for tables. Please simply list the aggregation function name as the column type and provide type information, when required. See Section 10.2.1, “SQL-Standard Functions” for a list of the functions and required parameter expressions for which you must provide type information.

Consider the next example that declares a table with columns for different aggregation functions (not a comprehensive example of all possible aggregation functions):

create table MyStats (
  myKey string primary key,
  myAvedev avedev(int), // column holds a mean deviation of int-typed values
  myAvg avg(double), // column holds an average of double-typed values
  myCount count(*), // column holds a count
  myMax max(int), // column holds a highest int-typed value
  myMedian median(float), // column holds the median of float-typed values
  myStddev stddev(java.math.BigDecimal), // column holds a standard deviation of BigDecimal values
  mySum sum(long), // column holds a sum of long values
  myFirstEver firstever(string), // column holds a first-ever value of type string
  myCountEver countever(*) // column holds the count-ever (regardless of data windows)
)

Additional keywords such as distinct can be used as well. If your aggregation will be associated with a filter expression, you must add boolean to the parameters in the column type declaration.

For example, the next EPL declares a table with aggregation-type columns that hold an average of filtered double-typed values and an average of distinct double-typed values:

create table MyStatsMore (
  myKey string primary key,
  myAvgFiltered avg(double, boolean), // column holds an average of double-typed values
                      // and filtered by a boolean expression to be provided
  myAvgDistinct avg(distinct double) // column holds an average of distinct double-typed values
)

6.3.1.2. Column Types for Event Aggregation Functions

The event aggregation functions can be used as column types for tables. For event aggregation functions you must specify the event type using the @type(name) annotation.

The window event aggregation function requires the * wildcard. The first and last cannot be used in a declaration, please use window instead and access as described in Section 6.3.3.2, “Using Aggregation Methods to Access Aggregation State with the Dot Operator”.

The sorted, maxbyever and minbyever event aggregation functions require the criteria expression as a parameter. The criteria expression must only use properties of the provided event type. The maxby and minby cannot be used in a declaration, please use sorted instead and access as described in Section 6.3.3.2, “Using Aggregation Methods to Access Aggregation State with the Dot Operator”.

In this example the table declares sample event aggregations (not a comprehensive example of all possible aggregations):

create table MyEventAggregationTable (
  myKey string primary key,
  myWindow window(*) @type(MyEvent), // column holds a window of MyEvent events
  mySorted sorted(mySortValue) @type(MyEvent), // column holds MyEvent events sorted by mySortValue
  myMaxByEver maxbyever(mySortValue) @type(MyEvent) // column holds the single MyEvent event that 
        // provided the highest value of mySortValue ever
)

6.3.1.3. Column Types for Plug-In Custom Aggregation Functions

Any custom single-function and multi-function aggregation can be used as a table column type. If the aggregation has multiple different return values and aggregations share common state, the multi-function aggregation is the preferred API.

For example, the next EPL declares a table with a single column that holds the state of the aggregation function myAggregation:

create table MyStatsCustom (myCustom myAggregation('some code', 100))

The above example passes the values some code and 100 to show how to pass constants to your custom aggregation function at declaration time.

6.3.1.4. Dropping or Removing Tables

There is no syntax to drop or remove a table.

Undeploying the deployment that created the table also removes the table.

The stop method on the EPStatement that created the table has no effect.

6.3.2. Aggregating Into Table Rows: The `Into Table` Clause

Use the into table keywords to instruct the runtime to aggregate into table columns. A given statement can only aggregate into a single table.

For example, consider a table that holds the count of intrusion events keyed by the combination of from-address and to-address:

create table IntrusionCountTable (
  fromAddress string primary key,
  toAddress string primary key,
  countIntrusion10Sec count(*),
  countIntrusion60Sec count(*)
)

The next sample statement updates the count considering the last 10 seconds of events:

into table IntrusionCountTable
select count(*) as countIntrusion10Sec
from IntrusionEvent#time(10)
group by fromAddress, toAddress

For statements that have a data window, like the example above, the into table instructs the runtime to update the aggregations that reside in the table column for events entering the data window and for events expiring from the data window. For statements that don't have a data window the into table instructs the runtime to update the aggregation for arriving events.

In the example above, when an IntrusionEvent enters the 10-second time window the runtime increments the count aggregation by one. When an IntrusionEvent gets removed from (expires from) the 10-second time window the runtime decrements the count aggregation by one.

Multiple statements can aggregate into the same table columns or different table columns. The co-aggregating ability allows you to co-locate aggregation state conveniently.

The sample shown below is very similar to the previous statement except that it updates the count considering the last 60 seconds of events:

into table IntrusionCountTable
select count(*) as countIntrusion60Sec
from IntrusionEvent#time(60)
group by fromAddress, toAddress

Considering the example above, when an intrusion event arrives and a row for the group-by key values (from and to-address) does not exists, the runtime creates a new row and updates the aggregation-type columns. If the row for the group-by key values exists, the runtime updates the aggregation-type columns of the existing row.

Tables can have no primary key columns. In this case a table either has a single row or is empty.

The next two statements demonstrate table use without a primary key column:

create table TotalIntrusionCountTable (totalIntrusions count(*))

into table TotalIntrusionCountTable select count(*) as totalIntrusions from IntrusionEvent

In conjunction with into table the unidirectional keyword is not supported.

6.3.2.1. Group-By Clause Requirements

The use of the into table clause requires that the group by clause must list group-by expressions that match the table's primary key declarations in terms of the number, return type and order of group-by expressions. It is not necessary that table column names match group-by expression texts.

For example consider a table with a single long-type primary key column:

create table MyTable (theKey long primary key, theCount count(*))

The following EPL are all not valid:

// Invalid: No group-by clause however the table declares a primary key
into table MyTable select count(*) as theCount from MyEvent

// Invalid: Two expressions in the group-by clause however the table declares a single primary key
into table MyTable select count(*) as theCount from MyEvent group by longPropertyOne, longPropertyTwo

// Invalid: The group-by clause expression returns a string-typed value however the table expects a long-type primary key
into table MyTable select count(*) as theCount from MyEvent group by stringProperty

You may use the rollup, cube and grouping sets keywords in conjunction with tables.

6.3.2.2. Aggregation State Requirements

The use of the into table clause requires that all aggregation state of the statement resides in table columns.

For example consider a simple table as follows:

create table MyTable (theKey long primary key, theCount count(*))

The following EPL is not valid:

// Invalid: the sum aggregation state is not available in a table column
into table MyTable select count(*) as theCount, sum(intProperty) from MyEvent group by longProperty

6.3.2.3. Aggregation Function Requirements

The use of the into table clause requires that all aggregation functions that are listed in the statement are compatible with table column types, and that the statement has at least one aggregation function.

For example consider a simple table as follows:

create table MyTable (theKey long primary key, theCount count(*))

The following EPL is not valid:

// Invalid: the sum aggregation state is not compatible with count(*) that was declared for the table column's type
into table MyTable select sum(intProperty) as theCount from MyEvent group by longProperty

If declared, the distinct keyword and filter expressions must also match. The event type information must match for event aggregation functions.

6.3.2.4. Column Naming Requirements

The use of the into table clause requires that the aggregation functions are named. You can name an expression two ways.

First, you can name the aggregation function expression by adding it to the select-clause and by providing the as-keyword followed by the table column name. The examples earlier use this technique.
Second, you can name the aggregation function by placing it into a declared expression that carries the same name as the table column.

This example demonstrates the second method of naming an aggregation function:

expression alias totalIntrusions {count(*)}
select totalIntrusions from IntrusionEvent

6.3.3. Table Column Keyed-Access Expressions

For accessing table columns by primary key, EPL provides a convenient syntax that allows you to read table column values simply by providing the table name, primary key value expressions (if required by the table) and the column name.

The synopsis for table-column access expressions is:

table-name[primary_key_expr [, primary_key_expr] [,...]][.column-name]

The expression starts with the table name. If the table declares primary keys you must provide the primary_key_expr value expressions for each primary key within square brackets. To access a specific column, add the (.) dot character and the column name.

For example, consider a table that holds the count of intrusion events keyed by the combination of from-address and to-address:

create table IntrusionCountTable (
  fromAddress string primary key,
  toAddress string primary key,
  countIntrusion10Sec count(*)
)

Assuming that a FireWallEvent has string-type properties named from and to, the next statement outputs the current 10-second intrusion count as held by the IntrusionCountTable row for the matching combination of keys:

select IntrusionCountTable[from, to].countIntrusion10Sec from FirewallEvent

The number of primary key expressions, the return type of the primary key expressions and the order in which they are provided must match the primary key columns that were declared for the table. If the table does not have any primary keys declared, you cannot provide any primary key expressions.

If a row for the primary key (or compound key) cannot be found, the runtime returns a null value.

An example table without primary key columns is shown next:

create table TotalIntrusionCountTable (totalIntrusions count(*))

A sample statement that outputs the current total count every 60 seconds is:

select TotalIntrusionCountTable.totalIntrusions from pattern[every timer:interval(60 sec)]

Table access expressions can be used anywhere in statements except as parameter expressions for data windows, the update istream, context declarations, output limit expressions, pattern observer and guard parameters, pattern every-distinct, pattern match-until bounds, pattern followed-by max and create window insert or select expression and as a create variable assignment expression.

6.3.3.1. Reading All Column Values

If your keyed-access expression emits the column name, the runtime returns all current column values.

An example EPL:

select IntrusionCountTable[from, to] from FirewallEvent

The runtime returns each column value, or null if no row is found. For aggregation-type columns it returns the current aggregation value.

6.3.3.2. Using Aggregation Methods to Access Aggregation State with the Dot Operator

Certain aggregation functions allow accessing aggregation state using the (.) dot operator. The methods that operate on aggregation state are called aggregation methods. Aggregation methods are explained further in Chapter 13, EPL Reference: Aggregation Methods.

The EPL shown below declares a table that keeps an unsorted set of events and a sorted set of events. This sample table has no primary key columns:

create table MyTable (
  theWindow window(*) @type(MyEvent),
  theSorted sorted(mySortValue) @type(MyEvent)
)

This statement uses the first and the maxBy aggregation methods to return the first (oldest) event and the greatest event according to the sort value:

select MyTable.theWindow.first(), MyTable.theSorted.maxBy() from SomeOtherEvent

6.3.3.3. Obtaining All Table Keys

Tables provide the keys method that returns all table keys. The synopsis is:

tablename.keys()

The return type of the method is object-array. Each element of the array is a table key. For composite table keys, each element of the array is itself of type object-array and each array element is the primary key value.

An example EPL:

select IntrusionCountTable.keys() from FirewallEvent

The runtime returns an array of primary keys.

6.3.4. Inserting Into Tables

The insert into clause inserts rows into a table. Your application must ensure that the column names and types match the declared column names and types of the table to be inserted into, when provided.

For inserting into a table and for simultaneously checking if the inserted row already exists in the table or for atomic update-insert operation on a table, or for targeting a specific partition, you must use on-merge as described in Section 6.8, “Triggered Upsert Using the On-Merge Clause”. On-merge is similar to the SQL merge clause and provides what is known as an "Upsert" operation: Update existing rows or if no existing rows(s) are found then insert a new row, all in one atomic operation provided by a single statement.

The following statement populates the example table declared earlier:

insert into IntrusionCountTable select fromAddress, toAddress from FirewallEvent

Note that when a row with the same primary key values already exists, your statement may encounter a unique index violation at runtime. If the inserted-into table does not have primary key columns, the table holds a maximum of one row and your statement may also encounter a unique index violation upon attempting to insert a second row. Use on-merge to prevent inserts of duplicate rows.

Table columns that are aggregation functions cannot be inserted-into and must be updated using into table instead.

You may also explicitly list column names as discussed earlier in Section 6.2.2, “Inserting Into Named Windows”. For insert-into, the context name must be the same context name as declared for the create table statement or the context name must be absent for both.

6.3.5. Selecting From Tables

A table can be referred to by any statement in the from-clause of the statement.

Tables do not provide an insert and remove stream. When a table appears alone in the from-clause (other than as part of a subquery), the statement produces output only when iterated (see pull API) or when executing a fire-and-forget (on-demand) query.

Assuming you have declared a table by name IntrusionCountTable as shown earlier, the following statement only returns rows when iterated or when executing the EPL as a fire-and-forget (on-demand) query or when adding an output snapshot:

select * from IntrusionCountTable

For tables, the contained-event syntax and specifying a data window is not supported. In a join, a table in the from-clause cannot be marked as unidirectional. You may not specify any of the retain-flags. Tables cannot be used in the from-clause of match-recognize statements, in context declarations, in pattern filter atoms and update istream.

The following are examples of invalid statements:

// invalid statement examples
select * from IntrusionCountTable#time(30 sec)   // data window not allowed
select * from IntrusionCountTable unidirectional, MyEvent   // tables cannot be marked as unidirectional

Tables can be used in subqueries and joins.

It follows a sample subselect and join against the table:

select
  (select * from IntrusionCountTable as intr
   where intr.fromAddress = firewall.fromAddress and intr.toAddress = firewall.toAddress) 
from IntrusionEvent as firewall

select * from IntrusionCountTable as intr, IntrusionEvent as firewall
where intr.fromAddress = firewall.fromAddress and intr.toAddress = firewall.toAddress

If the subselect or join specifies all of a table's primary key columns, please consider using the table-access expression instead. It offers a more concise syntax.

Note that for a subquery against a table that may return multiple rows, the information about subquery multi-row selection applies. For subselects, consider using @eventbean to preserve table type information in the output event.

Note that for joins against tables the compiler does not allow specifying table filter expressions in parenthesis, in the from clause. Filter expressions must instead be placed into the where-clause.

You may access aggregation state the same way as in table-access expressions, using the dot (.) operator.

The EPL shown below declares a table that keeps a set of events, and shows a join that selects window aggregation state:

create table MyWindowTable (theWindow window(*) @type(MyEvent))

select theWindow.first(), theWindow.last(), theWindow.window() from MyEvent, MyWindowTable

6.3.6. Resetting Table Columns and Aggregation State

For resetting plain table columns, you may simple assign a value using set name = value within an on-merge update- action to set new values. You may also use fire-and-forget queries.

For resetting aggregation-typed table columns, as they represent aggregation state, it is not possible to use a straight assignment. Instead, the runtime provides a reset method. The syntax for the reset method is column-name.reset() and is only available as part of on-merge and the update-action. You may also reset all aggregations of a table row by specifying table-alias.reset().

For example, consider a table that holds the count of intrusion events keyed by the combination of from-address and to-address:

create table IntrusionCountTable (
  fromAddress string primary key,
  toAddress string primary key,
  countIntrusion10Sec count(*),
  countIntrusion60Sec count(*)
)

Assume there is an event that triggers the reset of the countIntrusion10Sec and countIntrusion60Sec counts:

create schema IntrusionReset(fromAddress string, toAddress string)

Use on-merge to reset the count columns like so:

on IntrusionReset as resetEvent merge IntrusionCountTable as tableRow
where resetEvent.fromAddress = tableRow.fromAddress and resetEvent.toAddress = tableRow.toAddress
when matched then update set countIntrusion10Sec.reset(), countIntrusion60Sec.reset()

Alternatively you may specify the table alias to reset all of a table row's aggregation state:

on IntrusionReset as resetEvent merge IntrusionCountTable as tableRow
where resetEvent.fromAddress = tableRow.fromAddress and resetEvent.toAddress = tableRow.toAddress
when matched then update set tableRow.reset()

6.3.7. Initializing Table Columns and Aggregation State

For initializing plain table columns, you may simple assign a value using set name = value within an on-merge update- action to set new values. You may also use fire-and-forget queries.

For initializing aggregation-typed table columns, as they represent aggregation state, it is not possible to use a straight assignment. Instead, you must use into table since aggregation state is complex state and requires adding and removing values or events and therefore cannot be assigned to.

If the aggregation-typed column is a count, please declare the column as a sum instead and use sum(initial value) to initialize the value or sum(1) to increment the sum.

6.4. Triggered Select: The `On Select` Clause

The on select clause performs a one-time, non-continuous query on a named window or table every time a triggering event arrives or a triggering pattern matches. The statement can consider all rows, or only rows that match certain criteria, or rows that correlate with an arriving event or a pattern of arriving events.

The syntax for the on select clause is as follows:

on event_type[(filter_criteria)] [as stream_name]
[insert into insert_into_def]
select select_list
from window_or_table_name [as stream_name]
[where criteria_expression]
[group by grouping_expression_list]
[having grouping_search_conditions]
[order by order_by_expression_list]

The event_type is the name of the type of events that trigger the query against the named window or table. It is optionally followed by filter_criteria which are filter expressions to apply to arriving events. The optional as keyword can be used to assign a stream name. Patterns or named windows can also be specified in the on clause, see the samples in Section 6.7.1, “Using Patterns in the On-Delete Clause” (for a named window as a trigger only insert stream events trigger actions) (tables cannot be triggers).

The insert into clause works as described in Section 5.10, “Merging Streams and Continuous Insertion: The Insert Into Clause”. The select clause is described in Section 5.3, “Choosing Event Properties and Events: The Select Clause”. For all clauses the semantics are equivalent to a join operation: The properties of the triggering event or events are available in the select clause and all other clauses.

The window_or_table_name in the from clause is the name of the named window or table to select rows from. The as keyword is also available to assign a stream name to the table or named window. The as keyword is helpful in conjunction with wildcard in the select clause to select rows via the syntax select streamname.* .

The optional where clause contains a criteria_expression that correlates the arriving (triggering) event to the rows to be considered from the table or named window. The criteria_expression may also simply filter for rows to be considered by the statement.

The group by clause, the having clause and the order by clause are all optional and work as described in earlier chapters.

Statements that use tables and named windows work the same. The examples herein use the OrdersNamedWindow named window and the SecuritySummaryTable table to provide examples for each.

The sample statement below outputs, when a trigger event arrives, the count of all rows held by the SecuritySummaryTable table:

on QueryEvent select count(*) from SecuritySummaryTable

This sample statement outputs the total volume per symbol ordered by symbol ascending and only non-zero volumes of all rows held by the OrdersNamedWindow named window:

on QueryEvent
select symbol, sum(volume) from OrdersNamedWindow
group by symbol having volume > 0 order by symbol

When using wildcard (*) to select from streams in an on-select clause, each stream, that is the triggering stream and the selected-upon table or named window, are selected, similar to a join. Therefore your wildcard select returns two columns: the triggering event and the selection result row, for each row.

on QueryEvent as queryEvent
select * from OrdersNamedWindow as win

The statement above returns a queryEvent column and a win column for each event. If only a single stream's event is desired in the result, use select win.* instead.

Upon arrival of a QueryEvent event, this statement selects all rows in the OrdersNamedWindow named window:

on QueryEvent select win.* from OrdersNamedWindow as win

The runtime executes the statement on arrival of a triggering event, in this case a QueryEvent. It posts the statement results to any listeners to the statement, in a single invocation, as the new data array.

The where clause filters and correlates rows in the table or named window with the triggering event, as shown next:

on QueryEvent(volume>0) as query
select query.symbol, query.volume, win.symbol  from OrdersNamedWindow as win
where win.symbol = query.symbol

Upon arrival of a QueryEvent, if that event has a value for the volume property that is greater than zero, the runtime executes the statement. The statement considers all events currently held by the OrdersNamedWindow that match the symbol property value of the triggering QueryEvent event.

6.4.1. Notes on On-Select With Named Windows

To trigger an on-select when an update to the selected named window occurs or when the triggering event is the same event that is being inserted into the named window, specify the named window name as the event type.

The next statement fires the select for every change to the named window OrdersNamedWindow:

on OrdersNamedWindow as trig 
select onw.symbol, sum(onw.volume) 
from OrdersNamedWindow as onw 
where onw.symbol = trig.symbol

For named windows, the iterator of the EPStatement object representing the on select clause returns the last batch of selected events in response to the last triggering event, or null if the last triggering event did not select any rows.

6.4.2. Notes on On-Select With Tables

For tables, the iterator of the EPStatement object representing the on select clause returns no events.

6.4.3. `On-Select` Compared to Join

A join is evaluated when any of the streams participating in the join have new events (insert stream) or events leaving data windows (remove stream). A join is therefore bi-directional or multi-directional. However, the on select statement has one triggering event or pattern that causes the statement to be evaluated and is thus uni-directional.
The query within the on select statement is not continuous: It executes only when a triggering event or pattern occurs. Aggregation and groups are computed anew considering the contents of the table or named window at the time the triggering event arrives.

On-select and the unidirectional join can be compared as follows.

On-select, on-merge, on-insert, on-delete, on-update and on-select-and-delete operate only on named windows or tables. Unidirectional joins however can operate on any stream. If the unidirectional join is between a single named window or table and a triggering event or pattern and that triggering event or pattern is marked unidirectional, the unidirectional join is equivalent to on-select.

A unidirectional join does not execute under a named window context partition lock and instead is a consumer relationship to the named window.

6.5. Triggered Select+Delete: The `On Select Delete` Clause

The on select delete clause performs a one-time, non-continuous query on a table or named window every time a triggering event arrives or a triggering pattern matches, similar to on-select as described in the previous section. In addition, any selected rows are also deleted.

The syntax for the on select delete clause is as follows:

on trigger
select [and] delete select_list...
... (please see on-select for insert into, from, group by, having, order by)...

The syntax follows the syntax of on-select as described earlier. The select clause follows the optional and keyword and the delete keyword. The from-clause can list either a table or a named window.

The example statement below selects and deletes all rows from OrdersNamedWindow named window when a QueryEvent arrives:

on QueryEvent select and delete window(win.*) as rows from OrdersNamedWindow as win

The sample EPL above also shows the use of the window aggregation function. It specifies the window aggregation function to instruct the runtime to output a single event, regardless of the number of rows in the named window, and that contains a column rows that contains a collection of the selected event's underlying objects.

Note

On Select Delete deletes all rows that match the where-clause. When there is no where-clause it deletes all rows regardless of what the output looks like. The having-clause is relevant to output only and does not narrow down the rows that are deleted.

6.6. Updating Data: The `On Update` Clause

An on update clause updates rows held by a table or named window. The clause can be used to update all rows, or only rows that match certain criteria, or rows that correlate with an arriving event or a pattern of arriving events.

For updating a table or named window and for simultaneously checking if the updated row exists or for atomic update-insert operation on a named window or table, consider using on-merge as described in Section 6.8, “Triggered Upsert Using the On-Merge Clause”. On-merge is similar to the SQL merge clause and provides what is known as an "Upsert" operation: Update existing events or if no existing event(s) are found then insert a new event, all in one atomic operation provided by a single statement.

The syntax for the on update clause is as follows:

on event_type[(filter_criteria)] [as stream_name]
update window_or_table_name [as stream_name]
set mutation_expression [, mutation_expression [,...]]
[where criteria_expression]

The event_type is the name of the type of events that trigger an update of rows in a named window. It is optionally followed by filter_criteria which are filter expressions to apply to arriving events. The optional as keyword can be used to assign a name for use in expressions and the where clause. Patterns and named windows can also be specified in the on clause.

The window_or_table_name is the name of the table or named window to update rows. The as keyword is also available to assign a name to the named window or table.

After the set keyword follows a list of comma-separated mutation_expression expressions. A mutation expression is any valid EPL expression and including the below. Subqueries may by part of expressions however aggregation functions and the prev or prior function may not be used in expressions.

The below table shows some typical mutation expessions:

Table 6.1. Mutation Expressions in Update and Merge

Description	Syntax and Examples
Assignment	property_name = value_expression price = 10, side = 'BUY'
Array Element Assignment	arrayproperty_name[array_index_expression] = value_expression values[position] = 10
Event Method Invocation (not available for tables)	alias_or_windowname.methodname(...) orderWindow.clear()
Property Method Invocation	property_name.methodname(...) accountMap.clear()
Any Expression, For Example: User-Defined Function Call	functionname(...) clearQuantities(orderRow)

The optional where clause contains a criteria_expression that correlates the arriving (triggering) event to the rows to be updated in the table or named window. The criteria_expression may also simply filter for rows to be updated.

Statements that use tables and named windows work the same. We use the term property and column interchangeably. The examples herein use the OrdersNamedWindow named window and the SecuritySummaryTable table to provide examples for each. Let's look at a couple of examples.

In the simplest form, this statement updates all rows in the named window OrdersNamedWindow when any UpdateOrderEvent event arrives, setting the price property to zero for all rows currently held by the named window:

on UpdateOrderEvent update OrdersNamedWindow set price = 0

This example demonstrates the use of a where clause and updates the SecuritySummaryTable table. Upon arrival of a triggering ResetEvent it updates the active column value to false for all table rows that have an active column value of true:

on ResetEvent update SecuritySummaryTable set active = false where active = true

The next example shows a more complete use of the syntax, and correlates the triggering event with rows held by the OrdersNamedWindow named window:

on NewOrderEvent(volume>0) as myNewOrders
update OrdersNamedWindow as myNamedWindow 
set price = myNewOrders.price
where myNamedWindow.symbol = myNewOrders.symbol

In the above sample statement, only if a NewOrderEvent event with a volume greater then zero arrives does the statement trigger. Upon triggering, all rows in the named window that have the same value for the symbol property as the triggering NewOrderEvent event are then updated (their price property is set to that of the arriving event). The statement also showcases the as keyword to assign a name for use in the where expression.

Your application can subscribe a listener to your on update statements to determine update events. The statement post any rows that are updated to all listeners attached to the statement as new data, and the events prior to the update as old data.

The following example shows the use of tags and a pattern. It sets the price value of orders to that of either a FlushOrderEvent or OrderUpdateEvent depending on which arrived:

on pattern [every ord=OrderUpdateEvent(volume>0) or every flush=FlushOrderEvent] 
update OrdersNamedWindow as win
set price = case when ord.price is null then flush.price else ord.price end
where ord.id = win.id or flush.id = win.id

The runtime executes assignments in the order they are listed. When performing multiple assignments, the runtime takes the most recent column value according to the last assignment, if any. To instruct the runtime to use the initial value before update, prefix the column name with the literal initial. The initial prefix is only available for use with named windows and not for use with tables.

The following statement illustrates:

on UpdateEvent as upd
update MyWindow as win
set field_a = 1, 
  field_b = win.field_a, // assigns the value 1 
  field_c = initial.field_a // assigns the field_a original value before update

The next example assumes that your application provides a user-defined function copyFields that receives 3 parameters: The update event, the new row and the initial state before-update row.

on UpdateEvent as upd update MyWindow as win set copyFields(win, upd, initial)

You may invoke a method on a value object, for those properties that hold value objects, as follows:

on UpdateEvent update MyWindow as win set someproperty.clear()

For named windows only, you may also invoke a method on the named window event type.

The following example assumes that your event type provides a method by name populateFrom that receives the update event as a parameter:

on UpdateEvent as upd update MyWindow as win set win.populateFrom(upd)

The following restrictions apply:

Each property to be updated via assignment must be writable. For tables, all columns are always writable.

For underlying event representations that are Java objects, a event object class must implement the java.io.Serializable interface as discussed in Section 5.20.1, “Immutability and Updates” and must provide setter methods for updated properties.
When using an XML underlying event type, event properties in the XML document representation are not available for update.
Nested properties are not supported for update. Variant streams may also not be updated.

6.6.1. Notes on On-Update With Named Windows

Statements that reference the named window receive the new event in the insert stream and the event prior to the update in the remove stream.

The iterator of the EPStatement object representing the on update clause does not return any rows.

6.6.2. Notes on On-Update With Tables

On-Update may not update primary key columns.

The iterator of the EPStatement object representing the on update clause does not return any rows.

6.7. Deleting Data: The `On Delete` Clause

An on delete clause removes rows from a named window or table. The clause can be used to remove all rows, or only rows that match certain criteria, or rows that correlate with an arriving event or a pattern of arriving events.

The syntax for the on delete clause is as follows:

on event_type[(filter_criteria)] [as stream_name]
delete from window_or_table_name [as stream_name]
[where criteria_expression]

The event_type is the name of the type of events that trigger removal from the table or named window. It is optionally followed by filter_criteria which are filter expressions to apply to arriving events. The optional as keyword can be used to assign a name for use in the where clause. Patterns and named windows can also be specified in the on clause as described in the next section.

The window_or_table_name is the name of the named window or table to delete rows from. The as keyword is also available to assign a name to the table or named window.

The optional where clause contains a criteria_expression that correlates the arriving (triggering) event to the rows to be removed. The criteria_expression may also simply filter for rows without correlating.

On-delete can be used against tables and named windows. The examples herein use the OrdersNamedWindow named window and the SecuritySummaryTable table to provide examples for each.

In the simplest form, this statement deletes all rows from the SecuritySummaryTable table when any ClearEvent arrives:

on ClearEvent delete from SecuritySummaryTable

The next example shows a more complete use of the syntax, and correlates the triggering event with events held by the OrdersNamedWindow named window:

on NewOrderEvent(volume>0) as myNewOrders
delete from OrdersNamedWindow as myNamedWindow 
where myNamedWindow.symbol = myNewOrders.symbol

In the above sample statement, only if a NewOrderEvent event with a volume greater then zero arrives does the statement trigger. Upon triggering, all rows in the named window that have the same value for the symbol property as the triggering NewOrderEvent event are removed. The statement also showcases the as keyword to assign a name for use in the where expression.

6.7.1. Using Patterns in the `On-Delete` Clause

By means of patterns the on delete clause and on select clause (described below) can look for more complex conditions to occur, possibly involving multiple events or the passing of time. The syntax for on delete with a pattern expression is show next:

on pattern [pattern_expression] [as stream_name]
delete from window_or_table_name [as stream_name]
[where criteria_expression]

The pattern_expression is any pattern that matches zero or more arriving events. Tags can be used to name events in the pattern and can occur in the optional where clause to correlate to events to be removed from a named window.

In the next example the triggering pattern fires every 10 seconds. The effect is that every 10 seconds the statement removes all rows from the SecuritySummaryTable table:

on pattern [every timer:interval(10 sec)] delete from SecuritySummaryTable

The following example shows the use of tags in a pattern and executes against the OrdersNamedWindow named window instead:

on pattern [every ord=OrderEvent(volume>0) or every flush=FlushOrderEvent] 
delete from OrdersNamedWindow as win
where ord.id = win.id or flush.id = win.id

The pattern above looks for OrderEvent events with a volume value greater then zero and tags such events as 'ord'. The pattern also looks for FlushOrderEvent events and tags such events as 'flush'. The where clause deletes from the OrdersNamedWindow named window any rows that match in the value of the 'id' property either of the arriving events.

6.7.2. Silent Delete With Named Windows

When using on-delete with named windows you may specify @hint('silent_delete') to suppress output of the deleted events to listeners of the statement that declares the named window. This is useful when listeners need to receive only expired events and not explicitly deleted events.

The @hint('silent_delete') instruction for use with on-delete removes the dispatches for all listeners of the statement that declares the named window in respect to the deleted events, thereby listeners of the statement that declares the named window don't receive the deleted events.

6.7.3. Notes on On-Delete With Named Windows

Statements that reference the named window receive the deleted event as part of the remove stream.

The iterator of the EPStatement object representing the on delete clause does not return any rows.

6.7.4. Notes on On-Update With Tables

The iterator of the EPStatement object representing the on delete clause does not return any rows.

6.8. Triggered Upsert Using the `On-Merge` Clause

The on merge clause is similar to the SQL merge clause. It provides what is known as an "Upsert" operation: Update existing rows or if no existing row(s) are found then insert a new row, all in an atomic operation provided by a single statement.

The syntax for the on merge clause has two forms.

6.8.1. `On-Merge` Insert

Use on merge with insert to inserts one row. This syntax is:

on event_type[(filter_criteria)] [as stream_name]
merge [into] window_or_table_name [as stream_name]
insert [ (property_name [, property_name] [,...]) ] select select_expression [, select_expression[,...]]

The event_type is the name of the type of events that trigger the merge. It is optionally followed by filter_criteria which are filter expressions to apply to arriving events. The optional as keyword can be used to assign a name for use in the where clause. Patterns and named windows can also be specified in the on clause as described in prior sections.

The window_or_table_name is the name of the named window to insert a row into. The as keyword is also available to assign a name to the named window or table.

It follows the insert keyword and optionally the into keyword. Optionally you can provide a list of property names in parenthesis.

It follows the required select keyword and one or more select-clause expressions. The wildcard (*) is available in the select-clause as well.

On-merge can be used with tables and named windows. This examples uses the SecuritySummaryTable table that was defined earlier.

This example statement inserts a row into the SecuritySummaryTable table when a SecuritySummary arrives:

on SecuritySummary merge SecuritySummaryTable insert select ipAddress, userId

The following EPL is equivalent to the statement above and uses the upsert syntax instead, described next.

on SecuritySummary merge SecuritySummaryTable where 1=2 when not matched then insert select ipAddress, userId

6.8.2. `On-Merge` Update, Delete, Select (Upsert)

The syntax for on merge for update-insert (upsert) is as follows:

on event_type[(filter_criteria)] [as stream_name]
merge [into] window_or_table_name [as stream_name]
[where criteria_expression]
  when [not] matched [and search_condition]
    then [
      insert [into streamname]
          [ (property_name [, property_name] [,...]) ] 
          select select_expression [, select_expression[,...]]
          [where filter_expression]
      |
      update set mutation_expression [, mutation_expression [,...]]
          [where filter_expression]
      |
      delete
          [where filter_expression]
    ]
    [then [insert|update|delete]] [,then ...]
  [when ...  then ... [...]]

The window_or_table_name is the name of the named window or table to insert, update or delete rows. The as keyword is also available to assign a name to the named window or table.

The optional where clause contains a criteria_expression that correlates the arriving (triggering) event to the rows to be considered of the table or named window. We recommend specifying a criteria expression that is as specific as possible.

Following the where clause is one or more when matched or when not matched clauses in any order. Each may have an additional search condition associated.

After each when [not] matched follow one or more then clauses that each contains the action to take: Either an insert, update or delete keyword.

After when not matched only insert action(s) are available. After when matched any insert, update and delete action(s) are available.

After insert follows, optionally, the into keyword followed by the stream name or named window to insert-into. If no into and stream name is specified, the insert applies to the current table or named window. It follows an optional list of columns inserted. It follows the required select keyword and one or more select-clause expressions. The wildcard (*) is available in the select-clause as well. It follows an optional where-clause that may return Boolean false to indicate that the action should not be applied.

After update follows the set keyword and one or more mutation expressions. For mutation expressions please see Section 6.6, “Updating Data: The On Update Clause”. It follows an optional where-clause that may return Boolean false to indicate that the action should not be applied.

After delete follows an optional where-clause that may return Boolean false to indicate that the action should not be applied.

When according to the where-clause criteria_expression the runtime finds no rows in the named window or table that match the condition, the runtime evaluates each when not matched clause. If the optional search condition returns true or no search condition was provided then the runtime performs all of the actions listed after each then.

When according to the where-clause criteria_expression the runtime finds one or more rows in the named window or table that match the condition, the runtime evaluates each when matched clause. If the optional search condition returns true or no search condition was provided the runtime performs all of the actions listed after each then.

The runtime executes when matched and when not matched in the order specified. If the optional search condition returns true or no search condition was specified then the runtime takes the associated action (or multiple actions for multiple then keywords). When the block of actions completed the runtime proceeds to the next matching row, if any. After completing all matching rows the runtime continues to the next triggering event if any.

On-merge can be used with tables and named windows. The examples herein declare a ProductWindow named window and also use the SecuritySummaryTable table to provide examples for each.

This example statement updates the SecuritySummaryTable table when a ResetEvent arrives setting the active column's value to false:

on ResetEvent merge SecuritySummaryTable
when matched and active = true then update set active = false

A longer example utilizing a named window follows. You start by declaring a schema that provides a product id and that holds a total price:

create schema ProductTotalRec as (productId string, totalPrice double)

We create a named window that holds a row for each unique product:

create window ProductWindow#unique(productId) as ProductTotalRec

The events for this example are order events that hold an order id, product id, price, quantity and deleted-flag declared by the next schema:

create schema OrderEvent as (orderId string, productId string, price double, 
    quantity int, deletedFlag boolean)

The following statement utilizes on-merge to total up the price for each product based on arriving order events:

on OrderEvent oe
  merge ProductWindow pw
  where pw.productId = oe.productId
  when matched
    then update set totalPrice = totalPrice + oe.price
  when not matched 
    then insert select productId, price as totalPrice

In the above example, when an order event arrives, the runtime looks up in the product named window the matching row or rows for the same product id as the arriving event. In this example the runtime always finds no row or one row as the product named window is declared with a unique data window based on product id. If the runtime finds a row in the named window, it performs the update action adding up the price as defined under when matched. If the runtime does not find a row in the named window it performs the insert action as defined under when not matched, inserting a new row.

The insert keyword may be followed by a list of columns as shown in this EPL snippet:

// equivalent to the insert shown in the last 2 lines in above EPL
...when not matched 
    then insert(productId, totalPrice) select productId, price

The second example demonstrates the use of a select-clause with wildcard, a search condition and the delete keyword. It creates a named window that holds order events and employs on-merge to insert order events for which no corresponding order id was found, update quantity to the quantity provided by the last arriving event and delete order events that are marked as deleted:

create window OrderWindow#keepall as OrderEvent

on OrderEvent oe
  merge OrderWindow pw
  where pw.orderId = oe.orderId
  when not matched 
    then insert select *
  when matched and oe.deletedFlag=true
    then delete
  when matched
    then update set pw.quantity = oe.quantity, pw.price = oe.price

In the above example the oe.deletedFlag=true search condition instructs the runtime to take the delete action only if the deleted-flag is set.

You may specify multiple actions by providing multiple then keywords each followed by an action. Each of the insert, update and delete actions can itself have a where-clause as well. If a where-clause exists for an action, the runtime evaluates the where-clause and applies the action only if the where-clause returns Boolean true.

This example specifies two update actions and uses the where-clause to trigger different update behavior depending on whether the order event price is less than zero. This example assumes that the host application defined a clearorder user-defined function, to demonstrate calling a user-defined function as part of the update mutation expressions:

on OrderEvent oe
  merge OrderWindow pw
  where pw.orderId = oe.orderId
  when matched
    then update set clearorder(pw) where oe.price < 0
    then update set pw.quantity = oe.quantity, pw.price = oe.price where oe.price >= 0

To insert events into another stream and not the named window, use insert into streamname.

In the next example each matched-clause contains two actions, one action to insert a log event and a second action to insert, delete or update:

on OrderEvent oe
  merge OrderWindow pw
  where pw.orderId = oe.orderId
  when not matched 
    then insert into LogEvent select 'this is an insert' as name
    then insert select *
  when matched and oe.deletedFlag=true
    then insert into LogEvent select 'this is a delete' as name
    then delete
  when matched
    then insert into LogEvent select 'this is a update' as name
    then update set pw.quantity = oe.quantity, pw.price = oe.price

While the runtime evaluates and executes all actions listed under the same matched-clause in order, you may not rely on updated field values of an earlier action to trigger the where-clause of a later action. Similarly you should avoid simultaneous update and delete actions for the same match: the runtime does not guarantee whether the update or the delete take final affect.

Your application can subscribe a listener to on merge statements to determine inserted, updated and removed events. Statements post any events that are inserted to, updated or deleted from a named window to all listeners attached to the statement as new data and removed data.

The following limitations apply to on-merge statements:

Aggregation functions and the prev and prior operators are not available in conditions and the select-clause.

6.8.3. Notes on On-Merge With Named Windows

Statements that reference the named window receive an insert and remove stream represening the insertions, changes and deletions to named window rows.

The iterator of the EPStatement object representing the on merge clause does not return any rows.

6.8.4. Notes on On-Merge With Tables

On-Merge may not update primary key columns.

The iterator of the EPStatement object representing the on merge clause does not return any rows.

6.9. Explicitly Indexing Named Windows and Tables

You may explicitly create an index on a table or a named window. The runtime considers explicitly-created as well as implicitly-allocated indexes (named windows only) in query planning and execution of the following types of usages of tables and named windows:

Fire-and-forget (on-demand, non-continuous) queries as described in Section 16.7, “Execute Fire-and-Forget Queries Using EPFireAndForgetService”.
On-select, on-merge, on-update, on-delete and on-insert.
Subqueries against tables and named windows.
For joins (including outer joins) with named windows the runtime considers the filter criteria listed in parenthesis using the syntax
```
name_window_name(filter_criteria)
```
for index access.
For joins with tables the runtime considers the primary key columns (if any) as well as any table indexes.

The syntax to create an explicit index on a named window or table is:

create [unique] index index_name on window_or_table_name (
  column_expression [hash|btree|index_type_expression] 
  [, column_expression] [hash|btree|index_type_expression] 
  [,...]
)

The optional unique keyboard indicates that the column expressions uniquely identify rows. If unique is not specified the index allows duplicate rows.

The index_name is the name assigned to the index. The name uniquely identifies the index and is used in compiler query plan logging.

The window_or_table_name is the name of an existing table or named window. If the named window or table has rows already, the runtime builds an index for the rows.

After the table name or named window name follows a list of pairs of column_expression column expression and index type.

A column expression is the expression that is subject to index building. Typically a column expression is an event property or column name. For special application-provided or spatial indexes other column expressions are allowed and such indexes may allow multiple columns to be combined. Further detail on key expressions can be found at Section 5.2.13, “Composite Keys and Array Values as Keys”.

Following each column expression you may specify the index type by providing the optional hash or btree keywords or an index_type_expression. For special application-provided or spatial indexes please use the index_type_expression.

If you specify no keyword or the hash keyword for a property, the index will be a hash-based (unsorted) index in respect to that property. If you specify the btree keyword, the index will be a binary-tree-based sorted index in respect to that property. You may combine hash and btree properties for the same index. Specify btree for a property if you expect to perform numerical or string comparison using relational operators (<, >, >=, <=), the between or the in keyword for ranges and inverted ranges. Use hash (the default) instead of btree if you expect to perform exact comparison using =.

For hash and btree index types the column expression must be an event property or column name. Expressions such as col+1 are not currently supported for hash and btree index types but are supported for other index types.

The create table syntax is the same for tables and named windows. The examples herein create a new UserProfileWindow named window and also use the SecuritySummaryTable table.

This sample EPL creates an non-unique index on the active column of table SecuritySummaryTable:

create index MyIndex on SecuritySummaryTable(active)

We list a few example statements next that create a named window and create a single index:

// create a named window
create window UserProfileWindow#time(1 hour) select * from UserProfile

// create a non-unique index (duplicates allowed) for the user id property only
create index UserProfileIndex on UserProfileWindow(userId)

Next, execute a fire-and-forget query as shown below; this sample uses the prepared version to demonstrate:

String query = "select * from UserProfileWindow where userId='Joe'";
EPFireAndForgetPreparedQuery prepared = epRuntime.getFireAndForgetService().prepareQuery(query);
// query performance excellent in the face of large number of rows
EPFireAndForgetQueryResult result = prepared.execute();
// ...later ...
prepared.execute();	// execute a second time

A unique index is generally preferable over non-unique indexes. For named windows, if your data window declares a unique data window (#unique, #firstunique, including intersections and grouped unique data windows) it is not necessary to create a unique index unless index sharing is enabled, since the compiler and runtime considers the unique data window declaration in query planning.

The runtime enforces uniqueness (e.g. unique constraint) for unique indexes. If your application inserts a duplicate row the runtime raises a runtime exception when processing the statement and discards the row. The default error handler logs such an exception and continues.

For example, if the user id together with the profile id uniquely identifies an entry into the named window, your application can create a unique index as shown below:

// create a unique index on user id and profile id
create unique index UserProfileIndex on UserProfileWindow(userId, profileId)

By default, the runtime builds a hash code -based index useful for direct comparison via equals (=). Filter expressions that look for ranges or use in, between do not benefit from the hash-based index and should use the btree keyword. For direct comparison via equals (=) then compiler does not use btree indexes.

The next example creates a composite index over two fields symbol and buyPrice:

// create a named window
create window TickEventWindow#time(1 hour) as (symbol string, buyPrice double)

// create a non-unique index 
create index idx1 on TickEventWindow(symbol hash, buyPrice btree)

A sample fire-and-forget query is shown below (this time the API calls are not shown):

// query performance excellent in the face of large number of rows
select * from TickEventWindow where symbol='GE' and buyPrice between 10 and 20

Note

A table that does not declare one or more primary key columns cannot have a secondary index, as the table holds a maximum of one row.

6.10. Using Fire-and-Forget Queries With Named Windows and Tables

Fire-and-Forget queries can be run against both tables and named windows. We use the term property and column interchangeably.

For selecting from named windows and tables, please see the examples in Section 16.7, “Execute Fire-and-Forget Queries Using EPFireAndForgetService”.

For data manipulation (insert, update, delete) queries, the fire-and-forget query API returns the inserted, updated or deleted rows when the query executes against a named window.

6.10.1. Inserting Data

Your application can insert rows into a table or named window using fire-and-forget (on-demand, non-continuous) queries as described in Section 16.7, “Execute Fire-and-Forget Queries Using EPFireAndForgetService”.

The compiler allows the standard SQL syntax and values keyword and also supports using select to provide values. The values keyword allows inserting multiple rows in a single query.

The syntax using the values keyword is:

insert into window_or_table_name [(property_names)]
values (value_expressions) [, (value_expressions) ...]

The syntax using select is as follows:

insert into window_or_table_name [(property_names)]
select value_expressions

The window_or_table_name is the name of the table or named window to insert rows into.

After the named window or table name you can optionally provide a comma-separated list of property names.

When providing property names, the order of value expressions in the values list or select clause must match the order of property names specified. Column names provided in the select-clause, if specified, are ignored.

When not providing property names and when specifying the values keyword, the order of values must match the order of properties declared for the named window or table. When not providing property names and when specifying the select-clause, expressions must name the properties to be inserted into by assigning a column name using the as keyword.

The example code snippet inserts a new order row into the OrdersWindow named window:

String query = 
  "insert into OrdersWindow(orderId, symbol, price) values ('001', 'GE', 100)";
runtime.getFireAndForgetService().executeQuery(query);

After the values keyword there can be multiple comma-separated lists of values for insertion. A maximum of up to 1000 rows can be inserted per query.

The sample query below inserts two order rows:

insert into OrdersWindow(orderId, symbol, price) values ('001', 'GE', 100), ('002', 'IBM', 75)

Instead of the values keyword you may specify a select-clause as this example shows:

String query = 
  "insert into OrdersWindow(orderId, symbol, price) select '001', 'GE', 100";
runtime.getFireAndForgetService().executeQuery(query);

The following EPL inserts the same values as above but specifies property names as part of the select-clause expressions:

insert into OrdersWindow
select '001' as orderId, 'GE' as symbol, 100 as price

The next EPL inserts the same values as above and does not specify property names thereby populating the first 3 properties of the type of the named window:

insert into OrdersWindow values ('001', 'GE', 100)

6.10.2. Updating Data

Your application can update table and named window rows using fire-and-forget (on-demand, non-continuous) queries as described in Section 16.7, “Execute Fire-and-Forget Queries Using EPFireAndForgetService”.

The syntax for the update clause is as follows:

update window_or_table_name [as stream_name]
set mutation_expression [, mutation_expression [,...]]
[where criteria_expression]

The window_or_table_name is the name of the table or named window to remove rows from. The as keyword is also available to assign a name to the table or named window.

After the set keyword follows a comma-separated list of mutation expressions. For fire-and-forget queries the following restriction applies: Aggregation functions and the prev or prior function may not be used in expressions. Mutation expressions are detailed in Section 6.6, “Updating Data: The On Update Clause”.

The optional where clause contains a criteria_expression that identifies rows to be updated.

The example code snippet updates those rows of the named window that have a negative value for volume:

String query = "update OrdersNamedWindow set volume = 0 where volumne = 0";
runtime.getFireAndForgetService().executeQuery(query);

To instruct the runtime to use the initial property value before update, prefix the property name with the literal initial.

6.10.3. Deleting Data

Your application can delete rows from a named window or table using fire-and-forget (on-demand, non-continuous) queries as described in Section 16.7, “Execute Fire-and-Forget Queries Using EPFireAndForgetService”.

The syntax for the delete clause is as follows:

delete from window_or_table_name [as stream_name]
	[where criteria_expression]

The window_or_table_name is the name of the named window or table to delete rows from. The as keyword is also available to assign a name to the named window or table.

The optional where clause contains a criteria_expression that identifies rows to be removed from the named window or table.

The example code snippet deletes from a named window all rows that have a negative value for volume:

String query = "delete from OrdersNamedWindow where volume <= 0";
runtime.getFireAndForgetService().executeQuery(query);

6.11. Events as Property

Columns in a named window and table may also hold an event or multiple events. More information on the insert into clause providing event columns is in Section 5.10.5, “Event as a Property”.

A sample declaration for a named window and a table is:

create schema InnerData (value string)

create table ContainerTable (innerdata InnerData)

create window ContainerWindow#time(30) as (innerdataArray InnerData[]) // array of events

The second sample creates a named window that specifies two columns: A column that holds an OrderEvent, and a column by name priceTotal. A matching insert into statement is also part of the sample:

create window OrdersWindow#time(30) as select this, price as priceTotal from OrderEvent

insert into OrdersWindow select order, price * unit as priceTotal  
from ServiceOrderEvent as order

Note that the this proprerty must exist on the event and must return the event class itself (JavaBean events only). The property type of the additional priceTotal column is the property type of the existing price property.

Chapter 7. EPL Reference: Patterns

7.1. Event Pattern Overview

Event patterns match when an event or multiple events occur that match the pattern's definition. Patterns can also be time-based.

Pattern expressions consist of pattern atoms and pattern operators:

Pattern atoms are the basic building blocks of patterns. Atoms are filter expressions, observers for time-based events and plug-in custom observers that observe external events not under the control of the runtime.
Pattern operators control expression lifecycle and combine atoms logically or temporally.

The below table outlines the different pattern atoms available:

Table 7.1. Pattern Atoms

Pattern Atom	Example
Filter expressions specify an event to look for.	StockTick(symbol='ABC', price > 100)
Time-based event observers specify time intervals or time schedules.	timer:interval(10 seconds) timer:at(, 16, , , ) timer:schedule(....)
Custom plug-in observers can add pattern language syntax for observing application-specific events.	myapplication:myobserver("http://someResource")

Pattern Atom

Example

Filter expressions specify an event to look for.

StockTick(symbol='ABC', price > 100)

Time-based event observers specify time intervals or time schedules.

timer:interval(10 seconds)

timer:at(*, 16, *, *, *)

timer:schedule(....)

Custom plug-in observers can add pattern language syntax for observing application-specific events.

myapplication:myobserver("http://someResource")

There are 4 types of pattern operators:

Operators that control pattern sub-expression repetition: every, every-distinct, [num] and until
Logical operators: and, or, not
Temporal operators that operate on event order: -> (followed-by)
Guards are where-conditions that control the lifecycle of subexpressions. Examples are timer:within, timer:withinmax and while-expression. Custom plug-in guards may also be used.

Pattern expressions can be nested arbitrarily deep by including the nested expression(s) in () round parenthesis.

Underlying the pattern matching is a hierarchical finite state machine and behavior tree that allocates, transitions and destroys branch and leaf nodes of state based on arriving events and based on time advancing. A single event or advancing time may cause a reaction in multiple parts of your active pattern state. Patterns are stateful as the runtime maintains pattern state. There is a walkthrough of how a sample pattern behaves in Section 7.7, “Event Pattern Walkthrough”.

7.2. How to Use Patterns

7.2.1. Pattern Syntax

This is an example pattern expression that matches on every ServiceMeasurement events in which the value of the latency event property is over 20 seconds, and on every ServiceMeasurement event in which the success property is false. Either one or the other condition must be true for this pattern to match.

every spike=ServiceMeasurement(latency>20000) 
 or every error=ServiceMeasurement(success=false)

In the example above, the pattern expression or operator indicates that the pattern should fire when either of the filter expressions fire. The every operator indicates to fire for every matching event and not just the first matching event. The left hand of the or operator filters for events with a high latency value. The right hand of the or operator filters for events with error status. Filter expressions are explained in Section 7.4, “Filter Expressions in Patterns”.

The example above assigned the tags spike and error to the events in the pattern. The tags are important since the runtime only places tagged events into the output event(s) that a pattern generates, and that the runtime supplies to listeners of the pattern statement. The tags can further be selected in the select-clause of a statement as discussed in Section 5.4.2, “Pattern-Based Event Streams”.

Patterns can also contain comments within the pattern as outlined in Section 5.2.2, “Using Comments”.

7.2.2. Patterns in EPL

A pattern may appear anywhere in the from clause of a statement including joins and subqueries. Patterns may therefore be used in combination with the where clause, group by clause, having clause as well as output rate limiting and insert into.

In addition, you may use data window with a pattern. A data window declared for a pattern only serves to retain pattern matches, for use in joins or for iterating via the iterator API. A data window declared onto a pattern does not limit, cancel, remove or delete intermediate pattern matches of the pattern when pattern matches leave the data window.

This example statement demonstrates the idea by selecting a total price per customer over pairs of events (ServiceOrder followed by a ProductOrder event for the same customer id within 1 minute), occurring in the last 2 hours, in which the sum of price is greater than 100, and using a where clause to filter on name:

select a.custId, sum(a.price + b.price)
from pattern [every a=ServiceOrder -> 
    b=ProductOrder(custId = a.custId) where timer:within(1 min)]#time(2 hour) 
where a.name in ('Repair', b.name)
group by a.custId
having sum(a.price + b.price) > 100

7.2.3. Subscribing to Pattern Events

When a pattern fires it publishes one or more events to any listeners to the pattern statement. The listener interface is the com.espertech.esper.runtime.client.UpdateListener interface.

The example below shows an anonymous implementation of the UpdateListener interface. The example adds the anonymous listener implementation to the myPattern statement created earlier. The listener code simply extracts the underlying event class.

myPattern.addListener(new UpdateListener() {
  public void update(EventBean[] newEvents, EventBean[] oldEvents, EPStatement statement, EPRuntime runtime) {
    ServiceMeasurement spike = (ServiceMeasurement) newEvents[0].get("spike");
    ServiceMeasurement error = (ServiceMeasurement) newEvents[0].get("error");
    ... // either spike or error can be null, depending on which occurred
    ... // add more logic here
  }
});

Listeners receive an array of EventBean instances in the newEvents parameter. There is one EventBean instance passed to the listener for each combination of events that matches the pattern expression. At least one EventBean instance is always passed to the listener.

The properties of each EventBean instance contain the underlying events that caused the pattern to fire, if events have been named in the filter expression via the name=eventType syntax. The property name is thus the name supplied in the pattern expression, while the property type is the type of the underlying class, in this example ServiceMeasurement.

7.2.4. Pulling Data From Patterns

Data can also be obtained from pattern statements via the safeIterator() and iterator() methods on EPStatement (the pull API) If the pattern had fired at least once and the @IterableUnbound annotation is declared for the statement, then the iterator returns the last event for which it fired. The hasNext() method can then be used to determine if the pattern had fired.

if (myPattern.iterator().hasNext()) {
	ServiceMeasurement event = (ServiceMeasurement) statement.iterator().next().get("alert");
    ... // some more code here to process the event
}
else {
    ... // no matching events at this time
}

Further, if a data window is defined onto a pattern, the iterator returns the pattern matches according to the data window expiry policy.

This pattern specifies a length window of 10 elements that retains the last 10 matches of A and B events, for use via iterator or for use in a join or subquery:

select * from pattern [every (A or B)]#length(10)

7.2.5. Pattern Error Reporting

While the pattern compiler analyzes your pattern and verifies its integrity, it may not detect certain pattern errors that may occur at runtime. Sections of this pattern documentation point out common cases where the pattern runtime will log a runtime error. We recommend turning on the log warning level at project development time to inspect and report on warnings logged. If a statement name is assigned to a statement then the statement name is logged as well.

7.2.6. Suppressing Same-Event Matches

Any given event can contribute to multiple matches.

For example, consider the following pattern:

every a=A -> B

Given this sequence of events:

A₁ A₂ B₁

When event B₁ arrives the pattern matches for both the combination {A₁, B₁} and the combination {A₂, B₁}. The runtime indicates both matches to the listener or subscriber by delivering an array containing both matches in a single listener or subscriber invocation.

Use the @SuppressOverlappingMatches pattern-level annotation to instruct the runtime to discard all but the first match among multiple overlapping matches.

The same example with the pattern-level annotation is:

select * from pattern @SuppressOverlappingMatches [every a=A -> b=B]

When event B₁ arrives the pattern outputs only the first combination that matches, namely the combination {A₁, B₁}. The runtime discards the second combination ({A₂, B₁}) that matches as it detects that event B₁ overlaps between the first and the second match.

Note

The runtime only considers tagged events for detecting overlap.
Suppression takes place among multiple simultaneously occurring matches as a result of a single event arriving or time advancing.
Partially completed patterns are not impacted and existing pattern state does not change as a result of suppression.
Limitation: The annotation cannot be used with patterns in joins.

7.2.7. Discarding Partially Completed Patterns

Partially-completed patterns are incomplete matches that are not yet indicated by the runtime because the complete pattern condition is not satisfied. Any given event can be part of multiple partially-completed patterns.

For example, consider the following pattern:

every a=A -> B and C(id=a.id)

Given this sequence of events:

A₁{id='id1'} A₂{id='id2'} B₁

According to the sequence above there are no matches. The pattern is partially completed waiting for C events. The combination {A₁, B₁} is waiting for a C{id='id1'} event before the pattern match is complete for that combination. The combination {A₂, B₁} is waiting for a C{id='id2'} event before the pattern match is complete for that combination.

Assuming event C₁{id='id1') arrives the pattern outputs the combination {A₁, B₁, C₁}. Assuming event C₂{id='id2') arrives the pattern outputs the combination {A₂, B₁, C₂}. Note that event B₁ is part of both partially-completed patterns.

Use the @DiscardPartialsOnMatch pattern-level annotation to instruct the runtime that when any matches occur to discard partially completed patterns that overlap in terms of the events that make up the match (or matches if there are multiple matches).

The same example using the @DiscardPartialsOnMatch pattern-level annotation is:

select * from pattern @DiscardPartialsOnMatch [every a=A -> B and C(id=a.id)]

When event C₁{id='id1') arrives the pattern outputs the match combination {A₁, B₁, C₁}. Upon indication of the match the runtime discards all partially-completed patterns that refer to either of the A₁, B₁ and C₁ events. Since event B₁ is part of a partially-completed pattern waiting for C{id='id2'}, the runtime discards that partially-completed pattern. Therefore when C₂{id='id2'} arrives the runtime outputs no matches.

When specifying both @DiscardPartialsOnMatch and @SuppressOverlappingMatches the runtime discards the partially-completed patterns that overlap all matches including suppressed matches.

Note

The runtime only considers tagged events for detecting overlap.
Only partially completed patterns are impacted.
Limitation: The annotation cannot be used with patterns in joins.
Limitation: Removing partially completed patterns is not an undo operation. There is no reversal in truth-value, i.e. past matches are not retracted or indicated as a remove stream. The not operator can change truth value to permanently false. The change in truth-value does not get reversed when a match removes the event that caused the not operator to become permanently false.

7.3. Operator Precedence

The operators at the top of this table take precedence over operators lower on the table.

Table 7.2. Pattern Operator Precedence


MyEvent where timer:within(1 sec)
every MyEvent
timer:interval(5 min) and not MyEvent
[5] MyEvent
every (MyEvent and MyOtherEvent)
every (MyEvent or MyOtherEvent)
every (MyEvent -> MyOtherEvent)

Precedence	Operator	Description	Example
1	guard postfix	`where timer:within` and `while (expression)` (incl. withinmax and plug-in pattern guard)	MyEvent where timer:within(1 sec) a=MyEvent while (a.price between 1 and 10)
2	unary	`every, not, every-distinct`	every MyEvent timer:interval(5 min) and not MyEvent
3	repeat	`[num]`, `until`	[5] MyEvent [1..3] MyEvent until MyOtherEvent
4	and	`and`	every (MyEvent and MyOtherEvent)
5	or	`or`	every (MyEvent or MyOtherEvent)
6	followed-by	`->`	every (MyEvent -> MyOtherEvent)

If you are not sure about the precedence, please consider placing parenthesis () around your subexpressions. Parenthesis can also help make expressions easier to read and understand.

The following table outlines sample equivalent expressions, with and without the use of parenthesis for subexpressions.

Table 7.3. Equivalent Pattern Expressions

Expression	Equivalent	Reason
every A or B	(every A) or B	The `every` operator has higher precedence then the `or` operator.
every A -> B or C	(every A) -> (B or C)	The `or` operator has higher precedence then the `followed-by` operator.
A -> B or B -> A	A -> (B or B) -> A	The `or` operator has higher precedence then the `followed-by` operator, specify as (A -> B) or (B -> A) instead.
A and B or C	(A and B) or C	The `and` operator has higher precedence then the `or` operator.
A -> B until C -> D	A -> (B until C) -> D	The `until` operator has higher precedence then the `followed-by` operator.
[5] A or B	([5] A) or B	The `[num]` repeat operator has higher precedence then the `or` operator.
every A where timer:within(10)	every (A where timer:within(10))	The `where` postfix has higher precedence then the `every` operator.

7.4. Filter Expressions in Patterns

com.mypackage.myevents.RfidEvent

To make the event pattern fire for every RfidEvent and not just the first event, use the every keyword.

every com.mypackage.myevents.RfidEvent

The example above specifies the fully-qualified Java class name as the event type. Via configuration, the event pattern above can be simplified by using the name that has been defined for the event type.

every RfidEvent

Interfaces and superclasses are also supported as event types. In the below example IRfidReadable is an interface class, and the statement matches any event that implements this interface:

every org.myorg.rfid.IRfidReadable

The filtering criteria to filter for events with certain event property values are placed within parenthesis after the event type name:

RfidEvent(category="Perishable")

All expressions can be used in filters, including static method invocations that return a boolean value:

RfidEvent(com.mycompany.MyRFIDLib.isInRange(x, y) or (x<0 and y < 0))

Filter expressions can be separated via a single comma ','. The comma represents a logical AND between expressions:

RfidEvent(zone=1, category=10)
...is equivalent to...
RfidEvent(zone=1 and category=10)

For more information on filters please see Section 5.4.1, “Filter-Based Event Streams”. Contained-event selection on filters in patterns is further described in Section 5.19, “Contained-Event Selection”.

Filter criteria can also refer to events matching prior named events in the same expression. Below pattern is an example in which the pattern matches once for every RfidEvent that is preceded by an RfidEvent with the same asset id.

every e1=RfidEvent -> e2=RfidEvent(assetId=e1.assetId)

The syntax shown above allows filter criteria to reference prior results by specifying the event name tag of the prior event, and the event property name. The tag names in the above example were e1 and e2. This syntax can be used in all filter operators or expressions including ranges and the in set-of-values check:

every e1=RfidEvent -> 
  e2=RfidEvent(MyLib.isInRadius(e1.x, e1.y, x, y) and zone in (1, e1.zone))

An arriving event changes the truth value of all expressions that look for the event. Consider the pattern as follows:

every (RfidEvent(zone > 1) and RfidEvent(zone < 10))

The pattern above is satisfied as soon as only one event with zone in the interval [2, 9] is received.

7.4.1. Filter Expressions and Filter Indexes

Important

A detailed description of how filters become active and are indexed is provided at Section 2.18.2.2, “Filter Index Pattern Example”.

An expression such as a=A -> B(id=a.id) (A followed-by B with the same id as A) is not just a state change, in fact the runtime registers new B-filter instances dynamically and in runtime-wide shared filter indexes. This means that while such a pattern seems to be slow if you are sending A-events, the runtime can filter, match or discard B-events very fast as for B-events it only needs to perform a lookup in filter indexes. If you are looking for best performance and don't expect to need filter indexes, or if you compare to another technology that doesn't have the concept of filter indexes, please use match-recognize instead.

The runtime analyzes all filter expressions within a pattern and determines the filter indexes to use or to create. Indexing filter values to match event properties of incoming events enables the runtime to match incoming events faster to pattern subexpressions.

More information on filter indexes in general can be found at Section 2.18.2, “Filter Indexes”.

More information on the operators relevant to filter indexes can be found at Section 5.4.1.2, “Specifying Filter Criteria”.

7.4.2. Controlling Event Consumption

An arriving event applies to all filter expressions for which the event matches. In other words, an arriving event is not consumed by any specify filter expression(s) but applies to all active filter expressions of all pattern sub-expressions.

You may provide the @consume annotation as part of a filter expression to control consumption of an arriving event. If an arriving event matches the filter expression marked with @consume it is no longer available to other filter expressions of the same pattern that also match the arriving event.

The @consume can include a level number in parenthesis. A higher level number consumes the event first. The default level number is 1. Multiple filter expressions with the same level number for @consume all match the event.

Consider the next sample pattern:

a=RfidEvent(zone='Z1') and b=RfidEvent(assetId='0001')

This pattern fires when a single RfidEvent event arrives that has zone 'Z1' and assetId '0001'. The pattern also matches when two RfidEvent events arrive, in any order, wherein one has zone 'Z1' and the other has assetId '0001'.

Mark a filter expression with @consume to indicate that if an arriving event matches multiple filter expressions that the runtime prefers the marked filter expression and does not match any other filter expression.

This updated pattern statement uses @consume to indicate that a match against zone is preferred:

a=RfidEvent(zone='Z1')@consume and b=RfidEvent(assetId='0001')

This pattern no longer fires when a single RfidEvent arrives that has zone 'Z1' and assetId '0001', because when the first filter expression matches the pattern runtime consumes the event. The pattern only matches when two RfidEvent events arrive in any order. One event must have zone 'Z1' and the other event must have a zone other than 'Z1' and an assetId '0001'.

The next sample pattern provides a level number for each @consume:

a=RfidEvent(zone='Z1')@consume(2) 
  or b=RfidEvent(assetId='0001')@consume(1) 
  or c=RfidEvent(category='perishable'))

The pattern fires when an RfidEvent arrives with zone 'Z1'. In this case the output event populates property 'a' but not properties 'b' and 'c'. The pattern also fires when an RfidEvent arrives with a zone other than 'Z1' and an asset id of '0001'. In this case the output event populates property 'b' but not properties 'a' and 'c'. The pattern also fires when an RfidEvent arrives with a zone other than 'Z1' and an asset id other than '0001' and a category of 'perishable'. In this case the output event populates property 'c' but not properties 'a' and 'b'.

7.4.3. Use With Named Windows and Tables

When your filter expression provides the name of a named window then the filter expression matches each time an event is inserted into the named window that matches the filter conditions.

For example, assume a named window that holds the last order event per order id:

create window LastOrderWindow#unique(orderId) as OrderEvent

Assume that all order events are inserted into the named window using insert-into:

insert into LastOrderWindow select * from OrderEvent

This sample pattern fires 10 seconds after an order event with a price greater then 100 was inserted:

select * from pattern [every o=LastOrderWindow(price >= 100) -> timer:interval(10 sec)]

The pattern above fires only for events inserted-into the LastOrderWindow named window and does not fire when an order event was updated using on-update or merged using on-merge.

If your application would like to have the pattern fire for any change to the named window events including updates and merges, you must select from the named window as follows:

insert into OrderWindowChangeStream select * from LastOrderWindow

select * from pattern [every o=OrderWindowChangeStream(price >= 100) -> timer:interval(10 sec)]

A table cannot be listed as part of a pattern filter, however any filter EPL expressions can have tables access expressions and subqueries against tables.

Assuming that MyTable is a table, the following is not allowed:

// not allowed
select * from pattern [every MyTable -> timer:interval(10 sec)]

7.5. Pattern Operators

7.5.1. Every

The every operator indicates that the pattern sub-expression should restart when the subexpression qualified by the every keyword evaluates to true or false. Without the every operator the pattern sub-expression stops when the pattern sub-expression evaluates to true or false.

As a side note, please be aware that a single invocation to the UpdateListener interface may deliver multiple events in one invocation, since the interface accepts an array of values.

Thus the every operator works like a factory for the pattern sub-expression contained within. When the pattern sub-expression within it fires and thus quits checking for events, the every causes the start of a new pattern sub-expression listening for more occurrences of the same event or set of events.

Every time a pattern sub-expression within an every operator turns true the runtime starts a new active subexpression looking for more event(s) or timing conditions that match the pattern sub-expression. If the every operator is not specified for a subexpression, the subexpression stops after the first match was found.

This pattern fires when encountering an A event and then stops looking.

This pattern keeps firing when encountering A events, and doesn't stop looking.

every A

When using every operator with the -> followed-by operator, each time the every operator restarts it also starts a new subexpression instance looking for events in the followed-by subexpression.

Let's consider an example event sequence as follows.

A₁ B₁ C₁ B₂ A₂ D₁ A₃ B₃ E₁ A₄ F₁ B₄

Table 7.4. Every Operator Examples

Example	Description
every ( A -> B )	Detect an A event followed by a B event. At the time when B occurs the pattern matches, then the pattern matcher restarts and looks for the next A event. Matches on B₁ for combination {A₁, B₁} Matches on B₃ for combination {A₂, B₃} Matches on B₄ for combination {A₄, B₄}
every A -> B	The pattern fires for every A event followed by a B event. Matches on B₁ for combination {A₁, B₁} Matches on B₃ for combination {A₂, B₃} and {A₃, B₃} Matches on B₄ for combination {A₄, B₄}
A -> every B	The pattern fires for an A event followed by every B event. Matches on B₁ for combination {A₁, B₁}. Matches on B₂ for combination {A₁, B₂}. Matches on B₃ for combination {A₁, B₃} Matches on B₄ for combination {A₁, B₄}
every A -> every B	The pattern fires for every A event followed by every B event. Matches on B₁ for combination {A₁, B₁}. Matches on B₂ for combination {A₁, B₂}. Matches on B₃ for combination {A₁, B₃} and {A₂, B₃} and {A₃, B₃} Matches on B₄ for combination {A₁, B₄} and {A₂, B₄} and {A₃, B₄} and {A₄, B₄}

The examples show that it is possible that a pattern fires for multiple combinations of events that match a pattern expression. Each combination is posted as an EventBean instance to the update method in the UpdateListener implementation.

Let's consider the every operator in conjunction with a subexpression that matches 3 events that follow each other:

every (A -> B -> C)

The pattern first looks for A events. When an A event arrives, it looks for a B event. After the B event arrives, the pattern looks for a C event. Finally, when the C event arrives the pattern fires. The runtime then starts looking for an A event again.

Assume that between the B event and the C event a second A₂ event arrives. The pattern would ignore the A₂ event entirely since it's then looking for a C event. As observed in the prior example, the every operator restarts the subexpression A -> B -> C only when the subexpression fires.

In the next statement the every operator applies only to the A event, not the whole subexpression:

every A -> B -> C

This pattern now matches for each A event that is followed by a B event and then a C event, regardless of when the A event arrives. Note that for each A event that arrives the pattern runtime starts a new subexpression looking for a B event and then a C event, outputting each combination of matching events.

7.5.1.1. `Every` Operator Equivalence

A pattern that only has the every operator and a single filter expression is equivalent to selecting the same filter in the from clause:

select * from StockTickEvent(symbol='GE')	     // Prefer this
// ... equivalent to ...
select * from pattern[every StockTickEvent(symbol='GE')]

7.5.1.2. Limiting Subexpression Lifetime

As the introduction of the every operator states, the operator starts new subexpression instances and can cause multiple matches to occur for a single arriving event.

New subexpressions also take a very small amount of system resources and thereby your application should carefully consider when subexpressions must end when designing patterns. Use the timer:within construct and the and not constructs to end active subexpressions. The data window onto a pattern stream does not serve to limit pattern sub-expression lifetime.

Lets look at a concrete example. Consider the following sequence of events arriving:

A₁ A₂ B₁

This pattern matches on arrival of B₁ and outputs two events (an array of length 2 if using a listener). The two events are the combinations {A₁, B₁} and {A₂, B₁}:

every a=A -> b=B

The and not operators are used to end an active subexpression.

The next pattern matches on arrival of B₁ and outputs only the last A event which is the combination {A₂, B₁}:

every a=A -> (b=B and not A)

The and not operators cause the subexpression looking for {A₁, B?} to end when A₂ arrives.

every timer:interval(1 sec) -> b=B

Again the and not operators can end subexpressions that are not intended to match any longer:

every timer:interval(1 sec) -> (b=B and not timer:interval(1 sec))
// equivalent to
every timer:interval(1 sec) -> (b=B where timer:within(1 sec))

7.5.1.3. `Every` Operator Example

This example considers a generic pattern in which the pattern must match for each A event followed by a B event and followed by a C event, in which both the B event and the C event must arrive within 1 hour of the A event. The first approach to the pattern is as follows:

every A  -> (B -> C) where timer:within(1 hour)

Consider the following sequence of events arriving:

A₁ A₂ B₁ C₁ B₂ C₂

First, the pattern as above never stops looking for A events since the every operator instructs the pattern to keep looking for A events.

When A₁ arrives, the pattern starts a new subexpression that keeps A₁ in memory and looks for any B event. At the same time, it also keeps looking for more A events.

When A₂ arrives, the pattern starts a new subexpression that keeps A₂ in memory and looks for any B event. At the same time, it also keeps looking for more A events.

After the arrival of A₂, there are 3 subexpressions active:

The first active subexpression with A₁ in memory, looking for any B event.
The second active subexpression with A₂ in memory, looking for any B event.
A third active subexpression, looking for the next A event.

In the pattern above, there is a 1-hour lifetime for subexpressions looking for B and C events. Thus, if no B and no C event arrive within 1 hour after A₁, the first subexpression goes away. If no B and no C event arrive within 1 hour after A₂, the second subexpression goes away. The third subexpression however stays around looking for more A events.

The pattern as shown above thus matches on arrival of C₁ for combination {A₁, B₁, C₁} and for combination {A₂, B₁, C₁}, provided that B₁ and C₁ arrive within an hour of A₁ and A₂.

You may now ask how to match on {A₁, B₁, C₁} and {A₂, B₂, C₂} instead, since you may need to correlate on a given property.

The pattern as discussed above matches every A event followed by the first B event followed by the next C event, and doesn't specifically qualify the B or C events to look for based on the A event. To look for specific B and C events in relation to a given A event, specify correlating properties of the A event, for example:

every a=A -> (B(id=a.id) -> C(id=a.id)) where timer:within(1 hour)

The pattern as shown above thus matches on arrival of C₁ for combination {A₁, B₁, C₁} and on arrival of C₂ for combination {A₂, B₂, C₂}.

7.5.1.4. Sensor Example

This example looks at temperature sensor events named Sample. The pattern detects when 3 sensor events indicate a temperature of more then 50 degrees uninterrupted within 90 seconds of the first event, considering events for the same sensor only.

every sample=Sample(temp > 50) ->
( (Sample(sensor=sample.sensor, temp > 50) and not Sample(sensor=sample.sensor, temp <= 50))   
  ->
  (Sample(sensor=sample.sensor, temp > 50) and not Sample(sensor=sample.sensor, temp <= 50))   
 ) where timer:within(90 seconds))

The pattern starts a new subexpression in the round braces after the first followed-by operator for each time a sensor indicated more then 50 degrees. Each subexpression then lives a maximum of 90 seconds. Each subexpression ends if a temperature of 50 degress or less is encountered for the same sensor. Only if 3 temperature events in a row indicate more then 50 degrees, and within 90 seconds of the first event, and for the same sensor, does this pattern fire.

7.5.2. Every-Distinct

Similar to the every operator in most aspects, the every-distinct operator indicates that the pattern sub-expression should restart when the subexpression qualified by the every-distinct keyword evaluates to true or false. In addition, the every-distinct eliminates duplicate results received from an active subexpression according to its distinct-value expressions.

The synopsis for the every-distinct pattern operator is:

every-distinct(distinct_value_expr [, distinct_value_exp[...][, expiry_time_period])

Within parenthesis are one or more distinct_value_expr expressions that return the values by which to remove duplicates. Further detail on key expressions can be found at Section 5.2.13, “Composite Keys and Array Values as Keys”.

You may optionally specify an expiry_time_period time period. If present, the pattern runtime expires and removes distinct key values that are older then the time period, removing their associated memory and allowing such distinct values to match again. When your distinct value expressions return an unlimited number of values, for example when your distinct value is a timestamp or auto-increment column, you should always specify an expiry time period.

When specifying properties in the distinct-value expression list, you must ensure that the event types providing properties are tagged. Only properties of event types within filter expressions that are sub-expressions to the every-distinct may be specified.

For example, this pattern keeps firing for every A event with a distinct value for its aprop property:

every-distinct(a.aprop) a=A

Note that the pattern above assigns the a tag to the A event and uses a.prop to identify the prop property as a value of the a event A.

A pattern that returns the first Sample event for each sensor, assuming sensor is a field that returns a unique id identifying the sensor that originated the Sample event, is:

every-distinct(s.sensor) s=Sample

The next pattern looks for pairs of A and B events and returns only the first pair for each combination of aprop of an A event and bprop of a B event:

every-distinct(a.aprop, b.bprop) (a=A and b=B)

The following pattern looks for A events followed by B events for which the value of the aprop of an A event is the same value of the bprop of a B event but only for each distinct value of aprop of an A event:

every-distinct(a.aprop) a=A -> b=B(bprop = a.aprop)

When specifying properties as part of distinct-value expressions, properties must be available from tagged event types in sub-expressions to the every-distinct.

The following patterns are not valid:

// Invalid: event type in filter not tagged
every-distinct(aprop) A
			
// Invalid: property not from a sub-expression of every-distinct
a=A -> every-distinct(a.aprop) b=B

When an active subexpression to every-distinct becomes permanently false, the distinct-values seen from the active subexpression are removed and the sub-expression within is restarted.

For example, the below pattern detects each A event distinct by the value of aprop.

every-distinct(a.aprop) (a=A and not B)

In the pattern above, when a B event arrives, the subexpression becomes permanently false and is restarted anew, detecting each A event distinct by the value of aprop without considering prior values.

When your distinct key is a timestamp or other non-unique property, specify an expiry time period.

The following example returns every distinct A event according to the timestamp property on the A event, retaining each timestamp value for 10 seconds:

every-distinct(a.timestamp, 10 seconds) a=A

In the example above, if for a given A event and its timestamp value the same timestamp value occurs again for another A event before 10 seconds passed, the A event is not a match. If 10 seconds passed the pattern indicates a second match.

You may not use every-distinct with a timer-within guard to expire keys: The expiry time notation as above is the recommended means to expire keys.

// This is not the same as above; It does not expire transaction ids and is not recommended
every-distinct(a.timestamp) a=A where timer:within(10 sec)

7.5.3. Repeat

The repeat operator fires when a pattern sub-expression evaluates to true a given number of times. The synopsis is as follows:

[match_count] repeating_subexpr

The repeat operator is very similar to the every operator in that it restarts the repeating_subexpr pattern sub-expression up to a given number of times.

match_count is a positive number that specifies how often the repeating_subexpr pattern sub-expression must evaluate to true before the repeat expression itself evaluates to true, after which the runtime may indicate a match.

For example, this pattern fires when the last of five A events arrives:

[5] A

Parenthesis must be used for nested pattern sub-expressions. This pattern fires when the last of a total of any five A or B events arrives:

[5] (A or B)

Without parenthesis the pattern semantics change, according to the operator precedence described earlier. This pattern fires when the last of a total of five A events arrives or a single B event arrives, whichever happens first:

[5] A or B

Tags can be used to name events in filter expression of pattern sub-expressions. The next pattern looks for an A event followed by a B event, and a second A event followed by a second B event. The output event provides indexed and array properties of the same name:

[2] (a=A -> b=B)

Using tags with repeat is further described in Section 7.5.4.6, “Tags and the Repeat Operator”.

Consider the following pattern that demonstrates the behavior when a pattern sub-expression becomes permanently false:

[2] (a=A and not C)

In the case where a C event arrives before 2 A events arrive, the pattern above becomes permanently false.

Lets add an every operator to restart the pattern and thus keep matching for all pairs of A events that arrive without a C event in between each pair:

every [2] (a=A and not C)

Since pattern matches return multiple A events, your select clause should use tag a as an array, for example:

select a[0].id, a[1].id from pattern [every [2] (a=A and not C)]

7.5.4. Repeat-Until

The repeat until operator provides additional control over repeated matching.

The repeat until operator takes an optional range, a pattern sub-expression to repeat, the until keyword and a second pattern sub-expression that ends the repetition. The synopsis is as follows:

[range] repeated_pattern_expr until end_pattern_expr

Without a range, the runtime matches the repeated_pattern_expr pattern sub-expression until the end_pattern_expr evaluates to true, at which time the expression turns true.

An optional range can be used to indicate the minimum number of times that the repeated_pattern_expr pattern sub-expression must become true.

The optional range can also specify a maximum number of times that repeated_pattern_expr pattern sub-expression evaluates to true and retains tagged events. When this number is reached, the runtime stops the repeated_pattern_expr pattern sub-expression.

The until keyword is always required when specifying a range and is not required if specifying a fixed number of repeat as discussed in the section before.

7.5.4.1. Unbound Repeat

In the unbound repeat, without a range, the runtime matches the repeated_pattern_expr pattern sub-expression until the end_pattern_expr evaluates to true, at which time the expression turns true. The synopsis is:

repeated_pattern_expr until end_pattern_expr

This is a pattern that keeps looking for A events until a B event arrives:

A until B

Nested pattern sub-expressions must be placed in parenthesis since the until operator has precedence over most operators. This example collects all A or B events for 10 seconds and places events received in indexed properties 'a' and 'b':

(a=A or b=B) until timer:interval(10 sec)

7.5.4.2. Bound Repeat Overview

The synopsis for the optional range qualifier is:

[ [low_endpoint] : [high_endpoint] ]

low_endpoint is an optional number that appears on the left of a colon (:), after which follows an optional high_endpoint number.

A range thus consists of a low_endpoint and a high_endpoint in square brackets and separated by a colon (:) characters. Both endpoint values are optional but either one or both must be supplied. The low_endpoint can be omitted to denote a range that starts at zero. The high_endpoint can be omitted to denote an open-ended range.

Some examples for valid ranges might be:

[3 : 10]
[:3]    // range starts at zero
[2:]    // open-ended range

The low_endpoint, if specified, defines the minimum number of times that the repeated_pattern_expr pattern sub-expression must become true in order for the expression to become true.

The high_endpoint, if specified, is the maximum number of times that the repeated_pattern_expr pattern sub-expression becomes true. If the number is reached, the runtime stops the repeated_pattern_expr pattern sub-expression.

In all cases, only at the time that the end_pattern_expr pattern sub-expression evaluates to true does the expression become true. If end_pattern_expr pattern sub-expression evaluates to false, then the expression evaluates to false.

7.5.4.3. Bound Repeat - Open Ended Range

An open-ended range specifies only a low endpoint and not a high endpoint.

Consider the following pattern which requires at least three A events to match:

[3:] A until B

In the pattern above, if a B event arrives before 3 A events occurred, the expression ends and evaluates to false.

7.5.4.4. Bound Repeat - High Endpoint Range

A high-endpoint range specifies only a high endpoint and not a low endpoint.

In this sample pattern the runtime will be looking for a maximum of 3 A events. The expression turns true as soon as a single B event arrives regardless of the number of A events received:

[:3] A until B

The next pattern matches when a C or D event arrives, regardless of the number of A or B events that occurred:

[:3] (a=A or b=B) until (c=C or d=D)

In the pattern above, if more then 3 A or B events arrive, the pattern stops looking for additional A or B events. The 'a' and 'b' tags retain only the first 3 (combined) matches among A and B events. The output event contains these tagged events as indexed properties.

7.5.4.5. Bound Repeat - Bounded Range

A bounded range specifies a low endpoint and a high endpoint.

The next pattern matches after at least one A event arrives upon the arrival of a single B event:

[1:3] a=A until B

If a B event arrives before the first A event, then the pattern does not match. Only the first 3 A events are returned by the pattern.

7.5.4.6. Tags and the Repeat Operator

The tags assigned to events in filter subexpressions within a repeat operator are available for use in filter expressions and also in any EPL clause.

This sample pattern matches 2 A events followed by a B event. Note the filter on B events: only a B event that has a value for the "beta" property that equals any of the "id" property values of the two A events is considered:

[2] A -> B(beta in (a[0].id, a[1].id))

The next statement returns pairs of A events:

select a, a[0], a[0].id, a[1], a[1].id
from pattern [ every [2] a=A ]

The select clause of the statement above showcases different ways of accessing tagged events:

The tag itself can be used to select an array of underlying events. For example, the 'a' expression above returns an array of underlying events of event type A.
The tag as an indexed property returns the underlying event at that index. For instance, the 'a[0]' expression returns the first underlying A event, or null if no such A event was matched by the repeat operator.
The tag as a nested, indexed property returns a property of the underlying event at that index. For example, the 'a[1].id' expression returns the 'id' property value of the second A event, or null if no such second A event was matched by the repeat operator.

7.5.4.7. Note on Indexed Tags

You may not use indexed tags defined in the sub-expression to the repeat operator in the same subexpression. For example, in the following pattern the subexpression to the repeat operator is (a=A() -> b=B(id=a[0].id)) and the tag a cannot be used in its indexed form in the filter for event B:

// invalid
every [2] (a=A() -> b=B(id=a[0].id))

You can use tags without an index:

// valid
every [2] (a=A() -> b=B(id=a.id))

7.5.5. And

Similar to the Java && operator the and operator requires both nested pattern expressions to turn true before the whole expression turns true (a join pattern).

This pattern matches when both an A event and a B event arrive, at the time the last of the two events arrive:

A and B

This pattern matches on any sequence of an A event followed by a B event and then a C event followed by a D event, or a C event followed by a D and an A event followed by a B event:

(A -> B) and (C -> D)

Note that in an and pattern expression it is not possible to correlate events based on event property values. For example, this is an invalid pattern:

// This is NOT valid
a=A and B(id = a.id)

The above expression is invalid as it relies on the order of arrival of events, however in an and expression the order of events is not specified and events fulfill an and condition in any order. The above expression can be changed to use the followed-by operator:

// This is valid
a=A -> B(id = a.id)
// another example using 'and'...
a=A -> (B(id = a.id) and C(id = a.id))

Consider a pattern that looks for the same event:

A and A

The pattern above fires when a single A event arrives. The first arriving A event triggers a state transition in both the left and the right hand side expression.

In order to match after two A events arrive in any order, there are two options to express this pattern. The followed-by operator is one option and the repeat operator is the second option, as the next two patterns show:

A -> A
// ... or ...
[2] A

7.5.6. Or

Similar to the Java “||” operator the or operator requires either one of the expressions to turn true before the whole expression turns true.

Look for either an A event or a B event. As always, A and B can itself be nested expressions as well.

A or B

The next EPL outputs all A and B events:

every A or every B

Elaborating further, the expression every A or every B is equivalent to every (A or B). Prefer every A or every B as the every keyword lets the runtime know that filters for A and B can remain active. Consider the expression every A or every timer:interval(10 sec) which is not equivalent to every (A or timer:interval(10 sec)). This is because in the latter expression when an A event arrives the interval restarts.

7.5.7. Not

The not operator negates the truth value of an expression. Pattern expressions prefixed with not are automatically defaulted to true upon start, and turn permanently false when the expression within turns true.

The not operator is generally used in conjunction with the and operator or subexpressions as the below examples show.

This pattern matches only when an A event is encountered followed by a B event but only if no C event was encountered before either an A event and a B event, counting from the time the pattern is started:

(A -> B) and not C

Assume we'd like to detect when an A event is followed by a D event, without any B or C events between the A and D events:

A -> (D and not (B or C))

It may help your understanding to discuss a pattern that uses the or operator and the not operator together:

a=A -> (b=B or not C)

In the pattern above, when an A event arrives then the runtime starts the subexpression B or not C. As soon as the subexpression starts, the not operator turns to true. The or expression turns true and thus your listener receives an invocation providing the A event in the property 'a'. The subexpression does not end and continues listening for B and C events. Upon arrival of a B event your listener receives a second invocation. If instead a C event arrives, the not turns permanently false however that does not affect the or operator (but would end an and operator).

To test for absence of an event, use timer:interval together with and not operators. The sample statement reports each 10-second interval during which no A event occurred:

every (timer:interval(10 sec) and not A)

In many cases the not operator, when used alone, does not make sense. The following example is invalid and will log a warning when the runtime is started:

// not a sensible pattern
(not a=A) -> B(id=a.id)

7.5.8. Followed-By

The followed by -> operator specifies that first the left hand expression must turn true and only then is the right hand expression evaluated for matching events.

Look for an A event and if encountered, look for a B event. As always, A and B can itself be nested event pattern expressions.

A -> B

This is a pattern that fires when 2 status events indicating an error occur one after the other.

StatusEvent(status='ERROR') -> StatusEvent(status='ERROR')

A pattern that takes all A events that are not followed by a B event within 5 minutes:

every A -> (timer:interval(5 min) and not B)

A pattern that takes all A events that are not preceded by B within 5 minutes:

every (timer:interval(5 min) and not B -> A)

7.5.8.1. Limiting Sub-Expression Count

The followed-by -> operator can optionally be provided with an expression that limits the number of sub-expression instances of the right-hand side pattern sub-expression.

The synopsis for the followed-by operator with limiting expression is:

lhs_expression -[limit_expression]> rhs_expression

Each time the lhs_expression pattern sub-expression turns true the pattern runtime starts a new rhs_expression pattern sub-expression. The limit_expression returns an integer value that defines a maximum number of pattern sub-expression instances that can simultaneously be present for the same rhs_expression.

When the limit is reached the pattern runtime issues a com.espertech.esper.common.client.hook.condition.ConditionPatternSubexpressionMax notification object to any condition handlers registered with the runtime as described in Section 16.11, “Condition Handling” and does not start a new pattern sub-expression instance for the right-hand side pattern sub-expression.

For example, consider the following pattern which returns for every A event the first B event that matches the id field value of the A event:

every a=A -> b=B(id = a.id)

In the above pattern, every time an A event arrives (lhs) the pattern runtime starts a new pattern sub-expression (rhs) consisting of a filter for the first B event that has the same value for the id field as the A event.

In some cases your application may want to limit the number of right-hand side sub-expressions because of memory concerns or to reduce output. You may add a limit expression returning an integer value as part of the operator.

This example employs the followed-by operator with a limit expression to indicate that maximally 2 filters for B events (the right-hand side pattern sub-expression) may be active at the same time:

every a=A -[2]> b=B(id = a.id)

Note that the limit expression in the example above is not a limit per value of id field, but a limit counting all right-hand side pattern sub-expression instances that are managed by that followed-by sub-expression instance.

If your followed-by operator lists multiple sub-expressions with limits, each limit applies to the immediate right-hand side. For example, the pattern below limits the number of filters for B events to 2 and the number of filters for C events to 3:

every a=A -[2]> b=B(id = a.id) -[3]> c=C(id = a.id)

7.5.8.2. Limiting Runtime-Wide Sub-Expression Count

The runtime allows setting a maximum number of pattern sub-expressions in the configuration, applicable to all followed-by operators of all statements.

If your application has patterns in multiple statements and all such patterns should count towards a total number of pattern sub-expression counts, you may consider setting a maximum number of pattern sub-expression instances, runtime-wide, via the configuration described in Section 17.6.4.1, “Followed-By Operator Maximum Subexpression Count”.

When the limit is reached the pattern runtime issues a notification object to any condition handlers registered with the runtime as described in Section 16.11, “Condition Handling”. Depending on your configuration the runtime can prevent the start of a new pattern sub-expression instance for the right-hand side pattern sub-expression, until pattern sub-expression instances end or statements are undeployed.

The notification object issued to condition handlers is an instance of com.espertech.esper.common.client.hook.condition.ConditionPatternRuntimeSubexpressionMax. The notification object contains information which statement triggered the limit and the pattern counts per statement for all statements.

For information on configuration please consult Section 17.6.4.1, “Followed-By Operator Maximum Subexpression Count”.

7.5.9. Pattern Guards

Guards are where-conditions that control the lifecycle of subexpressions. Custom guard functions can also be used. The section Chapter 22, Integration and Extension outlines guard plug-in development in greater detail.

The pattern guard where-condition has no relationship to the EPL where clause that filters sets of events.

Take as an example the following pattern expression:

MyEvent where timer:within(10 sec)

In this pattern the timer:within guard controls the subexpression that is looking for MyEvent events. The guard terminates the subexpression looking for MyEvent events after 10 seconds after start of the pattern. Thus the pattern alerts only once when the first MyEvent event arrives within 10 seconds after start of the pattern.

The every keyword requires additional discussion since it also controls subexpression lifecycle. Let's add the every keyword to the example pattern:

every MyEvent where timer:within(10 sec)

The difference to the pattern without every is that each MyEvent event that arrives now starts a new subexpression, including a new guard, looking for a further MyEvent event. The result is that, when a MyEvent arrives within 10 seconds after pattern start, the pattern execution will look for the next MyEvent event to arrive within 10 seconds after the previous one.

By placing parentheses around the every keyword and its subexpression, you can have the every under the control of the guard:

(every MyEvent) where timer:within(10 sec)

In the pattern above, the guard terminates the subexpression looking for all MyEvent events after 10 seconds after start of the pattern. This pattern alerts for all MyEvent events arriving within 10 seconds after pattern start, and then stops.

Guards do not change the truth value of the subexpression of which the guard controls the lifecycle, and therefore do not cause a restart of the subexpression when used with the every operator. For example, the next pattern stops returning matches after 10 seconds unless a match occurred within 10 seconds after pattern start:

every ( (A and B) where timer:within(10 sec) )

7.5.9.1. The `timer:within` Pattern Guard

The timer:within guard acts like a stopwatch. If the associated pattern expression does not turn true within the specified time period it is stopped and permanently false.

The synopsis for timer:within is as follows:

timer:within(time_period_expression)

The time_period_expression is a time period (see Section 5.2.1, “Specifying Time Periods”) or an expression providing a number of seconds as a parameter. The interval expression may contain references to properties of prior events in the same pattern as well as variables and substitution parameters.

This pattern fires if an A event arrives within 5 seconds after statement deployment.

A where timer:within (5 seconds)

This pattern fires for all A events that arrive within 5 seconds. After 5 seconds, this pattern stops matching even if more A events arrive.

(every A) where timer:within (5 seconds)

This pattern matches for any one A or B event in the next 5 seconds.

( A or B ) where timer:within (5 sec)

This pattern matches for any 2 errors that happen 10 seconds within each other.

every (StatusEvent(status='ERROR') -> StatusEvent(status='ERROR') where timer:within (10 sec))

The following guards are equivalent:

timer:within(2 minutes 5 seconds)
timer:within(125 sec)
timer:within(125)

7.5.9.2. The `timer:withinmax` Pattern Guard

The timer:withinmax guard is similar to the timer:within guard and acts as a stopwatch that additionally has a counter that counts the number of matches. It ends the subexpression when either the stopwatch ends or the match counter maximum value is reached.

The synopsis for timer:withinmax is as follows:

timer:withinmax(time_period_expression, max_count_expression)

The time_period_expression is a time period (see Section 5.2.1, “Specifying Time Periods”) or an expression providing a number of seconds.

The max_count_expression provides the maximum number of matches before the guard ends the subexpression.

Each parameter expression may also contain references to properties of prior events in the same pattern as well as variables and substitution parameters.

This pattern fires for every A event that arrives within 5 seconds after statement deployment but only up to the first two A events:

(every A) where timer:withinmax (5 seconds, 2)

If the result of the max_count_expression is 1, the guard ends the subexpression after the first match and indicates the first match.

This pattern fires for the first A event that arrives within 5 seconds after statement deployment:

(every A) where timer:withinmax (5 seconds, 1)

If the result of the max_count_expression is zero, the guard ends the subexpression upon the first match and does no indicate any matches.

This example receives every A event followed by every B event (as each B event arrives) until the 5-second subexpression timer ends or X number of B events have arrived (assume X was declared as a variable):

every A -> (every B) where timer:withinmax (5 seconds, X)

7.5.9.3. The `while` Pattern Guard

The while guard is followed by an expression that the runtime evaluates for every match reported by the guard pattern sub-expression. When the expression returns false the pattern sub-expression ends.

The synopsis for while is as follows:

while (guard_expression)

The guard_expression is any expression that returns a boolean true or false. The expression may contain references to properties of prior events in the same pattern as well as variables and substitution parameters.

Each time the subexpression indicates a match, the runtime evaluates guard_expression and if true, passes the match and when false, ends the subexpression.

This pattern fires for every A event until an A event arrives that has a value of zero or less for its size property (assuming A events have an integer size property).

(every a=A) while (a.size > 0)

Note the parenthesis around the every subexpression. They ensure that, following precedence rules, the guard applies to the every operator as well.

7.5.9.4. Guard Time Interval Expressions

The timer:within and timer:withinmax guards may be parameterized by an expression that contains one or more references to properties of prior events in the same pattern.

As a simple example, this pattern matches every A event followed by a B event that arrives within delta seconds after the A event:

every a=A -> b=B where timer:within (a.delta seconds)

Herein A event is assumed to have a delta property that provides the number of seconds to wait for B events. Each arriving A event may have a different value for delta and the guard is therefore parameterized dynamically based on the prior A event received.

When multiple events accumulate, for example when using the match-until or repeat pattern elements, an index must be provided:

[2] a=A -> b=B where timer:within (a[0].delta + a[1].delta)

The above pattern matches after 2 A events arrive followed by a B event within a time interval after the A event that is defined by the sum of the delta properties of both A events.

7.5.9.5. Combining Guard Expressions

You can combine guard expression by using parenthesis around each subexpression.

The below pattern matches for each A event while A events of size greater then zero arrive and only within the first 20 seconds:

((every a=A) while (a.size > 0)) where timer:within(20)

7.6. Pattern Atoms

7.6.1. Filter Atoms

Filter atoms have been described in section Section 7.4, “Filter Expressions in Patterns”.

7.6.2. Observer Atoms Overview

Observers observe time-based events for which the thread-of-control originates by the runtime timer or external timer event. Custom observers can also be developed that observe timer events or other runtime-external application events such as a file-exists check. The section Chapter 22, Integration and Extension outlines observer plug-in development in greater detail.

7.6.3. Interval (`timer:interval`)

The timer:interval pattern observer waits for the defined time before the truth value of the observer turns true. The observer takes a time period (see Section 5.2.1, “Specifying Time Periods”) as a parameter, or an expression that returns the number of seconds.

The observer may be parameterized by an expression that contains one or more references to properties of prior events in the same pattern, or may also reference variables, substitution parameters or any other expression returning a numeric value.

After an A event arrived wait 10 seconds then indicate that the pattern matches.

A -> timer:interval(10 seconds)

The pattern below fires every 20 seconds.

every timer:interval(20 sec)

The next example pattern fires for every A event that is not followed by a B event within 60 seconds after the A event arrived. The B event must have the same "id" property value as the A event.

every a=A -> (timer:interval(60 sec) and not B(id=a.id))

Consider the next example, which assumes that the A event has a property waittime:

every a=A -> (timer:interval(a.waittime + 2) and not B(id=a.id))

In the above pattern the logic waits for 2 seconds plus the number of seconds provided by the value of the waittime property of the A event.

7.6.4. Crontab (`timer:at`)

The timer:at pattern observer is similar in function to the Unix “crontab” command. At a specified time the expression turns true. The at operator can also be made to pattern match at regular intervals by using an every operator in front of the timer:at operator.

The syntax is:

timer:at (minutes, hours, days of month, months, days of week [, seconds [, time zone [, milliseconds [, microseconds]]]])

The value for seconds, time zone, milliseconds and microseconds is optional. Each element allows wildcard * values. Ranges can be specified by means of lower bounds then a colon ‘:’ then the upper bound. The division operator */x can be used to specify that every x_th value is valid. Combinations of these operators can be used by placing these into square brackets ([]).

The timer:at observer may also be parameterized by an expression that contains one or more references to properties of prior events in the same pattern, or may also reference variables, substitution parameters or any other expression returning a numeric value. The frequency division operator */x and parameters lists within brackets ([]) are an exception: they may only contain variables, substitution parameters or numeric values.

This expression pattern matches every 5 minutes past the hour.

every timer:at(5, *, *, *, *)

The below timer:at pattern matches every 15 minutes from 8am to 5:45pm (hours 8 to 17 at 0, 15, 30 and 45 minutes past the hour) on even numbered days of the month as well as on the first day of the month.

timer:at (*/15, 8:17, [*/2, 1], *, *)

The below table outlines the fields, valid values and keywords available for each field:

Table 7.5. Crontab Fields

Field Name	Mandatory?	Allowed Values	Additional Keywords
Minutes	yes	0 - 59
Hours	yes	0 - 23
Days Of Month	yes	1 - 31	last, weekday, lastweekday
Months	yes	1 - 12
Days Of Week	yes	0 (Sunday) - 6 (Saturday)	last
Seconds	no (required if specifying a time zone, milliseconds or microseconds)	0 - 59
Time Zone	no (required if specifying milliseconds or microseconds)	any string (not validated, see TimeZone javadoc; use `*` for any value)
Milliseconds	no (required if specifying microseconds)	0 - 999
Microseconds	no	0 - 999 (only relevant when using microseconds as the runtime time unit)

The keyword last used in the days-of-month field means the last day of the month (current month). To specify the last day of another month, you must provide a value for the month field. For example: timer:at(*, *, last,2,*) is the last day of February.

The last keyword in the day-of-week field by itself simply means Saturday. If used in the day-of-week field after another value, it means "the last xxx day of the month" - for example "5 last" means "the last Friday of the month". So the last Friday of the current month will be: timer:at(*, *, *, *, 5 last). And the last Friday of June: timer:at(*, *, *, 6, 5 last).

The keyword weekday is used to specify the weekday (Monday-Friday) nearest the given day. Variant could include month like in: timer:at(*, *, 30 weekday, 9, *) which for year 2007 is Friday September 28th (no jump over month).

The keyword lastweekday is a combination of two parameters, the last and the weekday keywords. A typical example could be: timer:at(*, *, *, lastweekday, 9, *) which will define Friday September 28th (example year is 2007).

The time zone is a string-type value that specifies the time zone of the schedule. You must specify a value for seconds when specifying a time zone. The runtime relies on the java.util.TimeZone to interpret the time zone value. Note that TimeZone does not validate time zone strings.

The following timer:at pattern matches at 5:00 pm Pacific Standard Time (PST):

timer:at (0, 17, *, *, *, *, 'PST')

Any expression may occur among the parameters. This example invokes a user-defined function computeHour to return an hour:

timer:at (0, computeHour(), *, *, *, *)

The following restrictions apply to crontab parameters:

It is not possible to specify both Days Of Month and Days Of Week.

The return value for method that returns a crontab wildcard is WildcardParameter and for a crontab range is RangeParameter.

7.6.4.1. timer:at and the `every` Operator

When using timer:at with the every operator the crontab-like timer computes the next time at which the timer should fire based on the specification and the current time. When using every, the current time is the time the timer fired or the statement start time if the timer has not fired once.

For example, this pattern fires every 1 minute starting at 1:00pm and ending at 1:59pm, every day:

every timer:at(*, 13, *, *, *)

Assume the above statement gets started at 1:05pm and 20 seconds. In such case the above pattern fires every 1 minute starting at 1:06pm and ending at 1:59pm for that day and 1:00pm to 1:59pm every following day.

To get the pattern to fire only once at 1pm every day, explicitly specify the minute to start. The pattern below fires every day at 1:00pm:

every timer:at(0, 13, *, *, *)

By specifying a second resolution the timer can be made to fire every second, for instance:

every timer:at(*, *, *, *, *, *)

7.6.5. Schedule (`timer:schedule`)

The timer:schedule observer is a flexible observer for scheduling.

The observer implements relevant parts of the ISO 8601 specification however it is not necessary to use ISO 8601 formats. The ISO 8601 standard is an international standard covering the exchange of date and time-related data. The standard specifies a date format, a format for time periods and a format for specifying the number of repetitions. Please find more information on ISO 8601 at Wikipedia.

The observer takes the following named parameters:

Table 7.6. Timer Schedule Parameters

Name	Description
`iso`	An expression returning a string-type ISO 8601 formatted date, time period and/or number of repetitions.
`repetitions`	An expression returning a numeric value that specifies a number of repetitions. Provide a value of `-1` for an unlimited number of repetitions. If unspecified, the number of repetitions is one.
`date`	An expression returning a string-type ISO 8601 formatted date, or an expression that returns any of these types: `long`, `Date`, `Calendar`, `LocalDateTime`, `ZonedDateTime`.
`period`	An expression returning a time period, see Section 5.2.1, “Specifying Time Periods”

In summary, for example, the below pattern schedules two callbacks: The first callback 2008-03-01 at 13:00:00 UTC and the second callback on 2009-05-11 at 15:30:00 UTC.

select * from pattern[every timer:schedule(iso: 'R2/2008-03-01T13:00:00Z/P1Y2M10DT2H30M')]

The number of repetitions, date and period can be separated and do not have to be ISO 8601 strings, allowing each part to be an own expression.

This example specifies separate expressions. The equivalent schedule to the above example is:

select * from pattern[every timer:schedule(repetitions: 2, date: '2008-03-01T13:00:00Z', period: 1 year 2 month 10 days 2 hours 30 minutes)]

When providing the iso parameter, it must be the only parameter. The repetitions parameter is only allowed in conjunction with other parameters.

7.6.5.1. Specifying ISO8601 Dates, Periods and Repetition

7.6.5.1.1. Specifying Dates

The complete document for ISO 8601, the international standard for the representation of dates and times, can be found at http://www.w3.org/TR/NOTE-datetime.

The supported ISO 8601 date formats are:

Table 7.7. ISO 8601 Period Examples

Description Format Example

Complete date plus hours, minutes and seconds (zero milliseconds, zero microseconds):

Description	Format	Example
Complete date plus hours, minutes and seconds (zero milliseconds, zero microseconds):	`YYYY-MM-DDThh:mm:ssTZD`	`1997-07-16T19:20:30Z` (i.e. GMT+00:00, UTC) `1997-07-16T19:20:30+01:00` (i.e. GMT+01:00) `1997-07-16T19:20:30` (i.e. local time zone)
Complete date plus hours, minutes, seconds and a decimal fraction of a second (zero microseconds)	`YYYY-MM-DDThh:mm:ss.sTZD`	`1997-07-16T19:20:30.45Z` (i.e. GMT+00:00, UTC) `1997-07-16T19:20:30.45+01:00` (i.e. GMT+01:00) `1997-07-16T19:20:30.45` (i.e. local time zone)

YYYY-MM-DDThh:mm:ssTZD

1997-07-16T19:20:30Z

(i.e. GMT+00:00, UTC)

1997-07-16T19:20:30+01:00

(i.e. GMT+01:00)

1997-07-16T19:20:30

(i.e. local time zone)

Complete date plus hours, minutes, seconds and a decimal fraction of a second (zero microseconds)

YYYY-MM-DDThh:mm:ss.sTZD

1997-07-16T19:20:30.45Z

(i.e. GMT+00:00, UTC)

1997-07-16T19:20:30.45+01:00

(i.e. GMT+01:00)

1997-07-16T19:20:30.45

(i.e. local time zone)

7.6.5.1.2. Specifying Periods

In ISO 8601, periods are specified by a P and an optional year, month, week and day count. If there is a time part, add T and optionally provide the hour, minute and seconds. The format does not have any whitespace. The synopsis is:

P [nY] [nM] [nW] [nD] [T [nH [nM] [nS] ]

The Y stands for years, the M for month as well as minutes, the W for weeks and the D for days. The H stands for hours and the S means seconds.

Table 7.8. ISO 8601 Period Examples

Example	Description
P10M	10 months
PT10M	10 minutes
P1Y3M12D	1 year, 3 month and 12 days
P10DT5M	10 days and 5 minutes
P1Y2M3DT4H5M6S	1 year, 2 month, 3 days, 4 hours, 5 minutes, 6 seconds

7.6.5.1.3. Specifying Repetitions

In ISO 8601, repetition is specified by an R and an optional number of repetitions without any white space. The synopsis is:

R [nn]

For example R5 means 5 repetitions, and just R means unlimited repetitions.

7.6.5.2. Scheduling a Callback to Occur for a Given Date (Non-Repeating)

To instruct the runtime to observe a date, provide a date to the observer. When time advances to the specified date, the pattern subexpression fires.

For example, this pattern fires once when time reaches 2012-10-01 at 5:52:00 (UTC):

timer:schedule(iso:'2012-10-01T05:52:00Z')

This equivalent pattern specifies separate expressions:

every timer:schedule(date: '2012-10-01T05:52:00Z')

When the observer fires, the pattern subexpression becomes permanently false and the runtime does not restart the observer.

If the provided date is a past date as compared to runtime time, the pattern subexpression becomes permanently false on start.

7.6.5.3. Scheduling a Callback to Occur After a Given Period (Non-Repeating)

To instruct the runtime to observe a period starting from the current runtime time, provide a period. When time advances to the current runtime time plus the specified period, the pattern subexpression fires.

Assuming the current runtime time is 2012-10-01 at 5:52:00 (UTC), this pattern fires once when time reaches 5:53:00:

timer:schedule(iso:'PT1M')

This equivalent pattern specifies separate expressions:

every timer:schedule(period: 1 minute)

When the observer fires, the pattern subexpression becomes permanently false and the runtime does not restart the observer.

7.6.5.4. Scheduling a Callback to Occur After a Given Date and Period (Non-Repeating)

To instruct the runtime to observe a period starting from a given date, provide a date and a period. When time advances to the date plus the specified period, the pattern subexpression fires.

Assuming the current runtime time is 5:52:00 (UTC), this pattern fires once when time reaches 2012-10-01 at 5:53:00:

timer:schedule(iso:'2012-10-01T05:52:00Z/PT1M')

This equivalent pattern specifies separate expressions:

every timer:schedule(date: '2012-10-01T05:52:00Z', period: 1 minute)

When the observer fires, the pattern subexpression becomes permanently false and the runtime does not restart the observer.

7.6.5.5. Scheduling a Callback to Occur Periodically (Repeating)

To instruct the runtime to observe a period starting from the current runtime time and repeatedly thereafter anchored to current runtime time, provide a number of repetitions and a period (see synopsis provided earlier), like this:

repetitions/period

timer:schedule(iso: 'R2/PT1M')

When time advances to the current runtime time plus the specified period, the pattern subexpression fires for the first time. Repeatedly when time advances to the current runtime time plus a multiple of the specified period, the pattern subexpression fires, up to the number of repetitions specified (if any).

This pattern specifies a repetition of two. Assuming the current runtime time is 2012-10-01 at 5:52:00 (UTC), it fires when time reaches 5:53:00 and again when time reaches 5:54:00:

every timer:schedule(iso: 'R2/PT1M')

This equivalent pattern specifies separate expressions:

every timer:schedule(repetitions: 2, period: 1 minute)

All schedule computations are relative to (i.e. anchored to) current runtime time at observer start. Once the number of repetitions is reached relative to the current runtime time at observer start, the pattern subexpression becomes permanently false and the runtime does not restart the observer.

Note

Please specify the every operator for repeating schedules.

7.6.5.6. Scheduling a Callback to Occur Periodically Starting From a Given Date (Repeating)

To instruct the runtime to observe a period starting from a given date and repeatedly thereafter anchored to the provide date, provide a number of repetitions and a date and a period (see synopsis provided earlier), like this:

repetitions/date/period

When time advances to the date, the pattern subexpression fires for the first time. You may specify a date older than current runtime time as an anchor. Repeatedly when time advances to the date plus a multiple of the specified period, the pattern subexpression fires, up to the number of repetitions specified (if any).

This pattern specifies a repetition of two. The pattern fires when time reaches 2012-10-01 at 5:52:00 (UTC) and again when time reaches 5:53:00.

every timer:schedule(iso: 'R2/2012-10-01T05:52:00Z/PT1M')

This equivalent pattern specifies separate expressions:

every timer:schedule(repetitions: 2, date:'2012-10-01T05:52:00Z', period: 1 minute)

All schedule computations are relative to (i.e. anchored to) the provided date. Once the number of repetitions is reached relative to the provided date, the pattern subexpression becomes permanently false and the runtime does not restart the observer.

Note

Please specify the every operator for repeating schedules.

7.6.5.7. Additional Usage Examples

The pattern below outputs every MyEvent event after the MyEvent arrived and upon the next round 15 seconds:

select * from pattern[every e=MyEvent -> timer:schedule(iso: 'R/1980-01-01T00:00:00Z/PT15S']

Assuming a MyEvent event arrives on 2012-10-01 at 5:51:07 the output for that event occurs at 5:51:15.

All parameters can be expressions. The date parameter could, for example, be used with current_timestamp to compute a schedule:

select * from pattern[date: current_timestamp.withTime(9, 0, 0, 0)]

The above statement fires only at 9am and not after 9am on the same day (one repetition).

7.6.5.8. Samples With Equivalent EPL

The following EPL is equivalent:

select * from pattern[every timer:schedule(iso: 'R2/2008-03-01T13:00:00Z/P1Y2M10DT2H30M')]

select * from pattern[every (timer:schedule(iso: '2008-03-01T13:00:00Z') or timer:schedule(iso: '2009-05-11T15:30:00Z'))]

select * from pattern[every (timer:schedule(iso: '2008-03-01T13:00:00Z') or timer:schedule(iso: '2008-03-01T13:00:00Z/P1Y2M10DT2H30M'))]

7.6.5.9. Implementation Notes

Your environment should have the JAXB date parser available. The runtime uses javax.xml.datatype.DatatypeFactory.newInstance().newXMLGregorianCalendar(dateText).toGregorianCalendar() for date parsing.

7.7. Event Pattern Walkthrough

This walkthrough discusses the following pattern:

every a=LoginEvent -> (timer:interval(1 min) and not LogoutEvent(userId=a.userId))]

This pattern detects when a LoginEvent is not followed by a LogoutEvent arriving within 1 minute of the LoginEvent.

The runtime parses the pattern expression and builds a expression tree. At the root of the expression tree, for this specific pattern, is the followed-by (->) operator as followed-by has the lowest precedence (see precedence).

The pattern expression tree looks like this.

Followed-by operator (->)
  |
  | ---- Every
  |       |
  |       | ---- a=LoginEvent
  |
  | ---- And
          |
          | ---- timer:interval(1 min)
          |                  
          | ---- Not
                 |
                 | ---- LogoutEvent(userId=a.userId)

The followed-by (->) operator has two subexpressions that it manages, the Every expression that itself has the a=LoginEvent subexpression as a child, and the And expression that itself has two subexpressions as children.

When the runtime activates a pattern or pattern subexpression, it activates it from a top-down direction. In this example the runtime activates the followed-by (->) expression. The followed-by expression activates only its own left-most subexpression, which is the Every expression. The Every expression in turn activates the a=LoginEvent subexpression which registers a filter looking for a LoginEvent in the global filter index (see Section 2.18.2, “Filter Indexes”).

In this example, the And subexpression and its subexpressions do not get activated. Therefore after this pattern initially activates there is no filter active time looking for LogoutEvent event and there is no time tracking of any kind at this point.

Let time t₀ be the time of statement deployment. As discussed, upon deployment of the statement the runtime activates the pattern subexpression every a=LoginEvent only. At this time there is 1 active subexpression:

every a=LoginEvent

As you can see, the tree of active subexpressions (the tree of states) is not the same as the tree of expressions. After the pattern activated there are no active subexpressions (no states) for the And-part of the example expression tree.

Let's assume that at time t₁ a LoginEvent with user id 10 arrives. This causes the a=LoginEvent subexpression to become true. In turn, this causes the Every subexpression to become true. This causes the left-hand-side of the followed-by subexpression to become true. This in turn causes activation of the And subexpression.

The activation of the And-subexpression causes activation of both timer:interval(1 min) (from t₁) and the Not-subexpression. The activation of the And-subexpression registers a filter looking for a LogoutEvent that has a user id value of 10 in the global filter index and registers a timer callback for t₁+1 minute.

The runtime does not terminate the subexpression a=LoginEvent as it lives under an Every operator which means it should keep looking for more LoginEvent events.

There now are 2 active subexpressions:

every a=LoginEvent
timer:interval(t1+1 min) and not LogoutEvent(userId=10)

Let's assume that at time t₂ another LoginEvent with user id 20 arrives (assume t₂ – t₁ < 1 min). This again causes the a=LoginEvent subexpression to become true. In turn, this causes the Every subexpression to become true. This causes the left-hand-side of the followed-by subexpression to become true. This in turn causes activation of the And subexpression.

The activation of the And-subexpression causes activation of both timer:interval(1 min) (from t₂) and the Not-subexpression. The activation of the And-subexpression registers a filter looking for a LogoutEvent that has a user id value of 20 in the global filter index and registers a timer callback for t₂+1 minute.

There now are 3 active subexpressions:

every a=LoginEvent
timer:interval(t1+1 min) and not LogoutEvent(userId=10)
timer:interval(t2+1 min) and not LogoutEvent(userId=20)

In this scenario at time t₃ one minute has passed since t₁ and a matching LogoutEvent was not received.

The subexpression timer:interval(t1+1 min) becomes true. In turn, this causes the And-subexpression to become true as the Not-subexpression is already true on start. In turn, this causes the followed-by subexpression to become true. In turn, this causes an output of the pattern match.

The runtime terminates the subexpression not LogoutEvent(userId=10) which unregisters the filter looking for a LogoutEvent that has a user id value of 10 from the global filter index.

There now are 2 active subexpressions:

every a=LoginEvent
timer:interval(t2+1 min) and not LogoutEvent(userId=20)

Let's assume that at time t₄ a LogoutEvent with user id 20 arrives (assume t₄ – t₂ < 1 min). This again causes the LogoutEvent(userId=20) subexpression to become true. In turn, this causes the Not subexpression to become permanently false (as Not reverses the truth value). This causes the And-subexpression to become permanently false. This causes the pattern subexpression timer:interval(t2+1 min) and not LogoutEvent(userId=20) to terminate which causes the timer callback t₂+1 minute to unregister.

Finally there is 1 active subexpression remaining:

every a=LoginEvent

Chapter 8. EPL Reference: Match Recognize

8.1. Overview

8.2. Comparison of Match Recognize and EPL Patterns

8.3. Syntax

8.3.1. Syntax Example

8.4. Pattern and Pattern Operators

8.4.1. Operator Precedence
8.4.2. Concatenation
8.4.3. Alternation
8.4.4. Quantifiers Overview
8.4.5. Permutations
8.4.6. Variables Can Be Singleton or Group
8.4.7. Eliminating Duplicate Matches
8.4.8. Greedy or Reluctant
8.4.9. Quantifier - One or More (+ and +?)
8.4.10. Quantifier - Zero or More (* and *?)
8.4.11. Quantifier - Zero or One (? and ??)
8.4.12. Repetition - Exactly N Matches
8.4.13. Repetition - N or More Matches
8.4.14. Repetition - Between N and M Matches
8.4.15. Repetition - Between Zero and M Matches
8.4.16. Repetition Equivalence

8.5. Define Clause

8.5.1. The Prev Operator

8.6. Measure Clause

8.7. Datawindow-Bound

8.8. Interval

8.9. Interval-or-Terminated

8.10. Use With Different Event Types

8.11. Limiting Runtime-Wide State Count

8.12. Limitations

8.1. Overview

Using match recognize patterns are defined in the familiar syntax of regular expressions.

The match recognize syntax presents an alternative way to specify pattern detection as compared to the EPL pattern language described in the previous chapter. A comparison of match recognize and EPL patterns is below.

The match recognize syntax is a proposal for incorporation into the SQL standard. It is thus subject to change as the standard evolves and finalizes (it has not finalized yet). Please consult "row-pattern-recogniton-11-public.pdf" for further information.

You may be familiar with regular expressions in the context of finding text of interest in a string, such as particular characters, words, or patterns of characters. Instead of matching characters, match recognize matches sequences of events of interest.

EPL can apply match-recognize patterns in real-time upon arrival of new events in a stream of events (also termed incrementally, streaming or continuous). EPL can also match patterns on-demand via the iterator pull-API, if specifying a named window or data window on a stream (tables cannot be used in the from-clause with match-recognize). The runtime maintains state for partial pattern matches and match-recognize patterns are therefore stateful constructs.

8.2. Comparison of Match Recognize and EPL Patterns

This section compares pattern detection via match recognize and via the EPL pattern language.

Table 8.1. Comparison Match Recognize to EPL Patterns

Category	EPL Patterns	Match Recognize
Purpose	Pattern detection in sequences of events.	Same.
Standards	Not standardized, similar to Rapide pattern language.	Proposal for incorporation into the SQL standard.
Real-time Processing	Yes.	Yes.
On-Demand query via Iterator	No.	Yes.
Language	Nestable expressions consisting of boolean `AND`, `OR`, `NOT` and time or arrival-based constructs such as `->` (followed-by), `timer:within` and `timer:interval`.	Regular expression consisting of variables each representing conditions on events.
Event Types	An EPL pattern may react to multiple different types of events.	The input is a single type of event (unless used with variant streams).
Data Window Interaction	Disconnected, i.e. an event leaving a data window does not change pattern state.	Connected, i.e. an event leaving a data window removes the event from match selection.
Semantic Evaluation	Truth-value based: A EPL pattern such as `(A and B)` can fire when a single event arrives that satisfies both A and B conditions.	Sequence-based: A regular expression `(A B)` requires at least two events to match.
Time Relationship Between Events	The `timer:within`, `timer:interval` and `NOT` operator can expressively search for absence of events or other more complex timing relationships.	Some support for detecting absence of events using the `interval` clause.
Extensibility	Custom pattern objects, user-defined functions.	User-defined functions, custom aggregation functions.
Memory Use	Likely between 500 bytes to 2k per open sequence, depends on pattern.	Likely between 100 bytes to 1k per open sequence, depends on pattern.

8.3. Syntax

The synopsis is as follows:

match_recognize (
  [ partition by partition_expression [, partition_expression] [,...]  ]
  measures  measure_expression as col_name [, measure_expression as col_name ] [,...]
  [ all matches ]
  [ after match skip (past last row | to next row | to current row) ]
  pattern ( variable_regular_expr [, variable_regular_expr] [,...] )
  [ interval time_period [or terminated] ]
  [ define  variable as variable_condition [, variable as variable_condition]  [,...] ]
)

The match_recognize keyword starts the match recognize definition and occurs right after the from clause in an EPL select statement. It is followed by parenthesis that surround the match recognize definition.

Partition by is optional and may be used to specify that events are to be partitioned by one or more event properties or expressions. If there is no Partition by then all rows of the table constitute a single partition. The regular expression applies to events in the same partition and not across partitions. Further detail on key expressions can be found at Section 5.2.13, “Composite Keys and Array Values as Keys”.

The measures clause defines columns that contain expressions over the pattern variables. The expressions can reference partition columns, singleton variables, aggregates as well as indexed properties on the group variables. Each measure_expression expression must be followed by the as keyword and a col_name column name.

The all matches keywords are optional and instructs the runtime to find all possible matches. By default matches are ranked and the runtime returns a single match following an algorithm to eliminate duplicate matches, as described below. When specifying all matches, matches may overlap and may start at the same row.

The after match skip keywords are optional and serve to determine the resumption point of pattern matching after a match has been found. By default the behavior is after match skip past last row. This means that after eliminating duplicate matches, the logic skips to resume pattern matching at the next event after the last event of the current match.

The pattern component is used to specify a regular expression. The regular expression is built from variable names, and may use quantifiers such as *, +, ?, *?, +?, ??, {repetition} and | alteration (concatenation is indicated by the absence of any operator sign between two successive items in a pattern).

With the optional interval keyword, time period and or terminated you can control how long the runtime should wait for further events to arrive that may be part of a matching event sequence, before indicating a match (or matches) (not applicable to on-demand pattern matching).

Define is optional and is used to specify the boolean condition(s) that define some or all variable names that are declared in the pattern. A variable name does not require a definition and if there is no definition, the default is a predicate that is always true. Such a variable name can be used to match any row.

8.3.1. Syntax Example

For illustration, the examples in this chapter use the TemperatureSensorEvent event. Each event has 3 properties: the id property is a unique event id, the device is a sensor device number and the temp property is a temperature reading. An event described as "id=E1, device=1, temp=100" is a TemperatureSensorEvent event with id "E1" for device 1 with a reading of 100.

This example statement looks for two TemperatureSensorEvent events from the same device, directly following each other, that indicate a jump in temperature of 10 or more between the two events:

select * from TemperatureSensorEvent
match_recognize (
  partition by device
  measures A.id as a_id, B.id as b_id, A.temp as a_temp, B.temp as b_temp
  pattern (A B)
  define 
    B as Math.abs(B.temp - A.temp) >= 10
)

The partition by ensures that the regular expression applies to sequences of events from the same device.

The measures clause provides a list of properties or expressions to be selected from matching events. Each property name must be prefixed by the variable name.

In the pattern component the statement declares two variables: A and B. As a matter of convention, variable names are uppercase characters.

The define clause specifies no condition for variable A. This means that A defaults to true and any event matches A. Therefore, the pattern can start at any event.

The pattern A B indicates to look for a pattern in which an event that fulfills the condition for variable A is immediately followed by an event that fulfills the condition for variable B. A pattern thus defines the sequence (or sequences) of conditions that must be met for the pattern to fire.

Below table is an example sequence of events and output of the pattern:

Table 8.2. Example

Arrival Time	Tuple	Output Event (if any)
1000	id=E1, device=1, temp=50
2000	id=E2, device=1, temp=55
3000	id=E3, device=1, temp=60
4000	id=E4, device=1, temp=70	a_id = E3, b_id = E4, a_temp = 60, b_temp = 70
5000	id=E5, device=1, temp=85
6000	id=E6, device=1, temp=85
7000	id=E7, device=2, temp=100

At time 4000 when event with id E4 (or event E4 or just E4 for short) arrives the pattern matches and produces an output event. Matching then skips past the last event of the current match (E4) and begins at event E5 (the default skip clause is past last row). Therefore events E4 and E5 do not constitute a match.

At time 3000, events E1 and E3 do not constitute a match as E3 does not immediately follow E, since there is E2 in between.

At time 7000, event E7 does not constitute a match as it is from device 2 and thereby not in the same partition as prior events.

8.4. Pattern and Pattern Operators

The pattern specifies the pattern to be recognized in the ordered sequence of events in a partition using regular expression syntax. Each variable name in a pattern corresponds to a boolean condition, which is specified later using the define component of the syntax. Thus the pattern can be regarded as implicitly declaring one or more variable names; the definition of those variable names appears later in the syntax. If a variable is not defined the variable defaults to true.

It is permitted for a variable name to occur more than once in a pattern, for example pattern (A B A).

8.4.1. Operator Precedence

The operators at the top of this table take precedence over operators lower on the table.

Table 8.3. Match Recognize Regular Expression Operator Precedence

Precedence	Operator	Description	Example
1	Grouping	`()`	(A B)
2	Quantifiers	`* + ? {repetition}`	A* B+ C?
3	Concatenation	(no operator)	A B
4	Alternation	`\|`	A \| B

If you are not sure about the precedence, please consider placing parenthesis () around your groups. Parenthesis can also help make expressions easier to read and understand.

8.4.2. Concatenation

The concatenation is indicated by the absence of any operator sign between two successive items in a pattern.

In below pattern the two items A and B have no operator between them. The pattern matches for any event immediately followed by an event from the same device that indicates a jump in temperature over 10:

select * from TemperatureSensorEvent
match_recognize (
  partition by device
  measures A.id as a_id, B.id as b_id, A.temp as a_temp, B.temp as b_temp
  pattern (A B)
  define 
    B as Math.abs(B.temp - A.temp) >= 10
)

Please see the Section 8.3.1, “Syntax Example” for a sample event sequence.

8.4.3. Alternation

The alternation operator is a vertical bar ( | ).

The alternation operator has the lowest precedence of all operators. It tells the runtime to match either everything to the left of the vertical bar, or everything to the right of the vertical bar. If you want to limit the reach of the alternation, you will need to use parentheses for grouping.

This example pattern looks for a sequence of an event with a temperature over 50 followed immediately by either an event with a temperature less than 45 or an event that indicates the temperature jumped by 10 (all for the same device):

select * from TemperatureSensorEvent
match_recognize (
  partition by device
  measures A.id as a_id, B.id as b_id, C.id as c.id
  pattern (A (B | C))
  define 
    A as A.temp >= 50,
    B as B.temp <= 45,
    C as Math.abs(C.temp - A.temp) >= 10)

Below table is an example sequence of events and output of the pattern:

Table 8.4. Example

Arrival Time	Tuple	Output Event (if any)
1000	id=E1, device=1, temp=50
2000	id=E2, device=1, temp=45	a_id=E1, b_id=E2, c_id=null
3000	id=E3, device=1, temp=46
4000	id=E4, device=1, temp=48
5000	id=E5, device=1, temp=50
6000	id=E6, device=1, temp=60	a_id = E5, b_id = null, c_id=E6

8.4.4. Quantifiers Overview

Quantifiers are postfix operators with the following choices:

Table 8.5. Quantifiers

Quantifier	Meaning
*	Zero or more matches (greedy).
+	One or more matches (greedy).
?	Zero or one match (greedy).
*?	Zero or more matches (reluctant).
+?	One or more matches (reluctant).
??	Zero or one match (reluctant).

Quantifiers that control the number of repetitions are:

Table 8.6. Quantifiers

Quantifier	Meaning
{n}	Exactly n matches.
{n, }	n or more matches.
{n, m}	Between n and m matches (inclusive).
{ ,m}	Between zero and m matches (inclusive).

Repetition quantifiers can be combined with other quantifiers and grouping. For example A?{2} or (A B){2} are valid.

8.4.5. Permutations

To detect patterns that consist of a permutation of variables you may use match_recognize_permute. It is possible to express a permutation as alternations but it becomes clumsy when many variables are involved. For example, if all permutations of three variables A B C are needed you could express it as: (A B C | A C B | B A C | B C A | C A B | C B A).

You may use match_recognize_permute followed by a comma-separated list of variables, grouping, alternations or concatenations.

The following table outlines sample equivalent permutations.

Table 8.7. Equivalent Pattern Expressions

Pattern	Equivalent
match_recognize_permute(A)	A
match_recognize_permute(A,B)	(A B\|B A)
match_recognize_permute(A,B,C)	A B C\|A C B\|B A C\|B C A\|C A B\|C B A
match_recognize_permute((A B), C)	(A B) C\|C (A B)
A match_recognize_permute(B,C) D	A (B C\|C B) D
match_recognize_permute(A, match_recognize_permute(B, C))	A (B C\|C B)\|(B C\|C B) A

This sample pattern looks for either an event with temperature less than 100 and then an event with temperature greater or equal to 100, or an event with temperature greater or equal to 100 and then an event with temperature less than 100.

select * from TemperatureSensorEvent
match_recognize (
  partition by device
  measures A.id as a_id, B.id as b_id
  pattern (match_recognize_permute(A, B))
  define 
    A as A.temp < 100, 
    B as B.temp >= 100)

An example sequence of events that matches the pattern above is:

Table 8.8. Example

Arrival Time	Tuple	Output Event (if any)
1000	id=E1, device=1, temp=99
2000	id=E2, device=1, temp=100	a_id = E1, b_id = E2
3000	id=E3, device=1, temp=100
4000	id=E4, device=1, temp=99	a_id = E4, b_id = E3
5000	id=E5, device=1, temp=98

8.4.6. Variables Can Be Singleton or Group

A singleton variable is a variable in a pattern that does not have a quantifier or has a zero-or-one quantifier (? or ??) and occurs only once in the pattern (except with alteration). In the measures clause a singleton variable can be selected as:

variableName.propertyName

Variables with a zero-or-more or one-or-more quantifier, or variables that occur multiple places in a pattern (except when using alteration), may match multiple events and are group variables. In the measures clause a group variable must be selected either by providing an index or via any of the aggregation functions, such as first, last, count and sum:

variableName[index].propertyName

last(variableName.propertyName)

Enumeration methods can also be applied to group variables. An example is provided in Section 11.4.11, “Match-Recognize Group Variable”.

Please find examples of singleton and group variables and example measures and define clauses below.

8.4.6.1. Additional Aggregation Functions

For group variables all existing aggregation functions can be used and in addition the following aggregation functions may be used (measures-clause only):

Table 8.9. Syntax and Results of Aggregate Functions

Aggregate Function	Result
first([all\|distinct] expression)	Returns the first value.
last([all\|distinct] expression)	Returns the last value.

8.4.7. Eliminating Duplicate Matches

The execution of match recognize is continuous and real-time by default. This means that every arriving event, or batch of events if using batching, evaluates against the pattern and matches are immediately indicated. Elimination of duplicate matches occurs between all matches of the arriving events (or batch of events) at a given time.

As an alternative, and if your application does not require continuous pattern evaluation, you may use the iterator API to perform on-demand matching of the pattern. For the purpose of indicating to the runtime to not generate continuous results, specify the @Hint('iterate_only') hint.

When using one-or-more, zero-or-more or zero-or-one quantifiers (?, +, *, ??, +?, *?), the output of statement can differ from the output of the on-demand iterator execution: The statement will output a match (or multiple matches) as soon as matches are detected at a given time upon arrival of events (not knowing what further events may arrive). The on-demand execution, since it knows all possible events in advance, can determine the longest match(es). Thus elimination of duplicate matches can lead to different results between real-time and on-demand use.

If the all matches keywords are specified, then all matches are returned as the result and no elimination of duplicate matches as below occurs.

Otherwise matches to a pattern in a partition are ordered by preferment. Preferment is given to matches based on the following priorities:

A match that begins at an earlier row is preferred over a match that begins at a later row.
Of two matches matching a greedy quantifier, the longer match is preferred.
Of two matches matching a reluctant quantifier, the shorter match is preferred.

After ranking matches by preferment, matches are chosen as follows:

The first match by preferment is taken.
The pool of matches is reduced as follows based on the SKIP TO clause: If SKIP PAST LAST ROW is specified, all matches that overlap the first match are discarded from the pool. If SKIP TO NEXT ROW is specified, then all matches that overlap the first row of the first match are discarded. If SKIP TO CURRENT ROW is specified, then no matches are discarded.
The first match by preferment of the ones remaining is taken.
Step 2 is repeated to remove more matches from the pool.
Steps 3 and 4 are repeated until there are no remaining matches in the pool.

8.4.8. Greedy or Reluctant

Reluctant quantifiers are indicated by an additional question mark (*?, +?, ??,). Reluctant quantifiers try to match as few rows as possible, whereas non-reluctant quantifiers are greedy and try to match as many rows as possible.

Greedy and reluctant come into play only for match selection among multiple possible matches. When specifying all matches there is no difference between greedy and reluctant quantifiers.

Consider the below example. The conditions may overlap: an event with a temperature reading of 105 and over matches both A and B conditions:

select * from TemperatureSensorEvent
match_recognize (
  partition by device
  measures A.id as a_id, B.id as b_id
  pattern (A?? B?)
  define 
    A as A.temp >= 100
    B as B.temp >= 105)

A sample sequence of events and pattern matches:

Table 8.10. Example

Arrival Time	Tuple	Output Event (if any)
1000	id=E1, device=1, temp=99
2000	id=E2, device=2, temp=106	a_id=null, b_id=E2
3000	id=E3, device=1, temp=100	a_id=E3, b_id=null

As the ? qualifier on condition B is greedy, event E2 matches the pattern and is indicated as a B event by the measure clause (and not as an A event therefore a_id is null).

8.4.9. Quantifier - One or More (+ and +?)

The one-or-more quantifier (+) must be matched one or more times by events. The operator is greedy and the reluctant version is +?.

In the below example with pattern (A+ B+) the pattern consists of two variable names, A and B, each of which is quantified with +, indicating that they must be matched one or more times.

The pattern looks for one or more events in which the temperature is over 100 followed by one or more events indicating a higher temperature:

select * from TemperatureSensorEvent
match_recognize (
  partition by device
  measures first(A.id) as first_a, last(A.id) as last_a, B[0].id as b0_id, B[1].id as b1_id
  pattern (A+ B+)
  define 
	A as A.temp >= 100,
	B as B.temp > A.temp)

An example sequence of events that matches the pattern above is:

Table 8.11. Example

Arrival Time	Tuple	Output Event (if any)
1000	id=E1, device=1, temp=99
2000	id=E2, device=1, temp=100
3000	id=E3, device=1, temp=100
4000	id=E4, device=1, temp=101	first_a = E2, last_a = E3, b0_id = E4, b1_id = null
5000	id=E5, device=1, temp=102

Note that for statements there is no match that includes event E5 since after the pattern matches for E4 the pattern skips to start fresh at E5 (by default skip clause). When performing on-demand matching via iterator, event E5 gets included in the match and the output is first_a = E2, last_a = E3, b0_id = E4, b1_id = E5.

8.4.10. Quantifier - Zero or More (* and *?)

The zero-or-more quantifier (*) must be matched zero or more times by events. The operator is greedy and the reluctant version is *?.

The pattern looks for a sequence of events in which the temperature starts out below 50 and then stays between 50 and 60 and finally comes over 60:

select * from TemperatureSensorEvent
match_recognize (
  partition by device
  measures A.id as a_id, count(B.id) as count_b, C.id as c_id
  pattern (A B* C)
  define 
	A as A.temp < 50,
	B as B.temp between 50 and 60,
	C as C.temp > 60)

An example sequence of events that matches the pattern above is:

Table 8.12. Example

Arrival Time	Tuple	Output Event (if any)
1000	id=E1, device=1, temp=55
2000	id=E2, device=1, temp=52
3000	id=E3, device=1, temp=49
4000	id=E4, device=1, temp=51
5000	id=E5, device=1, temp=55
6000	id=E5, device=1, temp=61	a_id=E3, count_b=2, c_id=E6

8.4.11. Quantifier - Zero or One (? and ??)

The zero-or-one quantifier (?) must be matched zero or one time by events. The operator is greedy and the reluctant version is ??.

The pattern looks for a sequence of events in which the temperature is below 50 and then dips to over 50 and then to under 50 before indicating a value over 55:

select * from TemperatureSensorEvent
match_recognize (
  partition by device
  measures A.id as a_id, B.id as b_id, C.id as c_id, D.id as d_id
  pattern (A B? C? D)
  define 
	A as A.temp < 50,
	B as B.temp > 50,
	C as C.temp < 50,
	D as D.temp > 55)

An example sequence of events that matches the pattern above is:

Table 8.13. Example

Arrival Time	Tuple	Output Event (if any)
1000	id=E1, device=1, temp=44
2000	id=E2, device=1, temp=49
3000	id=E3, device=1, temp=51
4000	id=E4, device=1, temp=49
5000	id=E5, device=1, temp=56	a_id=E2, b_id=E3, c_id=E4, d_id=E5
6000	id=E5, device=1, temp=61

8.4.12. Repetition - Exactly N Matches

The exactly-n quantifier ({n}) must be matched exactly N times. The repetition quantifier can be combined with other non-repetition quantifiers and can be used with grouping.

In the below example the pattern (A{2}) consists of one variable names, A, quantified with {2}, indicating that the condition must match exactly two times.

This sample pattern looks for two events in which the temperature is over 100:

select * from TemperatureSensorEvent
match_recognize (
  partition by device
  measures A[0].id as a0_id, A[1].id as a1_id
  pattern (A{2})
  define 
	A as A.temp >= 100)

An example sequence of events that matches the pattern above is:

Table 8.14. Example

Arrival Time	Tuple	Output Event (if any)
1000	id=E1, device=1, temp=99
2000	id=E2, device=1, temp=100
3000	id=E3, device=1, temp=100	a0_id = E2, a1_id = E3
4000	id=E4, device=1, temp=101
5000	id=E5, device=1, temp=102	a0_id = E4, a1_id = E5

The next sample applies the quantifier to a group. This sample pattern looks for a four events in which the temperature is, in sequence, 100, 101, 100 and 101:

select * from TemperatureSensorEvent
match_recognize (
  partition by device
  measures A[0].id as a0_id, A[1].id as a1_id
  pattern (A B){2}
  define 
	A as A.temp = 100,
	B as B.temp = 101)

8.4.13. Repetition - N or More Matches

The quantifier {n, } must be matched N or more times. The repetition quantifier can be combined with other non-repetition quantifiers and can be used with grouping.

In the below example the pattern (A{2,} B) consists of two variable names, A and B. The condition A must match two or more times and the B condition must match once.

This sample pattern looks for two or more events in which the temperature is over 100 and thereafter an event with a temperature over 102:

select * from TemperatureSensorEvent
match_recognize (
  partition by device
  measures A[0].id as a0_id, A[1].id as a1_id, A[2].id as a2_id, B.id as b_id
  pattern (A{2,} B)
  define 
	A as A.temp >= 100,
	B as B.temp >= 102)

An example sequence of events that matches the pattern above is:

Table 8.15. Example

Arrival Time	Tuple	Output Event (if any)
1000	id=E1, device=1, temp=99
2000	id=E2, device=1, temp=100
3000	id=E3, device=1, temp=100
4000	id=E4, device=1, temp=101
5000	id=E5, device=1, temp=102	a0_id = E2, a1_id = E3, a2_id = E4, b_id = E5

8.4.14. Repetition - Between N and M Matches

The quantifier {n, m} must be matched between N and M times. The repetition quantifier can be combined with other non-repetition quantifiers and can be used with grouping.

In the below example the pattern (A{2,3} B) consists of two variable names, A and B. The condition A must match two or three times and the B condition must match once.

This sample pattern looks for two or three events in which the temperature is over 100 and thereafter an event with a temperature over 102:

select * from TemperatureSensorEvent
match_recognize (
  partition by device
  measures A[0].id as a0_id, A[1].id as a1_id, A[2].id as a2_id, B.id as b_id
  pattern (A{2,3} B)
  define 
	A as A.temp >= 100,
	B as B.temp >= 102)

An example sequence of events that matches the pattern above is:

Table 8.16. Example

Arrival Time	Tuple	Output Event (if any)
1000	id=E1, device=1, temp=99
2000	id=E2, device=1, temp=100
3000	id=E3, device=1, temp=100
4000	id=E4, device=1, temp=101
5000	id=E5, device=1, temp=102	a0_id = E2, a1_id = E3, a2_id = E4, b_id = E5

8.4.15. Repetition - Between Zero and M Matches

The quantifier {, m} must be matched between zero and M times. The repetition quantifier can be combined with other non-repetition quantifiers and can be used with grouping.

In the below example the pattern (A{, 2} B) consists of two variable names, A and B. The condition A must match zero, once or twice and the B condition must match once.

This sample pattern looks for between zero and two events in which the temperature is over 100 and thereafter an event with a temperature over 102:

select * from TemperatureSensorEvent
match_recognize (
  partition by device
  measures A[0].id as a0_id, A[1].id as a1_id, B.id as b_id
  pattern (A{,2} B)
  define 
	A as A.temp >= 100,
	B as B.temp >= 102)

An example sequence of events that matches the pattern above is:

Table 8.17. Example

Arrival Time	Tuple	Output Event (if any)
1000	id=E1, device=1, temp=99
2000	id=E2, device=1, temp=100
3000	id=E3, device=1, temp=100
4000	id=E4, device=1, temp=101
5000	id=E5, device=1, temp=102	a0_id = E3, a1_id = E4, b_id = E5

8.4.16. Repetition Equivalence

The following table outlines sample equivalent patterns.

Table 8.18. Equivalent Pattern Expressions

Expression	Equivalent
Atom Examples
A{2}	A A
A{2, }	A A A*
A{2, 4}	A A A? A?
A{, 2}	A? A?
Group Examples
(A B){2}	(A B) (A B)
(A B){2, }	(A B) (A B) (A B)*
(A B){2, 4}	(A B) (A B) (A B)? (A B)?
(A B){, 2}	(A B)? (A B)?
Quantifier Examples
A+{2, }	A+ A+ A*
A?{2, }	A? A? A*
A+{2, 4}	A+ A+ A* A*
A+{, 2}	A* A*

8.5. `Define` Clause

Within define are listed the boolean conditions that defines a variable name that is declared in the pattern.

A variable name does not require a definition and if there is no definition, the default is a predicate that is always true. Such a variable name can be used to match any row.

The definitions of variable names may reference the same or other variable names as prior examples have shown.

If a variable in your condition expression is a singleton variable, then only individual columns may be referenced. If the variable is not matched by an event, a null value is returned.

If a variable in your condition expression is a group variable, then only indexed columns may be referenced. If the variable is not matched by an event, a null value is returned.

Aggregation functions are not allowed within expressions of the define clause. However define-clause expressions can utilize enumeration methods.

8.5.1. The `Prev` Operator

The prev function may be used in a define expression to access columns of the previous row of a variable name. If there is no previous row, the null value is returned.

The prev function can accept an optional non-negative integer argument indicating the offset to the previous rows. That argument must be a constant. In this case, the runtime returns the property from the N-th row preceding the current row, and if the row doesn’t exist, it returns null.

This function can access variables currently defined, for example:

Y as Y.price < prev(Y.price, 2)

It is not legal to use prev with another variable then the one being defined:

// not allowed
Y as Y.price < prev(X.price, 2)

The prev function returns properties of events in the same partition. Also, it returns properties of events according to event order-of-arrival. When using data windows or deleting events from a named window, the remove stream does not remove events from the prev function.

The pattern looks for an event in which the temperature is greater or equal 100 and that, relative to that event, has an event preceding it by 2 events that also had a temperature greater or equal 100:

select * from TemperatureSensorEvent
match_recognize (
  partition by device
  measures A.id as a_id
  pattern (A)
  define 
	A as A.temp > 100 and prev(A.temp, 2) > 100)

An example sequence of events that matches the pattern above is:

Table 8.19. Example

Arrival Time	Tuple	Output Event (if any)
1000	id=E1, device=1, temp=98
2000	id=E2, device=1, temp=101
3000	id=E3, device=1, temp=101
4000	id=E4, device=1, temp=99
5000	id=E5, device=1, temp=101	a_id=E5

8.6. `Measure` Clause

The measures clause defines exported columns that contain expressions over the pattern variables. The expressions can reference partition columns, singleton variables and any aggregation functions including last and first on the group variables.

Expressions in the measures clause must use the as keyword to assign a column name.

If a variable is a singleton variable then only individual columns may be referenced, not aggregates. If the variable is not matched by an event, a null value is returned.

If a variable is a group variable and used in an aggregate, then the aggregate is performed over all rows that have matched the variable. If a group variable is not in an aggregate function, its variable name must be post-fixed with an index. See Section 8.4.6, “Variables Can Be Singleton or Group” for more information.

8.7. Datawindow-Bound

When using match recognize with a named window or stream bound by a data window, all events removed from the named window or data window also removed the match-in-progress that includes the event(s) removed.

The next example looks for four sensor events from the same device immediately following each other and indicating a rising temperature, but only events that arrived in the last 10 seconds are considered:

select * from TemperatureSensorEvent#time(10 sec)
match_recognize (
partition by device
measures A.id as a_id
pattern (A B C D)
define 
B as B.temp > A.temp,
C as C.temp > B.temp,
D as D.temp > C.temp)

An example sequence of events that matches the pattern above is:

Table 8.20. Example

Arrival Time	Tuple	Output Event (if any)
1000	id=E1, device=1, temp=80
2000	id=E2, device=1, temp=81
3000	id=E3, device=1, temp=82
4000	id=E4, device=1, temp=81
7000	id=E5, device=1, temp=82
9000	id=E6, device=1, temp=83
13000	id=E7, device=1, temp=84	a_id=E4, a_id=E5, a_id=E6, a_id=E7
15000	id=E8, device=1, temp=84
20000	id=E9, device=1, temp=85
21000	id=E10, device=1, temp=86
26000	id=E11, device=1, temp=87

Note that E8, E9, E10 and E11 doe not constitute a match since E8 leaves the data window at 25000.

8.8. Interval

With the optional interval keyword and time period you can control how long the runtime should wait for further events to arrive that may be part of a matching event sequence, before indicating a match (or matches). This is not applicable to on-demand pattern matching.

The interval timer starts are the arrival of the first event matching a sequence for a partition. When the time interval passes and an event sequence matches, duplicate matches are eliminated and output occurs.

The next example looks for sensor events indicating a temperature of over 100 waiting for any number of additional events with a temperature of over 100 for 5 seconds before indicating a match:

select * from TemperatureSensorEvent
match_recognize (
partition by device
measures A.id as a_id, count(B.id) as count_b, first(B.id) as first_b, last(B.id) as last_b
pattern (A B*)
interval 5 seconds
define 
  A as A.temp > 100,
  B as B.temp > 100)

An example sequence of events that matches the pattern above is:

Table 8.21. Example

Arrival Time	Tuple	Output Event (if any)
1000	id=E1, device=1, temp=98
2000	id=E2, device=1, temp=101
3000	id=E3, device=1, temp=102
4000	id=E4, device=1, temp=104
5000	id=E5, device=1, temp=104
7000		a_id=E2, count_b=3, first_b=E3, last_b=E5

Notice that the runtime waits 5 seconds (5000 milliseconds) after the arrival time of the first event E2 of the match at 2000, to indicate the match at 7000.

8.9. Interval-or-Terminated

The interval keyword and time period can be followed by or terminated keywords. When or-terminated is specified, the runtime detects when a pattern state cannot match further and outputs matching event sequences collected so far that are otherwise only output at the end of the interval. This is not applicable to on-demand pattern matching.

Same as for interval alone, the interval timer starts are the arrival of the first event matching a sequence for a partition. Event arrival can terminate the interval and lead to immediate output as follows:

When an event arrives in the sequence that causes pattern state to terminate because no further match is possible, the event sequence matches, duplicate matches are eliminated and output occurs immediately (and not at the end of the interval), for the affected event sequence(s).
Otherwise, when the time interval passes and an event sequence matches, duplicate matches are eliminated and output occurs.

The next example looks for sensor events indicating a temperature of over 100, waiting for any number of additional events with a temperature of over 100 for 5 seconds or when the temperature falls to equal or below 100, whichever happens first:

select * from TemperatureSensorEvent
match_recognize (
partition by device
measures A.id as a_id, count(B.id) as count_b, first(B.id) as first_b, last(B.id) as last_b
pattern (A B*)
interval 5 seconds or terminated
define 
  A as A.temp > 100,
  B as B.temp > 100)

An example sequence of events that matches the pattern above is:

Table 8.22. Example

Arrival Time	Tuple	Output Event (if any)
1000	id=E1, device=1, temp=98
2000	id=E2, device=1, temp=101
3000	id=E3, device=1, temp=102
4000	id=E4, device=1, temp=101
5000	id=E5, device=1, temp=100	a_id=E2, count_b=2, first_b=E3, last_b=E4
7000		(no further output)

Note

Interval and Interval with or terminated make most sense for open-ended patterns such as, for example, pattern (A B*) or pattern (A B C+).

For patterns that terminate when a given event arrives, for example, pattern (A B), an Interval in combination with or terminated should not be specified and if specified have no effect on matching.

8.10. Use With Different Event Types

You may match different types of events using match-recognize by following any of these strategies:

Declare a variant stream.
Declare a supertype for your event types in the create schema syntax.
Have you event classes implement a common interface or extend a common base class.

A short example that demonstrates variant streams and match-recognize is listed below:

// Declare one sample type
create schema S0 as (col string)

// Declare second sample type
create schema S1 as (col string)

// Declare variant stream holding either type
create variant schema MyVariantStream as S0, S1

// Populate variant stream
insert into MyVariantStream select * from S0

// Populate variant stream
insert into MyVariantStream select * from S1

// Simple pattern to match S0 S1 pairs
select * from MyVariantType#time(1 min)
match_recognize (
  measures A.id? as a, B.id? as b
  pattern (A B)
  define
    A as typeof(A) = 'S0',
    B as typeof(B) = 'S1'
)

8.11. Limiting Runtime-Wide State Count

The runtime allows setting a maximum number of states in the configuration, applicable to all match-recognize constructs of all statements.

If your application uses match-recognize in multiple statements and all such match-recognize constructs should count towards a total number of states counts, you may consider setting a maximum number of states, runtime-wide, via the configuration described in Section 17.6.5.1, “Maximum State Count”.

When the limit is reached the match-recognize runtime issues a notification object to any condition handlers registered with the runtime as described in Section 16.11, “Condition Handling”. Depending on your configuration the runtime can prevent the allocation of a new state instance, until states are discarded or statements are undeployed or context partitions are terminated.

The notification object issued to condition handlers is an instance of com.espertech.esper.common.client.hook.condition.ConditionMatchRecognizeStatesMax. The notification object contains information which statement triggered the limit and the state counts per statement for all statements.

For information on configuration please consult Section 17.6.5.1, “Maximum State Count”.

8.12. Limitations

Please note the following limitations:

Subqueries are not allowed in expressions within match_recognize.
Joins and outer joins are not allowed in the same statement as match_recognize.
match_recognize may not be used within on-select or on-insert statements.
When using match_recognize on unbound streams (no data window provided) the iterator pull API returns no rows.

Chapter 9. EPL Reference: Operators

Arithmetic and logical operator precedence follows Java standard arithmetic and logical operator precedence.

9.1. Arithmetic Operators

The below table outlines the arithmetic operators available.

Table 9.1. Syntax and Results of Arithmetic Operators

Operator	Description
+, -	As unary operators they denote a positive or negative expression. As binary operators they add or subtract.
*, /	Multiplication and division are binary operators.
%	Modulo binary operator.

9.2. Logical and Comparison Operators

The below table outlines the logical and comparison operators available.

Table 9.2. Syntax and Results of Logical and Comparison Operators

Operator	Description
NOT	Returns true if the following condition is false, returns false if it is true.
OR	Returns true if either component condition is true, returns false if both are false.
AND	Returns true if both component conditions are true, returns false if either is false.
=, !=, <, > <=, >=, is, is not	Comparison.

9.2.1. Null-Value Comparison Operators

The null value is a special value, see http://en.wikipedia.org/wiki/Null_(SQL) (source:Wikipedia) for more information.

Thereby the following expressions all return null:

2 != null

null = null

2 != null or 1 = 2

2 != null and 2 = 2

Use the is and is not operators for comparing values that can be null.

The following expressions all return true:

2 is not null

null is not 2

null is null

2 is 2

The compiler allows is and is not with any expression, not only in connection with the null constant.

9.3. Concatenation Operators

The below table outlines the concatenation operators available.

Table 9.3. Syntax and Results of Concatenation Operators

Operator	Description
\|\|	Concatenates character strings

9.4. Binary Operators

The below table outlines the binary operators available.

Table 9.4. Syntax and Results of Binary Operators

Operator	Description
&	Bitwise AND if both operands are numbers; conditional AND if both operands are boolean.
\|	Bitwise OR if both operands are numbers; conditional OR if both operands are boolean.
^	Bitwise exclusive OR (XOR).

9.5. Array Definition Operator

The { and } curly braces are array definition operators following the Java array initialization syntax. Arrays can be useful to pass to user-defined functions or to select array data in a select clause.

Array definitions consist of zero or more expressions within curly braces. Any type of expression is allowed within array definitions including constants, arithmetic expressions or event properties. This is the syntax of an array definition:

{ [expression [,expression...]] }

Consider the next statement that returns an event property named actions. The runtime populates the actions property as an array of java.lang.String values with a length of 2 elements. The first element of the array contains the observation property value and the second element the command property value of RFIDEvent events.

select {observation, command} as actions from RFIDEvent

The compiler determines the array type based on the types returned by the expressions in the array definiton. For example, if all expressions in the array definition return integer values then the type of the array is java.lang.Integer[]. If the types returned by all expressions are compatible number types, such as integer and double values, the runtime coerces the array element values and returns a suitable type, java.lang.Double[] in this example. The type of the array returned is Object[] if the types of expressions cannot be coerced or return object values. Null values can also be used in an array definition.

Arrays can come in handy for use as parameters to user-defined functions:

select * from RFIDEvent where Filter.myFilter(zone, {1,2,3})

9.6. Array Element Operator

The [ and ] brackets are array element access operators following the Java syntax for accessing array elements. They can be used with any expression returning an array including with event types that have array inner events.

There must be a single expression returning an Integer or int-typed value within square brackets. Any type of expression is allowed as the index expression. The syntax is:

[index-expression]

Use size() to obtain array length (the identifier length cannot be used to obtain array length).

This example assumes the RFIDEvent has an array-type property called actions and an Integer-type property called actionIndex. The expression returns the value of the actions array at index actionIndex and array length:

select actions[actionIndex], actions.size() from RFIDEvent

Array element operators can be chained and can be used with the Dot-Operator in a chain.

Here is an example of a user-defined function returning an array:

@name('s0') inlined_class """
  public class Helper {
    public static int[] toArray(int a, int b) {
      return new int[] {a, b};
    }
  }
""" 
select Helper.toArray(1,2)[actionIndex] from RFIDEvent

If the array input value or the result of the index expression is null the operator returns null. If the index expression returns an index value greater than the array size the operator raises an exception.

9.7. Dot Operator

You can use the dot operator to invoke a method on the result of an expression. The dot operator uses the dot (.) or period character.

The dot-operator is relevant with enumeration methods: Enumeration methods perform tasks such as transformation, filtering, aggregation, sequence-matching, sorting and others on subquery results, named windows, tables, event properties or inputs that are or can be projected to a collection of events, scalar values or objects. See Chapter 11, EPL Reference: Enumeration Methods

Further the dot-operator is relevant to date-time methods. Date-time methods work on date-time values to add or subtract time periods, set or round calendar fields or query fields, among other tasks. See Chapter 12, EPL Reference: Date-Time Methods.

The dot-operator is also relevant to spatial methods and the use of spatial indexes. See Chapter 20, EPL Reference: Spatial Methods and Indexes.

This section only describes the dot-operator in relation to property instance methods, the special get and size indexed-property methods and duck typing.

The synopsis for the dot operator is as follows

expression.method([parameter [,...]])[.method(...)][...]

The expression to evaluate by the dot operator is in parenthesis. After the dot character follows the method name and method parameters in parenthesis.

You may use the dot operator when your expression returns an object that you want to invoke a method on. The dot operator allows duck typing and convenient array and collection access methods.

This example statement invokes the getZones method of the RFID event class by referring to the stream name assigned in the from-clause:

select rfid.getZones() from RFIDEvent as rfid

The size() method can be used to return the array length or collection size. Use the get method to return the value at a given index for an array or collection.

The next statement selects array size and returns the last array element:

select arrayproperty.size() as arraySize, 
  arrayproperty.get((arrayproperty).size - 1) as lastInArray 
  from ProductEvent

The dot-operator allows a property name, for use when the object returned by the expression exposes a getter-method that matches the property name (only a simple name is supported; not allowing all event property expressions; not applicable when using duck typing).

For instance, assume that productLookup is a user-defined function that returns an object that has a getPrice() method. The dot-operator returns the result of the call to getPrice():

select productLookup(e).price from ProductEvent as e

9.7.1. Duck Typing

Duck typing is when the compiler checks for the existence of a method regardless of object class inheritance hierarchies. This can be useful, for example, when a dynamic property returns an object which may or may not provide a method to return the desired value.

Duck typing is disabled in the default configuration to consistently enforce strong typing. Please enable duck typing via compiler expression settings as described in Section 17.5.6, “Compiler Settings Related to Expression Evaluation”.

The statement below selects a dynamic property by name productDesc and invokes the getCounter() method if that method exists on the property value, or returns the null value if the method does not exist for the dynamic property value of if the dynamic property value itself is null:

select (productDesc?).getCounter() as arraySize from ProductEvent

9.8. The '`In`' Keyword

The in keyword determines if a given value matches any value in a list. The syntax of the keyword is:

test_expression [not] in (expression [,expression...] )

The test_expression is any valid expression. The keyword is followed by a list of expressions to test for a match. The optional not keyword specifies that the result of the predicate be negated.

The result of an in expression is of type Boolean. If the value of test_expression is equal to any expression from the comma-separated list, the result value is true. Otherwise, the result value is false.

The next example shows how the in keyword can be applied to select certain command types of RFID events:

select * from RFIDEvent where command in ('OBSERVATION', 'SIGNAL')

The statement is equivalent to:

select * from RFIDEvent where command = 'OBSERVATION' or command = 'SIGNAL'

Expression may also return an array, a java.util.Collection or a java.util.Map. Thus event properties that are lists, sets or maps may provide values to compare against test_expression.

All expressions must be of the same type or a compatible type to test_expression. The in keyword may coerce number values to compatible types. If expression returns an array, then the component type of the array must be compatible, unless the component type of the array is Object.

If expression returns an array of component type Object, the operation compares each element of the array, applying equals semantics.

If expression returns a Collection, the operation determines if the collection contains the value returned by test_expression, applying contains semantics.

If expression returns a Map, the operation determines if the map contains the key value returned by test_expression, applying containsKey semantics.

Constants, arrays, Collection and Map expressions or event properties can be used combined.

For example, and assuming a property named 'mySpecialCmdList' exists that contains a list of command strings:

select * from RFIDEvent where command in ( 'OBSERVATION', 'SIGNAL', mySpecialCmdList)

When using prepared statements and substitution parameters with the in keyword, make sure to retain the parenthesis. Substitution values may also be arrays, Collection and Map values:

test_expression [not] in (? [,?...] )

Note that if there are no successes and at least one right-hand row yields null for the operator's result, the result of the any construct will be null, not false. This is in accordance with SQL's normal rules for Boolean combinations of null values.

9.8.1. '`In`' for Range Selection

The in keyword can be used to specify ranges, including open, half-closed, half-open and inverted ranges.

Ranges come in the following 4 varieties. The round () or square [] bracket indicate whether an endpoint is included or excluded. The low point and the high-point of the range are separated by the colon : character.

Open ranges that contain neither endpoint (low:high)
Closed ranges that contain both endpoints [low:high]. The equivalent 'between' keyword also defines a closed range.
Half-open ranges that contain the low endpoint but not the high endpoint [low:high)
Half-closed ranges that contain the high endpoint but not the low endpoint (low:high]

The following statement two statements are equivalent: Both statements select orders where the price is in the range of zero and 10000 (endpoints inclusive):

select * from OrderEvent where price in [0:10000]

select * from OrderEvent where price between 0 and 10000

The next statement selects order events where the price is greater then 100 and less-or-equal to 2000:

select * from OrderEvent where price in (100:2000]

Use the not in keywords to specify an inverted range.

The following statement selects an inverted range by selecting all order events where the price is less then zero or the price is greater or equal to 10000:

select * from OrderEvent where price not in (0:10000]

In case the value of low endpoint is less then the value of high endpoint the in operator reverses the range.

The following two statements are also equivalent:

select * from OrderEvent where price in [10000:0]

select * from OrderEvent where price >= 0 and price <= 1000

9.9. The '`Between`' Keyword

The between keyword specifies a range to test. The syntax of the keyword is:

test_expression [not] between begin_expression and end_expression

The test_expression is any valid expression and is the expression to test for in the range defined by begin_expression and end_expression. The not keyword specifies that the result of the predicate be negated.

The result of a between expression is of type Boolean. If the value of test_expression is greater then or equal to the value of begin_expression and less than or equal to the value of end_expression, the result is true.

The next example shows how the between keyword can be used to select events with a price between 55 and 60 (endpoints inclusive).

select * from StockTickEvent where price between 55 and 60

The equivalent expression without between is:

select * from StockTickEvent where price >= 55 and price <= 60

And also equivalent to:

select * from StockTickEvent where price between 60 and 55

While the between keyword always includes the endpoints of the range, the in operator allows finer control of endpoint inclusion.

In case the value of begin_expression is less then the value of end_expression the between operator reverses the range.

The following two statements are also equivalent:

select * from StockTickEvent where price between 60 and 55

select * from StockTickEvent where price >= 55 and price <= 60

9.10. The '`Like`' Keyword

The like keyword provides standard SQL pattern matching. SQL pattern matching allows you to use '_' to match any single character and '%' to match an arbitrary number of characters (including zero characters). SQL patterns are case-sensitive by default. The syntax of like is:

test_expression [not] like pattern_expression [escape string_literal]

The test_expression is any valid expression yielding a String-type or a numeric result. The optional not keyword specifies that the result of the predicate be negated. The like keyword is followed by any valid standard SQL pattern_expression yielding a String-typed result. The optional escape keyword signals the escape character to escape '_' and '%' values in the pattern. The default escape character is backslash (\).

The result of a like expression is of type Boolean. If the value of test_expression matches the pattern_expression, the result value is true. Otherwise, the result value is false.

An example for the like keyword is below.

select * from PersonLocationEvent where name like '%Jack%'

The escape character can be defined as follows. In this example the where-clause matches events where the suffix property is a single '_' character.

select * from PersonLocationEvent where suffix like '!_' escape '!'

9.11. The '`Regexp`' Keyword

The regexp keyword is a form of pattern matching based on regular expressions implemented through the Java java.util.regex package. The syntax of regexp is:

test_expression [not] regexp pattern_expression

The result of a regexp expression is of type Boolean. If the value of test_expression matches the regular expression pattern_expression, the result value is true. Otherwise, the result value is false.

An example for the regexp keyword is below.

select * from PersonLocationEvent where name regexp '.*Jack.*'

The rexexp function matches the entire region against the pattern via java.util.regex.Matcher.matches() method. Please consult the Java API documentation for more information or refer to Regular Expression Flavors.

9.12. The '`Any`' and '`Some`' Keywords

The any operator is true if the expression returns true for one or more of the values returned by a list of expressions including array, Collection and Map values.

The synopsis for the any keyword is as follows:

expression operator any (expression [,expression...] )

The left-hand expression is evaluated and compared to each expression result using the given operator, which must yield a Boolean result. The result of any is "true" if any true result is obtained. The result is "false" if no true result is found (including the special case where the expressions are collections that return no rows).

The operator can be any of the following values: =, !=, <>, <, <=, >, >=.

The some keyword is a synonym for any. The in construct is equivalent to = any.

Expression may also return an array, a java.util.Collection or a java.util.Map. Thus event properties that are lists, sets or maps may provide values to compare against.

All expressions must be of the same type or a compatible type. The any keyword coerces number values to compatible types. If expression returns an array, then the component type of the array must be compatible, unless the component type of the array is Object.

If expression returns an array, the operation compares each element of the array.

If expression returns a Collection, the operation determines if the collection contains the value returned by the left-hand expression, applying contains semantics. When using relationship operators <, <=, >, >= the operator applies to each element in the collection, and non-numeric elements are ignored.

If expression returns a Map, the operation determines if the map contains the key value returned by the left-hand expression, applying containsKey semantics. When using relationship operators <, <=, >, >= the operator applies to each key in the map, and non-numeric map keys are ignored.

Constants, arrays, Collection and Map expressions or event properties can be used combined.

The next statement demonstrates the use of the any operator:

select * from ProductOrder where category != any (categoryArray)

The above statement selects ProductOrder event that have a category field and a category array, and returns only those events in which the category value is not in the array.

9.13. The '`All`' Keyword

The all operator is true if the expression returns true for all of the values returned by a list of expressions including array, Collection and Map values.

The synopsis for the all keyword is as follows:

expression operator all (expression [,expression...] )

The left-hand expression is evaluated and compared to each expression result using the given operator, which must yield a Boolean result. The result of all is "true" if all rows yield true (including the special case where the expressions are collections that returns no rows). The result is "false" if any false result is found. The result is null if the comparison does not return false for any row, and it returns null for at least one row.

The operator can be any of the following values: =, !=, <>, <, <=, >, >=.

The not in construct is equivalent to != all.

Expression may also return an array, a java.util.Collection or a java.util.Map. Thus event properties that are lists, sets or maps may provide values to compare against.

All expressions must be of the same type or a compatible type. The all keyword coerces number values to compatible types. If expression returns an array, then the component type of the array must be compatible, unless the component type of the array is Object.

If expression returns an array, the operation compares each element of the array.

Constants, arrays, Collection and Map expressions or event properties can be used combined.

The next statement demonstrates the use of the all operator:

select * from ProductOrder where category = all (categoryArray)

The above statement selects ProductOrder event that have a category field and a category array, and returns only those events in which the category value matches all values in the array.

9.14. The '`New`' Keyword

The new has two uses:

Populate a new data structure by evaluating column names and assignment expressions.
Instantiate an object of a given class by its constructor.

9.14.1. Using '`New`' to Populate a Data Structure

The new data structure operator populates a new data structure by evaluating column names and assignment expressions. This is useful when an expression should return multiple results, for performing a transformation or inside enumeration method lambda expressions.

The synopsis is as follows:

new { column_name = [assignment_expression] [,column_name...] }

The result of the new-operator is a map data structure that contains column_name keys and values. If an assignment expression is provided for a column, the operator evaluates the expression and assigns the result to the column name. If no assignment expression is provided, the column name is assumed to be an event property name and the value is the event property value.

The next statement demonstrates the use of the new operator:

select new {category, price = 2*price} as priceInfo from ProductOrder

The above statement returns a single property priceInfo for each arriving ProductOrder event. The property value is itself a map that contains two entries: For the key name category the value of the category property and for the key name price the value of the price property multiplied by two.

The next EPL is an example of the new operator within an expression definition and a case-statement (one statement not multiple):

expression calcPrice {
  productOrder => case
    when category = 'fish' then new { sterialize = 'XRAY', priceFactor = 1.01 }
    when category = 'meat' then new { sterialize = 'UVL', priceFactor = 1 }
  end
}  

select calcPrice(po) as priceDetail from ProductOrder po

In above example the expression calcPrice returns both a sterialize string value and a priceFactor double value. The expression is evaluated as part of the select-clause and the map-type result placed in the priceDetail property of output events.

When used within the case operator, the operator validates that the data structure is compatible between each case-when result in terms of column names and types. The default value for else in case is null.

9.14.2. Using '`New`' to Instantiate an Object

The new instantiation operator can instantiate an object of the given class.

The synopsis is as follows:

new class-name([parameter [, parameter [,...]]])

The class-name is the name of the class to instantiate an object for. The classname can either be fully-qualified or you can add the package or classname to the imports.

After the classname follow parenthesis and any number of parameter expressions. The compiler expects that the class declares a public constructor matching the number and return types of parameter expressions.

Assuming that OrderHolder is an imported class, the next statement demonstrates the use of the new operator:

select new OrderHolder(po) as orderHolder from ProductOrder as po

9.14.3. Using '`New`' to Allocate an Array

The new instantiation operator can instantiate an array of the given type. The syntax follows common programming language syntax.

The synopsis for one-dimensional arrays is:

new class-name[dimension-one]

The synopsis for two-dimensional arrays is:

new class-name[dimension-one][dimension-two]

The class-name is the name of the class that is the component type of the array.

For a one-dimensional array provide single square brackets, i.e. [dimension-one], wherein the dimension-one expression returns an integer value providing the size of the array.

For a two-dimensional array provide two square brackets, i.e. [dimension-one][dimension-two], wherein the dimension-one expression returns an integer value providing the size of the first dimension and the dimension-two expression returns an integer value providing the size of the second dimension of the array.

The next EPL allocates a one-dimensional array of size 5 holding double-type values, and a two-dimensional array of size 2 by 4 holding string-type values:

select new double[5] as doubleArrayOneDimensional, new string[2][4] as stringArrayTwoDimensional from ProductOrder

The operator allocates the respective array without initializing and without setting array element values. Thereby array element values are the JVM default values for the respective type.

In the case that any dimension expression returns a null value the runtime throws an EPException.

Only one- or two-dimensional arrays are supported by the new-operator and otherwise arrays can also be allocated using a single-row function.

9.14.4. Using '`New`' to Allocate and Initialize an Array

The new instantiation operator can instantiate an array of the given type and initialize its array element values. The syntax follows common programming language syntax.

The synopsis for one-dimensional arrays is:

new class-name[] { [expression [,expression...]] }

The synopsis for two-dimensional arrays is:

new class-name[][] { {[expression [,expression...]]}, [{...}] }

The class-name is the name of the class that is the component type of the array.

The array initialization consist of zero or more expressions within curly ({ and }) braces. For two-dimensional arrays the curly braces must be nested. The array dimensions are implied by the number of initialization expressions.

This sample EPL allocates a one-dimensional array of size 5 holding double-type values and initializes the array to values between 1 and 5, and also allocates and initializes a two-dimensional array of size 2 by 4 holding string-type values:

select new double[] {1, 2, 3, 4, 5} as doubleArrayOneDimensional, 
  new string[][] {{'0-0', '0-1', '0-2', '0-3'}, {'1-0', '1-1', '1-2', '1-3'}} as stringArrayTwoDimensional from ProductOrder

In the case that the array component type is a primitive type (i.e. int, long, double etc.) and when any expression returns a null value the runtime throws an EPException.

Only one- or two-dimensional arrays are supported by the new-operator and otherwise arrays can also be allocated and initialized using a single-row function.

Chapter 10. EPL Reference: Functions

10.1. Single-Row Function Reference

10.1.1. The Case Control Flow Function
10.1.2. The Cast Function
10.1.3. The Coalesce Function
10.1.4. The Current_Evaluation_Context Function
10.1.5. The Current_Timestamp Function
10.1.6. The Event_Identity_Equals Function
10.1.7. The Exists Function
10.1.8. The Grouping Function
10.1.9. The Grouping_Id Function
10.1.10. The Instance-Of Function
10.1.11. The Istream Function
10.1.12. The Min and Max Functions
10.1.13. The Previous Function
10.1.14. The Previous-Tail Function
10.1.15. The Previous-Window Function
10.1.16. The Previous-Count Function
10.1.17. The Prior Function
10.1.18. The Type-Of Function

10.2. Aggregation Functions

10.2.1. SQL-Standard Functions
10.2.2. Event Aggregation Functions
10.2.3. Approximation Aggregation Functions
10.2.4. Additional Aggregation Functions

10.3. User-Defined Functions

10.4. Select-Clause Transpose Function

10.4.1. Transpose with Insert-Into

10.1. Single-Row Function Reference

Single-row functions return a single value for every single result row generated by your statement. These functions can appear anywhere where expressions are allowed.

EPL allows static Java library methods as single-row functions, and also features built-in single-row functions. In addition, EPL allows instance method invocations on named streams.

You may also register your own single-row function name with the compiler so that your statements become less cluttered. This is described in detail in Section 22.2, “Single-Row Function”. Single-row functions that return an object can be chained.

The compiler auto-imports the following Java library packages:

java.lang.*
java.math.*
java.text.*
java.util.*

Thus Java static library methods can be used in all expressions as shown in below example:

select symbol, Math.round(volume/1000)
from StockTickEvent#time(30 sec)

In general, arbitrary Java class names have to be fully qualified (e.g. java.lang.Math) but EPL provides a mechanism for user-controlled imports of classes and packages as outlined in Section 17.4.2, “Class and Package Imports”.

The below table outlines the built-in single-row functions available.

Table 10.1. Syntax and Results of Single-Row Functions

Single-row Function	Result
case value when compare_value then result [when compare_value then result ...] [else result] end	Returns `result` where the first `value` equals `compare_value`.
case when condition then result [when condition then result ...] [else result] end	Returns the `result` for the first condition that is true.
cast(expression, type_name)	Casts the result of an expression to the given type.
coalesce(expression, expression [, expression ...])	Returns the first non-`null` value in the list, or `null` if there are no non-`null` values.
current_evaluation_context()	Returns an object containing the runtime URI, statement name and context partition id (when applicable).
current_timestamp[()]	Returns the current runtime time as a `long` value. Reserved keyword with optional parenthesis.
exists(dynamic_property_name)	Returns true if the dynamic property exists for the event, or false if the property does not exist.
instanceof(expression, type_name [, type_name ...])	Returns true if the expression returns an object whose type is one of the types listed.
istream()	Returns true if the event is part of the insert stream and false if the event is part of the remove stream.
max(expression, expression [, expression ...])	Returns the highest numeric value among the 2 or more comma-separated expressions.
min(expression, expression [, expression ...])	Returns the lowest numeric value among the 2 or more comma-separated expressions.
prev(expression, event_property)	Returns a property value or all properties of a previous event, relative to the event order within a data window, or according to an optional index parameter (N) the positional Nth-from-last value.
prevtail(expression, event_property)	Returns a property value or all properties of the first event in a data window relative to the event order within a data window, or according to an optional index parameter (N) the positional Nth-from-first value.
prevwindow(event_property)	Returns a single property value of all events or all properties of all events in a data window in the order that reflects the sort order of the data window.
prevcount(event_property)	Returns the count of events (number of data points) in a data window.
prior(expression, event_property)	Returns a property value of a prior event, relative to the natural order of arrival of events
typeof(expression)	If expression is a stream name, returns the event type name of the evaluated event, often used with variant streams. If expression is a property name or expression, returns the name of the expression result type.

10.1.1. The `Case` Control Flow Function

The case control flow function has two versions. The first version takes a value and a list of compare values to compare against, and returns the result where the first value equals the compare value. The second version takes a list of conditions and returns the result for the first condition that is true.

The return type of a case expression is the compatible aggregated type of all return values.

The case expression is sometimes used with the new operator to return multiple results, see Section 9.14, “The 'New' Keyword”.

The example below shows the first version of a case statement. It has a String return type and returns the value 'one'.

select case myexpression when 1 then 'one' when 2 then 'two' else 'more' end from ...

The second version of the case function takes a list of conditions. The next example has a Boolean return type and returns the boolean value true.

select case when 1>0 then true else false end from ...

10.1.2. The `Cast` Function

The cast function casts the return type of an expression to a designated type. The function accepts two parameters: The first parameter is the property name or expression that returns the value to be casted. The second parameter is the type to cast to. You can use the as keyword instead of comma (,) to separate parameters.

Valid parameters for the second (type) parameter are:

Any of the Java built-in types: int, long, byte, short, char, double, float, string, BigInteger, BigDecimal, where string is a short notation for java.lang.String and BigInteger as well as BigDecimal are the classes in java.math. The type name is not case-sensitive. Use [] for array types. For example:
```
cast(price, double)
```
The fully-qualified class name of the class to cast to, for example:
```
cast(product as org.myproducer.Product)
```
For parsing date-time values, any of the date-time types: date, calendar, long, localdatetime, zoneddatetime, localdate, localtime. For these types the dateformat parameter is required as discussed below.

The cast function is often used to provide a type for dynamic (unchecked) properties. Dynamic properties are properties whose type is not known at compile type. These properties are always of type java.lang.Object.

The cast function as shown in the next statement casts the dynamic "price" property of an "item" in the OrderEvent to a double value.

select cast(item.price?, double) from OrderEvent

The cast function returns a null value if the expression result cannot be casted to the desired type, or if the expression result itself is null.

The cast function adheres to the following type conversion rules:

For all numeric types, the cast function utilitzes java.lang.Number to convert numeric types, if required.
For casts to string or java.lang.String, the function calls toString on the expression result.
For casts to other objects including application objects, the cast function considers a Java class's superclasses as well as all directly or indirectly-implemented interfaces by superclasses.

The cast function supports specifying a parameterized type, for example:

select cast(item.listOfAlternativeNames, java.util.List<String>) from OrderEvent

10.1.2.1. The `Cast` Function For Parsing Dates

The cast function can parse string-type date-time values to long-type milliseconds, Date, Calendar, LocalDateTime, ZonedDateTime, LocalDate and LocalTime objects.

You must provide the dateformat named parameter as the last parameter to the cast function. The dateformat parameter expression must return a String-typed value, a SimpleDateFormat-type value or a DateTimeFormatter-type value. Return a SimpleDateFormat for long/Date/Calendar. Return a DateTimeFormatter for LocalDateTime/ZonedDateTime/LocalDate/LocalTime.

The next EPL outputs the date May 2, 2010 as a Date-type value:

select cast('20100502', date, dateformat: 'yyyyMMdd') from OrderEvent

You may use date-time methods when cast is returning a date-time value. Expressions can be any expression and do not need to be string constants.

You may parse dates that are ISO 8601-formatted dates by specifying iso as the date format. The ISO 8601 date format is described in Section 7.6.5.1.1, “Specifying Dates”.

For example, assuming the orderDate property is a ISO 8601 formatted date, the runtime can convert it to a long millisecond value like this:

select cast(orderDate, long, dateformat: 'iso') from OrderEvent

The next table shows the recognized date types available:

Table 10.2. Date Types for Casting/Parsing

Value provided to Cast as the second parameter	Result Type
`date`, `java.util.Date`	`java.util.Date`
`calendar`, `java.util.Calendar`	`java.util.Calendar`
`long`, `java.lang.Long`	`long`
`localdatetime`, `java.time.LocalDateTime`	`java.time.LocalDateTime`
`localdate`, `java.time.LocalDate`	`java.time.LocalDate`
`localtime`, `java.time.LocalTime`	`java.time.LocalTime`
`zoneddatetime`, `java.time.ZonedDateTime`	`java.time.ZonedDateTime`

Additional examples are:

select cast(orderDate, localdatetime, dateformat:java.time.format.DateTimeFormatter.ISO_DATE_TIME) from OrderEvent

select cast(orderDate, calendar, dateformat:SimpleDateFormat.getInstance()) from OrderEvent

10.1.2.2. Casting Arrays

You may add array brackets ([]) to the type to indicate that the cast-to type is an array. You may add array brackets with the primitive keyword ([primitive]) for casting to an array of primitive values.

Some examples are:

select cast(string_array, string[]), cast(int_primitive_array, int[primitive]), 
  cast(int_boxed_array, int[]), cast(two_dimensional, string[][]), cast(object_array, java.lang.Object[]) from MyEvent

10.1.3. The `Coalesce` Function

The result of the coalesce function is the first expression in a list of expressions that returns a non-null value. The return type is the compatible aggregated type of all return values.

This example returns a String-typed result of value 'foo':

select coalesce(null, 'foo') from ...

10.1.4. The `Current_Evaluation_Context` Function

The current_evaluation_context function takes no parameters and returns expression evaluation contextual information as an object of type com.espertech.esper.common.client.hook.expr.EPLExpressionEvaluationContext. The object provides the runtime URI, the statement name and the context partition id of the currently-evaluated expression.

For example:

select current_evaluation_context().getRuntimeURI() as runtimeURI from MyEvent

The context partition id will be -1 when the statement is not associated to a context.

10.1.5. The `Current_Timestamp` Function

The current_timestamp function is a reserved keyword and requires no parameters. The result of the current_timestamp function is the long-type value of the current runtime time.

The function returns the current runtime timestamp at the time of expression evaluation. When using external-timer events, the function provides the last value of the externally-supplied time at the time of expression evaluation.

This example selects the current runtime time:

select current_timestamp from MyEvent
// equivalent to
select current_timestamp() from MyEvent

10.1.6. The `Event_Identity_Equals` Function

The event_identity_equals function returns a boolean value indicating whether two events are the same event. The function accepts two parameters and both parameters must be a stream name. It validates that the event type of each stream is the same event type.

In a subquery, the function can be used to exclude the currently-arriving event from results, as shown below.

select * from OrderEvent as arrivingEvent 
  where exists (select * from OrderEvent#time(5) as last5 
        where not event_identity_equals(arrivingEvent, last5) and arrivingEvent.orderId = last5.orderId)

You may also use this function with enumeration methods that process events. The next sample totals up window amounts excluding the current event.

select orderId, window(*)
  .aggregate(0d, (result, e) => result + (case when event_identity_equals(oe, e) then 0d else e.amount end)) as c0 
from OrderEvent#time(10) as oe

The function returns null when either of the streams have no event.

10.1.7. The `Exists` Function

The exists function returns a boolean value indicating whether the dynamic property, provided as a parameter to the function, exists on the event. The exists function accepts a single dynamic property name as its only parameter.

The exists function is for use with dynamic (unchecked) properties. Dynamic properties are properties whose type is not known at compile type. Dynamic properties return a null value if the dynamic property does not exists on an event, or if the dynamic property exists but the value of the dynamic property is null.

The exists function as shown next returns true if the "item" property contains an object that has a "serviceName" property. It returns false if the "item" property is null, or if the "item" property does not contain an object that has a property named "serviceName" :

select exists(item.serviceName?) from OrderEvent

10.1.8. The `Grouping` Function

The grouping function is a SQL-standard function useful in statements that have a group by-clause and that utilize one of the rollup, cube or grouping sets keywords. The function can be used only in the select-clause, having-clause and order by-clauses.

The function takes a single expression as a parameter and returns an integer value of zero or one indicating whether a specified expression in a group-by-clause is aggregated or not. The function returns 1 for aggregated or 0 for not aggregated.

The grouping function can help you distinguish null values returned because of the output row's aggregation level from null values returned by event properties or other expressions.

The parameter expression must match exactly one of the expressions in the group-by-clause.

Please see an example in the next section.

10.1.9. The `Grouping_Id` Function

The grouping_id function is a SQL-standard function useful in statements that have a group by-clause and that utilize one of the rollup, cube or grouping sets keywords. The function can be used only in the select-clause, having-clause and order by-clauses.

The function takes one or more expressions as a parameter and returns an integer value indicating grouping level. The runtime computes the grouping level by taking the results of multiple grouping functions and concatenating them into a bit vector (a string of ones and zeros).

Assume a car event that has a property for name, place and number of cars:

create schema CarEvent(name string, place string, numcars int)

The next EPL computes the total number of cars for each of the following groupings: per name and place, per name, per place and overall.

select name, place, sum(numcars), grouping(name), grouping(place), grouping_id(name, place)
from CarEvent group by grouping sets((name, place),name, place,())

Assume your application processes a car event with properties like so: CarEvent={name='skoda', place='france', numcars=100}.

The runtime outputs 4 rows as shown in the next table:

Table 10.3. Example Output for Grouping and Grouping_id Functions (CarEvent 1)

name	place	sum(numcars)	grouping(name)	grouping(place)	grouping_id(name, place)
skoda	france	100	0	0	0
skoda	null	100	0	1	1
null	france	100	1	0	2
null	null	100	1	1	3

Assume your application processes a second car event: CarEvent={name='skoda', place='germany', numcars=75}.

The runtime outputs 4 rows as shown in the next table:

Table 10.4. Example Output for Grouping and Grouping_id Functions (CarEvent 2)

name	place	sum(numcars)	grouping(name)	grouping(place)	grouping_id(name, place)
skoda	germany	75	0	0	0
skoda	null	175	0	1	1
null	germany	75	1	0	2
null	null	175	1	1	3

The parameter expressions must match exactly to expressions in the group-by-clause.

10.1.10. The `Instance-Of` Function

The instanceof function returns a boolean value indicating whether the type of value returned by the expression is one of the given types. The first parameter to the instanceof function is an expression to evaluate. The second and subsequent parameters are Java type names.

The function determines the return type of the expression at runtime by evaluating the expression, and compares the type of object returned by the expression to the defined types. If the type of object returned by the expression matches any of the given types, the function returns true. If the expression returned null or a type that does not match any of the given types, the function returns false.

The instanceof function is often used in conjunction with dynamic (unchecked) properties. Dynamic properties are properties whose type is not known at compile type.

This example uses the instanceof function to select different properties based on the type:

select case when instanceof(item, com.mycompany.Service) then serviceName?
  when instanceof(item, com.mycompany.Product) then productName? end 
  from OrderEvent

The instanceof function returns false if the expression tested by instanceof returned null.

Valid parameters for the type parameter list are:

Any of the Java built-in types: int, long, byte, short, char, double, float, string, where string is a short notation for java.lang.String. The type name is not case-sensitive. For example, the next function tests if the dynamic "price" property is either of type float or type double:
```
instanceof(price?, double, float)
```
The fully-qualified class name of the class to cast to, for example:
```
instanceof(product, org.myproducer.Product)
```

The function considers an event class's superclasses as well as all the directly or indirectly-implemented interfaces by superclasses.

10.1.11. The `Istream` Function

The istream function returns a boolean value indicating whether within the context of expression evaluation the current event or set of events (joins) are part of the insert stream (true) or part of the remove stream (false). The function takes no parameters.

Use the istream function with data windows and select irstream and insert irstream into.

In the following example the istream function always returns boolean true since no data window is declared:

select irstream *, istream() from OrderEvent

The next example declares a data window. For newly arriving events the function returns boolean true, for events that expire after 10 seconds the function returns boolean false:

select irstream *, istream() from OrderEvent#time(10 sec)

The istream function returns true for all cases where insert or remove stream does not apply, such as when used in parameter expressions to data windows or in stream filter expressions.

10.1.12. The `Min` and `Max` Functions

The min and max function take two or more parameters that itself can be expressions. The min function returns the lowest numeric value among the 2 or more comma-separated expressions, while the max function returns the highest numeric value. The return type is the compatible aggregated type of all return values.

The next example shows the max function that has a Double return type and returns the value 1.1.

select max(1, 1.1, 2 * 0.5) from ...

The min function returns the lowest value. The statement below uses the function to determine the smaller of two timestamp values.

select symbol, min(ticks.timestamp, news.timestamp) as minT
	from StockTickEvent#time(30 sec) as ticks, NewsEvent#time(30 sec) as news
	where ticks.symbol = news.symbol

10.1.13. The `Previous` Function

The prev function returns the property value or all event properties of a previous event. For data windows that introduce a sort order other than the order of arrival, such as the sorted data window and the time order data window, the function returns the event at the specified position.

The prev function is not an aggregation function and therefore does not return results per group when used with group by. Please consider the last, lastever or first aggregation functions instead as described in Section 10.2.2, “Event Aggregation Functions”. You must use an aggregation function instead of prev when querying a named window or table.

The first parameter to the prev function is an index parameter and denotes the i-th previous event, in the order established by the data window. If no index is provided, the default index is 1 and the function returns the previous event. The second parameter is a property name or stream name. If specifying a property name, the function returns the value for the previous event property value. If specifying a stream name, the function returns the previous event underlying object.

This example selects the value of the price property of the 2nd-previous event from the current Trade event:

select prev(2, price) from Trade#length(10)

By using the stream alias in the previous function, the next example selects the trade event itself that is immediately previous to the current Trade event

select prev(1, trade) from Trade#length(10) as trade

Since the prev function takes the order established by the data window into account, the function works well with sorted windows.

In the following example the statement selects the symbol of the 3 Trade events that had the largest, second-largest and third-largest volume.

select prev(0, symbol), prev(1, symbol), prev(2, symbol)
  from Trade#sort(3, volume desc)

The i-th previous event parameter can also be an expression returning an Integer-type value. The next statement joins the Trade data window with an RankSelectionEvent event that provides a rank property used to look up a certain position in the sorted Trade data window:

select prev(rank, symbol) from Trade#sort(10, volume desc), RankSelectionEvent unidirectional

Use the prev function in combination with a grouped data window to access a previous event per grouping criteria.

The example below returns the price of the previous Trade event for the same symbol, or null if for that symbol there is no previous Trade event:

select prev(1, price) from Trade#groupwin(symbol)#length(2)

The prev function returns a null value if the data window does not currently hold the i-th previous event. The example below illustrates this using a time batch window. Here the prev function returns a null value for any events in which the previous event is not in the same batch of events. Note that the prior function as discussed below can be used if a null value is not the desired result.

select prev(1, symbol) from Trade#time_batch(1 min)

An alternative form of the prev function allows the index to not appear or appear after the property name if the index value is a constant and not an expression:

select prev(1, symbol) from Trade
// ... equivalent to ...
select prev(symbol) from Trade
// ... and ...
select prev(symbol, 1) from Trade

The combination of the prev function and #groupwin returns the property value for a previous event in the given data window group.

The following example returns for each event the current smallest price per symbol:

select symbol, prev(0, price) as topPricePerSymbol 
from Trade#groupwin(symbol)#sort(1, price asc)

10.1.13.1. Restrictions

The following restrictions apply to the prev functions and its results:

The function always returns a null value for remove stream (old data) events.
The function requires a data window, or #groupwin and a data window. See Section 14.3, “Data Windows” for data windows.

10.1.13.2. Comparison to the `Prior` Function

The prev function is similar to the prior function. The key differences between the two functions are as follows:

The prev function returns previous events in the order provided by the data window, while the prior function returns prior events in the order of arrival as posted by a stream's declared windows.
The prev function requires a data window while the prior function does not have any such requirement.
The prev function returns the previous event grouped by a criteria by combining the #groupwin and a data window. The prior function returns prior events posted regardless of data window grouping.
The prev function returns a null value for remove stream events, i.e. for events leaving a data window. The prior function does not have this restriction.

10.1.14. The `Previous-Tail` Function

The prevtail function returns the property value or all event properties of the positional-first event in a data window. For data windows that introduce a sort order other than the order of arrival, such as the sorted data window and the time order data window, the function returns the first event at the specified position.

The prevtail function is not an aggregation function and therefore does not return results per group when used with group by. Please consider the first, firstever or window aggregation functions instead as described in Section 10.2.2, “Event Aggregation Functions”. You must use an aggregation function instead of prevtail when querying a named window or table.

The first parameter is an index parameter and denotes the i-th from-first event in the order established by the data window. If no index is provided the default is zero and the function returns the first event in the data window. The second parameter is a property name or stream name. If specifying a property name, the function returns the value for the previous event property value. If specifying a stream name, the function returns the previous event underlying object.

This example selects the value of the price property of the first (oldest) event held in the length window:

select prevtail(price) from Trade#length(10)

By using the stream alias in the prevtail function, the next example selects the trade event itself that is the second event held in the length window:

select prevtail(1, trade) from Trade#length(10) as trade

Since the prevtail function takes the order established by the data window into account, the function works well with sorted windows.

In the following example the statement selects the symbol of the 3 Trade events that had the smallest, second-smallest and third-smallest volume.

select prevtail(0, symbol), prevtail(1, symbol), prevtail(2, symbol)
  from Trade#sort(3, volume asc)

select prevtail(rank, symbol) from Trade#sort(10, volume asc), RankSelectionEvent unidirectional

The prev function returns a null value if the data window does not currently holds positional-first or the Nth-from-first event. For batch data windows the value returned is relative to the current batch.

The following example returns the first and second symbol value in the batch:

select prevtail(0, symbol), prevtail(1, symbol) from Trade#time_batch(1 min)

An alternative form of the prevtail function allows the index to not appear or appear after the property name if the index value is a constant and not an expression:

select prevtail(1, symbol) from Trade
// ... equivalent to ...
select prevtail(symbol) from Trade
// ... and ...
select prevtail(symbol, 1) from Trade

The combination of the prevtail function and #groupwin returns the property value for a positional first event in the given data window group.

Let's look at an example. This statement outputs the oldest price per symbol retaining the last 10 prices per symbol:

select symbol, prevtail(0, price) as oldestPrice
from Trade#groupwin(symbol)#length(10)

10.1.14.1. Restrictions

The following restrictions apply to the prev functions and its results:

The function always returns a null value for remove stream (old data) events.
The function requires a data window, or a #groupwin and a data window. See Section 14.3, “Data Windows” for built-in data windows.

10.1.15. The `Previous-Window` Function

The prevwindow function returns property values or all event properties for all events in a data window. For data windows that introduce a sort order other than the order of arrival, such as the sorted data window and the time order data window, the function returns the event data sorted in that order, otherwise it returns the events sorted by order of arrival with the newest arriving event first.

The prevwindow function is not an aggregation function and therefore does not return results per group when used with group by. Please consider the window aggregation function instead as described in Section 10.2.2, “Event Aggregation Functions”. You must use an aggregation function instead of prevwindow when querying a named window or table.

The single parameter is a property name or stream name. If specifying a property name, the function returns the value of the event property for all events held by the data window. If specifying a stream name, the function returns the event underlying object for all events held by the data window.

This example selects the value of the price property of all events held in the length window:

select prevwindow(price) from Trade#length(10)

By using the stream alias in the prevwindow function, the next example selects all trade events held in the length window:

select prevwindow(trade) from Trade#length(10) as trade

When used with a data window that introduces a certain sort order, the prevwindow function returns events sorted according to that sort order.

The next statement outputs for every arriving event the current 10 underying trade event objects that have the largest volume:

select prevwindow(trade) from Trade#sort(10, volume desc) as trade

The prevwindow function returns a null value if the data window does not currently hold any events.

The combination of the prevwindow function and #groupwin returns the property value(s) for all events in the given data window group.

This example statement outputs all prices per symbol retaining the last 10 prices per symbol:

select symbol, prevwindow(price) from Trade#groupwin(symbol)#length(10)

10.1.15.1. Restrictions

The following restrictions apply to the prev functions and its results:

The function always returns a null value for remove stream (old data) events.
The function requires a data window, or #groupwin and a data window. See Section 14.3, “Data Windows” for built-in data windows.

10.1.16. The `Previous-Count` Function

The prevcount function returns the number of events held in a data window.

The prevcount function is not an aggregation function and therefore does not return results per group when used with group by. Please consider the count(*) or countever aggregation functions instead as described in Section 10.2, “Aggregation Functions”. You must use an aggregation function instead of prevcount when querying a named window or table.

The single parameter is a property name or stream name of the data window to return the count for.

This example selects the number of data points for the price property held in the length window:

select prevcount(price) from Trade#length(10)

By using the stream alias in the prevcount function the next example selects the count of trade events held in the length window:

select prevcount(trade) from Trade#length(10) as trade

The combination of the prevcount function and #groupwin returns the count of events in the given data window group.

This example statement outputs the number of events retaining the last 10 events per symbol:

select symbol, prevcount(price) from Trade#groupwin(symbol)#length(10)

10.1.16.1. Restrictions

The following restrictions apply to the prev functions and its results:

The function always returns a null value for remove stream (old data) events.
The function requires a data window, or a #groupwin and a data window. See Section 14.3, “Data Windows” for built-in data windows.

10.1.17. The `Prior` Function

The prior function returns the property value of a prior event. The first parameter is an expression returning a constant integer-typed value that denotes the i-th prior event in the natural order of arrival. The second parameter is a property name for which the function returns the value for the prior event. The second parameter is a property name or stream name. If specifying a property name, the function returns the property value for the prior event. If specifying a stream name, the function returns the prior event underlying object.

This example selects the value of the price property of the 2nd-prior event to the current Trade event.

select prior(2, price) from Trade

By using the stream alias in the prior function, the next example selects the trade event itself that is immediately prior to the current Trade event

select prior(1, trade) from Trade as trade

The prior function can be used on any event stream and does not require declaring a data window. The function operates on the order of arrival of events by the event stream that provides the events.

The next statement uses a time batch window to compute an average volume for 1 minute of Trade events, posting results every minute. The select-clause employs the prior function to select the current average and the average before the current average:

select average, prior(1, average) 
    from TradeAverages#time_batch(1 min)#uni(volume)

10.1.18. The `Type-Of` Function

The typeof function, when parameterized by a stream name, returns the event type name of the evaluated event which can be useful with variant streams. When parameterized by an expression or property name, the function returns the type name of the expression result or null if the expression result is null.

In summary, the function determines the return type of the expression at runtime by evaluating the expression and returns the type name of the expression result.

The typeof function is often used in conjunction with variant streams. A variant stream is a predefined stream into which events of multiple disparate event types can be inserted. The typeof function, when passed a stream name alias, returns the name of the event type of each event in the stream.

The following example elaborates on the use of variant streams with typeof. The first statement declares a variant stream SequencePatternStream:

create variant schema SequencePatternStream as *

The next statement inserts all order events and is followed by a statement to insert all product events:

insert into SequencePatternStream select * from OrderEvent;

insert into SequencePatternStream select * from PriceEvent;

This example statement returns the event type name for each event in the variant stream:

select typeof(sps) from SequencePatternStream as sps

The next example statement detects a pattern by utilizing the typeof function to find pairs of order event immediately followed by product event:

select * from SequencePatternStream match_recognize(
  measures A as a, B as b
  pattern (A B)
  define A as typeof(A) = "OrderEvent",
         B as typeof(B) = "ProductEvent"
  )

When passing a property name to the typeof function, the function evaluates whether the property type is event type (a fragment event type). If the property type is event type, the function returns the type name of the event in the property value or null if not provided. If the property type is not event type, the function returns the simple class name of the property value.

When passing an expression to the typeof function, the function evaluates the expression and returns the simple class name of the expression result value or null if the expression result value is null.

This example statement returns the simple class name of the value of the dynamic property prop of events in stream MyStream, or a null value if the property is not found for an event or the property value itself is null:

select typeof(prop?) from MyStream

When using subclasses or interface implementations as event classes or when using Map-event type inheritance, the function returns the event type name provided when the class or Map-type event was registered, or if the event type was not registered, the function returns the fully-qualified class name.

10.2. Aggregation Functions

Aggregation functions are stateful and consider sets of events or value points. The group by clause is often used in conjunction with aggregation functions to group the result-set by one or more columns.

Aggregation functions can be a column type for table declarations. This allows easy sharing of aggregated state, co-location of aggregations and other data as well as co-aggregation by multiple statements into the same aggregation state. Please see Section 6.1.2, “Table Overview” for details.

The EPL language extends the standard SQL aggregation functions by allowing filters and by further useful aggregation functions that can track a data window or compute event rates, for example. Your application may also add its own aggregation function as Section 22.5, “Aggregation Function” describes.

The EPL language allows each aggregation function to specify its own grouping criteria. Please find further information in Section 5.6.4, “Specifying Grouping for Each Aggregation Function”.

The EPL language allows each aggregation function to specify its own filter criteria. Please find further information in Section 5.6.5, “Specifying a Filter Expression for Each Aggregation Function”.

Aggregation values are always computed incrementally: Insert and remove streams result in aggregation value changes. The exceptions are fire-and-forget queries and joins when using the unidirectional keyword. Aggregation functions are optimized to retain the minimal information necessary to compute the aggregated result, and to share aggregation state between eligible other aggregation functions in the same statement so that same-kind aggregation state is never held multiple times unless required.

Most aggregation functions can also be used with unbound streams when no data window is specified. A few aggregation functions require a data window or named window as documented below.

10.2.1. SQL-Standard Functions

The SQL-standard aggregation functions are shown in below table.

Table 10.5. Syntax and Results of SQL-Standard Aggregation Functions

Aggregate Function Result

avedev([all|distinct] expression [, filter_expr])

Mean deviation of the (distinct) values in the expression, returning a value of double type.

The optional filter expression limits the values considered for computing the mean deviation.

avg([all|distinct] expression [, filter_expr])

Average of the (distinct) values in the expression, returning a value of double type.

The optional filter expression limits the values considered for computing the average.

count([all|distinct] expression [, filter_expr])

Number of the (distinct) non-null values in the expression, returning a value of long type.

The optional filter expression limits the values considered for the count.

Further detail on key expressions that return arrays can be found at Section 5.2.13, “Composite Keys and Array Values as Keys”.

count(* [, filter_expr])

Number of events, returning a value of long type.

The optional filter expression limits the values considered for the count.

max([all|distinct] expression)

fmax([all|distinct] expression, filter_expr)

Highest (distinct) value in the expression, returning a value of the same type as the expression itself returns.

Use fmax to provide a filter expression that limits the values considered for computing the maximum.

Consider using maxby instead if return values must include additional properties.

maxever([all|distinct] expression)

fmaxever([all|distinct] expression, filter_expr)

Highest (distinct) value - ever - in the expression, returning a value of the same type as the expression itself returns.

Use fmaxever to provide a filter expression that limits the values considered for computing the maximum.

Consider using maxbyever instead if return values must include additional properties.

median([all|distinct] expression [, filter_expr])

Median (distinct) value in the expression, returning a value of double type. Double Not-a-Number (NaN) values are ignored in the median computation.

The optional filter expression limits the values considered for computing the median.

min([all|distinct] expression)

fmin([all|distinct] expression, filter_expr)

Lowest (distinct) value in the expression, returning a value of the same type as the expression itself returns.

Use fmin to provide a filter expression that limits the values considered for computing the maximum.

Consider using minby instead if return values must include additional properties.

minever([all|distinct] expression)

fminever([all|distinct] expression, filter_expr)

Lowest (distinct) value - ever - in the expression, returning a value of the same type as the expression itself returns.

Use fminever to provide a filter expression that limits the values considered for computing the maximum.

Consider using minbyever instead if return values must include additional properties.

stddev([all|distinct] expression [, filter_expr])

Standard deviation of the (distinct) values in the expression, returning a value of double type.

The optional filter expression limits the values considered for computing the standard deviation.

sum([all|distinct] expression [, filter_expr])

Totals the (distinct) values in the expression, returning a value of long, double, float or integer type depending on the expression.

The optional filter expression limits the values considered for computing the total.

If your application provides double-type values to an aggregation function, avoid using Not-a-Number (NaN) and infinity. Also when using double-type values, round-off errors (or rounding errors) may occur due to double-type precision. Consider rounding your result value to the desired precision.

Each of the aggregation functions above takes an optional filter expression as a parameter. The filter expression must return a boolean-type value and applies to the events considered for the aggregation. If a filter expression is provided, then only if the filter expression returns a value of true does the runtime update the aggregation for that event or combination of events.

Consider the following example, which computes the quantity fraction of buy orders among all orders:

select sum(quantity, side='buy') / sum(quantity) as buy_fraction from Orders

Use the fmin and fmax aggregation functions instead of the min and max aggregation functions when providing a filter expression (the min and max functions are also single-row functions).

The next example computes the minimum quantity for buy orders and a separate minimum quantity for sell orders:

select fmin(quantity, side='buy'), fmin(quantity, side = 'sell') from Orders

This sample statement demonstrates specifying grouping criteria for an aggregation function using the group_by named parameter. It computes, for the last one minute of orders, the ratio of orders per account compared to all orders:

select count(*)/count(*, group_by:()) as ratio from Orders#time(1 min) group by account

10.2.2. Event Aggregation Functions

The event aggregation functions return one or more events or event properties. When used with group by the event aggregation functions return one or more events or event properties per group.

The sorted and the window event aggregation functions require that a data window or named window is declared for the applicable stream. They cannot be used on unbound streams.

The below table summarizes the event aggregation functions available:

Table 10.6. Event Aggregation Functions

Function	Result
first(...)	Returns the first event or an event property value of the first event. Section 10.2.2.1, “First Aggregation Function”.
last(...)	Returns the last event or an event property value of the last event. Section 10.2.2.2, “Last Aggregation Function”.
maxby(criteria)	Returns the event with the highest sorted value according to criteria expressions. Section 10.2.2.3, “Maxby Aggregation Function”.
maxbyever(criteria)	Returns the event with the highest sorted value, ever, according to criteria expressions. Section 10.2.2.4, “Maxbyever Aggregation Function”.
minby(criteria)	Returns the event with the lowest sorted value according to criteria expressions. Section 10.2.2.5, “Minby Aggregation Function”.
minbyever(criteria)	Returns the event with the lowest sorted value, ever, according to criteria expressions. Section 10.2.2.6, “Minbyever Aggregation Function”.
sorted(criteria)	Returns events sorted according to criteria expressions. Section 10.2.2.7, “Sorted Aggregation Function”. Also see Section 13.3, “Aggregation Methods for Sorted Aggregations” for methods such as `lowerKey` or `higherKey` that operate on `sorted` aggregation state.
window(...)	Returns all events or all event's property values. Section 10.2.2.8, “Window Aggregation Function”. Also see Section 13.4, “Aggregation Methods for Window Aggregations” for methods such as `first` or `countEvents` that operate on `window` aggregation state.

In connection with named windows and tables, event aggregation functions can also be used in on-select, selects with named window or table in the from clause, subqueries against named windows or tables and fire-and-forget queries.

The event aggregation functions are often useful in connection with enumeration methods and they can provide input events for enumeration. Please see Chapter 11, EPL Reference: Enumeration Methods for more information.

When comparing the last aggregation function to the prev function, the differences are as follows. The prev function is not an aggregation function and thereby not sensitive to the presence of group by. The prev function accesses data window contents directly and respects the sort order of the data window. The last aggregation function returns results based on arrival order and tracks data window contents in a separate shared data structure.

When comparing the first aggregation function to the prevtail function, the differences are as follows. The prevtail function is not an aggregation function and thereby not sensitive to the presence of group by. The prevtail function accesses data window contents directly and respects the sort order of the data window. The first aggregation function returns results based on arrival order and tracks data window contents in a separate shared data structure.

When comparing the window aggregation function to the prevwindow function, the differences are as follows. The prevwindow function is not an aggregation function and thereby not sensitive to the presence of group by. The prevwindow function accesses data window contents directly and respects the sort order of the data window. The window aggregation function returns results based on arrival order and tracks data window contents in a separate shared data structure.

When comparing the count aggregation function to the prevcount function, the differences are as follows. The prevcount function is not an aggregation function and thereby not sensitive to the presence of group by.

When comparing the last aggregation function to the nth aggregation function, the differences are as follows. The nth aggregation function does not consider out-of-order deletes (for example with on-delete and sorted windows) and does not revert to the prior expression value when the last event or nth-event was deleted from a data window. The last aggregation function tracks the data window and reflects out-of-order deletes.

From an implementation perspective, the first, last and window aggregation functions share a common data structure for each stream. The sorted, minby and maxby aggregation functions share a common data structure for each stream.

10.2.2.1. `First` Aggregation Function

The synopsis for the first aggregation function is:

first(*|stream.*|value_expression [, index_expression] [, filter:filter_expression])

The first aggregation function returns properties of the very first event. When used with group by, it returns properties of the first event for each group. When specifying an index expression, the function returns properties of the Nth-subsequent event to the first event, all according to order of arrival.

The first parameter to the function is required and defines the event properties or expression result to return. The second parameter is an optional index_expression that must return an integer value used as an index to evaluate the Nth-subsequent event to the first event.

You may specify the wildcard (*) character in which case the function returns the underlying event of the single selected stream. When selecting a single stream you may specify no parameter instead of wildcard. For joins and subqueries you must use the stream wildcard syntax below.

You may specify the stream name and wildcard (*) character in the stream.* syntax. This returns the underlying event for the specified stream.

You may specify a value_expression to evaluate for the first event. The value expression may not select properties from multiple streams.

The index_expression is optional. If no index expression is provided, the function returns the first event. If present, the function evaluates the index expression to determine the value for N, and evaluates the Nth-subsequent event to the first event. A value of zero returns the first event and a value of 1 returns the event subsequent to the first event. You may not specify event properties in the index expression.

The function returns null if there are no events or when the index is larger than the number of events held. When used with group by, it returns null if there are no events for that group or when the index is larger than the number of events held for that group.

To explain, consider the statement below which selects the underlying event of the first sensor event held by the length window of 2 events.

select first(*) from SensorEvent#length(2)

Assume event E1, event E2 and event E3 are of type SensorEvent. When event E1 arrives the statement outputs the underlying event E1. When event E2 arrives the statement again outputs the underlying event E1. When event E3 arrives the statement outputs the underlying event E2, since event E1 has left the data window.

The stream wildcard syntax is useful for joins and subqueries. This example demonstrates a subquery that returns the first SensorEvent when a DoorEvent arrives:

select (select first(se.*) from SensorEvent#length(2) as se) from DoorEvent

The following example shows the use of an index expression. The output value for f1 is the temperature property value of the first event, the value for f2 is the temperature property value of the second event:

select first(temperature, 0) as f1, first(temperature, 1) as f2
from SensorEvent#time(10 sec)

You may use dot-syntax to invoke a method on the first event. You may also append a property name using dot-syntax.

10.2.2.2. `Last` Aggregation Function

The synopsis for the last aggregation function is:

last(*|stream.*|value_expression [, index_expression][, filter:filter_expression])

The last aggregation function returns properties of the very last event. When used with group by, it returns properties of the last event for each group. When specifying an index expression, the function returns properties of the Nth-prior event to the last event, all according to order of arrival.

The index_expression is optional. If no index expression is provided, the function returns the last event. If present, the function evaluates the index expression to determine the value for N, and evaluates the Nth-prior event to the last event. A value of zero returns the last event and a value of 1 returns the event prior to the last event. You may not specify event properties in the index expression.

The next statement selects the underlying event of the first and last sensor event held by the time window of 10 seconds:

select first(*), last(*) from SensorEvent#time(10 sec)

The statement shown next selects the last temperature (f1) and the prior-to-last temperature (f1) of sensor events in the last 10 seconds:

select last(temperature, 0) as f1, select last(temperature, 1) as f2
from SensorEvent#time(10 sec)

10.2.2.3. `Maxby` Aggregation Function

The synopsis for the maxby aggregation function is:

maxby(sort_criteria_expression [asc/desc][, sort_criteria_expression [asc/desc]...][, filter:filter_expression])

The maxby aggregation function returns the greatest of all events, compared by using criteria expressions. When used with group by, it returns the greatest of all events per group.

This example statement returns the sensor id and the temperature of the sensor event that had the highest temperature among all sensor events:

select maxby(temperature).sensorId, maxby(temperature).temperature from SensorEvent

The next EPL returns the sensor event that had the highest temperature and the sensor event that had the lowest temperature, per zone, among the last 10 seconds of sensor events:

select maxby(temperature), minby(temperature) from SensorEvent#time(10 sec) group by zone

Your EPL may specify multiple criteria expressions. If the sort criteria expression is descending please append the desc keyword.

The following EPL returns the sensor event with the highest temperature and if there are multiple sensor events with the highest temperature the statement returns the sensor event that has the newest timestamp value:

select maxby(temperature asc, timestamp desc) from SensorEvent

Event properties that are listed in criteria expressions must refer to the same event stream and cannot originate from different event streams.

If your statement does not define a data window and does not refer to a named window, the semantics of maxby are the same as maxbyever.

10.2.2.4. `Maxbyever` Aggregation Function

The synopsis for the maxbyever aggregation function is:

maxbyever(sort_criteria_expression [asc/desc][, sort_criteria_expression [asc/desc]...][, filter:filter_expression])

The maxbyever aggregation function returns the greatest of all events that ever occurred, compared by using criteria expressions. When used with group by, it returns the greatest of all events that ever occurred per group.

Compared to the maxby aggregation function the maxbyever does not consider the data window or named window contents and instead considers all arriving events.

The next EPL computes the difference, per zone, between the maximum temperature considering all events and the maximum temperature considering only the events in the last 10 seconds:

select maxby(temperature).temperature - maxbyever(temperature).temperature 
from SensorEvent#time(10) group by zone

10.2.2.5. `Minby` Aggregation Function

The synopsis for the minby aggregation function is:

minby(sort_criteria_expression [asc/desc][, sort_criteria_expression [asc/desc]...][, filter:filter_expression])

Similar to the maxby aggregation function, the minby aggregation function returns the lowest of all events, compared by using criteria expressions. When used with group by, it returns the lowest of all events per group.

Please review the section on maxby for more information.

10.2.2.6. `Minbyever` Aggregation Function

Please review the section on maxbyever for more information.

10.2.2.7. `Sorted` Aggregation Function

The synopsis for the sorted aggregation function is:

sorted(sort_criteria_expression [asc/desc][, sort_criteria_expression [asc/desc]...][, filter:filter_expression])

The sorted aggregation function maintains a list of events sorted according to criteria expressions. When used with group by, it maintains a list of events sorted according to criteria expressions per group.

The sorted aggregation offers a number of additional lookup methods such as lowerKey or higherKey, see Section 13.3, “Aggregation Methods for Sorted Aggregations”.

The sample EPL listed next returns events sorted according to temperature ascending for the same zone:

select sorted(temperature) from SensorEvent group by zone

Your EPL may specify multiple criteria expressions. If the sort criteria expression is descending please append the desc keyword.

Enumeration methods can be useful in connection with sorted as the function provides the sorted events as input.

This statement finds the sensor event that when sorted according to temperature is the first sensor event for a Friday timestamp among sensor events for the same zone:

select sorted(temperature).firstOf(v => timestamp.getDayOfWeek()=6) from SensorEvent

Event properties that are listed in criteria expressions must refer to the same event stream and cannot originate from different event streams.

If used in a regular select statement, the use of sorted requires that your EPL defines a data window for the stream or utilizes a named window.

10.2.2.8. `Window` Aggregation Function

The synopsis for the window aggregation function is:

window(*|stream.*|value_expression [, filter:filter_expression])

The window aggregation function returns all rows. When used with group by, it returns the rows for each group.

The window aggregation offers a number of additional lookup methods such as last or listReference, see Section 13.4, “Aggregation Methods for Window Aggregations”.

Similar to the first aggregation function described above, you may specify the wildcard (*) character or stream name and wildcard (*) character or a value_expression to evaluate for all events.

The function returns null if there are no rows. When used with group by, it returns null if there are no rows for that group.

The next statement selects the underlying event of all events held by the time window of 10 seconds:

select window(*) from SensorEvent#time(10 sec)

If used in a regular select statement, the window aggregation function requires that your stream is bound by a data window or a named window. You may not use the window aggregation function on unbound streams with the exception of fire-and-forget queries or subqueries.

This example statement assumes that the OrderWindow named window exists. For each event entering or leaving the OrderWindow named window it outputs the total amount removing negative amounts:

select window(*).where(v => v.amount > 0).aggregate(0d, (r, v) => r + v.amount) from OrderWindow

10.2.3. Approximation Aggregation Functions

Approximation aggregation functions are aggregations that perform approximate analysis. Compared to the previously-introduced aggregation functions, the functions discussed here have a degree of accuracy and probabilistic behavior.

10.2.3.1. Count-Min Sketch

Count-min sketch (or CM sketch) is a probabilistic sub-linear space streaming algorithm (source: Wikipedia). Count-min sketch computes an approximate frequency, without retaining distinct values in memory, making the algorithm suitable for summarizing very large spaces of distinct values. The estimated count can be used for estimated top-K and estimated heavy-hitters, for example.

The original and detail of the algorithm is described in the paper by Graham Cormode and S. Muthukrishnan. An improved data stream summary: The Count-min sketch and its applications (2004. 10.1016/j.jalgor.2003.12.001).

Count-min sketch can only be used with tables and is not available as an aggregation function other than in a table declaration.

Count-min sketch does not consider events leaving a data window and does not process a remove stream.

10.2.3.1.1. Declaration

The table column type for Count-min sketch is countMinSketch.

For example, the next EPL declares a table that holds a Count-min sketch (does not provision a top-K):

create table WordCountTable(wordcms countMinSketch())

You can parameterize the algorithm by providing a JSON-format structure to the declaration. The available parameters are all optional:

Table 10.7. Count-min Sketch Parameters

Name	Description
`epsOfTotalCount`	Specifies the accuracy (number of values counted * accuracy >= number of errors) of type double. This value defaults to 0.0001.
`confidence`	Provides the certainty with which you reach the accuracy of type double. The default is 0.99.
`seed`	A seed value for computing hash codes of type integer. This default is 123456.
`topk`	The number of top-K values as an integer. If null, the algorithm maintains no top-K list. This value defaults to null (no top-K available).
`agent`	The agent is an extension API class that can interact with Count-min sketch state and also receives the value objects. The agent defines the type of the values that can be counted. The default agent only allows string-type values and utilizes UTF-16 charset encoding. The default agent is `com.espertech.esper.common.client.util.CountMinSketchAgentStringUTF16`.

The next example EPL declares all available parameters:

create table WordCountTable (wordcms countMinSketch({
  epsOfTotalCount: 0.000002,
  confidence: 0.999,
  seed: 38576,
  topk: 20,
  agent: 'com.mycompany.CountMinSketchCustomAgent'
}))

The default for the topk parameter is null. Thereby the runtime by default does not compute top-K. By specifying a positive integer value for topk the algorithm maintains a list of values representing the top estimated counts.

By default, the Count-min sketch group of aggregation functions operates on string-type values only. The aggregation function allows registering an agent that can handle any other type of value objects and that allows overriding behavior. The agent class must implement the interface com.espertech.esper.common.client.util.CountMinSketchAgent. Please see the JavaDoc for implementing an agent. The agent API is an extension API and is subject to change between versions.

10.2.3.1.2. Counting Values

The countMinSketchAdd function counts value(s). It expects a single parameter expression returning the value(s) to be counted. The function can only be used with statements that utilize into table and can accept a filter: filter expression as a parameter.

This example EPL counts words:

into table WordCountTable select countMinSketchAdd(word) as wordcms from WordEvent

10.2.3.1.3. Estimating Current Count

The countMinSketchFrequency function returns an estimated count for a given value. It expects a single parameter expression returning the value(s) for which to estimate and return the long-type count. The function can only be used as a table-access function against a table column that declares the aggregation countMinSketch.

The next example EPL returns, when a EstimateWordCountEvent event arrives, the estimated frequency of a given word:

select WordCountTable.wordcms.countMinSketchFrequency(word) from EstimateWordCountEvent

10.2.3.1.4. Obtaining Top-K

The countMinSketchTopK function returns top-K. The function expects no parameters. The function can only be used as a table-access function against a table column that declares the aggregation countMinSketch and only if the Count-min sketch was parameterized with a non-null topk parameter (the default is null, see declaration above).

The function returns an array of com.espertech.esper.common.client.util.CountMinSketchTopK.

The following EPL outputs top-K every 10 seconds:

select WordCountTable.wordcms.countMinSketchTopk() from pattern[every timer:interval(10 sec)]

10.2.3.1.5. Agent API Example

We provide a sample agent code that handles String-type values below. The complete code is available for class CountMinSketchAgentStringUTF16 as part of sources.

public class CountMinSketchAgentStringUTF16 implements CountMinSketchAgent {
  public Class[] getAcceptableValueTypes() {
    return new Class[] {String.class};
  }

  public void add(CountMinSketchAgentContextAdd ctx) {
    String text = (String) ctx.getValue();
    if (text == null) {
      return;
    }
    byte[] bytes = toBytesUTF16(text);
    ctx.getState().add(bytes, 1);  // increase count by 1
  }

  public Long estimate(CountMinSketchAgentContextEstimate ctx) {
    String text = (String) ctx.getValue();
    if (text == null) {
      return null;
    }
    byte[] bytes = toBytesUTF16(text);
    return ctx.getState().frequency(bytes);
  }
}

10.2.4. Additional Aggregation Functions

EPL provides the following additional aggregation functions beyond those in the SQL standard:

Table 10.8. Syntax and Results of EPL Aggregation Functions

Aggregate Function	Result
countever(* [, filter_expr]) countever(expression [, filter_expr])	The `countever` aggregation function returns the number of events ever. When used with `group by` it returns the number of events ever for that group. When used with a data window, the result of the function does not change as data points leave a data window. Use the `count()` or `prevcount` function to return counts relative to a data window. The optional filter expression limits the values considered for counting rows. The `distinct` keyword is not allowed. When an expression is provided instead of wildcard, counts the non-null values. The next example statement outputs the count-ever for sell orders: select countever(, side='sell') from Order
firstever(expression [, filter_expr])	The `firstever` aggregation function returns the very first value ever. When used with `group by` it returns the first value ever for that group. When used with a data window, the result of the function does not change as data points leave a data window. Use the `first` or `prevtail` function to return values relative to a data window. The optional filter expression limits the values considered for retaining the first-ever value. The next example statement outputs the first price ever for sell orders: select firstever(price, side='sell') from Order
lastever(expression [, filter_expr])	Returns the last value or last value per group, when used with `group by`. This sample statement outputs the total price, the first price and the last price per symbol for the last 30 seconds of events and every 5 seconds: select symbol, sum(price), lastever(price), firstever(price) from StockTickEvent#time(30 sec) group by symbol output every 5 sec When used with a data window, the result of the function does not change as data points leave a data window (for example when all data points leave the data window). Use the `last` or `prev` function to return values relative to a data window. The optional filter expression limits the values considered for retaining the last-ever value. The next example statement outputs the last price (ever) for sell orders: select lastever(price, side='sell') from Order
leaving([filter:filter_expression])	Returns true when any remove stream data has passed, for use in the `having` clause to output only when a data window has filled. The `leaving` aggregation function is useful when you want to trigger output after a data window has a remove stream data point. Use the `output after` syntax as an alternative to output after a time interval. This sample statement uses `leaving()` to output after the first data point leaves the data window, ignoring the first datapoint: select symbol, sum(price) from StockTickEvent#time(30 sec) having leaving()
nth(expression, N_index [, filter:filter_expression])	Returns the Nth oldest element; If N=0 returns the most recent value. If N=1 returns the value before the most recent value. If N is larger than the events held in the data window for this group, returns null. A maximum N historical values are stored, so it can be safely used to compare recent values in data windows with a large number of events without incurring excessive overhead. As compared to the `prev` row function, this aggregation function works within the current `group by` group, see Section 2.15, “Basic Aggregated Statement Types”. This statement outputs every 2 seconds the groups that have new data and their last price and the previous-to-last price: select symbol, nth(price, 1), last(price) from StockTickEvent group by symbol output last every 2 sec
rate(number_of_seconds [, filter:filter_expression])	Returns an event arrival rate per second over the provided number of seconds, computed based on runtime time. Returns null until events fill the number of seconds. Useful with `output snapshot` to output a current rate. This function footprint is for use without a data window onto the stream(s). A sample statement to output, every 2 seconds, the arrival rate per second considering the last 10 seconds of events is shown here: select rate(10) from StockTickEvent output snapshot every 2 sec The aggregation function retains a runtime timestamp value for each arriving event.
rate(timestamp_property[, accumulator] [, filter:filter_expression])	Returns an event arrival rate over the data window including the last remove stream event. The timestamp_property is the name of a long-type property of the event that provides a timestamp value. The first parameter is a property name or expression providing millisecond timestamp values. The optional second parameter is a property or expression for computing an accumulation rate: If a value is provided as a second parameter then the accumulation rate for that quantity is returned (e.g. turnover in dollars per second). This footprint is designed for use with a data window and requires a data window declared onto the stream. Returns null until events start leaving the window. This sample statement outputs event rate for each group (symbol) with fixed sample size of four events (and considering the last event that left). The `timestamp` event property must be part of the event for this to work. select colour, rate(timestamp) as rate from StockTickEvent#groupwin(symbol)#length(4) group by symbol

Built-in aggregation functions can be disabled via configuration (see Section 17.5.6.3, “Extended Built-in Aggregation Functions”). A custom aggregation function of the same name as a built-on function may be registered to override the built-in function.

10.3. User-Defined Functions

A user-defined function (UDF) is a single-row function that can be invoked anywhere as an expression itself or within an expresson. The function must simply be a public static method of a public class that the classloader can resolve at statement deployment time. The compiler resolves the function reference at compile-time and verifies parameter types.

For information on calling external services via instance method invocation, please see Section 5.17.5, “Class and Event-Type Variables”. For invoking methods on events, please see Section 5.4.5, “Using the Stream Name”

You may register your own function name for the user-defined function. Please see the instructions in Section 22.2, “Single-Row Function” for registering a function name for a user-defined single-row function.

A single-row function that has been registered with a function name can simply be referenced as function_name(parameters) thus statements can be less cluttered as no class name is required. The compiler also optimizes evaluation of such registered single-row functions when used in filter predicate expressions as described in Section 22.2.4, “Single-Row Functions in Filter Predicate Expressions”.

An example statement that utilizes the discount function is shown next (assuming that function has been registered).

select discount(quantity, price) from OrderEvent

When selecting from a single stream, use the wildcard (*) character to pass the underlying event:

select discount(*) from OrderEvent

Alternatively use the stream alias or EPL pattern tag to pass an event:

select discount(oe) from OrderEvent as oe

User-defined functions can be also be invoked on instances of an event: Please see Section 5.4.5, “Using the Stream Name” to invoke event instance methods on a named stream.

Note that user-defined functions (not single-row functions) are candidate for caching their return result if the parameters passed are constants and they are not used chained. Please see below for details and configuration.

The example below assumes a class MyClass that exposes a public static method myFunction accepting 2 parameters, and returing a numeric type such as double.

select 3 * com.mycompany.MyClass.myFunction(price, volume) as myValue 
from StockTick#time(30 sec)

User-defined functions also take array parameters as this example shows. The section on Section 9.5, “Array Definition Operator” outlines in more detail the types of arrays produced.

select * from RFIDEvent where com.mycompany.rfid.MyChecker.isInZone(zone, {10, 20, 30})

Java class names have to be fully qualified (e.g. java.lang.Math) but EPL provides a mechanism for user-controlled imports of classes and packages as outlined in Section 17.4.2, “Class and Package Imports”.

User-defined functions can return any value including null, Java objects or arrays. Therefore user-defined functions can serve to transform, convert or map events, or to extract information and assemble further events.

The following statement is a simple pattern that looks for events of type E1 that are followed by events of type E2. It assigns the tags "e1" and "e2" that the function can use to assemble a final event for output:

select MyLib.mapEvents(e1, e2) from pattern [every e1=E1 -> e2=E2]

User-defined functions may also be chained: If a user-defined function returns an object then the object can itself be the target of the next function call and so on.

Assume that there is a calculator function in the MyLib class that returns a class which provides the search method taking two parameters. The EPL that takes the result of the calculator function and that calls the search method on the result and returns its return value is shown below:

select MyLib.calculator().search(zonevariable, zone) from RFIDEvent]

A user-defined function should be implemented thread-safe.

10.3.1. Event Type Conversion via User-Defined Function

A function that converts from one event type to another event type is shown in the next example. The first statement declares a stream that consists of MyEvent events. The second statement employs a conversion function to convert MyOtherEvent events to events of type MyEvent:

insert into MyStream select * from MyEvent
	insert into MyStream select MyLib.convert(other) from MyOtherEvent as other

In the example above, assuming the event classes MyEvent and MyOtherEvent are Java classes, the static method should have the following footprint:

public static MyEvent convert(MyOtherEvent otherEvent)

10.3.2. User-Defined Function Result Cache

For user-defined functions that take no parameters or only constants as parameters the runtime automatically caches the return result of the function, and invokes the function only once. This is beneficial to performance if your function indeed returns the same result for the same input parameters.

You may disable caching of return values of user-defined functions via configuration as described in Section 17.5.6.2, “User-Defined Function or Static Method Cache”.

10.3.3. Parameter Matching

EPL follows Java standards in terms of widening, performing widening automatically in cases where widening type conversion is allowed without loss of precision, for both boxed and primitive types.

When user-defined functions are overloaded, the function with the best match is selected based on how well the arguments to a function can match up with the parameters, giving preference to the function that requires the least number of widening conversions.

User-defined functions that can receive an arbitrary number of parameter values can use varargs, i.e. can define a function such as function(T arg1, T... args) {...}.

Boxing and unboxing of arrays is not supported in UDF as it is not supported in Java. For example, an array of Integer and an array of int are not compatible types.

When passing the event or underlying event to your method, either declare the parameter to take EventBean (i.e. myfunc(EventBean event)) or as the underlying event type (i.e. myfunc(OrderEvent event)).

When using {} array syntax in EPL, the resulting type is always a boxed type: "{1, 2}" is an array of Integer (and not int since it may contain null values), "{1.0, 2d}" is an array of Double and "{'A', "B"}" is an array of String, while "{1, "B", 2.0}" is an array of Object (Object[]).

10.3.4. Receiving a Context Object

The runtime can pass an object containing contextual information such as statement name, function name, runtime URI and context partition id to your method. The container for this information is EPLMethodInvocationContext in package com.espertech.esper.common.client.hook.expr. Please declare your method to take EPLMethodInvocationContext as the last parameter. The runtime then passes the information along.

A sample method footprint and EPL are shown below:

public static double computeSomething(double number, EPLMethodInvocationContext context) {...}

select MyLib.computeSomething(10) from MyEvent

10.4. Select-Clause `Transpose` Function

The transpose function is only valid in the select-clause and indicates that the result of the parameter expression should become the underlying event object of the output event.

The transpose function takes a single expression as a parameter. The result object of the parameter expression is subject to transposing as described below.

The function can be useful with insert into to allow an object returned by an expression to become the event itself in the output stream.

Any expression returning a Java object can be used with the transpose function. Typical examples for expressions are a static method invocation, the result of an enumeration method, a plug-in single row function or a subquery.

The examples herein assume that a single-row function by name makeEvent returns an OrderEvent instance (a POJO object, not shown).

The following EPL takes the result object of the invocation of the makeEvent method (assumed to be an OrderEvent instance) and returns the OrderEvent instance as the underlying event of the output event:

select transpose(makeEvent(oi)) from OrderIndication oi

Your select-clause can select additional properties or expressions. In this case the output event underlying object is a pair of the expression result object and the additional properties.

The next EPL also selects the origin property of the order indication event. The output event is a pair of the OrderEvent instance and a map containing the property name and value of origin:

select origin, transpose(makeEvent(oi)) from OrderIndication oi

If the transpose function is not a top-level function, i.e. if it occurs within another expression or within any other clause then the select-clause, the function simply returns the expression result of the parameter expression.

10.4.1. Transpose with Insert-Into

You may insert transposed output events into another stream.

If the stream name in the insert-into clause is already associated to an event type, the compiler checks whether the event type associated to the stream name provided in the insert-into clause matches the event type associated to the object returned by the expression. If the stream name in the insert-into clause is not already associated to an existing event type the compiler associates a new event type using the stream name provided in the insert-into clause.

The type returned by the expression must match the event representation that is defined for the stream, i.e. must be a subtype or implementation of the respective class (POJO, object-array or Map).

For example, the next statement associates the stream name OrderEvent with a class:

create schema OrderEvent as com.mycompany.OrderEvent

A statement can insert into the OrderEvent stream the OrderEvent instance returned by the makeEvent method, as follows:

insert into OrderEvent select transpose(makeEvent(oi)) from OrderIndication oi

It is not valid to select additional properties or expressions in this case, as they would not be part of the output event. The following is not valid:

// not valid
insert into OrderEvent select origin, transpose(makeEvent(oi)) from OrderIndication oi

Chapter 11. EPL Reference: Enumeration Methods

11.1. Overview

11.2. Example Events

11.3. How to Use

11.3.1. Syntax
11.3.2. Introductory Examples
11.3.3. Index and Size Formal Parameters
11.3.4. Input, Output and Limitations

11.4. Inputs

11.4.1. Subquery Results
11.4.2. Named Window
11.4.3. Table
11.4.4. Event Property and Insert-Into With @eventbean
11.4.5. Event Aggregation Function
11.4.6. Prev, Prevwindow and Prevtail Single-Row Functions as Input
11.4.7. Single-Row Function, User-Defined Function and Enum Types
11.4.8. Declared Expression
11.4.9. Variables
11.4.10. Substitution Parameters
11.4.11. Match-Recognize Group Variable
11.4.12. Pattern Repeat and Repeat-Until Operators

11.5. Example

11.6. Reference

11.6.1. Aggregate
11.6.2. AllOf
11.6.3. AnyOf
11.6.4. ArrayOf
11.6.5. Average
11.6.6. CountOf
11.6.7. DistinctOf
11.6.8. Except
11.6.9. FirstOf
11.6.10. GroupBy
11.6.11. Intersect
11.6.12. LastOf
11.6.13. LeastFrequent
11.6.14. Max
11.6.15. MaxBy
11.6.16. Min
11.6.17. MinBy
11.6.18. MostFrequent
11.6.19. OrderBy and OrderByDesc
11.6.20. Reverse
11.6.21. SelectFrom
11.6.22. SequenceEqual
11.6.23. SumOf
11.6.24. Take
11.6.25. TakeLast
11.6.26. TakeWhile
11.6.27. TakeWhileLast
11.6.28. ToMap
11.6.29. Union
11.6.30. Where

11.1. Overview

EPL provides enumeration methods that work with lambda expressions to perform common tasks on subquery results, named windows, tables, event properties or inputs that are or can be projected to a collection of events, scalar values or objects.

Enumeration methods are stateless and the use of enumeration methods alone does not cause the runtime to retain any events or other state (with the possible exception of short-lived caching of evaluation results).

A lambda expression is an anonymous expression. Lambda expressions are useful for encapsulating user-defined expressions that are applied to each element in a collection. This section discusses built-in enumeration methods and their lambda expression parameters.

Lambda expressions use the lambda operator =>, which is read as "goes to" (-> may be used and is equivalent). The left side of the lambda operator specifies the lambda expression input parameter(s) (if any) and the right side holds the expression. The lambda expression x => x * x is read "x goes to x times x.". Lambda expressions are also used for expression declaration as discussed in Section 5.2.9, “Expression Declaration”.

When writing lambdas, you do not have to specify a type for the input parameter(s) or output result(s) because the compiler can infer all types based on the input and the expression body. So if you are querying an RFIDEvent, for example, then the input variable is inferred to be an RFIDEvent event, which means you have access to its properties and methods.

The term element in respect to enumeration methods means a single event, scalar value or object in a collection that is the input to an enumeration method. The term collection means a sequence or group of elements.

The below table summarizes the built-in enumeration methods available:

Table 11.1. Enumeration Methods

Method Result

aggregate(seed, (result, value) lambda)

aggregate(seed, (result, value, index) lambda)

aggregate(seed, (result, value, index, size) lambda)

Aggregate elements by using seed as an initial accumulator value and applying an accumulator expression consisting of the result and the value.

Section 11.6.1, “Aggregate”.

allof(predicate lambda)

allof( (predicate, index) lambda)

allof( (predicate, index, size) lambda)

Return true when all elements satisfy a condition.

Section 11.6.2, “AllOf”.

anyof(predicate lambda)

anyof( (predicate, index) lambda)

anyof( (predicate, index, size) lambda)

Return true when any element satisfies a condition.

Section 11.6.3, “AnyOf”.

arrayOf()

Returns an array of the scalar elements.

Section 11.6.4, “ArrayOf”.

arrayOf(value-selector lambda)

arrayOf( (value-selector, index) lambda)

arrayOf( (value-selector, index, size) lambda)

Returns the array containing each value returned by the value-selector expression when applied to each element.

Section 11.6.4, “ArrayOf”.

average()

Computes the average of values obtained from numeric elements.

Section 11.6.5, “Average”.

average(projection lambda)

average( (projection, index) lambda)

average( (projection, index, size) lambda)

Computes the average of values obtained from elements by invoking a projection expression on each element.

Section 11.6.5, “Average”.

countof()

Returns the number of elements.

Section 11.6.6, “CountOf”.

countof(predicate lambda)

countof( (predicate, index) lambda)

countof( (predicate, index, size) lambda)

Returns the number of elements that satisfy a condition.

Section 11.6.6, “CountOf”.

distinctOf()

Returns distinct elements according to default hash and equals semantics.

Section 11.6.7, “DistinctOf”.

distinctOf(key-selector lambda)

distinctOf( (key-selector, index) lambda)

distinctOf( (key-selector, index, size) lambda)

Returns distinct elements according using the key function provided.

Section 11.6.7, “DistinctOf”.

except(source)

Produces the set difference of the two collections.

Section 11.6.8, “Except”.

firstof()

Returns the first element.

Section 11.6.9, “FirstOf”.

firstof(predicate lambda)

firstof( (predicate, index) lambda)

firstof( (predicate, index, size) lambda)

Returns the first element that satisfies a condition.

Section 11.6.9, “FirstOf”.

groupby(key-selector lambda)

groupby( (key-selector, index) lambda)

groupby( (key-selector, index, size) lambda)

Groups the elements according to a specified key-selector expression.

Section 11.6.10, “GroupBy”.

groupby(key-selector lambda, value-selector lambda)

groupby( (key-selector, index) lambda, (value-selector, index) lambda)

groupby( (key-selector, index, size) lambda, (value-selector, index, size) lambda)

Groups the elements according to a key-selector expression mapping each element to a value according to a value-selector.

Section 11.6.10, “GroupBy”.

intersect(source)

Produces the set intersection of the two collections.

Section 11.6.11, “Intersect”.

lastof()

Returns the last element.

Section 11.6.12, “LastOf”.

lastof(predicate lambda)

lastof( (predicate, index) lambda)

lastof( (predicate, index, size) lambda)

Returns the last element that satisfies a condition.

Section 11.6.12, “LastOf”.

leastFrequent()

Returns the least frequent value among a collection of values.

Section 11.6.13, “LeastFrequent”.

leastFrequent(transform lambda)

leastFrequent( (transform, index) lambda)

leastFrequent( (transform, index, size) lambda)

Returns the least frequent value returned by the transform expression when applied to each element.

Section 11.6.13, “LeastFrequent”.

max()

Returns the maximum value among a collection of elements.

Section 11.6.14, “Max”.

max(value-selector lambda)

max( (value-selector, index) lambda)

max( (value-selector, index, size) lambda)

Returns the maximum value returned by the value-selector expression when applied to each element.

Section 11.6.14, “Max”.

maxby(value-selector lambda)

maxby( (value-selector, index) lambda)

maxby( (value-selector, index, size) lambda)

Returns the element that provides the maximum value returned by the value-selector expression when applied to each element.

Section 11.6.15, “MaxBy”.

min()

Returns the minimum value among a collection of elements.

Section 11.6.14, “Max”.

min(value-selector lambda)

min( (value-selector, index) lambda)

min( (value-selector, index, size) lambda)

Returns the minimum value returned by the value-selector expression when applied to each element.

Section 11.6.16, “Min”.

minby(value-selector lambda)

minby( (value-selector, index) lambda)

minby( (value-selector, index, size) lambda)

Returns the element that provides the minimum value returned by the value-selector expression when applied to each element..

Section 11.6.17, “MinBy”.

mostFrequent()

Returns the most frequent value among a collection of values.

Section 11.6.18, “MostFrequent”.

mostFrequent(transform lambda)

mostFrequent( (transform, index) lambda)

mostFrequent( (transform, index, size) lambda)

Returns the most frequent value returned by the transform expression when applied to each element.

Section 11.6.18, “MostFrequent”.

orderBy()

Sorts the elements in ascending order.

orderBy(key-selector lambda)

orderBy( (key-selector, index) lambda)

orderBy( (key-selector, index, size) lambda)

Sorts the elements in ascending order according to a key.

orderByDesc()

Sorts the elements in descending order.

orderByDesc(key-selector lambda)

orderByDesc( (key-selector, index) lambda)

orderByDesc( (key-selector, index, size) lambda)

Sorts the elements in descending order according to a key.

Section 11.6.20, “Reverse”.

reverse

Reverses the order of elements.

selectFrom(transform lambda)

selectFrom( (transform, index) lambda)

selectFrom( (transform, index, size) lambda)

Transforms each element resulting in a collection of transformed elements.

Section 11.6.21, “SelectFrom”.

sequenceEqual(second)

Determines whether two collections are equal by comparing each element (equals semantics apply).

Section 11.6.22, “SequenceEqual”.

sumOf()

Computes the sum from a collection of numeric elements.

Section 11.6.23, “SumOf”.

sumOf(projection lambda)

sumOf( (projection, index) lambda)

sumOf( (projection, index, size) lambda)

Computes the sum by invoking a projection expression on each element.

Section 11.6.23, “SumOf”.

take(numElements)

Returns a specified number of contiguous elements from the start.

Section 11.6.24, “Take”.

takeLast(numElements)

Returns a specified number of contiguous elements from the end.

Section 11.6.25, “TakeLast”.

takeWhile(predicate lambda)

takeWhile( (predicate, index) lambda)

takeWhile( (predicate, index, size) lambda)

Returns elements from the start as long as a specified condition is true.

Section 11.6.26, “TakeWhile”.

takeWhileLast(predicate)

takeWhileLast( (predicate, index) lambda)

takeWhileLast( (predicate, index, size) lambda)

Returns elements from the end as long as a specified condition is true.

Section 11.6.27, “TakeWhileLast”.

toMap(key-selector lambda, value-selector lambda)

toMap( (key-selector, index) lambda, (value-selector, index) lambda)

toMap( (key-selector, index, size) lambda, (value-selector, index, size) lambda)

Returns a Map according to specified key selector and value-selector expressions.

Section 11.6.28, “ToMap”.

union(source)

Forms a union of the input elements with source elements.

Section 11.6.29, “Union”.

where(predicate lambda)

where( (predicate, index) lambda)

where( (predicate, index, size) lambda)

Filters elements based on a predicate.

Section 11.6.30, “Where”.

11.2. Example Events

The examples in this section come out of the domain of location report (aka. RFID, asset tracking etc.) processing:

The Item event is a report of the location of a certain item. An item can be either a piece of luggage or a passenger.
The LocationReport event contains a list of Item items for which it reports location.
The Zone event describes areas that items may move through.

The examples use example single-row functions for computing the distance (distance) and for determining if a location falls within a rectangle (inrect) that are not provided by the EPL language. These example UDF functions are not enumeration methods and are used in statements to provide a sensible example.

The Item event contains an assetId id, a (x,y) location, a luggage flag to indicate whether the item represents a luggage (true) or passenger (false), and the assetIdPassenger that holds the asset id of the associated passenger when the item is a piece of luggage.

The Item event is defined as follows (access methods not shown for brevity):

public class Item {
  String assetId;             // passenger or luggage asset id
  Location location;          // (x,y) location
  boolean luggage;            // true if this item is a luggage piece
  String assetIdPassenger;    // if the item is luggage, contains passenger associated
...

The LocationReport event contains a list of Item items for which it reports events.

The LocationReport event is defined as follows:

public class LocationReport {
  List<Item> items;
...

The Zone event contains a zone name and (x1, y1, x2, y2) rectangle.

The Zone event is defined as follows:

public class Zone {
  String name;
  Rectangle rectangle;
...

The Location object is a nested object to Item and provides the current (x,y) location:

public class Location {
  int x;
  int y;
...

The Rectangle object is a nested object to Zone and provides a zone rectangle(x1,y1,x2,y2):

public class Rectangle {
  int x1;
  int y1;
  int x2;
  int y2;
...

11.3. How to Use

11.3.1. Syntax

The syntax for enumeration methods is the same syntax as for any chained invocation:

input_coll.enum_method_name( [method_parameter [, method_parameter [,...]]])
	  .[ [enum_method_name(...) [...]] | property_name]

Following the input_coll input collection (options outlined below), is the . (dot) operator and the enum_method_name enumeration method name. It follows in parenthesis a comma-separated list of method parameter expressions. Additional enumeration methods can be chained thereafter. An event property name can follow for those enumeration methods returning an event-typed (non-scalar) element.

If the method parameter is a lambda expression with a single lambda-parameter, specify the lambda-parameter name followed by the => lambda operator and followed by the expression. The synopsis for use with a single lambda-parameter is:

method_parameter: lambda_param => lambda_expression

If the method parameter is a lambda expression with two or more lambda-parameters, specify the lambda parameter names in parenthesis followed by the => lambda operator followed by the expression. The synopsis for use with multiple lambda-parameters is:

method_parameter: (lambda_param [,lambda_param [,...]]) => lambda_expression

Generally for lambda expressions, the runtime applies the lambda expression to each element in the input collection. The expression yields a result that, depending on the particular enumeration method, is used for aggregation, as a filter or for output, for example.

11.3.2. Introductory Examples

Let's look at a statement that employs the where enumeration method and a lambda expression. This example returns items that have a (x, y) location of (0, 0):

select items.where(i => i.location.x = 0 and i.location.y = 0) as zeroloc
from LocationReport

As enumeration methods can be chained, this selection is equivalent:

select items.where(i => i.location.x = 0).where(i => i.location.y = 0) as zeroloc
from LocationReport

According to above statement the runtime outputs in field zeroloc a collection of Item objects matching the condition.

The where enumeration method has a second version that has two lambda-parameters. The second parameter is the name of the index property which represents the current index of the element within the collection.

This sample statement returns a collection that consists of the first 3 items. This sample statement does not use the item lambda parameter:

select items.where( (item, indexElement) => indexElement < 3) as firstThreeItems
from LocationReport

11.3.3. Index and Size Formal Parameters

All enumeration methods that have a lambda expression as a parameter allow the lambda expression to declare index and size formal parameters. Index is always the second formal parameter. Size is always the third formal parameter and must appear with the index formal parameter.

The synopsis for passing the index of each collection element to the lambda expression is:

method_parameter: (lambda_param,index_lambda_param) => lambda_expression

The index value starts at zero for the first item.

For example, the next lambda expression concatenates to each element an underscore and the index of each element:

{'A', 'B', 'C'}.selectFrom( (v, i) => v || '_' || Integer.toString(i))   // Returns {'A_0', 'B_1', 'C_2'}

The synopsis for passing the index of each collection element as well as the size of the collection to the lambda expression is:

method_parameter: (lambda_param, index_lambda_param, size_lambda_param) => lambda_expression

The following lambda expression concatenates to each element the index and size separated by underscore:

{'A', 'B', 'C'}.selectFrom( (v, i, s) => v || '_' || Integer.toString(i) || '_' || Integer.toString(s))   // Returns {'A_0_3', 'B_1_3', 'C_2_3'}

11.3.4. Input, Output and Limitations

It is not necessary to use classes for event representation. The example above applies the same to Object-array, Map or XML underlying events.

For most enumeration methods the input can be any collection of events, scalar values or objects. For some enumeration methods limitations apply that are documented below. For example, the sumOf enumeration method requires a collection of numeric scalar values if used without parameters. If the input to sumOf is a collection of events or scalar values the enumeration method requires a lambda expression as parameter that yields the numeric value to use to compute the sum.

Many examples of this section operate on the collection returned by the event property items in the LocationReport event class. There are many other inputs yielding collections as listed below. Most examples herein use an event property as a input simply because the example can thus be brief and does not need to refer to a subquery, named window, table or other concept.

For enumeration methods that return a collection, for example where and orderBy, the runtime outputs an implementation of the Collection interface that contains the selected value(s). The collection returned must be considered read-only. As Java does not allow resettable iterators, the Collection interface allows more flexibility to query size and navigate among collection elements. We recommend against down-casting a collection returned by the runtime to a more specific subclass of the Collection interface.

For enumeration methods that return an element, for example firstOf, lastOf, minBy and maxBy the runtime outputs the scalar value or the underlying event if operating on events. You may add an event property name after the enumeration method to return a property value.

Enumeration methods generally retain the order of elements provided by the collection.

The following restrictions apply to enumeration methods:

Enumeration methods returning a collection return a read-only implementation of the Collection interface. You may not use any of the write-methods such as add or remove on a result collection.

11.4. Inputs

The input of data for built-in enumeration methods is a collection of scalar values, events or other objects. Input can originate from any of the following:

A subquery.
A named window.
A table.
A property of an event that is itself a collection of events or classes, for example indexed properties, or selected properties when using insert-into and @eventbean.
Any of the event aggregation functions (window, first, last, sorted, maxby, minby, maxbyever, minbyever).
The special prevwindow, prev and prevtail single-row functions.
A plug-in single-row function, a user-defined function, a script or an enum type.
A declared expression.
Another enumeration method that returns a collection.
An array returned by the {} array operator.
A collection or array returned by a method call on an event or a method call on a variable.
A variable. Usually variables declared as an array.
A substitution parameter value provided by a prepared statement.
In a match-recognize pattern, a group variable.
In an EPL pattern, events collected in a repeat ([...]) and a repeat-until (... until ...).

11.4.1. Subquery Results

Subqueries can return the rows of another stream's data window or rows from a named window or table. By providing a where-clause the rows returned by a subquery can be correlated to data provided by stream(s) in the from-clause. See Section 5.11, “Subqueries”.

A subquery that selects (*) wildcard provides a collection of events as input. A subquery that selects a single value expression provides a collection of scalar values as input. Subqueries that select multiple value expressions are not allowed as input to enumeration methods.

The following example uses a subquery to retrieve all zones for each location report item where the location falls within the rectangle of the zone. Please see a description of example events and functions above.

select assetId,
  (select * from Zone#unique(name)).where(z => inrect(z.rectangle, location)) as zones
from Item

You may place the subquery in an expression declaration to reuse the subquery in multiple places of the same statement.

This sample EPL declares the same statement as above in an expression declaration:

expression myquery {itm =>
  (select * from Zone#unique(name)).where(z => inrect(z.rectangle, itm.location))
}
select assetId, myquery(item) as subq, 
    myquery(item).where(z => z.zone = 'Z01') as assetItem
from Item as item

The above statement also demonstrates how an enumeration method, in the example the where-method, can be run across the results returned by a subquery in an expression declaration.

Place a single column in the subquery select-clause to provide a collection of scalar values as input.

The next example selects all names of zones and orders the names returning an order collection of string names every 30 seconds:

select (select name from Zone#unique(name)).orderBy() as orderedZones
from pattern[every timer:interval(30)]

The next example utilizes a subquery that counts zone events per name and finds those that have a count greater than 1:

select (select name, count(*) as cnt from Zone#keepall group by name)
  .where(v => cnt > 1) from LocationReport]

When the subquery selects a single column that is itself an event, the result of the subquery is a collection of events of that type and can provide input to enumeration methods.

For example:

create schema SettlementEvent (symbol string);

create schema PriceEvent (symbol string, price double);

create schema OrderEvent (orderId string, pricedata PriceEvent);

select (select pricedata from OrderEvent#unique(orderId))
  .anyOf(v => v.symbol = 'GE') as has_ge from SettlementEvent(symbol = 'GE')

Note that the runtime can cache intermediate results thereby is not forced to re-evaluate the subquery for each occurrence in the select-clause.

11.4.2. Named Window

Named windows are globally-visible data windows. See Section 6.2, “Named Window Usage”.

You may specify the named window name as input for an enumeration method and can optionally provide a correlation where-clause. The syntax is equivalent to a sub-query against a named window but much shorter.

Synopsis:

named-window-name[(correlation-expression)].enum-method-name(...)

When selecting all events in a named window you do not need the correlation-expression. To select a subset of data in the named window, specify a correlation-expression. From the perspective of best runtime performance, a correlation expression is preferred to reduce the number of rows returned.

The following example first declares a named window to hold the last zone event per zone name:

create window ZoneWindow#unique(name) as Zone

Then you create a statement to insert zone events that arrive to the named window:

insert into ZoneWindow select * from Zone

Finally this statement queries the named window to retrieve all zones for each location report item where the location falls within the rectangle of the zone:

select ZoneWindow.where(z => inrect(z.rectangle, location)) as zones from Item

If you have a filter or correlation expression, append the expression to the named window name and place in parenthesis.

This slightly modified statement is the example above except that it adds a filter expression such that only zones with name Z1, Z2 or Z3 are considered:

select ZoneWindow(name in ('Z1', 'Z2', 'Z3')).where(z => inrect(z.rectangle, location)) as zones 
from Item

You may prefix property names provided by the named window with the name to disambiguate property names.

This sample statement prefixed the name property and returns the count of matching zones:

select ZoneWindow(ZoneWindow.name in ('Z1', 'Z2', 'Z3')).countof()) as zoneCount
from Item

The runtime internally interprets the shortcut syntax and creates a subquery from it. Thus all indexing and query planning for subqueries against named windows apply here as well.

11.4.3. Table

Tables are globally-visible data structures. See Section 6.3, “Table Usage”.

Tables can hold aggregation state such as the window and sorted aggregation state.

The example EPL below declares a table to hold StockTick events in a column named theTicks:

create table MyTable(theTicks window(*) @type(StockTick))

The table column can be input to an enumeration method, for example:

select MyTable.theTicks.anyOf(v=> price > 100) from MyEvent

11.4.4. Event Property and Insert-Into With `@eventbean`

Event properties that are a collection, array or iterable of scalar values or objects can be an input to enumeration methods. Event properties that hold a collection or array of events can be an input to enumeration methods. Use @eventbean in the select-clause with insert into to select events.

The LocationReport event from the example contains a list of Item events. Any indexed property (list, array, collection, iterable) is eligible for use as input to an enumeration method. If the indexed property contains non-scalar objects the objects are treated as events and can be used as input to enumeration methods as a collection of events.

The next sample statement returns items that are less than 20 units away from the center, taking the items event property provided by each LocationReport event as input:

select items.where(p => distance(0, 0, p.location.x, p.location.y) < 20) as centeritems
from LocationReport

The next three statements declare an OrderEvent type that contains OrderDetail events and a statement that enumerates order detail:

create schema OrderDetail(itemId string)

create schema OrderEvent(details OrderDetail[])

select details.where(i => i.itemId = '001') from OrderEvent

The first statement in this two-statement explanation selects the events that have a price of greater than 100 from a 10-second time window if the count of events is at least 10. It inserts the events into stream TicksLarge. The use of @eventbean means that the event property ticksLargePrice holds events (and not underlying objects). The second statement further filters the events and returns a collection of events that have price of less than 100.

insert into TicksLarge select window(*).where(e => e.price > 100) @eventbean as ticksLargePrice
from StockTick#time(10) having count(*) > 10

select ticksLargePrice.where(e => e.price < 200) as ticksLargeLess200 from TicksLarge

11.4.5. Event Aggregation Function

Event aggregation functions return an event or multiple events. They are aggregation functions and as such sensitive to the presence of group by. See Section 10.2.2, “Event Aggregation Functions”.

You can use window, first or last event aggregation functions as input to an enumeration method. Specify the * wildcard as the parameter to the event aggregation function to provide a collection of events as input. Or specify a property name as the parameter to event aggregation function to provide a collection of scalar values as input.

You can use the sorted, maxby, minby, maxbyever or minbyever event aggregation functions as input to an enumeration method. Specify one or more criteria expressions that provide the sort order as parameters to the event aggregation function.

Aggregation methods are used in conjunction with aggregations and can also provide input to an enumeration method. Please go to Section 13.2.5, “Using Dot to Enumerate Events”.

In this example statement the window(*) aggregation function returns the last 10 seconds of item location reports for the same asset id as the incoming event. Among that last 10 seconds of events for the same asset id, the enumeration method returns those item location reports where the distance to center is less than 20, for each arriving Item event.

Sample statement:

select window(*).where(p => distance(0, 0, p.location.x, p.location.y) < 20) as centeritems
from Item(type='P')#time(10) group by assetId

The next sample statement instead selects the asset id property of all events and returns an ordered collection:

select window(assetId).orderBy() as orderedAssetIds
from Item#time(10) group by assetId

The following example outputs the 5 highest prices per symbol among the last 10 seconds of stock ticks:

select sorted(price desc).take(5) as highest5PricesPerSymbol
from StockTick#time(10) group by symbol

11.4.6. `Prev`, `Prevwindow` and `Prevtail` Single-Row Functions as Input

The prev, prevwindow and prevtail single-row functions allow access into a stream's data window however are not aggregation functions and and as such not sensitive to the presence of group by. See Section 10.1.15, “The Previous-Window Function”.

When using any of the prev single-row functions as input to a built-in enumeration method you can specify the stream name as a parameter to the function or an event property. The input to the enumeration method is a collection of events if you specify the stream name, or a collection of scalar value if you specify an event property.

In this example statement the prevwindow(stream) single-row function returns the last 10 seconds of item location reports, among which the enumeration method filters those item location reports where the distance to center is less than 20, for each Item event that arrived in the last 10 seconds considering passenger-type Item events only (see filter type = 'P').

Sample statement:

select prevwindow(items)
    .where(p => distance(0, 0, p.location.x, p.location.y) < 20) as centeritems
from Item(type='P')#time(10) as items

This sample statement demonstrates the use of the prevwindow function to return a collection of scalar values (collection of asset id) as input to orderby:

select prevwindow(assetId).orderBy() as orderedAssetIds
from Item#time(10) as items

11.4.7. Single-Row Function, User-Defined Function and Enum Types

Your single-row or user-defined function can return either an array or any collection that implements either the Collection or Iterable interface. For arrays, the array component type and for collections, the collection or iterable generic type should be the class providing event properties.

As an example, assume a ZoneFactory class exists and a static method getZones() returns a list of zones to filter items, for example:

public class ZoneFactory {
  public static Iterable<Zone> getZones() {
    List<Zone> zones = new ArrayList<Zone>();
    zones.add(new Zone("Z1", new Rectangle(0, 0, 20, 20)));
    return zones;
  }
}

Import the class through runtime or static configuration, or add the method above as a plug-in single-row function.

The following statement returns for each Item event all zones that the item belongs to:

select ZoneFactory.getZones().where(z => inrect(z.rectangle, item.location)) as zones
from Item as item

If the class and method were registered as a plug-in single-row function, you can leave the class name off, for example:

select getZones().where(z => inrect(z.rectangle, item.location)) as zones
from Item as item

Your single-row or user-defined function can also return an array, collection or iterable or scalar values.

For example, the static method getZoneNames() returns a list of zone names:

public static String[] getZoneNames() { 
  return new String[] { "Z1", "Z2"};
}

The following statement returns zone names every 30 seconds and excludes zone Z1:

select getZoneNames().where(z => z != "Z1")
from pattern[every timer:interval(30)]

An enum type can also be a useful source for enumerable values.

The following sample Java declares an enum type EnumOfZones:

public enum EnumOfZones {
  ZONES_OUTSIDE(new String[] {"z1", "z2"}),
  ZONES_INSIDE(new String[] {"z3", "z4"})

  private final String[] zones;

  private EnumOfZones(String[] zones) {
	  this.zones = zones;
  }

  public String[] getZones() {
    return zones;
  }
}

A sample statement that utilizes the enum type is shown next:

select EnumOfZones.ZONES_OUTSIDE.getZones().anyOf(v => v = zone) from Item

11.4.8. Declared Expression

A declared expression may return input data for an enumeration method.

The below statement declares an expression that returns all passenger location reports among the items in the location report event in a column named passengerCollection. The statement uses the result returned by the declared expression a second time to filter through the list returning the passenger location report where the asset id is a given value in a column named passengerP01.

Sample statement:

expression passengers {
  lr => lr.items.where(l => l.type='P')
}
select passengers(lr) as passengerCollection,
  passengers(lr).where(x => assetId = 'P01') as passengerP01
from LocationReport lr

The runtime applies caching techniques to avoid re-evaluating the declared expression multiple times.

11.4.9. Variables

A variable may provide input data for an enumeration method.

This constant of array type carries a list of invalid zones:

create constant variable string[] invalid_zones = { 'Z1', 'Z2' };

Sample statement:

select invalid_zones.anyOf(v => v = name) as flagged from Zone

11.4.10. Substitution Parameters

A substitution parameter may provide input data for an enumeration method. The value of the parameter must be array-typed.

Sample statement:

select ?.anyOf(v => v = name) as flagged from Zone

11.4.11. Match-Recognize Group Variable

In a match-recognize pattern, the term group variables refers to identifiers that can collect multiple events.

This example assumes an order event type and each order has an item id. This sample match-recognize pattern finds a sequence of order events that concludes with an item id matching any of the collected item ids since the last pattern match:

select * from Order
match_recognize (
  measures A as a_array, B as b
  pattern (A* B)
  define
  B as A.anyOf(v=> v.itemId = B.itemId)
)

Both the define and the measures clause can contain expressions utilizing enumeration methods.

11.4.12. Pattern Repeat and Repeat-Until Operators

In an EPL pattern, the repeat and repeat-until pattern operators may collect multiple events.

The following pattern fires when two order events arrive followed by an order amendment event that has an amount larger than the largest amount of any of the preceding order events:

select * from pattern [
  ([2] a=Order) -> b=OrderAmendment(amount > a.max(i => i.amount))
]

11.5. Example

Following the RFID asset tracking example as introduced earlier, this section introduces two use cases solved by enumeration methods.

The first use case requires us to find any luggage that is more than 20 units away from the passenger that the luggage belongs to. The declared expression lostLuggage solves this question.

The second question to answer is: For each of such lost luggage what single other passenger is nearest to that luggage. The declared expression nearestOwner which uses lostLuggage answers this question.

Below is the complete statement (one statement not multiple):

// expression to return a collection of lost luggage
expression lostLuggage {
  lr => lr.items.where(l => l.type='L' and
    lr.items.some(p => p.type='P' and p.assetId=l.assetIdPassenger 
      and LRUtil.distance(l.location.x, l.location.y, p.location.x, p.location.y) > 20))
}

// expression to return all passengers
expression passengers {
  lr => lr.items.where(l => l.type='P')
}

// expression to find the nearest owner
expression nearestOwner {
  lr => lostLuggage(lr).toMap(key => key.assetId,
    value => passengers(lr).minBy(
        p => LRUtil.distance(value.location.x, value.location.y, p.location.x, p.location.y))
    )
}

select lostLuggage(lr) as val1, nearestOwner(lr) as val2 from LocationReport lr

11.6. Reference

11.6.1. Aggregate

The aggregate enumeration method takes an expression providing the initialization value (seed) and an accumulator lambda expression. The return value is the final accumulator value.

Via the aggregate method you may perform a calculation over elements. The method initializes the aggregated value by evaluating the expression provided in the first parameter. The method then calls the lambda expression of the second parameter once for each element in the input. The lambda expression receives the last aggregated value and the element from the input. The result of the expression replaces the previous aggregated value and returns the final result after completing all elements. The collection element index and collection size are additional optional formal parameters.

Some expression examples with scalar values as input and with the optional collection element index and collection size formal parameters are:

{1, 2, 3}.aggregate(0, (result, value) => result + value)  // Returns 6
{1, 2, 3}.aggregate(0, (result, value, index) => result + value + index*10)  // Returns 36 = 1 + 12 + 23
{1, 2, 3}.aggregate(0, (result, value, index, size) => result + value + index*10 + size*100)  // Returns 936 = 301 + 312 + 323

The example below aggregates price of each OrderEvent in the last 10 seconds computing a total price:

// Initialization value is zero.
// Aggregate by adding up the price.
select window(*).aggregate(0, (result, order) => result + order.price) as totalPrice
from OrderEvent#time(10)

In the statement above, the initialization value is zero, result is used for the last aggregated value and order denotes the element that the expression adds the value of the price property.

This example aggregation builds a comma-separated list of all asset ids of all items:

select items.aggregate('', 
  (result, item) => result || (case when result='' then '' else ',' end) || item.assetId) as assets			
from LocationReport

In above statement, the empty string represents the initialization value. The name result is used for the last aggregated value and the name item is used to denote the element.

The type value returned by the initialization expression must match to the type of value returned by the accumulator lambda expression.

If the input is null the method returns null. If the input is empty the method returns the initialization value.

11.6.2. AllOf

The allof enumeration method determines whether all elements satisfy the predicate condition.

The method takes a single parameter: The predicate lambda expression that must yield a Boolean result. The enumeration method applies the lambda expression to each element and if the expression returns true for all elements, the method returns true.

The statement below returns true when all items are within 1000 unit distance of center:

{1, 2, 3}.allOf(v => v > 0)    // Returns true as all values are > 0
{1, 2, 3}.allOf(v => v > 1)    // Returns false
{1, 2, 3}.allOf( (v, index) => case when index < 2 then true else v > 1 end)    // Returns true
{1, 2, 3}.allOf( (v, index, size) => v > 1 or size >= 3)    // Returns true

select items.allof(i => distance(i.location.x, i.location.y, 0, 0) < 1000) as centered			
from LocationReport

If the input is null the method returns null. If the input is empty the method returns true.

11.6.3. AnyOf

The anyof enumeration method determines whether any element satisfies the predicate condition.

The only parameter is the predicate lambda expression that must yield a Boolean result. The enumeration method applies the lambda expression to each element and if the expression returns true for all elements, the method returns true.

The statement below return true when any of the items are within 10 unit distance of center:

{1, 2, 3}.anyOf(v => v > 0)    // Returns true
{1, 2, 3}.anyOf(v => v > 1)    // Returns true
{1, 2, 3}.anyOf(v => v > 3)    // Returns false
{1, 2, 3}.anyOf( (v, index) => case when index < 2 then false else v = 3 end)    // Returns true
{1, 2, 3}.anyOf( (v, index, size) => v > 100 or size >= 3)    // Returns true

select items.anyof(i => distance(i.location.x, i.location.y, 0, 0) < 10) as centered			
from LocationReport

If the input is null the method returns null. If the input is empty the method returns false.

11.6.4. ArrayOf

The arrayOf enumeration method returns values as an array.

If no value-selector lambda expression is provided, the method accepts only scalar input and returns the array of scalar values.

If a value-selector lambda expression is provided, the enumeration method invokes a value-selector lambda expression on each element and returns the result values as an array. The type of array returned follows the return type of the lambda expression that was provided as the sole parameter.

The next expressions are examples with scalar input:

{1, 2, 3}.arrayOf()   // Returns [1, 2, 3] as an array of Integer
{1, 2, 3}.arrayOf(v => v+1)   // Returns [2, 3, 4] as an array of Integer
{1, 2, 3}.arrayOf((v, index) => v+index)   // Returns [1, 3, 5] as an array of Integer
{1, 2, 3}.arrayOf((v, index, size) => v+index+size)   // Returns [4, 6, 8] as an array of Integer

The next statement returns the array of distances of any item from center:

select items.arrayOf(i => distance(i.location.x, i.location.y, 0, 0)) as distancesArray
from LocationReport

If the input is null the method returns null. If the input is empty the method returns an empty array.

11.6.5. Average

The average enumeration method computes the average of scalar values. If passing a projection lambda expression the method computes the average obtained by invoking the projection lambda expression on each element.

The method takes a projection lambda expression yielding a numeric value as a parameter. It applies the lambda expression to each element and computes the average of the result, returning a Double value. A BigDecimal is returned for expressions returning BigInteger or BigDecimal.

The following are expression examples with scalar values:

{1, 2, 3}.average()    // Returns 2
{1, 2, 3}.average(v => v+1)    // Returns 3
{1, 2, 3}.average((v, index) => v+10*index)    // Returns 12 = (1+12+23)/3
{1, 2, 3}.average((v, index, size) => v+10*index + 100*size)    // Returns 312 = (301+312+323)/3

The statement as shown next computes the average distance from center among all items in the location report event:

select items.average(i => distance(i.location.x, i.location.y, 0, 0)) as avgdistance
from LocationReport

If the input is null the method returns null. If the input is empty the method returns double zero or BigDecimal zero. For BigDecimal precision and rounding, please see Section 17.5.6.5, “Math Context”.

11.6.6. CountOf

The countof enumeration method returns the number of elements, or the number of elements that satisfy a condition.

The enumeration method has two versions: The first version takes no parameters and computes the number of elements. The second version takes a predicate lambda expression that must yield Boolean true or false, and computes the number of elements that satisfy the condition.

The next sample statement counts the number of items:

{1, 2, 3}.countOf()    // Returns 3
{1, 2, 3}.countOf(v => v < 2)    // Returns 1
{1, 2, 3}.countOf( (v, index) => v > index)    // Returns 3
{1, 2, 3}.countOf( (v, index, size) => v >= size)    // Returns 1

select items.countOf() as cnt from LocationReport

This example statement counts the number of items that have a distance to center that is less than 20 units:

select items.countOf(i => distance(i.location.x, i.location.y, 0, 0) < 20) as cntcenter
from LocationReport

If the input is null the method returns null. If the input is empty the method returns integer zero.

11.6.7. DistinctOf

The distinctOf enumeration method returns distinct elements.

The enumeration method can take a single key-selector lambda expression as parameter and returns distinct elements according to the key yielded by the expression. For same-value keys, distinct returns the first element for that key. Further detail on key expressions can be found at Section 5.2.13, “Composite Keys and Array Values as Keys”.

An expression example with scalar values:

{2, 3, 2, 1}.distinctOf()   // Returns {2, 3, 1}
{2, 3, 2, 1}.distinctOf(v => case when v > 1 then 0 else -1 end)   // Returns {2, 1}
{2, 3, 2, 1}.distinctOf((v, index) => case when index = 0 then 1 else 2 end)   // Returns {2, 3}
{2, 3, 2, 1}.distinctOf((v, index, size) => case when index+1=size then 1 else 2 end)   // Returns {2, 1}

This example returns items distinct by item id returning the first item for each distinct item id:

select items.distinctOf(i => itemId) as itemsNearFirst
from LocationReport

The key-selector lambda expression, when provided, must return a comparable type: Any primitive or boxed or Comparable type is permitted.

If the input is null the method returns null. If the input is empty the method returns an empty collection.

11.6.8. Except

The except enumeration method forms a set difference of the input elements with the elements that the parameter expression yields.

The enumeration method takes a single parameter that must itself return a collection of events, objects or scalar values. The method returns the elements of the first collection that do not appear in the second collection.

The following statement compares the items of the last location report against all items in the previous 10 location reports, and reports for each combination only those items in the current item report that are not also in the location report compared to:

{1, 2, 3}.except({1})   // Returns {2, 3}

select za.items.except(zb.items) as itemsCompared 
from LocationReport as za unidirectional, LocationReport#length(10) as zb

If the input is null the method returns null. For scalar values and objects equals-semantics apply.

11.6.9. FirstOf

The firstOf enumeration method returns the first element or the first element that satisfies a condition.

The method has two versions: The first version takes no parameters and returns the first element. The second version takes a predicate lambda expression yielding true or false. It applies the lambda expression to each element and returns the first element for which the expression returns true. The return type is the element itself and not a collection. You may append a property name to return the property value for the first element.

The example expressions with scalar values are:

{1, 2, 3}.firstOf()   // Returns 1
{1, 2, 3}.firstOf(v => v / 2 > 1)   // Returns 3
{1, 2, 3}.firstOf((v, index) => index = 1)   // Returns 2
{1, 2, 3}.firstOf((v, index, size) => v = size-1)   // Returns 2

In the following EPL sample the statement returns the first item that has a distance to center that is less than 20 units:

select items.firstof(i => distance(i.location.x, i.location.y, 0, 0) < 20) as firstcenter
from LocationReport

The next sample EPL returns the first item's asset id:

select items.firstof().assetId as firstAssetId from LocationReport

If the input is null, empty or if none of the elements match the condition the method returns null.

11.6.10. GroupBy

The groupby enumeration method groups the elements according to a specified key-selector lambda expression. There are two version of the groupby method.

The first version of the method takes a key-selector lambda expression and returns a Map of key with each value a list of objects, one for each distinct key that was encountered. The result is a Map<Object, Collection<Object>> wherein object is the event underlying object.

The second version of the method takes a key-selector lambda expression and value-selector lambda expression and returns a Map of key with each value a list of values, one for each distinct key that was encountered. The result is a Map<Object, Collection<Object>> wherein object is the result of applying the value-selector expression.

The example expressions with scalar values are:

{1, 2, 3}.groupby(k => 'K' || Integer.toString(k))   // Returns map {K1=[1], K2=[2], K3=[3]}
{1, 2, 3}.groupby(k => 'K' || Integer.toString(k), v => 'V' || Integer.toString(v))   // Returns map {K1=[V1], K2=[V2], K3=[V3]}
{1, 2, 3}.groupby(
  (k, i) => 'K' || Integer.toString(k) || "_" || Integer.toString(i), 
  (v, i) => 'V' || Integer.toString(v) || "_" || Integer.toString(i)
  )  // Returns map {K1_0=[V1_0], K2_1=[V2_1], K3_2=[V3_2]}
{1, 2, 3}.groupby(
  (k, i, s) => 'K' || Integer.toString(k) || "_" || Integer.toString(s), 
  (v, i, s) => 'V' || Integer.toString(v) || "_" || Integer.toString(s)
  )  // Returns map {K1_3=[V1_3], K2_3=[V2_3], K3_3=[V3_3]}

The next statement filters out all luggage items using a where method and then groups by the luggage's passenger asset id. It returns a map of passenger asset id and the collection of luggage items for each passenger:

select items.where(type='L').groupby(i => assetIdPassenger) as luggagePerPerson
from LocationReport

The statement shown below generates a map of item asset id and distance to center:

select items.groupby(
    k => assetId, v => distance(v.location.x, v.location.y, 0, 0)) as distancePerItem
from LocationReport

If the input is null the method returns null. Null values as key and value are allowed.

11.6.11. Intersect

The intersect enumeration method forms a set intersection of the input elements with the elements that the parameter expression yields.

The following statement compares the items of the last location report against all items in the previous 10 location reports, and reports for each combination all items in the current item report that also occur in the other location report:

{1, 2, 3}.intersect({2, 3})   // Returns {2, 3}

select za.items.intersect(zb.items) as itemsCompared 
from LocationReport as za unidirectional, LocationReport#length(10) as zb

If the input is null the method returns null. For scalar values and objects equals-semantics apply.

11.6.12. LastOf

The lastOf enumeration method returns the last element or the last element that satisfies a condition.

The method has two versions: The first version takes no parameters and returns the last element. The second version takes a predicate lambda expression yielding true or false. It applies the lambda expression to each element and returns the last element for which the expression returns true. The return type is the element itself and not a collection. You may append a property name to return the property value for the last element.

In the following EPL sample the statement returns the last item that has a distance to center that is less than 20 units:

{1, 2, 3}.lastOf()   // Returns 3
{1, 2, 3}.lastOf(v => v < 3)   // Returns 2
{1, 2, 3}.lastOf((v, index) => index < 2 )   // Result 2
{1, 2, 3}.lastOf((v, index, size) => index < size - 2 )   // Result 1

select items.lastof(i => distance(i.location.x, i.location.y, 0, 0) < 20) as lastcenter 
from LocationReport

The next sample EPL returns the last item's asset id:

select items.lastof().assetId as lastAssetId from LocationReport

If the input is null, empty or if none of the elements match the condition the method returns null.

11.6.13. LeastFrequent

The leastFrequent enumeration method returns the least frequent value among a collection of values, or the least frequent value after applying a transform expression to each element.

The method has two versions: The first version takes no parameters and returns the least frequent value. The second version takes a transform lambda expression yielding the value to count occurrences for. The method applies the lambda expression to each element and returns the expression result value with the least number of occurrences. The return type is the type of value in the collection or the type of value returned by the transform lambda expression if one was provided.

The example EPL below returns the least frequent item type, counting the distinct item types among all items for the current LocationReport event:

{1, 2, 3, 2, 1}.leastFrequent()   // Returns 3
{1, 2, 3, 2, 1}.leastFrequent(v => case when v = 3 then 4 else v end)   // Returns 4
{1, 2, 3, 2, 1}.leastFrequent((v, index) => case when index = 2 then 4 else v end)   // Returns 4
{1, 2, 3, 2, 1}.leastFrequent((v, index, size) => case when index = size - 2 then 4 else v end)  // Returns 2

select items.leastFrequent(i => type) as leastFreqType from LocationReport

If the input is null or empty the method returns null. The transform expression may also yield null. A null value can be returned as the most frequent value if the most frequent value is null. If multiple values have the same number of occurrences the method returns the first value with the least number of occurrences considering the ordering of the collection.

11.6.14. Max

The max enumeration method returns the maximum value among a collection of values.

If no value-selector lambda expression is provided, the method finds the maximum.

If a value-selector lambda expression is provided, the enumeration method invokes a value-selector lambda expression on each element and returns the maximum value. The type of value returned follows the return type of the lambda expression that was provided as parameter.

The next statement returns the maximum distance of any item from center:

{1, 2, 3, 2, 1}.max()   // Returns 3
{1, 2, 3, 2, 1}.max(v => case when v >= 3 then 0 else v end)   // Returns 2
{1, 2, 3, 2, 1}.max((v, index) => case when index = 2 then 0 else v end)   // Returns 2
{1, 2, 3, 2, 1}.max((v, index, size) => case when index > size - 4 then 0 else v end)   // Returns 2

select items.max(i => distance(i.location.x, i.location.y, 0, 0)) as maxcenter 
from LocationReport

The value-selector lambda expression must return a comparable type: Any primitive or boxed type or Comparable type is permitted.

If the input is null, empty or if none of the elements when transformed return a non-null value the method returns null.

11.6.15. MaxBy

The maxBy enumeration method returns the element that provides the maximum value returned by the value-selector lambda expression when applied to each element.

The enumeration method returns the element itself. You may append an event property name to return a property value of the element.

The next statement returns the first item with the maximum distance to center:

{1, 2, 3, 2, 1}.maxby(v => v)   // Result 3
{1, 2, 3, 2, 1}.maxby((v, index) => case when index < 3 then -1 else 0 end)   // Result 2
{1, 2, 3, 2, 1}.maxby((v, index, size) => case when index < size - 2 then -1 else 0 end)   // Result 2

select items.maxBy(i => distance(i.location.x, i.location.y, 0, 0)) as maxItemCenter 
from LocationReport

The next sample returns the type of the item with the largest asset id (string comparison) among all items:

select items.maxBy(i => assetId).type as minAssetId from LocationReport

The transform expression must return a comparable type: Any primitive or boxed type or Comparable type is permitted.

If the input is null, empty or if none of the elements when transformed return a non-null value the method returns null.

11.6.16. Min

The min enumeration method returns the minimum value among a collection of values.

If no value-selector lambda expression is provided, the method finds the minimum.

If a value-selector lambda expression is provided, the enumeration method invokes a value-selector lambda expression on each element and returns the minimum value. The type of value returned follows the return type of the lambda expression that was provided as parameter.

Some expression examples with scalar values are:

{1, 2, 3, 2, 1}.min()   // Returns 1
{1, 2, 3, 2, 1}.min(v => v + 1)  // Returns 2
{1, 2, 3, 2, 1}.min((v, index) => v - index)   // Returns -3
{1, 2, 3, 2, 1}.min((v, index, size) => v - size)   // Returns -4

The next statement returns the minimum distance of any item to center:

select items.min(i => distance(i.location.x, i.location.y, 0, 0)) as mincenter 
from LocationReport

The transform expression must return a comparable type: Any primitive or boxed type or Comparable type is permitted.

If the input is null, empty or if none of the elements when transformed return a non-null value the method returns null.

11.6.17. MinBy

The minBy enumeration method returns the element that provides the minimum value returned by the value-selector lambda expression when applied to each element.

The enumeration method returns the element itself. You may append an event property name to return a property value of the element.

Some expression examples with scalar values are:

{1, 2, 3, 2, 1}.minby(v => v)   // Result 1
{1, 2, 3, 2, 1}.minby((v, index) => case when index < 3 then -1 else 0 end)   // Result 1
{1, 2, 3, 2, 1}.minby((v, index, size) => case when index < size - 2 then -1 else 0 end)   // Result 1

The next statement returns the first item with the minimum distance to center:

select items.minBy(i => distance(i.location.x, i.location.y, 0, 0)) as minItemCenter 
from LocationReport

The next sample returns the type of the item with the smallest asset id (string comparison) among all items:

select items.minBy(i => assetId).type as minAssetId from LocationReport

The transform expression must return a comparable type: Any primitive or boxed or Comparable type is permitted.

If the input is null, empty or if none of the elements when transformed return a non-null value the method returns null.

11.6.18. MostFrequent

The mostFrequent enumeration method returns the most frequent value among a collection of values, or the most frequent value after applying a transform expression to each element.

The method has two versions: The first version takes no parameters and returns the most frequent value. The second version takes a transform lambda expression yielding the value to count occurrences for. The method applies the lambda expression to each element and returns the expression result value with the most number of occurrences. The return type is the type of value in the collection or the type of value returned by the transform lambda expression if one was provided.

The example EPL below returns the least frequent item type, counting the distinct item types among all items for the current LocationReport event:

{1, 2, 3, 2, 1, 2}.mostFrequent()   // Returns 2
{1, 2, 3, 2, 1, 2}.mostFrequent(v => case when v = 2 then 10 else v end)   // Returns 10
{1, 2, 3, 2, 1, 2}.mostFrequent((v, index) => case when index > 2 then 4 else v end)   // Returns 4
{1, 2, 3, 2, 1, 2}.mostFrequent((v, index, size) => case when size > 3 then 0 else v end)   // Returns 0

select items.leastFrequent(i => type) as leastFreqType from LocationReport

If the input is null or empty the method returns null. The transform expression may also yield null. A null value can be returned as the most frequent value if the most frequent value is null. If multiple values have the same number of occurrences the method returns the first value with the most number of occurrences considering the ordering of the collection.

11.6.19. OrderBy and OrderByDesc

The orderBy enumeration method sorts elements in ascending order according to a key. The orderByDesc enumeration method sorts elements in descending order according to a key.

The enumeration method takes a single key-selector lambda expression as parameter and orders elements according to the key yielded by the expression. For same-value keys, it maintains the existing order.

This example orders all items from a location report according to their distance from center:

{2, 3, 2, 1}.orderBy()   // Returns {1, 2, 2, 3}
{2, 3, 2, 1}.orderBy(v => -v)    // Returns {3, 2, 2, 1}
{2, 3, 2, 1}.orderBy((v, index) => index)   // Returns {2, 3, 2, 1}
{2, 3, 2, 1}.orderBy((v, index, size) => case when index < size - 2 then v else -v end)   // Returns {2, 1, 2, 3}

select items.orderBy(i => distance(i.location.x, i.location.y, 0, 0)) as itemsNearFirst,
  items.orderByDesc(i => distance(i.location.x, i.location.y, 0, 0)) as itemsFarFirst
from LocationReport

The key-selector lambda expression must return a comparable type: Any primitive or boxed or Comparable type is permitted.

If the input is null the method returns null. If the input is empty the method returns an empty collection.

11.6.20. Reverse

The reverse enumeration method simply reverses the order of elements returning a collection.

The following EPL reverses the items:

{2, 3, 2, 1}.reverse()   // Returns {1, 2, 3, 2}

select items.reverse() as reversedItems from LocationReport

If the input is null the method returns null. If the input is empty the method returns an empty collection.

11.6.21. SelectFrom

The selectFrom enumeration method transforms each element resulting in a collection of transformed elements.

The enumeration method has two versions. The first version takes only a transformation lambda expression and applies that to each element and returns the result of each transformation as a collection. The second version takes a transformation lambda expression and index for use within the transformation expression.

Use the new operator to yield multiple values for each element, see Section 9.14, “The 'New' Keyword”.

The following are expression examples with scalar values:

{'A','B','C'}.selectFrom(v => '<' || v || '>')   // Returns [<A>, <B>, <C>]
{'A','B','C'}.selectFrom((v, index) => v || '_' || Integer.toString(index))   // Returns [A_0, B_1, C_2]
{'A','B','C'}.selectFrom((v, index, size) => v || '_' || Integer.toString(size))   // Returns [A_3, B_3, C_3]

The next statement returns a collection of asset ids:

select items.selectFrom(i => assetId) as itemAssetIds from LocationReport

This sample statement evaluates each item and returns the asset id as well as the distance from center for each item:

select items.selectFrom(i => 
  new {
    assetId, 
    distanceCenter = distance(i.location.x, i.location.y, 0, 0)
  } ) as itemInfo from LocationReport

The second version of the selectFrom enumeration method also represents the index of the input element starting at zero for the first element.

The example appends the passenger-type and the index returning a string-type value for each item:

select items.selectFrom((p, ind) => p.type || Integer.toString(ind)) from LocationReport

If the input is null the method returns null. If the input is empty the method returns an empty collection.

11.6.22. SequenceEqual

The sequenceEqual enumeration method determines whether two collections are equal by comparing each element.

The method enumerates the two source collections in parallel and compares corresponding elements by using the equals method to compare. The method takes a single parameter expression that must return a collection containing elements of the same type as the input. The method returns true if the two source sequences are of equal length and their corresponding elements are equal.

The following example compares the asset id of all items to the asset ids returned by a method ItemUtil.redListed() which is assumed to return a list of asset id of string type:

{1, 2, 3}.sequenceEqual({1})   // Returns false
{1, 2, 3}.sequenceEqual({1, 2, 3})   // Returns true

select items.selectFrom(i => assetId).sequenceEquals(ItemUtil.redListed()) from LocationReport

If the input is null the method returns null.

11.6.23. SumOf

The sumOf enumeration method computes the sum. If a projection lambda expression is provided, the method invokes the projection lambda expression on each element and computes the sum on each returned value.

The projection lambda expression should yield a numeric value, BigDecimal or BigInteger value. Depending on the type returned by the projection lambda expression the method returns either Integer, Long, Double, BigDecimal or BigInteger.

The following example computes the sum of the distance of each item to center:

{1, 2, 3}.sumOf()   // Returns 6
{1, 2, 3}.sumOf(v => v+1)    // Returns 9 = 2+3+4
{1, 2, 3}.sumOf((v, index) => v+index)    // Returns 9 = 1+3+5
{1, 2, 3}.sumOf((v, index, size) => v+index+size)    // Returns 18 = 4+6+8

select items.sum(i => distance(i.location.x, i.location.y, 0, 0) as totalAllDistances
from LocationReport

If the input is null or empty the method returns null.

11.6.24. Take

The take enumeration method returns a specified number of contiguous elements from the start.

The enumeration method takes a single size (non-lambda) expression that returns an Integer value.

The following example returns the first 5 items:

{1, 2, 3}.take(2)   // Returns {1, 2}

select items.take(5) as first5Items from LocationReport

If the input is null the method returns null. If the input is empty the method returns an empty collection.

11.6.25. TakeLast

The takeLast enumeration method returns a specified number of contiguous elements from the end.

The enumeration method takes a single size (non-lambda) expression that returns an Integer value.

The following example returns the last 5 items:

{1, 2, 3}.takeLast(2)   // Returns {2, 3}

select items.takeLast(5) as last5Items from LocationReport

If the input is null the method returns null. If the input is empty the method returns an empty collection.

11.6.26. TakeWhile

The takeWhile enumeration method returns elements from the start as long as a specified condition is true.

The enumeration method has two versions. The first version takes a predicate lambda expression and the second version takes a predicate lambda expression and index for use within the predicate expression. Both versions return elements from the start as long as the specified condition is true.

This example selects all items from a location report in the order provided until the first item that has a distance to center greater than 20 units:

{1, 2, 3}.takeWhile(v => v < 3)   // Returns {1, 2}
{1, 2, 3}.takeWhile((v,ind) => ind > 2)   // Returns {1, 2}
{1, 2, -1, 4, 5, 6}.takeWhile((v,ind,sz) => ind < sz - 5 and v > 0)   // Returns {1}

select items.takeWhile(i => distance(i.location.x, i.location.y, 0, 0) < 20)
from LocationReport

The second version of the where represents the index of the input element starting at zero for the first element.

The next example is similar to the statement above but also limits the result to the first 10 items:

select items.takeWhile((i, ind) => distance(i.location.x, i.location.y, 0, 0) < 20) and ind < 10)
from LocationReport

If the input is null the method returns null. If the input is empty the method returns an empty collection.

11.6.27. TakeWhileLast

The takeWhileLast enumeration method returns elements from the end as long as a specified condition is true.

The expression examples with scalar values are:

{1, 2, 3}.takeWhileLast(v => v < 3)   // Returns {} (empty collection)
{1, 2, 3}.takeWhileLast(v => v > 1)   // Returns {2, 3}
{1, 2, 3}.takeWhileLast((v,ind) => ind > 2)   // Returns {2, 3}
{1, 2, -1, 4, 5, 6}.takeWhileLast((v,ind) => ind < 5 and v > 0)  // Returns {4, 5, 6} (Take while index<5 amd value>0)
{1, 2, -1, 4, 5, 6}.takeWhileLast((v,ind,sz) => ind < sz-4 and v > 0)   // Returns {5, 6}

This example selects all items from a location report, starting from the last element and proceeding backwards, until the first item that has a distance to center greater than 20 units:

select items.takeWhile(i => distance(i.location.x, i.location.y, 0, 0) < 20)
from LocationReport

The second version provides the index of the input element starting at zero for the last element (reverse index).

The next example is similar to the statement above but also limits the result to the last 10 items:

select items.takeWhile((i, ind) => distance(i.location.x, i.location.y, 0, 0) < 20) and ind < 10)
from LocationReport

If the input is null the method returns null. If the input is empty the method returns an empty collection.

11.6.28. ToMap

The toMap enumeration method returns a Map according to specified key-selector lambda expression and value-selector lambda expression.

The enumeration method takes a key-selector expression and a value-selector expression. For each element the method applies the key-selector expression to determine the map key and the value-selector expression to determine the map value. If the key already exists in the map the value is overwritten.

The expression examples with scalar values are:

{1, 2, 3}.toMap(k => 'K' || Integer.toString(k), v => 'V' || Integer.toString(v))   // Returns map {K1=V], K2=V2, K3=V3}
{1, 2, 3}.toMap(
  (k, i) => 'K' || Integer.toString(k) || "_" || Integer.toString(i), 
  (v, i) => 'V' || Integer.toString(v) || "_" || Integer.toString(i)
  )  // Returns map {K1_0=V1_0, K2_1=V2_1, K3_2=V3_2}
{1, 2, 3}.toMap(
  (k, i, s) => 'K' || Integer.toString(k) || "_" || Integer.toString(s), 
  (v, i, s) => 'V' || Integer.toString(v) || "_" || Integer.toString(s)
  )  // Returns map {K1_3=V1_3, K2_3=V2_3, K3_3=V3_3}

The next example EPL outputs a map of item asset id and distance to center for each item:

select items.toMap(k => k.assetId, v => distance(v.location.x, v.location.y, 0, 0)) as assetDistance
from LocationReport

If the input is null the method returns null. If the input is empty the method returns an empty map.

11.6.29. Union

The union enumeration method forms a union of the input elements with the elements that the parameter expression yields.

The enumeration method takes a single parameter that must itself return a collection of events (input), objects or scalar values. It appends the collection to the input elements and returns the appended collection of elements.

This example selects a union of all items that have an asset id of L001 or that are of type passenger:

{1, 2, 3}.union({4, 5})   // Returns {1, 2, 3, 4, 5}

select items.where(i => i.assetId = 'L001')
    .union(items.where(i => i.type = 'P')) as itemsUnion
from LocationReport

If the input is null the method returns null.

11.6.30. Where

The where enumeration method filters elements based on a predicate.

The enumeration method has two versions. The first version takes a predicate lambda expression and the second version takes a predicate lambda expression and index for use within the predicate expression. Both versions return all elements for which the predicate expression is true.

This example selects all items from a location report that are passenger-type:

{1, 2, 3}.where(v => v != 2)   // Returns {1, 3}
{1, 2, 3}.where((v, index) => v != 2 and index < 2)   // Returns {1}
{1, 2, 3}.where((v, index, size) => v != 2 and index < size - 2)   // Returns {1}

select items.where(p => p.type = 'P') from LocationReport

The second version of the where represents the index of the input element starting at zero for the first element.

The example below selects all items from a location report that are passenger-type but ignores the first 3 elements:

select items.where((p, ind) => p.type = 'P' and ind > 2) from LocationReport

If the input is null the method returns null. If the input is empty the method returns an empty collection.

Chapter 12. EPL Reference: Date-Time Methods

12.1. Overview

EPL date-time methods work on date-time values to perform common tasks such as comparing times and time periods, adding or subtracting time periods, setting or rounding calendar fields and querying fields.

Date-time methods operate on:

Any expression or event property that returns one of the below values:
1. A long-type millisecond or microsecond value.
2. A java.util.Calendar object including subclasses.
3. A java.util.Date object including subclasses.
4. A java.time.LocalDateTime object including subclasses.
5. A java.time.ZonedDateTime object including subclasses.
Any event for which the event type declares a start timestamp property name and optionally also an end timestamp property name. Date-time methods operate on events by means of the stream-alias.method-name syntax.

Your application may add additional date-time methods by following the recipe described at Section 22.8, “Date-Time Method”.

The below table summarizes the built-in date-time methods available:

Table 12.1. Date-Time Methods

Method Result

after(event or timestamp)

Returns true if an event happens after another event, or a timestamp is after another timestamp.

Section 12.4.5, “After”.

before(event or timestamp)

Returns true if an event happens before another event, or a timestamp is before another timestamp.

Section 12.4.6, “Before”.

between(timestamp, timestamp, boolean, boolean)

Returns true if a timestamp is between two timestamps.

Section 12.3.1, “Between”.

coincides(event or timestamp)

Returns true if an event and another event happen at the same time, or two timestamps are the same value.

Section 12.4.7, “Coincides”.

during(event or timestamp)

Returns true if an event happens during the occurrence of another event, or when a timestamps falls within the occurrence of an event.

Section 12.4.8, “During”.

finishes(event or timestamp)

Returns true if an event starts after another event starts and the event ends at the same time as the other event.

Section 12.4.9, “Finishes”.

finishedBy(event or timestamp)

Returns true if an event starts before another event starts and ends at the same time as the other event.

Section 12.4.10, “Finished By”.

format()

format(format)

Formats the date-time returning a string.

Section 12.3.2, “Format”.

get(field)

Returns the value of the given date-time value field.

Section 12.3.3, “Get (By Field)”.

getMillisOfSecond()

getSecondOfMinute()

getMinuteOfHour()

getHourOfDay()

getDayOfWeek()

getDayOfMonth()

getDayOfYear()

getWeekyear()

getMonthOfYear()

getYear()

getEra()

Returns the value of the given date-time value field.

Section 12.3.4, “Get (By Name) ”.

includes(event or timestamp)

Returns true if the parameter event happens during the occurrence of the event, or when a timestamps falls within the occurrence of an event.

Section 12.4.11, “Includes”.

meets(event or timestamp)

Returns true if the event's end time is the same as another event's start time.

Section 12.4.12, “Meets”.

metBy(event or timestamp)

Returns true if the event's start time is the same as another event's end time.

Section 12.4.13, “Met By”.

minus(duration-millis)

Returns a date-time with the specified duration in long-type milliseconds taken away.

Section 12.3.5, “Minus”.

minus(time-period)

Returns a date-time with the specified duration in time-period syntax taken away.

Section 12.3.5, “Minus”.

overlaps(event or timestamp)

Returns true if the event starts before another event starts and finishes after the other event starts, but before the other event finishes (events have an overlapping period of time).

Section 12.4.14, “Overlaps”.

overlappedBy(event or timestamp)

Returns true if the parameter event starts before the input event starts and the parameter event finishes after the input event starts, but before the input event finishes (events have an overlapping period of time).

Section 12.4.15, “Overlapped By”.

plus(duration-millis)

Returns a date-time with the specified duration in long-type milliseconds added.

Section 12.3.6, “Plus”.

plus(time-period)

Returns a date-time with the specified duration in time-period syntax added.

Section 12.3.6, “Plus”.

roundCeiling(field)

Returns a date-time rounded to the highest whole unit of the date-time field.

Section 12.3.7, “RoundCeiling”.

roundFloor(field)

Returns a date-time rounded to the lowest whole unit of the date-time field.

Section 12.3.8, “RoundFloor”.

roundHalf(field)

Returns a date-time rounded to the nearest whole unit of the date-time field.

Section 12.3.9, “RoundHalf”.

set(field, value)

Returns a date-time with the specified field set to the value returned by a value expression.

Section 12.3.10, “Set (By Field)”.

starts(event or timestamp)

Returns true if an event and another event start at the same time and the event's end happens before the other event's end.

Section 12.4.16, “Starts”.

startedBy(event or timestamp)

Returns true if an event and another event start at the same time and the other event's end happens before the input event's end.

Section 12.4.17, “Started By”.

withDate(year,month,day)

Returns a date-time with the specified date, retaining the time fields.

Section 12.3.11, “WithDate”.

withMax(field)

Returns a date-time with the field set to the maximum value for the field.

Section 12.3.12, “WithMax”.

withMin(field)

Returns a date-time with the field set to the minimum value for the field.

Section 12.3.13, “WithMin”.

withTime(hour,minute,sec,msec)

Returns a date-time with the specified time, retaining the date fields.

Section 12.3.14, “WithTime”.

toCalendar()

Returns the Calendar object for this date-time value.

Section 12.3.15, “ToCalendar”.

toDate()

Returns the Date object for this date-time value.

Section 12.3.16, “ToDate”.

toMillisec()

Returns the long-type milliseconds value for this date-time value.

Section 12.3.17, “ToMillisec”.

12.2. How to Use

12.2.1. Syntax

The syntax for date-time methods is the same syntax as for any chained invocation:

input_val.datetime_method_name( [method_parameter [, method_parameter [,...]]])
	  .[ datetime_method_name(...) [...]]

Following the input_val input value is the . (dot) operator and the datetime_method_name date-time method name. It follows in parenthesis a comma-separated list of method parameter expressions. Additional date-time methods can be chained thereafter.

The input value can be any expression or event property that returns a value of type long or java.util.Calendar or java.util.Date or java.time.LocalDateTime or java.time.ZonedDateTime. If the input value is null, the expression result is also null.

The input value can also be an event. In this case the event type of the event must have the start timestamp property name defined and optionally also the end timestamp property name.

The following example statement employs the withTime date-time method. This example returns the current runtime time with the time-part set to 1 am:

select current_timestamp.withTime(1, 0, 0, 0) as time1am from MyEvent

As date-time methods can be chained, this EPL is equivalent:

select current_timestamp.set('hour', 1).set('min', 0).set('sec', 0).set('msec', 0) as time1am
from MyEvent

The statement above outputs in field time1am a long-type value (milliseconds or microseconds) reflecting 1am on the same date as runtime time. Since the input value is provided by the built-in current_timestamp function which returns current runtime time as a long-type value the output is also a long-type value.

You may apply a date-time method to an event property.

Assume that the RFIDEvent event type has a Date-type property by name timeTaken. The following statement rounds each time-taken value down to the nearest minute and outputs a Date-type value in column timeTakenRounded:

select timeTaken.roundFloor('min') as timeTakenRounded from RFIDEvent

You may apply a date-time method to events. This example assumes that the RFIDEvent and WifiEvent event types both have a timestamp property defined. The EPL compares the timestamps of the RFIDEvent and the WifiEvent:

select rfid.after(wifi) as isAfter 
from RFIDEvent#lastevent rfid, WifiEvent#lastevent wifi

For comparing date-time values and considering event duration (event start and end timestamps) we recommend any of the interval algebra methods. You may also compare long-type values using the between or in ranges and inverted ranges or relational operators (> , <, >=, <=).

From a performance perspective, the date-time method evaluation ensures that for each unique chain of date-time methods only a single calendar objects is copied or created when necessary.

12.3. Calendar and Formatting Reference

12.3.1. Between

The between date-time method compares the input date-time value to the two date-time values passed in and returns true if the input value falls between the two parameter values.

The synopsis is:

input_val.between(range_start, range_end [, include_start, include_end])

The method takes either 2 or 4 parameters. The first two parameters range_start and range_end are expressions or properties that yield either a long-typed, Date-typed or Calendar-typed range start and end value.

The next two parameters include_start and include_end are optional. If not specified, the range start value and range end value are included in the range i.e. specify a closed range where both endpoints are included. If specified, the expressions must return a boolean-value indicating whether to include the range start value and range end value in the range.

The example below outputs true when the time-taken property value of the RFID event falls between the time-start property value and the time-end property value (closed range includes endpoints):

select timeTaken.between(timeStart, timeEnd) from RFIDEvent

The example below performs the same test as above but does not include endpoints (open range includes neither endpoint):

select timeTaken.between(timeStart, timeEnd, false, false) from RFIDEvent

If the range end value is less then the range start value, the algorithm reverses the range start and end value.

If the input date-time value or any of the parameter values evaluate to null the method returns a null result value.

12.3.2. Format

The format date-time method formats the date-time returning a string.

The method takes either no parameter or a single format parameter.

12.3.2.1. Format with Default Formatter

When passing no parameter, the method returns the date-time value formatted using the default formatter as follows:

Table 12.2. RoundHalf Examples

Input	String Formatter
`long, Date, Calendar`	`new SimpleDateFormat()`
`java.time.LocalTimeDate`	`DateTimeFormatter.ISO_DATE_TIME`
`java.time.ZonedTimeDate`	`DateTimeFormatter.ISO_ZONED_DATE_TIME`

The example below outputs the time-taken property value of the RFID event:

select timeTaken.format() as timeTakenStr from RFIDEvent

12.3.2.2. Providing a Format

For input values that are long-typed, Date-typed or Calendar-typed you must provide an expression that returns either:

A String-type format that adheres to SimpleDateFormat rules.
A DateFormat instance.

For input values that are LocalDateTime-typed or ZonedDateTime-typed you must provide an expression that returns either:

A String-type format that adheres to DateTimeFormatter rules.
A DateTimeFormatter instance.

The runtime evaluates the format expression at statement compilation time therefore the format expression must return a value that is not computed from time or events.

The get date-time method returns the value of the given date-time value field.

select timeTaken.format('yyyy.MM.dd G 'at' HH:mm:ss') from RFIDEvent

select timeTaken.format(SimpleDateFormat.getDateInstance()) from RFIDEvent

select localDateTime.format(java.time.format.DateTimeFormatter.BASIC_ISO_DATE) from RFIDEvent

12.3.3. Get (By Field)

The method takes a single string-constant field name as parameter. Please see Section 5.2.1, “Specifying Time Periods” for a list of recognized keywords (not case-sensitive).

The method returns the numeric value of the field within the date-time value. The value returned adheres to Calendar-class semantics: For example, the value for month starts at zero and has a maximum of 11 (Note: for LocalDateTime and ZonedDateTime the range for month is 1 to 12).

The example below outputs the month value of the time-taken property value of the RFID event:

select timeTaken.get('month') as timeTakenMonth from RFIDEvent

12.3.4. Get (By Name)

The following list of getter-methods are available: getMillisOfSecond(), getSecondOfMinute(), getMinuteOfHour(), getHourOfDay(), getDayOfWeek(), getDayOfMonth(), getDayOfYear(), getWeekYear(), getMonthOfYear(), getYear() and getEra().

All get-methods take no parameter and return the numeric value of the field within the date-time value. The value returned adheres to Calendar-class semantics: For example, the value for month starts at zero and has a maximum of 11 (Note: for LocalDateTime and ZonedDateTime the range for month is 1 to 12).

The example below outputs the month value of the time-taken property value of the RFID event:

select timeTaken.getMonthOfYear() as timeTakenMonth from RFIDEvent

12.3.5. Minus

The minus date-time method returns a date-time with the specified duration taken away.

The method has two versions: The first version takes the duration as a long-type millisecond value. The second version takes the duration as a time-period expression, see Section 5.2.1, “Specifying Time Periods”.

The example below demonstrates the time-period parameter to subtract two minutes from the time-taken property value of the RFID event:

select timeTaken.minus(2 minutes) as timeTakenMinus2Min from RFIDEvent

The next example is equivalent but passes a millisecond-value instead:

select timeTaken.minus(2*60*1000) as timeTakenMinus2Min from RFIDEvent

12.3.6. Plus

The plus date-time method returns a date-time with the specified duration added.

The next example adds two minutes to the time-taken property value of the RFID event:

select timeTaken.plus(2 minutes) as timeTakenPlus2Min from RFIDEvent

The next example is equivalent but passes a millisecond-value instead:

select timeTaken.plus(2*60*1000) as timeTakenPlus2Min from RFIDEvent

12.3.7. RoundCeiling

The roundCeiling date-time method rounds to the highest whole unit of the date-time field.

The method takes a single string-constant field name as parameter. Please see Section 5.2.1, “Specifying Time Periods” for a list of recognized keywords (not case-sensitive).

The next example rounds-to-ceiling the minutes of the time-taken property value of the RFID event:

select timeTaken.roundCeiling('min') as timeTakenRounded from RFIDEvent

If the input time is 2002-05-30 09:01:23.050, for example, the output is 2002-05-30 09:02:00.000 (example timestamps are in format yyyy-MM-dd HH:mm:ss.SSS).

12.3.8. RoundFloor

The roundFloor date-time method rounds to the lowest whole unit of the date-time field.

The method takes a single string-constant field name as parameter. Please see Section 5.2.1, “Specifying Time Periods” for a list of recognized keywords (not case-sensitive).

The next example rounds-to-floor the minutes of the time-taken property value of the RFID event:

select timeTaken.roundFloor('min') as timeTakenRounded from RFIDEvent

If the input time is 2002-05-30 09:01:23.050, for example, the output is 2002-05-30 09:01:00.000 (example timestamps are in format yyyy-MM-dd HH:mm:ss.SSS).

12.3.9. RoundHalf

The roundFloor date-time method rounds to the nearest whole unit of the date-time field.

The method takes a single string-constant field name as parameter. Please see Section 5.2.1, “Specifying Time Periods” for a list of recognized keywords (not case-sensitive).

The next example rounds the minutes of the time-taken property value of the RFID event:

select timeTaken.roundHalf('min') as timeTakenRounded from RFIDEvent

The following table provides a few examples of the rounding (example timestamps are in format yyyy-MM-dd HH:mm:ss.SSS):

Table 12.3. RoundHalf Examples

Input	Output
2002-05-30 09:01:23.050	2002-05-30 09:01:00.000
2002-05-30 09:01:29.999	2002-05-30 09:01:00.000
2002-05-30 09:01:30.000	2002-05-30 09:02:00.000

This method is not support for LocalDateTime and ZonedDateTime input values.

12.3.10. Set (By Field)

The set date-time method returns a date-time with the specified field set to the value returned by an expression.

The method takes a string-constant field name and an expression returning an integer-value as parameters. Please see Section 5.2.1, “Specifying Time Periods” for a list of recognized keywords (not case-sensitive).

The method returns the new date-time value with the field set to the provided value. Note that value adheres to Calendar-class semantics: For example, the value for month starts at zero and has a maximum of 11 (Note: for LocalDateTime and ZonedDateTime the range for month is 1 to 12).

The example below outputs the time-taken with the value for month set to April:

select timeTaken.set('month', 3) as timeTakenMonth from RFIDEvent

12.3.11. WithDate

The withDate date-time method returns a date-time with the specified date, retaining the time fields.

The method takes three expressions as parameters: An expression for year, month and day.

The method returns the new date-time value with the date fields set to the provided values. For expressions returning null the method ignores the field for which null is returned. Note the Calendar-class semantics: For example, the value for month starts at zero and has a maximum of 11.

The example below outputs the time-taken with the date set to May 30, 2002:

select timeTaken.withDate(2002, 4, 30) as timeTakenDated from RFIDEvent

12.3.12. WithMax

The withMax date-time method returns a date-time with the field set to the maximum value for the field.

The method takes a string-constant field name as parameter. Please see Section 5.2.1, “Specifying Time Periods” for a list of recognized keywords (not case-sensitive).

The method returns the new date-time value with the specific date field set to the maximum value.

The example below outputs the time-taken property value with the second-part as 59 seconds:

select timeTaken.withMax('sec') as timeTakenMaxSec from RFIDEvent

12.3.13. WithMin

The withMin date-time method returns a date-time with the field set to the minimum value for the field.

The method takes a string-constant field name as parameter. Please see Section 5.2.1, “Specifying Time Periods” for a list of recognized keywords (not case-sensitive).

The method returns the new date-time value with the specific date field set to the minimum value.

The example below outputs the time-taken property value with the second-part as 0 seconds:

select timeTaken.withMin('sec') as timeTakenMaxSec from RFIDEvent

12.3.14. WithTime

The withTime date-time method returns a date-time with the specified time, retaining the date fields.

The method takes four expressions as parameters: An expression for hour, minute, second and millisecond.

The method returns the new date-time value with the time fields set to the provided values. For expressions returning null the method ignores the field for which null is returned.

The example below outputs the time-taken with the time set to 9am:

select timeTaken.withTime(9, 0, 0, 0) as timeTakenDated from RFIDEvent

12.3.15. ToCalendar

The toCalendar date-time method returns the Calendar object for this date-time value.

The method takes no parameters.

The example below outputs the time-taken as a Calendar object:

select timeTaken.toCalendar() as timeTakenCal from RFIDEvent

12.3.16. ToDate

The toDate date-time method returns the Date object for this date-time value.

The method takes no parameters.

The example below outputs the time-taken as a Date object:

select timeTaken.toDate() as timeTakenDate from RFIDEvent

12.3.17. ToMillisec

The toMillisec date-time method returns the long-typed millisecond value for this date-time value.

The method takes no parameters.

The example below outputs the time-taken as a long-typed millisecond value:

select timeTaken.toMillisec() as timeTakenLong from RFIDEvent

12.4. Interval Algebra Reference

Interval algebra methods compare start and end timestamps of events or timestamps in general.

When the expression input is only a timestamp value, such as a long-type value or a Date or Calendar object, the start and end timestamp represented by that value are the same timestamp value.

When expression input is an event stream alias, the compiler determine the event type for the stream. If the event type declares a start timestamp property name, the compiler uses that start timestamp property to determine the start timestamp for the event. If the event type also declares an end timestamp property name, the compiler uses that end timestamp property to determine the end timestamp for the event (i.e. an event with duration). If an end timestamp property name is not declared, the start and end timestamp for each event is the same value and the event is considered to have zero duration (i.e. a point-in-time event).

Interval algebra methods all return Boolean-type value. When the input value start timestamp is null, or the end timestamp (if declared for the event type) is null or any of the start timestamp and end timestamp (if declared for the event type) values of the first parameter is null, the result value is null.

12.4.1. Examples

The examples in this section simply use A and B as event type names. The alias a is used to represent A-type events and respectively the alias b represents B-type events.

The create-schema for types A and B is shown next. The two types are declared the same. The example declares the property providing start timestamp values as startts and the property providing end timestamp values as endts:

create schema A as (startts long, endts long) starttimestamp 'startts' endtimestamp 'endts'

create schema B as (startts long, endts long) starttimestamp 'startts' endtimestamp 'endts'

The sample EPL below joins the last A and the last B event. It detects A-B event combinations for which, when comparing timestamps, the last A event that occurs before the last B event. The example employs the before method:

select * from A#lastevent as a, B#lastevent as b where a.before(b)

For simplicity, the examples in this section refer to A and the alias a as the input event. The examples refer to B and the alias b as the parameter event.

12.4.2. Interval Algebra Parameters

The first parameter of each interval algebra methods is the event or timestamp to compare to.

All remaining parameters to interval algebra methods are intervals and can be any of the following:

A constant, an event property or more generally any expression returning a numeric value that is the number of seconds. For example, in the expression a.before(b, 2) the parameter 2 is interpreted to mean 2 seconds. The expression a.before(b, myIntervalProperty) is interpreted to mean myIntervalProperty seconds.
A time period expression as described in Section 12.4.11, “Includes”. For example: a.before(b, 1 hour 2 minutes).

When an interval parameter is provided and is null, the method result value is null.

12.4.3. Performance

The compiler analyzes interval algebra methods as well as the between date-time method in the where-clause and builds a query plan for execution of joins and subqueries. The query plan can include hash and btree index lookups using the start and end timestamps as computed by expressions or provided by events as applicable. Consider turning on query plan logging to obtain information on the query plan used.

The query planning is generally most effective when no additional thresholds or ranges are provided to interval algebra methods, as the query planner may not consider an interval algebra method that it cannot plan.

The query planner may also not optimally plan the query execution if events or expressions return different types of date representation. Query planning works best if all date representations use the same long, Date or Calendar types.

12.4.4. Limitations

Date-time method that change date or time fields, such as withTime, withDate, set or round methods set the end timestamp to the start timestamp.

For example, in the following expression the parameter to the after method has a zero duration, and not the end timestamp that the event B endts property provides.

a.after(b.withTime(9, 0, 0, 0))

12.4.5. After

The after date-time method returns true if an event happens after another event, or a timestamp is after another timestamp.

The method compares the input value's start timestamp (a.startTimestamp) to the first parameter's end timestamp (b.endTimestamp) to determine whether A happens after B.

If used with one parameter, for example in a.after(b), the method returns true if A starts after B ends.

If providing two parameters, for example in a.after(b, 5 sec), the method returns true if A starts at least 5 seconds after B ends.

select * from A#lastevent as a, B#lastevent as b where a.after(b)
// Above matches when:
//   a.startTimestamp - b.endTimestamp > 0

If providing three parameters, for example in a.after(b, 5 sec, 10 sec), the method returns true if A starts at least 5 seconds but no more then 10 seconds after B ends.

select * from A#lastevent as a, B#lastevent as b where a.after(b, 5 sec)
// Above matches when:
//   a.startTimestamp - b.endTimestamp >= 5 seconds

Negative values for the range are allowed. For example in a.after(b, -5 sec, -10 sec), the method returns true if A starts at least 5 seconds but no more then 10 seconds before B ends.

select * from A#lastevent as a, B#lastevent as b where a.after(b, 5 sec, 10 sec)
// Above matches when:
//   5 seconds <= a.startTimestamp - b.endTimestamp <= 10 seconds

If the range low endpoint is greater than the range high endpoint, the compiler automatically reverses them. Thus a.after(b, 10 sec, 5 sec) is the same semantics as a.after(b, 5 sec, 10 sec).

12.4.6. Before

The before date-time method returns true if an event happens before another event, or a timestamp is before another timestamp.

The method compares the input value's end timestamp (a.endTimestamp) and the first parameter's start timestamp (b.startTimestamp) to determine whether A happens before B.

If used with one parameter, for example in a.before(b), the method returns true if A ends before B starts.

If providing two parameters, for example in a.before(b, 5 sec), the method returns true if A ends at least 5 seconds before B starts.

select * from A#lastevent as a, B#lastevent as b where a.before(b)
// Above matches when:
//   b.startTimestamp - a.endTimestamp > 0

If providing three parameters, for example in a.before(b, 5 sec, 10 sec), the method returns true if A ends at least 5 seconds but no more then 10 seconds before B starts.

select * from A#lastevent as a, B#lastevent as b where a.before(b, 5 sec)
// Above matches when:
//   b.startTimestamp - a.endTimestamp >= 5 seconds

Negative values for the range are allowed. For example in a.before(b, -5 sec, -10 sec), the method returns true if A starts at least 5 seconds but no more then 10 seconds after B starts.

select * from A#lastevent as a, B#lastevent as b where a.before(b, 5 sec, 10 sec)
// Above matches when:
//   5 seconds <= b.startTimestamp - a.endTimestamp <= 10 seconds

If the range low endpoint is greater than the range high endpoint, the compiler automatically reverses them. Thus a.before(b, 10 sec, 5 sec) is the same semantics as a.before(b, 5 sec, 10 sec).

12.4.7. Coincides

The coincides date-time method returns true if an event and another event happen at the same time, or two timestamps are the same value.

The method compares the input value's start and end timestamp with the first parameter's start and end timestamp and determines if they equal.

If used with one parameter, for example in a.coincides(b), the method returns true if the start timestamp of A and B are the same and the end timestamps of A and B are also the same.

If providing two parameters, for example in a.coincides(b, 5 sec), the method returns true if the difference between the start timestamps of A and B is equal to or less then 5 seconds and the difference between the end timestamps of A and B is also equal to or less then 5 seconds.

select * from A#lastevent as a, B#lastevent as b where a.coincides(b)
// Above matches when:
//   a.startTimestamp = b.startTimestamp and a.endTimestamp = b.endTimestamp

If providing three parameters, for example in a.coincides(b, 5 sec, 10 sec), the method returns true if the difference between the start timestamps of A and B is equal to or less then 5 seconds and the difference between the end timestamps of A and B is equal to or less then 10 seconds.

select * from A#lastevent as a, B#lastevent as b where a.coincides(b, 5 sec)
// Above matches when:
//   abs(a.startTimestamp - b.startTimestamp) <= 5 sec and 
//   abs(a.endTimestamp - b.endTimestamp) <= 5 sec

A negative value for interval parameters is not allowed. If your interval parameter is itself an expression that returns a negative value the runtime logs a warning message and returns null.

select * from A#lastevent as a, B#lastevent as b where a.coincides(b, 5 sec, 10 sec)
// Above matches when:
//   abs(a.startTimestamp - b.startTimestamp) <= 5 seconds and 
//   abs(a.endTimestamp - b.endTimestamp) <= 10 seconds

12.4.8. During

The during date-time method returns true if an event happens during the occurrence of another event, or when a timestamps falls within the occurrence of an event..

The method determines whether the input value's start and end timestamp are during the first parameter's start and end timestamp. The symmetrical opposite is Section 12.4.11, “Includes”.

If used with one parameter, for example in a.during(b), the method returns true if the start timestamp of A is after the start timestamp of B and the end timestamp of A is before the end timestamp of B.

Sample EPL:

select * from A#lastevent as a, B#lastevent as b where a.during(b)
// Above matches when:
//   b.startTimestamp < a.startTimestamp <= a.endTimestamp < b.endTimestamp

If providing two parameters, for example in a.during(b, 5 sec), the method returns true if the difference between the start timestamps of A and B is equal to or less then 5 seconds and the difference between the end timestamps of A and B is also equal to or less then 5 seconds.

Sample EPL:

select * from A#lastevent as a, B#lastevent as b where a.during(b, 5 sec)
// Above matches when:
//   0 < a.startTimestamp - b.startTimestamp <= 5 sec and 
//   0 < a.endTimestamp - b.endTimestamp <= 5 sec

If providing three parameters, for example in a.during(b, 5 sec, 10 sec), the method returns true if the difference between the start timestamps of A and B and the difference between the end timestamps of A and B is between 5 and 10 seconds.

Sample EPL:

select * from A#lastevent as a, B#lastevent as b where a.during(b, 5 sec, 10 sec)
// Above matches when:
//   5 seconds <= a.startTimestamp - b.startTimestamp <= 10 seconds and 
//   5 seconds <= a.endTimestamp - b.endTimestamp <= 10 seconds

If providing five parameters, for example in a.during(b, 5 sec, 10 sec, 20 sec, 30 sec), the method returns true if the difference between the start timestamps of A and B is between 5 seconds and 10 seconds and the difference between the end timestamps of A and B is between 20 seconds and 30 seconds.

Sample EPL:

select * from A#lastevent as a, B#lastevent as b
  where a.during(b, 5 sec, 10 sec, 20 sec, 30 sec)
// Above matches when:
//   5 seconds <= a.startTimestamp - b.startTimestamp <= 10 seconds and 
//   20 seconds < a.endTimestamp - b.endTimestamp <= 30 seconds

12.4.9. Finishes

The finishes date-time method returns true if an event starts after another event starts and the event ends at the same time as the other event.

The method determines whether the input value's start timestamp is after the first parameter's start timestamp and the end timestamp of the input value and the first parameter are the same. The symmetrical opposite is Section 12.4.10, “Finished By”.

If used with one parameter, for example in a.finishes(b), the method returns true if the start timestamp of A is after the start timestamp of B and the end timestamp of A and B are the same.

Sample EPL:

select * from A#lastevent as a, B#lastevent as b where a.finishes(b)
// Above matches when:
//   b.startTimestamp < a.startTimestamp and a.endTimestamp = b.endTimestamp

If providing two parameters, for example in a.finishes(b, 5 sec), the method returns true if the start timestamp of A is after the start timestamp of B and the difference between the end timestamps of A and B is equal to or less then 5 seconds.

Sample EPL:

select * from A#lastevent as a, B#lastevent as b where a.finishes(b, 5 sec)
// Above matches when:
//   b.startTimestamp < a.startTimestamp and 
//   abs(a.endTimestamp - b.endTimestamp ) <= 5 seconds

A negative value for interval parameters is not allowed. If your interval parameter is itself an expression that returns a negative value the runtime logs a warning message and returns null.

12.4.10. Finished By

The finishedBy date-time method returns true if an event starts before another event starts and the event ends at the same time as the other event.

The method determines whether the input value's start timestamp happens before the first parameter's start timestamp and the end timestamp of the input value and the first parameter are the same. The symmetrical opposite is Section 12.4.9, “Finishes”.

If used with one parameter, for example in a.finishedBy(b), the method returns true if the start timestamp of A is before the start timestamp of B and the end timestamp of A and B are the same.

Sample EPL:

select * from A#lastevent as a, B#lastevent as b where a.finishedBy(b)
// Above matches when:
//   a.startTimestamp < b.startTimestamp and a.endTimestamp = b.endTimestamp

If providing two parameters, for example in a.finishedBy(b, 5 sec), the method returns true if the start timestamp of A is before the start timestamp of B and the difference between the end timestamps of A and B is equal to or less then 5 seconds.

Sample EPL:

select * from A#lastevent as a, B#lastevent as b where a.finishedBy(b, 5 sec)
// Above matches when:
//   a.startTimestamp < b.startTimestamp and 
//   abs(a.endTimestamp - b.endTimestamp ) <= 5 seconds

12.4.11. Includes

The includes date-time method returns true if the parameter event happens during the occurrence of the input event, or when a timestamps falls within the occurrence of an event.

The method determines whether the first parameter's start and end timestamp are during the input value's start and end timestamp. The symmetrical opposite is Section 12.4.8, “During”.

If used with one parameter, for example in a.includes(b), the method returns true if the start timestamp of B is after the start timestamp of A and the end timestamp of B is before the end timestamp of A.

Sample EPL:

select * from A#lastevent as a, B#lastevent as b where a.includes(b)
// Above matches when:
//   a.startTimestamp < b.startTimestamp <= b.endTimestamp < a.endTimestamp

If providing two parameters, for example in a.includes(b, 5 sec), the method returns true if the difference between the start timestamps of A and B is equal to or less then 5 seconds and the difference between the end timestamps of A and B is also equal to or less then 5 seconds.

Sample EPL:

select * from A#lastevent as a, B#lastevent as b where a.includes(b, 5 sec)
// Above matches when:
//   0 < b.startTimestamp - a.startTimestamp <= 5 sec and 
//   0 < a.endTimestamp - b.endTimestamp <= 5 sec

If providing three parameters, for example in a.includes(b, 5 sec, 10 sec), the method returns true if the difference between the start timestamps of A and B and the difference between the end timestamps of A and B is between 5 and 10 seconds.

Sample EPL:

select * from A#lastevent as a, B#lastevent as b where a.includes(b, 5 sec, 10 sec)
// Above matches when:
//   5 seconds <= a.startTimestamp - b.startTimestamp <= 10 seconds and 
//   5 seconds <= a.endTimestamp - b.endTimestamp <= 10 seconds

If providing five parameters, for example in a.includes(b, 5 sec, 10 sec, 20 sec, 30 sec), the method returns true if the difference between the start timestamps of A and B is between 5 seconds and 10 seconds and the difference between the end timestamps of A and B is between 20 seconds and 30 seconds.

Sample EPL:

select * from A#lastevent as a, B#lastevent as b
  where a.includes(b, 5 sec, 10 sec, 20 sec, 30 sec)
// Above matches when:
//   5 seconds <= a.startTimestamp - b.startTimestamp <= 10 seconds and 
//   20 seconds <= a.endTimestamp - b.endTimestamp <= 30 seconds

12.4.12. Meets

The meets date-time method returns true if the event's end time is the same as another event's start time.

The method compares the input value's end timestamp and the first parameter's start timestamp and determines whether they equal.

If used with one parameter, for example in a.meets(b), the method returns true if the end timestamp of A is the same as the start timestamp of B.

If providing two parameters, for example in a.meets(b, 5 sec), the method returns true if the difference between the end timestamp of A and the start timestamp of B is equal to or less then 5 seconds.

select * from A#lastevent as a, B#lastevent as b where a.meets(b)
// Above matches when:
//   a.endTimestamp = b.startTimestamp

A negative value for the interval parameter is not allowed. If your interval parameter is itself an expression that returns a negative value the runtime logs a warning message and returns null.

select * from A#lastevent as a, B#lastevent as b where a.meets(b, 5 sec)
// Above matches when:
//   abs(b.startTimestamp - a.endTimestamp) <= 5 seconds

12.4.13. Met By

The metBy date-time method returns true if the event's start time is the same as another event's end time.

The method compares the input value's start timestamp and the first parameter's end timestamp and determines whether they equal.

If used with one parameter, for example in a.metBy(b), the method returns true if the start timestamp of A is the same as the end timestamp of B.

If providing two parameters, for example in a.metBy(b, 5 sec), the method returns true if the difference between the end timestamps of B and the start timestamp of A is equal to or less then 5 seconds.

select * from A#lastevent as a, B#lastevent as b where a.metBy(b)
// Above matches when:
//   a.startTimestamp = b.endTimestamp

A negative value for the interval parameter is not allowed. If your interval parameter is itself an expression that returns a negative value the runtime logs a warning message and returns null.

select * from A#lastevent as a, B#lastevent as b where a.metBy(b, 5 sec)
// Above matches when:
//   abs(a.startTimestamp - b.endTimestamp) <= 5 seconds

12.4.14. Overlaps

The overlaps date-time method returns true if the event starts before another event starts and finishes after the other event starts, but before the other event finishes (events have an overlapping period of time).

The method determines whether the input value's start and end timestamp indicate an overlap with the first parameter's start and end timestamp, such that A starts before B starts and A ends after B started but before B ends.

If used with one parameter, for example in a.overlaps(b), the method returns true if the start timestamp of A is before the start timestamp of B and the end timestamp of A and is before the end timestamp of B.

If providing two parameters, for example in a.overlaps(b, 5 sec), the method returns true if, in addition, the difference between the end timestamp of A and the start timestamp of B is equal to or less then 5 seconds.

select * from A#lastevent as a, B#lastevent as b where a.overlaps(b)
// Above matches when:
//   a.startTimestamp < b.startTimestamp < a.endTimestamp < b.endTimestamp

If providing three parameters, for example in a.overlaps(b, 5 sec, 10 sec), the method returns true if, in addition, the difference between the end timestamp of A and the start timestamp of B is between 5 and 10 seconds.

select * from A#lastevent as a, B#lastevent as b where a.overlaps(b, 5 sec)
// Above matches when:
//   a.startTimestamp < b.startTimestamp < a.endTimestamp < b.endTimestamp and 
//   0 <= a.endTimestamp - b.startTimestamp <= 5 seconds

The overlappedBy date-time method returns true if the parameter event starts before the input event starts and the parameter event finishes after the input event starts, but before the input event finishes (events have an overlapping period of time).

select * from A#lastevent as a, B#lastevent as b where a.overlaps(b, 5 sec, 10 sec)
// Above matches when:
//   a.startTimestamp < b.startTimestamp < a.endTimestamp < b.endTimestamp and 
//   5 seconds <= a.endTimestamp - b.startTimestamp <= 10 seconds

12.4.15. Overlapped By

The method determines whether the input value's start and end timestamp indicate an overlap with the first parameter's start and end timestamp, such that B starts before A starts and B ends after A started but before A ends.

If used with one parameter, for example in a.overlappedBy(b), the method returns true if the start timestamp of B is before the start timestamp of A and the end timestamp of B and is before the end timestamp of A.

If providing two parameters, for example in a.overlappedBy(b, 5 sec), the method returns true if, in addition, the difference between the end timestamp of B and the start timestamp of A is equal to or less then 5 seconds.

select * from A#lastevent as a, B#lastevent as b where a.overlappedBy(b)
// Above matches when:
//   b.startTimestamp < a.startTimestamp < b.endTimestamp < a.endTimestamp

If providing three parameters, for example in a.overlappedBy(b, 5 sec, 10 sec), the method returns true if, in addition, the difference between the end timestamp of B and the start timestamp of A is between 5 and 10 seconds.

select * from A#lastevent as a, B#lastevent as b where a.overlappedBy(b, 5 sec)
// Above matches when:
//   b.startTimestamp < a.startTimestamp < b.endTimestamp < a.endTimestamp and 
//   0 <= b.endTimestamp - a.startTimestamp <= 5 seconds

The starts date-time method returns true if an event and another event start at the same time and the event's end happens before the other event's end.

select * from A#lastevent as a, B#lastevent as b where a.overlappedBy(b, 5 sec, 10 sec)
// Above matches when:
//   b.startTimestamp < a.startTimestamp < b.endTimestamp < a.endTimestamp and 
//   5 seconds <= b.endTimestamp - a.startTimestamp <= 10 seconds

12.4.16. Starts

The method determines whether the start timestamps of the input value and the first parameter are the same and the end timestamp of the input value is before the end timestamp of the first parameter.

If used with one parameter, for example in a.starts(b), the method returns true if the start timestamp of A and B are the same and the end timestamp of A is before the end timestamp of B.

If providing two parameters, for example in a.starts(b, 5 sec), the method returns true if the difference between the start timestamps of A and B is between is equal to or less then 5 seconds.

select * from A#lastevent as a, B#lastevent as b where a.starts(b)
// Above matches when:
//   a.startTimestamp = b.startTimestamp and a.endTimestamp < b.endTimestamp

A negative value for the interval parameter is not allowed. If your interval parameter is itself an expression that returns a negative value the runtime logs a warning message and returns null.

select * from A#lastevent as a, B#lastevent as b where a.starts(b, 5 sec)
// Above matches when:
//   abs(a.startTimestamp - b.startTimestamp) <= 5 seconds and 
//   a.endTimestamp < b.endTimestamp

12.4.17. Started By

The startedBy date-time method returns true if an event and another event start at the same time and the other event's end happens before the input event's end.

The method determines whether the start timestamp of the input value and the first parameter are the same and the end timestamp of the first parameter is before the end timestamp of the input value.

If used with one parameter, for example in a.startedBy(b), the method returns true if the start timestamp of A and B are the same and the end timestamp of B is before the end timestamp of A.

If providing two parameters, for example in a.startedBy(b, 5 sec), the method returns true if the difference between the start timestamps of A and B is between is equal to or less then 5 seconds.

select * from A#lastevent as a, B#lastevent as b where a.startedBy(b)
// Above matches when:
//   a.startTimestamp = b.startTimestamp and b.endTimestamp < a.endTimestamp

A negative value for the interval parameter is not allowed. If your interval parameter is itself an expression that returns a negative value the runtime logs a warning message and returns null.

select * from A#lastevent as a, B#lastevent as b where a.startedBy(b, 5 sec)
// Above matches when:
//   abs(a.startTimestamp - b.startTimestamp) <= 5 seconds and 
//    b.endTimestamp < a.endTimestamp

Chapter 13. EPL Reference: Aggregation Methods

13.1. Overview

13.2. How to Use

13.2.1. Syntax
13.2.2. Use with Aggregation Function
13.2.3. Use with Table Column
13.2.4. Using Dot to Obtain Property Values
13.2.5. Using Dot to Enumerate Events

13.3. Aggregation Methods for Sorted Aggregations

13.3.1. Overview
13.3.2. Specifying Composite Keys
13.3.3. CeilingEvent, FloorEvent, HigherEvent, LowerEvent, GetEvent
13.3.4. CeilingEvents, FloorEvents, HigherEvents, LowerEvents, GetEvents
13.3.5. CeilingKey, FloorKey, HigherKey, LowerKey
13.3.6. FirstEvent, LastEvent, MinBy, MaxBy
13.3.7. FirstEvents, LastEvents
13.3.8. FirstKey, LastKey
13.3.9. ContainsKey
13.3.10. CountEvents
13.3.11. CountKeys
13.3.12. EventsBetween
13.3.13. Submap
13.3.14. NavigableMapReference

13.4. Aggregation Methods for Window Aggregations

13.4.1. Overview
13.4.2. CountEvents
13.4.3. ListReference
13.4.4. First, Last

13.5. Aggregation Methods for CountMinSketch Aggregations

13.6. Aggregation Methods for Custom Plug-In Multi-Function Aggregations

13.1. Overview

Aggregation methods are methods that work with aggregations to perform lookups into aggregation state.

Aggregation methods are stateless and the use of aggregation methods alone does not cause the runtime to retain any events or other state.

There are four types of aggregations that offer aggregation methods:

The window aggregation, see Section 10.2.2.8, “Window Aggregation Function”.
The sorted aggregation, see Section 10.2.2.7, “Sorted Aggregation Function”.
The countminsketch aggregation, see Section 10.2.3, “Approximation Aggregation Functions”.
Plug-in multi-function aggregations, see Section 22.5.2, “Aggregation Multi-Function Development”.

Aggregation methods are handy when an aggregation organizes events or other data for further lookup. For example, the sorted aggregation organizes events according to sort criteria and offers operations for floor, ceiling, higher and lower keys, and many other operations.

Each aggregation makes different aggregation methods available. For example, sorted provides the higherKey aggregation method. The higherKey method is only available for use with the sorted aggregation and not with the window aggregation.

You may use aggregation methods together with aggregation functions without tables, and you may use aggregation methods with table columns that hold aggregations. Further examples are provided below.

You may also use aggregation methods with enumeration methods, date-time methods and the dot-syntax to access event properties.

13.2. How to Use

13.2.1. Syntax

The syntax for aggregation methods is the same syntax as for any chained invocation:

input_aggregation.aggregation_method_name( [method_parameter [, method_parameter [,...]]])
	  .[ [other_method(...) [...]] | property_name]

Following the input_aggregation input aggregation (options outlined below), is the . (dot) operator and the aggregation_method_name aggregation method name. It follows, in parenthesis, a comma-separated list of method parameter expressions. Additional enumeration or date-time methods can be chained thereafter. An event property name can follow for those aggregation or enumeration methods returning an event-typed (non-scalar) element.

The examples in this section focus on the sorted aggregation.

13.2.2. Use with Aggregation Function

You may use aggregation methods with the respective aggregation function.

The following statement outputs the greatest price strictly less than the current order event price, or null if there is no such value, considering the last 10 minutes of order events.

select sorted(price).lowerKey(price) as lowerPrice from OrderEvent#time(10 minutes)

13.2.3. Use with Table Column

You may use aggregation methods with table columns.

The sample statement below creates a simple un-keyed table that keeps order events sorted by price:

create table OrderPrices(prices sorted(price) @type('OrderEvent'))

The next statement aggregates order events into the table column prices considering the last 10 minutes of order events (the example specifies priority 1 so the runtime always updates the table before other selects):

@Priority(1) into table OrderPrices select sorted(*) as prices from OrderEvent#time(10 minutes)

Now the prices column of table OrderPrices is ready. The runtime keeps it continually updated from the last 10 minutes of order events.

You may access the table column by providing a table name, column name and the aggregation method. For more information please see Section 6.3.3, “Table Column Keyed-Access Expressions”.

The following statement access the table column to output the first (least) and last (greatest) current price for each order event:

select OrderPrices.prices.firstKey() as leastPrice,
    OrderPrices.prices.lastKey() as greatestPrice 
  from OrderEvent

You may access the table column by putting the table into the from-clause such as in a subquery or join.

The following statement uses a subquery and outputs the least price as well as the order event itself:

select (select prices.firstKey() from OrderPrices) as leastPrice, * from OrderEvent

13.2.4. Using Dot to Obtain Property Values

Use the dot (.) and the event property name to select an event property of the event, for aggregation methods that return a single event.

The example statement below selects the order id of the least price order:

select OrderPrices.prices.firstEvent().orderId as leastPriceOrderId from OrderEvent

13.2.5. Using Dot to Enumerate Events

Use the dot (.) and an enumeration method to enumerate events returned by aggregation methods that return one or more events.

The example statement below selects the first 3 events among the order events with prices between one and ten:

select OrderPrices.prices.eventsbetween(0, true, 10, true).take(3) as first3Events from OrderEvent

13.3. Aggregation Methods for `Sorted` Aggregations

13.3.1. Overview

This table lists methods for key-up and key-down:

Table 13.1. Sorted Aggregation Methods For Key Up/Down

Method	Result
ceilingEvent(key)	Returns the first event associated with the least key greater than or equal to the given key, or null if there is no such key. Section 13.3.3, “CeilingEvent, FloorEvent, HigherEvent, LowerEvent, GetEvent”.
ceilingEvents(key)	Returns all events associated with the least key greater than or equal to the given key, or null if there is no such key. Section 13.3.4, “CeilingEvents, FloorEvents, HigherEvents, LowerEvents, GetEvents”.
ceilingKey(key)	Returns the least key greater than or equal to the given key, or null if there is no such key. Section 13.3.5, “CeilingKey, FloorKey, HigherKey, LowerKey”.
floorEvent(key)	Returns the first event associated with the greatest key less than or equal to the given key, or null if there is no such key. Section 13.3.3, “CeilingEvent, FloorEvent, HigherEvent, LowerEvent, GetEvent”.
floorEvents(key)	Returns all events associated with the greatest key less than or equal to the given key, or null if there is no such key. Section 13.3.4, “CeilingEvents, FloorEvents, HigherEvents, LowerEvents, GetEvents”.
floorKey(key)	Returns the greatest key less than or equal to the given key, or null if there is no such key. Section 13.3.5, “CeilingKey, FloorKey, HigherKey, LowerKey”.
higherEvent(key)	Returns the first event associated with the least key strictly greater than the given key, or null if there is no such key. Section 13.3.3, “CeilingEvent, FloorEvent, HigherEvent, LowerEvent, GetEvent”.
higherEvents(key)	Returns all events associated with the least key strictly greater than the given key, or null if there is no such key. Section 13.3.4, “CeilingEvents, FloorEvents, HigherEvents, LowerEvents, GetEvents”.
higherKey(key)	Returns the least key strictly greater than the given key, or null if there is no such key. Section 13.3.5, “CeilingKey, FloorKey, HigherKey, LowerKey”.
lowerEvent(key)	Returns the first event associated with the greatest key strictly less than the given key, or null if there is no such key. Section 13.3.3, “CeilingEvent, FloorEvent, HigherEvent, LowerEvent, GetEvent”.
lowerEvents(key)	Returns all events associated with the greatest key strictly less than the given key, or null if there is no such key. Section 13.3.4, “CeilingEvents, FloorEvents, HigherEvents, LowerEvents, GetEvents”.
lowerKey(key)	Returns the greatest key strictly less than the given key, or null if there is no such key. Section 13.3.5, “CeilingKey, FloorKey, HigherKey, LowerKey”.

This table lists methods for least and greatest:

Table 13.2. Sorted Aggregation Methods For Key First/Last

Method	Result
firstEvent() or minBy()	Returns the first event associated with the least key, or null if empty. Section 13.3.6, “FirstEvent, LastEvent, MinBy, MaxBy”.
firstEvents()	Returns all events associated with the least key, or null if empty. Section 13.3.7, “FirstEvents, LastEvents”.
firstKey()	Returns the first (least) key. Section 13.3.8, “FirstKey, LastKey”.
lastEvent() or maxBy()	Returns the first event associated with the greatest key, or null if empty. Section 13.3.6, “FirstEvent, LastEvent, MinBy, MaxBy”.
lastEvents()	Returns all events associated with the greatest key, or null if empty. Section 13.3.7, “FirstEvents, LastEvents”.
lastKey()	Returns the last (greatest) key. Section 13.3.8, “FirstKey, LastKey”.

This table lists methods for getting for specific keys, checking contains and getting counts:

Table 13.3. Sorted Aggregation Methods For Get, Contains, Count

Method	Result
containsKey(key)	Returns true if the aggregation contains the specified key. Section 13.3.9, “ContainsKey”.
countEvents()	Returns the number of events in the aggregation. Section 13.3.10, “CountEvents”.
countKeys()	Returns the number of keys in the aggregation. Section 13.3.11, “CountKeys”.
getEvent(key)	Returns the first event for the specified key, or null if there is no such key. Section 13.3.3, “CeilingEvent, FloorEvent, HigherEvent, LowerEvent, GetEvent”.
getEvents(key)	Returns all events for the specified key, or null if there is no such key. Section 13.3.4, “CeilingEvents, FloorEvents, HigherEvents, LowerEvents, GetEvents”.

This table lists methods for sub-map, between-values and reference:

Table 13.4. Sorted Aggregation Methods For Submap, Between and Reference

Method Result

eventsBetween(fromKey, fromInclusive, toKey, toInclusive)

Returns all events of a portion of this aggregation whose keys range from fromKey to toKey.

Section 13.3.12, “EventsBetween”.

submap(fromKey, fromInclusive, toKey, toInclusive)

Returns a submap of type NavigableMap<Object, Object[]> that is a navigable map containing keys and events of the portion of the aggregation whose keys range from fromKey to toKey.

Section 13.3.13, “Submap”.

navigableMapReference()

NOTE: This method returns an object that the runtime may concurrently modify and that is only valid for the period of evaluation of the current event or time.

Returns a NavigableMap<Object, Collection<EventBean>> that is an unmodifiable view of keys and EventBean instances.

Section 13.3.14, “NavigableMapReference”.

13.3.2. Specifying Composite Keys

Use com.espertech.esper.common.client.util.HashableMultiKey when the sorted aggregation has multiple keys. E.g. for sorted(string, double) use new HashableMultiKey(orderId, price).

13.3.3. `CeilingEvent`, `FloorEvent`, `HigherEvent`, `LowerEvent`, `GetEvent`

The methods accept a single key parameter and return the event or null if an event was not found. If the result is multiple events, the method returns the oldest ceiling/floor/lower/higher/keyed event.

Table 13.5. Sorted Aggregation Methods - Keyed Returning Single Event

Method	Result
ceilingEvent(key)	Returns the first event associated with the least key greater than or equal to the given key, or null if there is no such key.
floorEvent(key)	Returns the first event associated with the greatest key less than or equal to the given key, or null if there is no such key.
higherEvent(key)	Returns the first event associated with the least key strictly greater than the given key, or null if there is no such key.
lowerEvent(key)	Returns the first event associated with the greatest key strictly less than the given key, or null if there is no such key.
getEvent(key)	Returns the first event for the specified key, or null if there is no such key.

Assuming the OrderPrices table exists as defined above, this example statement, when an order event arrives, returns the order event associated with the greatest price strictly less than the given price, or null if there is no such price:

select OrderPrices.prices.lowerEvent(price) as lowerPriceEvent from OrderEvent

13.3.4. `CeilingEvents`, `FloorEvents`, `HigherEvents`, `LowerEvents`, `GetEvents`

The methods accepts a single key parameter and return all events or null if no events are found.

Table 13.6. Sorted Aggregation Methods - Keyed Returning Multiple Events

Method	Result
ceilingEvents(key)	Returns all events associated with the least key greater than or equal to the given key, or null if there is no such key.
floorEvents(key)	Returns all events associated with the greatest key less than or equal to the given key, or null if there is no such key.
higherEvents(key)	Returns all events associated with the least key strictly greater than the given key, or null if there is no such key.
lowerEvents(key)	Returns all events associated with the greatest key strictly less than the given key, or null if there is no such key.
getEvents(key)	Returns all events for the specified key, or null if there is no such key.

Assuming the OrderPrices table exists as defined above, this example statement, when an order event arrives, returns all order events associated with the greatest price strictly less than the given price, or null if there is no such price:

select OrderPrices.prices.lowerEvents(price) as lowerPriceEvents from OrderEvent

13.3.5. `CeilingKey`, `FloorKey`, `HigherKey`, `LowerKey`

The methods accepts a single key parameter and return the key or null if no key is found.

Table 13.7. Sorted Aggregation Methods - Keyed Returning Key

Method	Result
ceilingKey(key)	Returns the least key greater than or equal to the given key, or null if there is no such key.
floorKey(key)	Returns the greatest key less than or equal to the given key, or null if there is no such key.
higherKey(key)	Returns the least key strictly greater than the given key, or null if there is no such key.
lowerKey(key)	Returns the greatest key strictly less than the given key, or null if there is no such key.

Assuming the OrderPrices table exists as defined above, this example statement, when an order event arrives, returns the greatest price strictly less than the given price, or null if there is no such price:

select OrderPrices.prices.lowerKey(price) as lowerPrice from OrderEvent

13.3.6. `FirstEvent`, `LastEvent`, `MinBy`, `MaxBy`

The methods have no parameter and return the first (least) or last (greatest) event or null if an event was not found. If the result is multiple events, the method returns the oldest first/last (least/greatest) event.

Table 13.8. Sorted Aggregation Methods - Unparameterized Returning Single Event

Method	Result
firstEvent() or minBy()	Returns the first event associated with the least key, or null if empty.
lastEvent() or maxBy()	Returns the first event associated with the greatest key, or null if empty.

Assuming the OrderPrices table exists as defined above, this example statement, when an order event arrives, returns the order event associated with the least price , or null if there is no such price:

select OrderPrices.prices.firstEvent() as leastPriceEvent from OrderEvent

13.3.7. `FirstEvents`, `LastEvents`

The methods have no parameter and return the first (least) or last (greatest) events or null if no events are found.

Table 13.9. Sorted Aggregation Methods - Unparameterized Returning Multiple Events

Method	Result
firstEvents()	Returns all events associated with the least key, or null if empty.
lastEvents()	Returns all events associated with the greatest key, or null if empty.

Assuming the OrderPrices table exists as defined above, this example statement, when an order event arrives, returns all order events associated with the least price or null if there is no such price:

select OrderPrices.prices.firstEvents(price) as lowerPriceEvents from OrderEvent

13.3.8. `FirstKey`, `LastKey`

The methods have no parameter and return the the first (least) or last (greatest) key or null if no key is found.

Table 13.10. Sorted Aggregation Methods - Unparameterized Returning Key

Method	Result
firstKey()	Returns the first (least) key.
lastKey()	Returns the last (greatest) key.

Assuming the OrderPrices table exists as defined above, this example statement, when an order event arrives, returns the least price or null if there is no such price:

select OrderPrices.prices.firstKey() as leastPrice from OrderEvent

13.3.9. `ContainsKey`

This method accepts a single key parameter and returns a boolean-type value indicating whether the key is found, or null if using tables and the table row is not found.

Assuming the OrderPrices table exists as defined above, this example statement, when an order event arrives, returns true if a price of zero exists:

select OrderPrices.prices.containsKey(0) as priceZeroFound from OrderEvent

13.3.10. `CountEvents`

This method has no parameters and returns an int-type count of the number of events, or null if using tables and the table row is not found.

Assuming the OrderPrices table exists as defined above, this example statement, when an order event arrives, returns the number of events:

select OrderPrices.prices.countEvents() as numEvents from OrderEvent

13.3.11. `CountKeys`

This method has no parameters and returns an int-type count of the number of keys, or null if using tables and the table row is not found.

Assuming the OrderPrices table exists as defined above, this example statement, when an order event arrives, returns the number of prices:

select OrderPrices.prices.countKeys() as numPrices from OrderEvent

13.3.12. `EventsBetween`

This method accepts four parameters, namely the from-key, the boolean-typed from-inclusive indicator, the to-key and the boolean-typed to-inclusive indicator. The method returns all events whose keys range from from-key to to-key. If from-key and to-key are equal, the returned value is empty unless from-inclusive and to-inclusive are both true. The from-key must be less or equal to to-key.

Assuming the OrderPrices table exists as defined above, this example statement, when an order event arrives, returns all events with prices between one (inclusive) and ten (inclusive):

select OrderPrices.prices.eventsBetween(1, true, 10, true) as eventsPricedOneToTen from OrderEvent

13.3.13. `Submap`

This method accepts four parameters, namely the from-key, the boolean-typed from-inclusive indicator, the to-key and the boolean-typed to-inclusive indicator. The method returns an object of type NavigableMap<Object, Object[]> that is a shallow copy of all keys of a portion of this aggregation whose keys range from fromKey to toKey. If from-key and to-key are equal, the returned value is empty unless from-inclusive and to-inclusive are both true. The from-key must be less or equal to to-key.

Assuming the OrderPrices table exists as defined above, this example statement, when an order event arrives, returns all keys and events as a navigable map with prices between one (inclusive) and ten (inclusive):

select OrderPrices.prices.submap(1, true, 10, true) as keysAndEventsPricedOneToTen from OrderEvent

The map keys are the key objects. Each map value is an array containing the event underlying object of the events for the key.

Modifications on the map returned by the method are not reflected back into the aggregation state. The map returned by the method is in effect a shallow copy.

13.3.14. `NavigableMapReference`

This method has no parameters and returns an object of type NavigableMap<Object, Collection<EventBean>> that is an unmodifiable view of keys and unmodifiable collections of EventBean instances.

Warning

This method returns an object that the runtime may concurrently modify and that is only valid for the period of evaluation of the current event or time.

Assuming the OrderPrices table exists as defined above, this example statement, when an order event arrives, returns all keys and events as a navigable map:

select OrderPrices.prices.navigableMapReference() as prices from OrderEvent

The map keys are the key objects. Each map value is an a collection of EventBean events for the key.

Modifications on the map returned by the method are not allowed. The map returned by the method is in effect a wrapper of the actual aggregation state.

13.4. Aggregation Methods for `Window` Aggregations

13.4.1. Overview

The below table summarizes the built-in aggregation methods for use with the window aggregation:

Table 13.11. Window Aggregation Methods

Method	Result
listReference()	NOTE: This method returns an object that the runtime may concurrently modify and that is only valid for the period of evaluation of the current event or time. Returns a `List<EventBean>` that is an unmodifiable view of `EventBean` instances. Section 13.4.3, “ListReference”.
countEvents()	Returns the number of events. Section 13.4.2, “CountEvents”.
first()	Returns the oldest event or null if the aggregation is empty. Section 13.4.4, “First, Last”.
last()	Returns the newest event or null if the aggregation is empty. Section 13.4.4, “First, Last”.

The sample statement below creates a simple un-keyed table that keeps order events:

create table OrdersTable(orders window(*) @type('OrderEvent'))

The next statement aggregates order events into the table column orders considering the last 10 minutes of order events (the example specifies priority 1 so the runtime always updates the table before other selects):

@Priority(1) into table OrdersTable select window(*) as orders from OrderEvent#time(10 minutes)

13.4.2. `CountEvents`

This method has no parameters and returns an int-type count of the number of events, or null if using tables and the table row is not found.

Assuming the OrdersTable table exists as defined above, this example statement, when an order event arrives, returns the number of events:

select OrdersTable.orders.countEvents() as numEvents from OrderEvent

13.4.3. `ListReference`

This method has no parameters and returns an object of type List<EventBean> that is an unmodifiable list of EventBean instances.

Warning

This method returns an object that the runtime may concurrently modify and that is only valid for the period of evaluation of the current event or time.

Assuming the OrdersTable table exists as defined above, this example statement, when an order event arrives, returns all events as a list:

select OrderPrices.orders.listReference() as prices from OrderEvent

Modifications on the list returned by the method are not allowed. The list returned by the method is in effect a wrapper of the actual aggregation state.

13.4.4. `First`, `Last`

The method has no parameter and return the first (oldest) or last (newest) event or null if an event was not found.

Assuming the OrdersTable table exists as defined above, this example statement, when an order event arrives, returns the oldest order event, or null if there is no such event:

select OrdersTable.orders.first() as oldestOrder from OrderEvent

You may optionally specify an event property name as a parameter, in which case the method returns the event property value of the oldest/newest event or null if there is no such event.

13.5. Aggregation Methods for `CountMinSketch` Aggregations

The aggregation methods for count-min sketch are countMinSketchFrequency and countMinSketchTopk and are already described in Section 10.2.3, “Approximation Aggregation Functions”.

13.6. Aggregation Methods for Custom Plug-In Multi-Function Aggregations

The extension code defines the aggregation methods and their parameters. For more information please see Section 22.5.2, “Aggregation Multi-Function Development”.

Chapter 14. EPL Reference: Data Windows

14.1. A Note on Data Window Name and Parameters

14.2. A Note on Batch Windows

14.3. Data Windows

14.3.1. Length Window (length or win:length)
14.3.2. Length Batch Window (length_batch or win:length_batch)
14.3.3. Time Window (time or win:time)
14.3.4. Externally-timed Window (ext_timed or win:ext_timed)
14.3.5. Time batch Window (time_batch or win:time_batch)
14.3.6. Externally-timed Batch Window (ext_timed_batch or win:ext_timed_batch)
14.3.7. Time-Length Combination Batch Window (time_length_batch or win:time_length_batch)
14.3.8. Time-Accumulating Window (time_accum or win:time_accum)
14.3.9. Keep-All Window (keepall or win:keepall)
14.3.10. First Length Window(firstlength or win:firstlength)
14.3.11. First Time Window (firsttime or win:firsttime)
14.3.12. Expiry Expression Window (expr or win:expr)
14.3.13. Expiry Expression Batch Window (expr_batch or win:expr_batch)
14.3.14. Unique Window (unique or std:unique)
14.3.15. Grouped Data Window (groupwin or std:groupwin)
14.3.16. Last Event Window (std:lastevent)
14.3.17. First Event Window (firstevent or std:firstevent)
14.3.18. First Unique Window (firstunique or std:firstunique)
14.3.19. Sorted Window (sort or ext:sort)
14.3.20. Ranked Window (rank or ext:rank)
14.3.21. Time-Order Window (time_order or ext:time_order)
14.3.22. Time-To-Live Window (timetolive or ext:timetolive)

14.4. Special Derived-Value Windows

14.4.1. Size Derived-Value Window (size) or std:size)
14.4.2. Univariate Statistics Derived-Value Window (uni or stat:uni)
14.4.3. Regression Derived-Value Window (linest or stat:linest)
14.4.4. Correlation Derived-Value Window (correl or stat:correl)
14.4.5. Weighted Average Derived-Value Window (weighted_avg or stat:weighted_avg)

This chapter outlines the data windows. The section on Chapter 2, Basic Concepts provides additional information on the relationship of filtering, windows and aggregation. Please also see Section 5.4.3, “Specifying Data Windows” for the use of windows in the from clause with streams, patterns and named windows.

Data windows retain incoming events until an expiry policy indicates to release events. Thus data windows are a means of indicating what subset of events to analyze.

Two or more data windows can be combined. This allows a sets of events retained by one data window to be placed into a union or an intersection with the set of events retained by one or more other data windows. Please see Section 5.4.4, “Multiple Data Windows” for more detail.

The keep-all data window counts as a data window but has no expiry policy: it retains all events received. The grouped-window declaration allocates a new data window per grouping criteria and thereby counts as a data window, but cannot appear alone.

The next table summarizes data windows:

Table 14.1. Built-in Data Windows

Data Window	Syntax	Description
Length Window	`length(`size`)`	Sliding length window extending the specified number of elements into the past.
Length Batch Window	`length_batch(`size`)`	Tumbling window that batches events and releases them when a given minimum number of events has been collected.
Time Window	`time(`time period`)`	Sliding time window extending the specified time interval into the past.
Externally-timed Window	`ext_timed(`timestamp expression, time period`)`	Sliding time window, based on the long-type time value supplied by an expression.
Time Batch Window	`time_batch(`time period[,optional reference point] [, flow control]`)`	Tumbling window that batches events and releases them every specified time interval, with flow control options.
Externally-timed Batch Window	`ext_timed_batch(`timestamp expression, time period[,optional reference point]`)`	Tumbling window that batches events and releases them every specified time interval based on the long-type value supplied by an expression.
Time-Length Combination Batch Window	`time_length_batch(`time period, size [, flow control]`)`	Tumbling multi-policy time and length batch window with flow control options.
Time-Accumulating Window	`time_accum(`time period`)`	Sliding time window accumulates events until no more events arrive within a given time interval.
Keep-All Window	`keepall`	The keep-all data window simply retains all events.
Sorted Window	`sort(`size, sort criteria`)`	Sorts by values returned by sort criteria expressions and keeps only the top events up to the given size.
Ranked Window	`rank(`unique criteria(s), size, sort criteria(s)`)`	Retains only the most recent among events having the same value for the criteria expression(s) sorted by sort criteria expressions and keeps only the top events up to the given size.
Time-Order Window	`time_order(`timestamp expression, time period`)`	Orders events that arrive out-of-order, using an expression providing timestamps to be ordered.
Time-To-Live Window	`timetolive(`timestamp expression`)`	Retains events until the time returned by the timestamp expression.
Unique Window	`unique(`unique criteria(s)`)`	Retains only the most recent among events having the same value for the criteria expression(s). Acts as a length window of size 1 for each distinct expression value.
Grouped Data Window	`groupwin(`grouping criteria(s)`)`	Groups events into sub-data-windows by the value of the specified expression(s), generally used to provide a separate data window per group.
Last Event Window	`lastevent`	Retains the last event, acts as a length window of size 1.
First Event Window	`firstevent`	Retains the very first arriving event, disregarding all subsequent events.
First Unique Window	`firstunique(`unique criteria(s)`)`	Retains only the very first among events having the same value for the criteria expression(s), disregarding all subsequent events for same value(s).
First Length Window	`firstlength(`size`)`	Retains the first size events, disregarding all subsequent events.
First Time Window	`firsttime(`time period`)`	Retains the events arriving until the time interval has passed, disregarding all subsequent events.
Expiry Expression Window	`expr(`expiry expression`)`	Expire events based on the result of an expiry expression passed as a parameter.
Expiry Expression Batch Window	`expr_batch(`expiry expression`)`	Tumbling window that batches events and releases them based on the result of an expiry expression passed as a parameter.

There is a special kind of data window that is used less frequently, and is called a derived-value window. They are windows that derive a new value from event streams and post the result as events of a new type. The table below summarizes these special derived-value windows.

Table 14.2. Built-in Derived-Value Data Windows

Data Window	Syntax	Description
Size	`size(`[expression, ...]`)`	Derives a count of the number of events in a data window, or in an insert stream if used without a data window, and optionally provides additional event properties as listed in parameters.
Univariate statistics	`uni(`value expression [,expression, ...]`)`	Calculates univariate statistics on the values returned by the expression.
Regression	`linest(`value expression, value expression [,expression, ...]`)`	Calculates regression on the values returned by two expressions.
Correlation	`correl(`value expression, value expression [,expression, ...]`)`	Calculates the correlation value on the values returned by two expressions.
Weighted average	`weighted_avg(`value expression, value expression [,expression, ...]`)`	Calculates weighted average given a weight expression and an expression to compute the average for.

14.1. A Note on Data Window Name and Parameters

The syntax for data windows starts with data window name and is followed by optional parameter expressions in parenthesis:

name(window_parameters)

This example specifies a time window of 5 seconds:

select * from StockTickEvent#time(5 sec)

EPL organizes built-in data windows in namespaces and names. Windows that provide sliding or tumbling data windows are in the win namespace. Other most commonly used windows are in the std namespace. The ext namespace are window that order events. The stat namespace is used for windows that derive statistical data.

Alternatively you may specify the namespace name and : colon character.

namespace:name(window_parameters)

The below examples all specify a time window of 5 seconds:

select * from StockTickEvent#time(5 sec)

select * from StockTickEvent#win:time(5 sec)

select * from StockTickEvent.win:time(5 sec)

All expressions are allowed as parameters to data windows, including expressions that contain variables or substitution parameters for prepared statements. Subqueries, the special prior and prev functions and aggregations (with the exception of the expression window and expression batch window) are not allowed as data window parameters.

For example, assuming a variable by name VAR_WINDOW_SIZE is defined:

select * from StockTickEvent#time(VAR_WINDOW_SIZE)

The system evaluates expression parameters for data windows at the time of context partition instantiation with the exception of the expression window (expr) and expression batch window (expr_batch).

Also consider multiple data windows in intersection or union (keywords retain-intersection and retain-union). Consider writing a custom plug-in data window if your application requires behavior that is not yet provided by any of the built-in windows.

If a window takes no parameters you may leave parenthesis off or the use empty parenthesis ().

The below examples all specify a keep-all window:

select * from StockTickEvent#keepall

select * from StockTickEvent#keepall()

select * from StockTickEvent.win:keepall()

select * from StockTickEvent.win:keepall

Expression parameters can reference context-provided properties. For example:

create schema ParameterEvent(windowSize int)

create context MyContext initiated by ParameterEvent as params terminated after 1 year

context MyContext select * from StockTickEvent#length(context.params.windowSize)

14.2. A Note on Batch Windows

Batch windows buffer events until a certain threshold is reached and then release the batched events for processing. The released events become the insert stream events and the previous batch of events constitutes the remove stream events. Batch windows thus retain the current and the last batch of events in memory.

It is often desirable to aggregate without retaining events in memory, or with just keeping the current events in memory (and not also the last batch of events). You can declare a context and define what starts and ends a "batch" instead. Contexts provide a large degree of freedom in allowing batches to overlap, in allowing batches to span multiple statements and in allowing batches to have complex start and end conditions. They are further described in Chapter 4, Context and Context Partitions.

This example declares a non-overlapping context that spans a time interval of 3 seconds (i.e. a batch of 3 seconds):

create context IntervalSpanning3Seconds start @now end after 3 sec

The next example EPL aggregates events without retaining events in memory and outputs at the end of each interval:

context IntervalSpanning3Seconds select count(*) from Events output snapshot when terminated

Here is an example that outputs all events when at least 10 events, in the 3-second interval, have collected:

context IntervalSpanning3Seconds select window(*) from Events#keepall having count(*) >= 10

For the examples above, at the end of each 3-second interval, the runtime discards all data windows and aggregation state. If your application would like 3-second intervals keyed by some fields please consider a nested context declaration with a keyed segmented context, for example:

create context PerSymbolInterval3Sec 
  context ById partition by symbol from StockTick, 
  context Interval3Sec start @now end after 3 sec

Batch windows keep not only the current batch in memory but also the previous batch of events. For example, let's say at time 0 an event arrives and enters the batch window. At time 3 seconds (3-second batch window) the event becomes an insert-stream event and the runtime now updates aggregations for that batch (i.e. count goes up to 1). At time 6 seconds the event becomes a remove-stream event and the runtime now updates aggregations for that batch (i.e. count goes down to 0). Since the runtime continually updates aggregations from insert and remove stream events, and does not re-compute aggregations, batch windows follow the same paradigm.

14.3. Data Windows

14.3.1. Length Window (`length` or `win:length`)

This window is a moving (sliding) length window extending the specified number of elements into the past. The window takes a single expression as a parameter providing a numeric size value that defines the window size:

length(size_expression)

The below example sums the price for the last 5 stock ticks for symbol GE.

select sum(price) from StockTickEvent(symbol='GE')#length(5)

The next example keeps a length window of 10 events of stock trade events, with a separate window for each symbol. The sum of price is calculated only for the last 10 events for each symbol and aggregates per symbol:

select sum(price) from StockTickEvent#groupwin(symbol)#length(10) group by symbol

A length window of 1 is equivalent to the last event window lastevent. The lastevent data window is the preferred notation:

select * from StockTickEvent#lastevent	// Prefer this
// ... equivalent to ...
select * from StockTickEvent#length(1)

14.3.2. Length Batch Window (`length_batch` or `win:length_batch`)

This window buffers events (tumbling window) and releases them when a given minimum number of events has been collected. Provide an expression defining the number of events to batch as a parameter:

length_batch(size_expression)

The next statement buffers events until a minimum of 10 events have collected. Listeners to updates posted by this window receive updated information only when 10 or more events have collected.

select * from StockTickEvent#length_batch(10)

14.3.3. Time Window (`time` or `win:time`)

This window is a moving (sliding) time window extending the specified time interval into the past based on the system time. Provide a time period (see Section 5.2.1, “Specifying Time Periods”) or an expression defining the number of seconds as a parameter:

time(time period)

time(seconds_interval_expression)

For the GE stock tick events in the last 1 second, calculate a sum of price.

select sum(price) from StockTickEvent(symbol='GE')#time(1 sec)

The following time windows are equivalent specifications:

time(2 minutes 5 seconds)
time(125 sec)
time(125)
time(MYINTERVAL)  // MYINTERVAL defined as a variable

14.3.4. Externally-timed Window (`ext_timed` or `win:ext_timed`)

Similar to the time window, this window is a moving (sliding) time window extending the specified time interval into the past, but based on the long-type time value supplied by a timestamp expression. The window takes two parameters: the expression to return long-typed timestamp values, and a time period or expression that provides a number of seconds:

ext_timed(timestamp_expression, time_period)

ext_timed(timestamp_expression, seconds_interval_expression)

The key difference comparing the externally-timed window to the regular time window is that the window slides not based on the runtime time, but strictly based on the result of the timestamp expression when evaluated against the events entering the window.

The algorithm underlying the window compares the timestamp value returned by the expression when the oldest event arrived with the timestamp value returned by the expression for the newest arriving event on event arrival. If the time interval between the timestamp values is larger then the timer period parameter, then the algorithm removes all oldest events tail-first until the difference between the oldest and newest event is within the time interval. The window therefore slides only when events arrive and only considers each event's timestamp property (or other expression value returned) and not runtime time.

This window holds stock tick events of the last 10 seconds based on the timestamp property in StockTickEvent.

select * from StockTickEvent#ext_timed(timestamp, 10 seconds)

The externally-timed data window expects strict ordering of the timestamp values returned by the timestamp expression. The window is not useful for ordering events in time order, please use the time-order window instead.

On a related subject, runtime time itself can be entirely under control of the application as described in Section 16.9, “Controlling Time-Keeping”, allowing control over all time-based aspects of processing in one place.

14.3.5. Time batch Window (`time_batch` or `win:time_batch`)

This window buffers events (tumbling window) and releases them every specified time interval in one update. The window takes a time period or an expression providing a number of seconds as a parameter, plus optional parameters described next.

time_batch(time_period [,optional_reference_point] [,flow_control])

time_batch(seconds_interval_expression [,optional_reference_point] [,flow_control])

The time batch window takes a second, optional parameter that serves as a reference point to batch flush times. If not specified, the arrival of the first event into the batch window sets the reference point. Therefore if the reference point is not specified and the first event arrives at time t₁, then the batch flushes at time t₁ plus time_period and every time_period thereafter.

Note

Please see Section 14.2, “A Note on Batch Windows” for information on what a batch window is and how to best to compute over intervals.

Note that using this window means that the runtime keeps events in memory until the time is up: Consider your event arrival rate and determine if this is the behavior you want. Use context declaration or output rate limiting such as output snapshot as an alternative.

The below example batches events into a 5 second window releasing new batches every 5 seconds. Listeners to updates posted by this window receive updated information only every 5 seconds.

select * from StockTickEvent#time_batch(5 sec)

By default, if there are no events arriving in the current interval (insert stream), and no events remain from the prior batch (remove stream), then the window does not post results to listeners. The window allows overriding this default behavior via flow control keywords.

The synopsis with flow control parameters is:

time_batch(time_period or seconds_interval_expr [,optional_reference_point] 
    [, "flow-control-keyword [, keyword...]"] )

The FORCE_UPDATE flow control keyword instructs the window to post an empty result set to listeners if there is no data to post for an interval. When using this keyword the irstream keyword should be used in the select clause to ensure the remove stream is also output. Note that FORCE_UPDATE is for use with listeners to the same statement and not for use with named windows. Consider output rate limiting instead.

The START_EAGER flow control keyword instructs the window to post empty result sets even before the first event arrives, starting a time interval at statement deployment time. As when using FORCE_UPDATE, the window also posts an empty result set to listeners if there is no data to post for an interval, however it starts doing so at time of statement deployment rather then at the time of arrival of the first event.

Taking the two flow control keywords in one sample statement, this example presents a window that waits for 10 seconds. It posts empty result sets after one interval after the statement gets deployed and keeps posting an empty result set as no events arrive during intervals:

select * from MyEvent#time_batch(10 sec, "FORCE_UPDATE, START_EAGER")

The optional reference point is provided as a long-value of milliseconds (or microseconds for microsecond runtime time unit) relative to January 1, 1970 and time 00:00:00.

The following example statement sets the reference point to 5 seconds and the batch size to 1 hour, so that each batch output is 5 seconds after each hour:

select * from OrderSummaryEvent#time_batch(1 hour, 5000L)

14.3.6. Externally-timed Batch Window (`ext_timed_batch` or `win:ext_timed_batch`)

Similar to the time batch window, this window buffers events (tumbling) and releases them every specified time interval in one update, but based on the long-type time value supplied by a timestamp expression. The window has two required parameters taking an expression that returns long-typed timestamp values and a time period or constant-value expression that provides a number of seconds:

ext_timed_batch(timestamp_expression, time_period [,optional_reference_point])

ext_timed_batch(timestamp_expression, seconds_interval_expression [,optional_reference_point])

The externally-timed batch window takes a third, optional parameter that serves as a reference point to batch flush times. If not specified, the arrival of the first event into the batch window sets the reference point. Therefore if the reference point is not specified and the first event arrives at time t₁, then the batch flushes at time t₁ plus time_period and every time_period thereafter.

The key difference comparing the externally-timed batch window to the regular time batch window is that the window tumbles not based on the runtime time, but strictly based on the result of the timestamp expression when evaluated against the events entering the window.

The algorithm underlying the window compares the timestamp value returned by the expression when the oldest event arrived with the timestamp value returned by the expression for the newest arriving event on event arrival. If the time interval between the timestamp values is larger then the timer period parameter, then the algorithm posts the current batch of events. The window therefore posts batches only when events arrive and only considers each event's timestamp property (or other expression value returned) and not runtime time.

The below example batches events into a 5 second window releasing new batches every 5 seconds. Listeners to updates posted by this window receive updated information only when event arrive with timestamps that indicate the start of a new batch:

select * from StockTickEvent#ext_timed_batch(timestamp, 5 sec)

The optional reference point is provided as a long-value of milliseconds (or microseconds) relative to January 1, 1970 and time 00:00:00.

The following example statement sets the reference point to 5 seconds and the batch size to 1 hour, so that each batch output is 5 seconds after each hour:

select * from OrderSummaryEvent#ext_timed_batch(timestamp, 1 hour, 5000L)

14.3.7. Time-Length Combination Batch Window (`time_length_batch` or `win:time_length_batch`)

This data window is a combination of time and length batch (tumbling) windows. Similar to the time and length batch windows, this batches events and releases the batched events when either one of the following conditions occurs, whichever occurs first: the data window has collected a given number of events, or a given time interval has passed.

The parameters take 2 forms. The first form accepts a time period or an expression providing a number of seconds, and an expression for the number of events:

time_length_batch(time_period, number_of_events_expression)

time_length_batch(seconds_interval_expression, number_of_events_expression)

The next example shows a time-length combination batch window that batches up to 100 events or all events arriving within a 1-second time interval, whichever condition occurs first:

 select * from MyEvent#time_length_batch(1 sec, 100)

In this example, if 100 events arrive into the window before a 1-second time interval passes, the window posts the batch of 100 events. If less then 100 events arrive within a 1-second interval, the window posts all events that arrived within the 1-second interval at the end of the interval.

By default, if there are no events arriving in the current interval (insert stream), and no events remain from the prior batch (remove stream), then the window does not post results to listeners. This window allows overriding this default behavior via flow control keywords.

The synopsis of the window with flow control parameters is:

time_length_batch(time_period or seconds_interval_expression, number_of_events_expression, 
    "flow control keyword [, keyword...]")

The FORCE_UPDATE flow control keyword instructs the window to post an empty result set to listeners if there is no data to post for an interval. The window begins posting no later then after one time interval passed after the first event arrives. When using this keyword the irstream keyword should be used in the select clause to ensure the remove stream is also output.

The START_EAGER flow control keyword instructs the window to post empty result sets even before the first event arrives, starting a time interval at statement deployment time. As when using FORCE_UPDATE, the window also posts an empty result set to listeners if there is no data to post for an interval, however it starts doing so at time of statement deployment rather then at the time of arrival of the first event.

Taking the two flow control keywords in one sample statement, this example presents a window that waits for 10 seconds or reacts when the 5th event arrives, whichever comes first. It posts empty result sets after one interval after the statement gets deployed and keeps posting an empty result set as no events arrive during intervals:

 select * from MyEvent#time_length_batch(10 sec, 5, "FORCE_UPDATE, START_EAGER")

14.3.8. Time-Accumulating Window (`time_accum` or `win:time_accum`)

This data window is a specialized moving (sliding) time window that differs from the regular time window in that it accumulates events until no more events arrive within a given time interval, and only then releases the accumulated events as a remove stream.

The window accepts a single parameter: the time period or seconds-expression specifying the length of the time interval during which no events must arrive until the window releases accumulated events. The synopsis is as follows:

time_accum(time_period)

time_accum(seconds_interval_expression)

The next example shows a time-accumulating window that accumulates events, and then releases events if within the time interval no more events arrive:

 select * from MyEvent#time_accum(10 sec)

This example accumulates events, until when for a period of 10 seconds no more MyEvent events arrive, at which time it posts all accumulated MyEvent events.

Your application may only be interested in the batches of events as events leave the data window. This can be done simply by selecting the remove stream of this data window, populated by the runtime as accumulated events leave the data window all-at-once when no events arrive during the time interval following the time the last event arrived:

 select rstream * from MyEvent#time_accum(10 sec)

If there are no events arriving, then the window does not post results to listeners.

14.3.9. Keep-All Window (`keepall` or `win:keepall`)

This keep-all data window simply retains all events. The window does not remove events from the data window, unless used with a named window and the on delete clause.

The window accepts no parameters. The synopsis is as follows:

keepall

The next example shows a keep-all window that accumulates all events received into the window:

 select * from MyEvent#keepall

Note that since the window does not release events, care must be taken to prevent retained events from using all available resources.

14.3.10. First Length Window(`firstlength` or `win:firstlength`)

The firstlength window retains the very first size_expression events.

The synopsis is:

firstlength(size_expression)

If used within a named window and an on-delete clause deletes events, the window accepts further arriving events until the number of retained events reaches the size of size_expression.

The below example creates a window that retains only the first 10 events:

select * from MyEvent#firstlength(10)

14.3.11. First Time Window (`firsttime` or `win:firsttime`)

The firsttime window retains all events arriving within a given time interval after statement start.

The synopsis is:

firsttime(time_period)

firsttime(seconds_interval_expression)

The below example creates a window that retains only those events arriving within 1 minute and 10 seconds of statement start:

select * from MyEvent#firsttime(1 minute 10 seconds)

14.3.12. Expiry Expression Window (`expr` or `win:expr`)

The expr data window applies an expiry expression and removes events from the data window when the expression returns false.

Use this window to implement rolling and dynamically shrinking or expanding time, length or other windows. Rolling can, for example, be controlled based on event properties of arriving events, based on aggregation values or based on the return result of user-defined functions. Use this window to accumulate events until a value changes or other condition occurs based on arriving events or change of a variable value.

The synopsis is:

expr(expiry_expression)

The expiry expression can be any expression including expressions on event properties, variables, aggregation functions or user-defined functions. The window applies this expression to the oldest event(s) currently in the window, as described next.

When a new event arrives or when a variable value referenced by the expiry expression changes then the window applies the expiry expression starting from the oldest event in the data window. If the expiry expression returns false for the oldest event, the window removes the event from the data window. The window then applies the expression to the next oldest event. If the expiry expression returns true for the oldest event, no further evaluation takes place and the window indicates any new and expired events through insert and remove stream.

By using variables in the expiry expression it is possible to change the behavior of the window dynamically at runtime. When one or more variables used in the expression are updated the window evaluates the expiry expression starting from the oldest event.

Aggregation functions, if present in the expiry expression, are continuously updated as events enter and leave the data window. Use the grouped data window with this window to compute aggregations per group.

The runtime makes the following built-in properties available to the expiry expression:

Table 14.3. Built-in Properties of the Expiry Expression Data Window

Name	Type	Description
`current_count`	int	The number of events in the data window including the currently-arriving event.
`expired_count`	int	The number of events expired during this evaluation.
`newest_event`	(same event type as arriving events)	The last-arriving event itself.
`newest_timestamp`	long	The runtime timestamp associated with the last-arriving event.
`oldest_event`	(same event type as arriving events)	The currently-evaluated event itself.
`oldest_timestamp`	long	The runtime timestamp associated with the currently-evaluated event.
`view_reference`	Object	The object handle to this data window.

This EPL declares an expiry expression that retains the last 2 events:

select * from MyEvent#expr(current_count <= 2)

The following example implements a dynamically-sized length window by means of a SIZE variable. As the SIZE variable value changes the window retains the number of events according to the current value of SIZE:

create variable int SIZE = 1000

select * from MyEvent#expr(current_count <= SIZE)

The next EPL retains the last 2 seconds of events:

select * from MyEvent#expr(oldest_timestamp > newest_timestamp - 2000)

The following example implements a dynamically-sized time window. As the SIZE long-type variable value changes the window retains a time interval accordingly:

create variable long SIZE = 1000

select * from MyEvent#expr(newest_timestamp - oldest_timestamp < SIZE)

The following example declares a KEEP variable and flushes all events from the data window when the variable turns false:

create variable boolean KEEP = true

select * from MyEvent#expr(KEEP)

The next example specifies a rolling window that removes the oldest events from the window until the total price of all events in the window is less then 1000:

select * from MyEvent#expr(sum(price) < 1000)

This example retains all events that have the same value of the flag event property. When the flag value changes, the data window expires all events with the old flag value and retains only the most recent event of the new flag value:

select * from MyEvent#expr(newest_event.flag = oldest_event.flag)

14.3.12.1. Limitations

You may not use subqueries or the prev and prior functions as part of the expiry expression. Consider using a named window and on-delete or on-merge instead.

When using variables in the expiry expression, the thread that updates the variable does not evaluate the window. The thread that updates the variable instead schedules a reevaluation and window evaluates by timer execution.

14.3.13. Expiry Expression Batch Window (`expr_batch` or `win:expr_batch`)

The expr_batch buffers events (tumbling window) and releases them when a given expiry expression returns true.

Use this window to implement dynamic or custom batching behavior, such as for dynamically shrinking or growing time, length or other batches, for batching based on event properties of arriving events, aggregation values or for batching based on a user-defined function.

The synopsis is:

expr_batch(expiry_expression, [include_triggering_event])

The optional second parameter include_triggering_event defines whether to include the event that triggers the batch in the current batch (true, the default) or in the next batch (false).

When a new event arrives or when a variable value referenced by the expiry expression changes or when events get removed from the data window then the window applies the expiry expression. If the expiry expression returns true the data window posts the collected events as the insert stream and the last batch of events as remove stream.

Aggregation functions, if present in the expiry expression, are continuously updated as events enter the data window and reset when the runtime posts a batch of events. Use the grouped data window with this window to compute aggregations per group.

The compiler makes the following built-in properties available to the expiry expression:

Table 14.4. Built-in Properties of the Expiry Expression Data Window

Name	Type	Description
`current_count`	int	The number of events in the data window including the currently-arriving event.
`newest_event`	(same event type as arriving events)	The last-arriving event itself.
`newest_timestamp`	long	The runtime timestamp associated with the last-arriving event.
`oldest_event`	(same event type as arriving events)	The currently-evaluated event itself.
`oldest_timestamp`	long	The runtime timestamp associated with the currently-evaluated event.
`view_reference`	Object	The object handle to this window.

This EPL declares an expiry expression that posts event batches consisting of 2 events:

select * from MyEvent#expr_batch(current_count >= 2)

The following example implements a dynamically-sized length batch window by means of a SIZE variable. As the SIZE variable value changes the window accumulates and posts the number of events according to the current value of SIZE:

create variable int SIZE = 1000

select * from MyEvent#expr_batch(current_count >= SIZE)

The following example accumulates events until an event arrives that has a value of postme for property myvalue:

select * from MyEvent#expr_batch(myvalue = 'postme')

The following example declares a POST variable and posts a batch of events when the variable turns true:

create variable boolean POST = false

select * from MyEvent#expr_batch(POST)

The next example specifies a tumbling window that posts a batch of events when the total price of all events in the window is greater then 1000:

select * from MyEvent#expr_batch(sum(price) > 1000)

Specify the second parameter as false when you want the triggering event not included in the current batch.

This example batches all events that have the same value of the flag event property. When the flag value changes, the data window releases the batch of events collected for the old flag value. The data window collects the most recent event and the future arriving events of the same new flag value:

select * from MyEvent#expr_batch(newest_event.flag != oldest_event.flag, false)

14.3.13.1. Limitations

You may not use subqueries or the prev and prior functions as part of the expiry expression. Consider using a named window and on-delete or on-merge instead.

14.3.14. Unique Window (`unique` or `std:unique`)

The unique window is a window that includes only the most recent among events having the same value(s) for the result of the specified expression or list of expressions.

The synopsis is:

unique(unique_expression [, unique_expression ...])

The window acts as a length window of size 1 for each distinct value returned by an expression, or combination of values returned by multiple expressions. It thus posts as old events the prior event of the same value(s), if any.

An expression may return a null value. The compiler treats a null value as any other value. An expression can also return a custom application object, whereby the application class should implement the hashCode and equals methods. Further detail on key expressions can be found at Section 5.2.13, “Composite Keys and Array Values as Keys”.

The below example creates a window that retains only the last event per symbol.

select * from StockTickEvent#unique(symbol)

The next example creates a window that retains the last event per symbol and feed.

select * from StockTickEvent#unique(symbol, feed)

When using unique the compiler plans statements applying an implicit unique index, where applicable. Specify @Hint('disable_unique_implicit_idx') to force the compiler to plan statement using a non-unique index.

14.3.15. Grouped Data Window (`groupwin` or `std:groupwin`)

Specifying #groupwin groups events into sub-data-window by the value returned by the specified expression or the combination of values returned by a list of expressions. The #groupwin takes a single expression to supply the group criteria values, or a list of expressions as parameters, as the synopsis shows:

groupwin(grouping_expression [, grouping_expression ...])

The grouping_expression expression(s) return one or more group keys, by which it creates a separate data window for each distinct group key. Note that the expression should not return an unlimited number of values: the grouping expression should not return a time value or otherwise unlimited key. Further detail on key expressions can be found at Section 5.2.13, “Composite Keys and Array Values as Keys”.

An expression may return a null value. The runtime treats a null value as any other value. An expression can also return a custom application object, whereby the application class should implement the hashCode and equals methods.

You can specify a single groupwin per stream. Multiple groupwin declarations for the same stream are not allowed.

Use group by instead of the grouped data window to control how aggregations are grouped.

A grouped data window with a length window of 1 is equivalent to the unique data window unique. The unique data window is the preferred notation:

select * from StockTickEvent#unique(symbol)	// Prefer this
// ... equivalent to ...
select * from StockTickEvent#groupwin(symbol)#length(1)

This example computes the total price for the last 5 events considering the last 5 events per each symbol, aggregating the price across all symbols (since no group by clause is specified the aggregation is across all symbols):

select symbol, sum(price) from StockTickEvent#groupwin(symbol)#length(5)

The @Hint("reclaim_group_aged=age_in_seconds") hint instructs the runtime to discard grouped data window state that has not been updated for age_in_seconds seconds. The optional @Hint("reclaim_group_freq=sweep_frequency_in_seconds") can be specified in addition to control the frequency at which the runtime sweeps data window state. If the hint is not specified, the frequency defaults to the same value as age_in_seconds. Use the hints when your group criteria returns a changing or unlimited number of values. By default and without hints the data window does not reclaim or remove data windows for group criteria values.

The updated sample statement with both hints:

// Remove data window for symbols not updated for 10 seconds or more and sweep every 30 seconds
@Hint('reclaim_group_aged=10,reclaim_group_freq=30')
select symbol, sum(price) from StockTickEvent#groupwin(symbol)#length(5)

Reclaim executes when an event arrives and not in the timer thread. In the example above reclaim can occur up to 40 seconds of runtime time after the newest event arrives. Reclaim may affect iteration order for the statement and iteration order becomes indeterministic with reclaim.

To compute the total price for the last 5 events considering the last 5 events per each symbol and outputting a price per symbol, add the group by clause:

select symbol, sum(price) from StockTickEvent#groupwin(symbol)#length(5) group by symbol

The groupwin grouped-window can also take multiple expressions that provide values to group by. This example computes the total price for each symbol and feed for the last 10 events per symbol and feed combination:

select sum(price) from StockTickEvent#groupwin(symbol, feed)#length(10)

The order in which the groupwin grouped-window appears controls the data the runtime derives from events for each group. The next 2 statements demonstrate this using a length window.

Without the groupwin declaration the same statement returns the total price per symbol for only the last 10 events across all symbols. Here the runtime allocates only one length window for all events:

select sum(price) from StockTickEvent#length(10)

We have learned that by placing the groupwin grouped-window before other data windows, these other data windows become part of the grouped set of windows. The runtime dynamically allocates a new window instance for each, every time it encounters a new group key such as a new value for symbol. Therefore, in groupwin(symbol)#length(10) the runtime allocates a new length window for each distinct symbol. However in length(10) alone the runtime maintains a single length window.

The groupwin can be used with multiple data windows to achieve a grouped intersection or union policy.

The next statement retains the last 4 events per symbol and only those events that are also not older then 10 seconds:

select * from StockTickEvent#groupwin(symbol)#length(4)#time(10)

Last, considers a grouped data window for two group criteria. Here, the statement results are total price per symbol and feed for the last 100 events per symbol and feed.

select sum(price) from StockTickEvent#groupwin(symbol, feed)#length(100)

Note

A note on grouped time windows: When using grouped-window with time windows, note that whether the runtime retains 5 minutes of events or retains 5 minutes of events per group, the result is the same from the perspective of retaining events as both policies retain, considering all groups, the same set of events. Therefore please specify the time window alone (ungrouped).

For example:

// Use this:
select sum(price) from StockTickEvent#time(1 minute)

// is equivalent to (don't use this):
// select sum(price) from StockTickEvent#groupwin(symbol)#time(1 minute)

// Use the group-by clause for grouping aggregation by symbol.

For advanced users: There is an optional declaration that can control how the groupwin grouped-window gets evaluated and that is #merge. The merge can only occur after a groupwin grouped-window. It controls the end of the grouped declaration.

Compare the following statements:

select * from Market#groupwin(ticker)#length(1000000)
    #weighted_avg(price, volume)#merge(ticker)
// ... and ...
select * from Market#groupwin(ticker)#length(1000000)#merge(ticker)
    #weighted_avg(price, volume)

If your statement does not specify the optional #merge, the semantics are the same as the first statement.

The first statement, in which the #mergeis added to the end (same as no merge), computes weighted average per ticker, considering, per-ticker, the last 1M Market events for each ticker. The second statement, in which the merge is added to the middle, computes weighted average considering, per-ticker, the last 1M Market events, computing the weighted average for all such events using a single data window rather then multiple data window instances with one window per ticker.

14.3.16. Last Event Window (`std:lastevent`)

This window exposes the last element:

lastevent

The window acts as a length window of size 1. It thus posts as old events the prior event in the stream, if any.

This example statement retains the last stock tick event for the symbol GE.

select * from StockTickEvent(symbol='GE')#lastevent

If you want to output the last event within a sliding window, please see Section 10.1.13, “The Previous Function”. That function accepts a relative (count) or absolute index and returns event properties or an event in the context of the specified data window.

14.3.17. First Event Window (`firstevent` or `std:firstevent`)

This window retains only the first arriving event:

firstevent

All events arriving after the first event are discarded.

If used within a named window and an on-delete clause deletes the first event, the window resets and will retain the next arriving event.

An example of a statement that retains the first ReferenceData event arriving is:

select * from ReferenceData#firstevent

If you want to output the first event within a sliding window, please see Section 10.1.13, “The Previous Function”. That function accepts a relative (count) or absolute index and returns event properties or an event in the context of the specified data window.

14.3.18. First Unique Window (`firstunique` or `std:firstunique`)

The firstunique window retains only the very first among events having the same value for the specified expression or list of expressions.

The synopsis is:

firstunique(unique_expression [, unique_expression ...])

Further detail on key expressions can be found at Section 5.2.13, “Composite Keys and Array Values as Keys”.

If used within a named window and an on-delete clause deletes events, the window resets and will retain the next arriving event for the expression result value(s) of the deleted events.

The below example creates a window that retains only the first event per category:

select * from ReferenceData#firstunique(category)

When using firstunique the compiler plans statements applying an implicit unique index, where applicable. Specify @Hint('disable_unique_implicit_idx') to force the compiler to plan statements using a non-unique index.

14.3.19. Sorted Window (`sort` or `ext:sort`)

This window sorts by values returned by the specified expression or list of expressions and keeps only the top (or bottom) events up to the given size.

This window retains all events in the stream that fall into the sort range. Use the ranked window as described next to retain events per unique key(s) and sorted.

The syntax is as follows:

sort(size_expression, 
    sort_criteria_expression [asc/desc][, sort_criteria_expression [asc/desc]...])

An expression may be followed by the optional asc or desc keywords to indicate that the values returned by that expression are sorted in ascending or descending sort order.

The window below retains only those events that have the highest 10 prices considering all events (and not only the last event per symbol, see rank below) and reports a total price:

select sum(price) from StockTickEvent#sort(10, price desc)

The following example sorts events first by price in descending order, and then by symbol name in ascending (alphabetical) order, keeping only the 10 events with the highest price (with ties resolved by alphabetical order of symbol).

select * from StockTickEvent#sort(10, price desc, symbol asc)

The sorted window is often used with the prev, prevwindow or prevtail single-row functions to output properties of events at a certain position or to output the complete data window according to sort order.

Use the grouped window to retain a separate sort window for each group. For example, the windows groupwin(market)#sort(10, price desc) instruct the runtime to retain, per market, the highest 10 prices.

14.3.20. Ranked Window (`rank` or `ext:rank`)

This window retains only the most recent among events having the same value for the criteria expression(s), sorted by sort criteria expressions and keeps only the top events up to the given size.

This window is similar to the sorted window in that it keeps only the top (or bottom) events up to the given size, however the window also retains only the most recent among events having the same value(s) for the specified uniqueness expression(s).

The syntax is as follows:

rank(unique_expression [, unique_expression ...],
    size_expression, 
    sort_criteria_expression [asc/desc][, sort_criteria_expression [asc/desc]...])

Specify the expressions returning unique key values first. Further detail on key expressions can be found at Section 5.2.13, “Composite Keys and Array Values as Keys”.

Then specify a constant value that is the size of the ranked window. Then specify the expressions returning sort criteria values. The sort criteria expressions may be followed by the optional asc or desc keywords to indicate that the values returned by that expression are sorted in ascending or descending sort order.

The window below retains only those events that have the highest 10 prices considering only the last event per symbol and reports a total price:

select sum(price) from StockTickEvent#rank(symbol, 10, price desc)

The following example retains, for the last event per market and symbol, those events that sort by price and quantity ascending into the first 10 ranks:

select * from StockTickEvent#rank(market, symbol, 10, price, quantity)

The ranked window is often used with the prev, prevwindow or prevtail single-row functions to output properties of events at a certain position or to output the complete data window according to sort order.

This example outputs every 5 seconds the top 10 events according to price descending and considering only the last event per symbol:

select prevwindow(*) from StockTickEvent#rank(symbol, 10, price desc)
  output snapshot every 5 seconds limit 1  // need only 1 row

Use the grouped window to retain a separate rank for each group. For example, the windows groupwin(market)#rank(symbol, 10, price desc) instruct the runtime to retain, per market, the highest 10 prices considering the last event per symbol.

14.3.21. Time-Order Window (`time_order` or `ext:time_order`)

This window orders events that arrive out-of-order, using timestamp-values provided by an expression, and by comparing that timestamp value to runtime time.

The syntax for this window is as follows.

time_order(timestamp_expression, time_period)

time_order(timestamp_expression, seconds_interval_expression)

The first parameter to the window is the expression that supplies timestamp values. The timestamp is expected to be a long-typed value that denotes an event's time of consideration by the window (or other expression). This is typically the time of arrival. The second parameter is a number-of-seconds expression or the time period specifying the time interval that an arriving event should maximally be held, in order to consider older events arriving at a later time.

Since the window compares timestamp values to runtime time, the window requires that the timestamp values and runtime time are both following the same clock. Therefore, to the extend that the clocks that originated both timestamps differ, the window may produce inaccurate results.

As an example, the next statement uses the arrival_time property of MyTimestampedEvent events to order and release events by arrival time:

insert rstream into ArrivalTimeOrderedStream
select rstream * from MyTimestampedEvent#time_order(arrival_time, 10 sec)

In the example above, the arrival_time property holds a long-typed timestamp value. On arrival of an event, the runtime compares the timestamp value of each event to the tail-time of the window. The tail-time of the window is, in this example, 10 seconds before runtime time (continuously sliding). If the timestamp value indicates that the event is older then the tail-time of the time window, the event is released immediately in the remove stream. If the timestamp value indicates that the event is newer then the tail-time of the window, the window retains the event until runtime time moves such that the event timestamp is older then tail-time.

The examples thus holds each arriving event in memory anywhere from zero seconds to 10 seconds, to allow for older events (considering arrival time timestamp) to arrive. In other words, the window holds an event with an arrival time equal to runtime time for 10 seconds. The window holds an event with an arrival time that is 2 seconds older then runtime time for 8 seconds. The window holds an event with an arrival time that is 10 or more seconds older then runtime time for zero seconds, and releases such (old) events immediately into the remove stream.

The insert stream of this sliding window consists of all arriving events. The remove stream of the window is ordered by timestamp value: The event that has the oldest timestamp value is released first, followed by the next newer events. The window preserves the order of events that arrived into the window for same timestamp values. Note the statement above uses the rstream keyword in both the insert into clause and the select clause to select ordered events only. It uses the insert into clause to makes such ordered stream available for subsequent statements to use.

It is up to your application to populate the timestamp property into your events or use a sensible expression that returns timestamp values for consideration by the window. The window also works well if you use externally-provided time via timer events.

14.3.22. Time-To-Live Window (`timetolive` or `ext:timetolive`)

This window retains events until runtime time reaches the value returned by the given timestamp expression.

The syntax for this window is as follows:

timetolive(timestamp_expression)

The only parameter to the window is the expression that supplies timestamp values. The timestamp is expected to be a long-typed value that denotes an event's time-to-live.

Since the window compares timestamp values to runtime time, the window requires that the timestamp values and runtime time are both following the same clock.

On arrival of an event, the runtime evaluates the timestamp expression and obtains a long-type timestamp. The runtime compares that timestamp value to runtime time:

If the timestamp is older than runtime time or the same as runtime time, the runtime releases the event immediately into the remove stream and does not retain the event at all.
If the timestamp value is newer than the runtime time, the data window retains the event until runtime time moves forward such that the timestamp is the same or older than runtime time.

As an example, the next statement uses the arrival_time property of MyTimestampedEvent events to release events by arrival time:

insert rstream into ArrivalTimeOrderedStream
select rstream * from MyTimestampedEvent#timetolive(arrival_time)

For example, assume runtime time is 8:00:00 (8 am).

If the arrival_time timestamp is 8:00:00 or older (such as 7:59:00), the data window does not retain the event at all, i.e. the runtime releases the event into the remove stream upon arrival.
If the arrival_time timestamp is after 8:00:00 the data window retains the event. Let's say the arrival_time timestamp is 8:02:00 the runtime retains the event until runtime time is 8:02:00 or newer.

The runtime evaluates the expression only once at the arrival of each event to determine that event's time-to-live. The time-to-live data structure organizes events by the value returned by the expression on event arrival into the data window.

The insert stream of this sliding window consists of all arriving events. The remove stream of the window is ordered by timestamp value: The event that has the oldest timestamp value is released first, followed by the next newer events. Note the statement above uses the rstream keyword in both the insert into clause and the select clause to select ordered events only. It uses the insert into clause to makes such ordered stream available for subsequent statements to use.

The time-to-live data window is fully equivalent to the time-order data window with a zero value for the time period.

14.4. Special Derived-Value Windows

The derived-value windows can be used combined with data windows or alone. Very similar to aggregation functions, these windows aggregate or derive information from an event stream. As compared to aggregation functions, statistics windows can post multiple derived fields all-in-one including properties from the last event that was received. The derived fields and event properties are available for querying in the where-clause and are often compared to prior values using the prior function. Derived-value window do not retain events.

14.4.1. Size Derived-Value Window (`size`) or `std:size`)

This window posts the number of events received from a stream or window plus any additional event properties or expression values listed as parameters. The synopsis is:

size([expression, ...] [ * ])

The window posts a single long-typed property named size. The window posts the prior size as old data, and the current size as new data to update listeners of the window. Via the iterator method of the statement the size value can also be polled (read). The window only posts output events when the size count changes and does not stay the same.

As optional parameters the window takes a list of expressions that the window evaluates against the last arriving event and provides along the size field. You may also provide the * wildcard selector to have the window output all event properties.

An alternative to receiving a data window event count is the prevcount function. Compared to the size window the prevcount function requires a data window while the size window does not. The related count(...) aggregation function provides a count per group when used with group by.

When combined with a data window, the size window reports the current number of events in the data window in the insert stream and the prior number of events in the data window as the remove stream. This example reports the number of tick events within the last 1 minute:

select size from StockTickEvent#time(1 min)#size

To select additional event properties you may add each event property to output as a parameter to the window.

The next example selects the symbol and feed event properties in addition to the size property:

select size, symbol, feed from StockTickEvent#time(1 min)#size(symbol, feed)

This example selects all event properties in addition to the size property:

select * from StockTickEvent#time(1 min)#size(*)

The size window is also useful in conjunction with a groupwin grouped-window to count the number of events per group. The EPL below returns the number of events per symbol.

select size from StockTickEvent#groupwin(symbol)#size

When used without a data window, the window simply counts the number of events:

select size from StockTickEvent#size

All windows can be used with pattern statements as well. The next EPL snippet shows a pattern that looks for tick events followed by trade events for the same symbol. The size window counts the number of occurrences of the pattern.

select size from pattern[every s=StockTickEvent -> TradeEvent(symbol=s.symbol)]#size

14.4.2. Univariate Statistics Derived-Value Window (`uni` or `stat:uni`)

This window calculates univariate statistics on a numeric expression. The window takes a single value expression as a parameter plus any number of optional additional expressions to return properties of the last event. The value expression must return a numeric value:

uni(value_expression [,expression, ...] [ * ])

After the value expression you may optionally list additional expressions or event properties to evaluate for the stream and return their value based on the last arriving event. You may also provide the * wildcard selector to have the window output all event properties.

Table 14.5. Univariate Statistics Derived Properties

Property Name	Description
`datapoints`	Number of values, equivalent to `count(*)` for the stream
`total`	Sum of values
`average`	Average of values
`variance`	Variance
`stddev`	Sample standard deviation (square root of variance)
`stddevpa`	Population standard deviation

The below example selects the standard deviation on price for stock tick events for the last 10 events.

select stddev from StockTickEvent#length(10)#uni(price)

To add properties from the event stream you may simply add all additional properties as parameters to the window.

This example selects all of the derived values, based on the price property, plus the values of the symbol and feed event properties:

select * from StockTickEvent#length(10)#uni(price, symbol, feed)

The following example selects all of the derived values plus all event properties:

select * from StockTickEvent#length(10)#uni(price, symbol, *)

14.4.3. Regression Derived-Value Window (`linest` or `stat:linest`)

This window calculates regression and related intermediate results on the values returned by two expressions. The window takes two value expressions as parameters plus any number of optional additional expressions to return properties of the last event. The value expressions must return a numeric value:

linest(value_expression, value_expression [,expression, ...] [ * ])

After the two value expressions you may optionally list additional expressions or event properties to evaluate for the stream and return their value based on the last arriving event. You may also provide the * wildcard selector to have the window output all event properties.

Table 14.6. Regression Derived Properties

Property Name	Description
`slope`	Slope.
`YIntercept`	Y intercept.
`XAverage`	X average.
`XStandardDeviationPop`	X standard deviation population.
`XStandardDeviationSample`	X standard deviation sample.
`XSum`	X sum.
`XVariance`	X variance.
`YAverage`	X average.
`YStandardDeviationPop`	Y standard deviation population.
`YStandardDeviationSample`	Y standard deviation sample.
`YSum`	Y sum.
`YVariance`	Y variance.
`dataPoints`	Number of data points.
`n`	Number of data points.
`sumX`	Sum of X (same as X Sum).
`sumXSq`	Sum of X squared.
`sumXY`	Sum of X times Y.
`sumY`	Sum of Y (same as Y Sum).
`sumYSq`	Sum of Y squared.

The next example calculates regression and returns the slope and y-intercept on price and offer for all events in the last 10 seconds.

select slope, YIntercept from StockTickEvent#time(10 seconds)#linest(price, offer)

To add properties from the event stream you may simply add all additional properties as parameters to the window.

This example selects all of the derived values, based on the price and offer properties, plus the values of the symbol and feed event properties:

select * from StockTickEvent#time(10 seconds)#linest(price, offer, symbol, feed)

The following example selects all of the derived values plus all event properties:

select * from StockTickEvent#time(10 seconds)#linest(price, offer, *)

14.4.4. Correlation Derived-Value Window (`correl` or `stat:correl`)

This window calculates the correlation value on the value returned by two expressions. The window takes two value expressions as parameters plus any number of optional additional expressions to return properties of the last event. The value expressions must be return a numeric value:

correl(value_expression, value_expression [,expression, ...] [ * ])

Table 14.7. Correlation Derived Properties

Property Name	Description
`correlation`	Correlation between two event properties

The next example calculates correlation on price and offer over all stock tick events for GE:

select correlation from StockTickEvent(symbol='GE')#correl(price, offer)

To add properties from the event stream you may simply add all additional properties as parameters to the window.

This example selects all of the derived values, based on the price and offer property, plus the values of the feed event property:

select * from StockTickEvent(symbol='GE')#correl(price, offer, feed)

The next example selects all of the derived values plus all event properties:

select * from StockTickEvent(symbol='GE')#correl(price, offer, *)

14.4.5. Weighted Average Derived-Value Window (`weighted_avg` or `stat:weighted_avg`)

This window returns the weighted average given an expression returning values to compute the average for and an expression returning weight. The window takes two value expressions as parameters plus any number of optional additional expressions to return properties of the last event. The value expressions must return numeric values:

weighted_avg(value_expression_field, value_expression_weight [,expression, ...] [ * ])

Table 14.8. Weighted Average Derived Properties

Property Name	Description
`average`	Weighted average

A statement that derives the volume-weighted average price for the last 3 seconds for a given symbol is shown below:

select average 
from StockTickEvent(symbol='GE')#time(3 seconds)#weighted_avg(price, volume)

To add properties from the event stream you may simply add all additional properties as parameters to the window.

This example selects all of the derived values, based on the price and volume properties, plus the values of the symbol and feed event properties:

select *
from StockTickEvent#time(3 seconds)#weighted_avg(price, volume, symbol, feed)

The next example selects all of the derived values plus the values of all event properties:

select *
from StockTickEvent#time(3 seconds)#weighted_avg(price, volume, *)

Aggregation functions could instead be used to compute the weighted average as well. The next example also posts weighted average per symbol considering the last 3 seconds of stock tick data:

select symbol, sum(price*volume)/sum(volume)
from StockTickEvent#time(3 seconds) group by symbol

The following example computes weighted average keeping a separate data window per symbol considering the last 5 events of each symbol:

select symbol, average
from StockTickEvent#groupwin(symbol)#length(5)#weighted_avg(price, volume)

Chapter 15. Compiler Reference

15.1. Introduction

15.2. Concepts

15.2.1. Module
15.2.2. EPL-objects
15.2.3. Dependencies
15.2.4. Dependency Resolution
15.2.5. Access Modifiers
15.2.6. Bus Modifier for Event Types

15.3. Compiling a Module

15.4. Reading and Writing a Compiled Module

15.5. Reading Module Content

15.6. Compiler Arguments

15.6.1. Compiler Configuration
15.6.2. Compiler Path
15.6.3. Compiler Options
15.6.4. Compiler Path Cache

15.7. Statement Object Model

15.7.1. Building an Object Model
15.7.2. Building Expressions
15.7.3. Building a Pattern Statement
15.7.4. Building a Select Statement
15.7.5. Building a Create-Variable and On-Set Statement
15.7.6. Building Create-Window, On-Delete and On-Select Statements

15.8. Substitution Parameters

15.9. OSGi, Class Loader, Class-For-Name

15.10. Authoring Tools

15.11. Testing Tools

15.12. Debugging

15.12.1. @Audit Annotation

15.13. Ordering Multiple Modules

15.14. Logging

15.15. Debugging Generated Code

15.16. Compiler Version and Runtime Version

15.17. Compiler Byte Code Optimizations

15.18. Compiler Filter Expression Analysis

15.18.1. Constituent Analysis
15.18.2. Value Composite Expression Analysis
15.18.3. Lookupable Composite Expression Analysis
15.18.4. Boolean Reusable Expression Analysis
15.18.5. Condition Analysis and Planning

15.19. Limitations

15.1. Introduction

The compiler provides the following functions:

Compiles a module to JVM byte code.
Compiles a fire-and-forget query to JVM byte code.
Parses a module producing a module object model.
Parses a statement producing a statement object model.
Validates the syntax of a module.
Reads a module from external sources.

The most important function of the compiler is to produce byte code for your module. Deploy the byte code into a runtime for execution.

The compiler interface is EPCompiler in package com.espertech.esper.compiler.client. Your application obtains a compiler instance by calling the getCompiler method of EPCompilerProvider.

Use the compiler as follows:

EPCompiler epCompiler = EPCompilerProvider.getCompiler();

The compiler is a stateless service. It does not have any state that it keeps between calls.
You may obtain and use any number of compiler instances in parallel.
You may share a compiler instance between threads.
All compiler methods are thread-safe.
The compiler internally uses multiple threads for compiling each statement in parallel, for modules that have multiple statements.

Tip

When using the compiler in separate threads, make sure the input to the compiler does not get modified while the compiler is using it. For example, the application should not modify compiler configuration or compiler path at the same time as invoking a compiler method.

15.2. Concepts

15.2.1. Module

A module contains zero, one or multiple statements. A module is a source code unit as the compiler turns a module into byte code. A module does not need to be a text - a module can also be an object model.

In module text, statements appear separated by the semicolon (;) character. If there is a single statement in the module the semicolon can be left off.

The synopsis of a module file is:

[module module_name;]
	[uses module_name; | import import_name;] [uses module_name; | import import_name;] [...]
	[epl_statement;] [epl_statement;] [...]

Use the module keyword followed a module_name identifier or a package (identifiers separated by dots) to declare the name of the module. The module name declaration must be at the beginning of the file, comments and whitespace excluded. The module name serves to check uses-dependences of other modules.

If a module file requires certain constructs that may be shared by other modules, such as named windows, tables, variables, event types, variant streams or inserted-into streams required by statements, a module file may specify dependent modules with the uses keyword. It can be used to avoid name conflicts and automatic deployment can use this information to determine deployment order.

If the statements in the module require Java classes such as for underlying events or user-defined functions, use the import keyword followed by the fully-qualified class name or package name in the format package.*. The uses and import keywords are optional and must occur after the module declaration.

Following the optional deployment instructions are any number of epl_statement statements that are separated by semicolon (;).

The following is a sample module file explained in detail thereafter:

// Declare the name for the module (optional).
module org.myorganization.switchmonitor;

// Declare other module(s) that this module depends on (optional).
// This can be used to resolve name conflicts.
uses org.myorganization.common;

// Import any Java/.NET classes of the given package name (optional). 
// Imports only handle classpath and do not import other modules.
import org.myorganization.events.*;

// Declare an event type based on a Java class in the package that was imported as above
create schema MySwitchEvent as MySwitchEventPOJO;

// Sample statement
@Name('Off-On-Detector')
insert into MyOffOnStream
select * from pattern[every-distinct(id) a=MySwitchEvent(status='off') 
  -> b=MySwitchEvent(id=a.id, status='on')];

// Sample statement
@Name('Count-Switched-On')
@Description('Count per switch id of the number of Off-to-On switches in the last 1 hour')
select id, count(*) from MyOffOnStream#time(1 hour) group by id;

The example above declares a module name of org.myorganization.switchmonitor. The example demonstrates the import keyword to make a package name known to the compiler for resolving classpath items, as the example assumes that MySwitchEventPOJO is a POJO event class. In addition the example module contains two statements separated by semicolon characters.

15.2.2. EPL-objects

The following types of EPL-objects are managed by the compiler and runtime:

Event types define stream type information and are added using create schema or by configuration.
Variables are free-form value holders and are added using create variable or by configuration.
Named windows are sharable named data windows and are added using create window.
Tables are sharable organized rows with columns that are simple, aggregation and complex types, and are added using create table.
Contexts define analysis lifecycle and are added using create context.
Expressions and Scripts are reusable expressions and are added using create expression.
Inlined-Classes are classes in Java (or C# for NEsper) that are part of the EPL code and are added using create inlined_class.
Indexes organize named window events and table rows for fast lookup and are added using create index.

Your application can pre-configure event types and variables in a Configuration object.

A module can create any number of EPL-objects.

A module can depend on EPL-objects that are pre-configured or other modules created.

15.2.3. Dependencies

A module usually depends on event types and may also depend on other EPL-objects such as named windows or tables, for example. The compiler resolves all dependencies at compile-time. It produces byte code based on the information associated with the EPL-object. Upon deploying a compiled module's byte code into the runtime the runtime validates that dependencies exist.

For example, consider the following module:

select accountId, amount from Withdrawal

The module above depends on the event type Withdrawal. The compiler resolves the event type name to an EventType instance. It produces code according to the event type. At time of deployment of the compiled module the runtime verifies that the Withdrawal event type exists.

Specifically, the compiler generates code like this:

If the Withdrawal event type is a Map-based event type, the compiler produces code such as event.get("accountId").
If the Withdrawal event type is an Object-Array-based event type, the compiler produces code such as event[index].
If the Withdrawal event type is a Bean-based event type, the compiler produces code such as event.getAccountId().

Note

The compiler only tracks dependencies on EPL-objects.

The compiler does not track classpath dependencies. The runtime does not validate classpath dependencies.

The runtime validates that EPL-object dependencies exist before deploying a compiled module.

The runtime does not validate that the information about the EPL-object is the same as at compile-time.

In other words, the runtime does not validate that event property names, event property types, table column names and types, variable types, index property names and other compile-time information matches the information that was provided at compile time.

15.2.4. Dependency Resolution

The compiler resolves an EPL-object by its name by looking at:

The EPL-objects created by the same module (also known as local)
The EPL-objects created by the other modules (also known as path).
The pre-configured event types and variables.

The term path encompasses the EPL-objects other modules define. The term local encompasses the EPL-objects the same module defines.

Coming back to the previous example:

select accountId, amount from Withdrawal

The compiler finds an event type by name Withdrawal by:

Checking whether Withdrawal is an event type that the same module defined by create schema (local).
Checking whether Withdrawal is an event type that another modules defined by create schema (path).
Checking whether Withdrawal is a pre-configured event type.

In case the name cannot be resolved the compilation fails.

In case the name is found multiple times, the compiler checks as follows:

If the name is a pre-configured EPL-object and the name is also found in path the validation fails.
If the name is found in local, and the name is found in path or preconfigured, the validation fails.
If the name is found in path for multiple modules, and if there is no module-uses provided, the validation fails.
If the name is found in path for multiple modules and there are module-uses module names provided the EPL object module name must match one of the module names in module-uses.

15.2.5. Access Modifiers

Access level modifiers determine whether other modules can use a particular EPL-object.

An EPL-object may be declared with the modifier public, in which case that EPL-object is visible to all other modules.

An EPL-object may be declared with the modifier protected, in which case that EPL-object is visible to other modules that have the same module name.

An EPL-object may be declared with the modifier private (the default), in which case that EPL-object is not visible to other modules.

Your application may set access modifiers by:

Using an annotation i.e. @public, @protected, @private.
Setting default access modifiers in the ConfigurationCompilerByteCode that is part of the Configuration object.
Computing access modifiers by providing a callback in CompilerOptions compiler options. Any computed value overrides the annotation or configuration default.

The following module declares a public named window to hold the last 10 seconds of withdrawal events:

@public create window WithdrawalWindow#time(10) as Withdrawal

15.2.6. Bus Modifier for Event Types

For event types there is a bus modifier that determines whether or not the event type is available for use with the sendEventType methods of the EPEventService runtime event service.

An event type may be declared with the bus modifier, in which case calls to sendEventType process the event.

An event type may be declared with the non-bus modifier (the default), in which case calls to sendEventType cause an exception to be thrown.

To understand this better, here is what sendEventType of EPEventService does: When your application calls any of the sendEventBean, sendEventMap, sendEventObjectArray, sendEventJson, sendEventXMLDOM or sendEventAvro methods of EPEventService, the runtime finds the event type using the event type name that is passed. It associates the event type to the event object for processing the given event. If the event type name is not recognized or the event type does not have the bus modifier it throws an exception.

The bus modifier is not required for pre-configured event types. The bus modifier requires public access.

Your application may set the bus modifier by:

Using the @buseventtype annotation.
Setting the default bus modifier in the ConfigurationCompilerByteCode that is part of the Configuration object.
Computing a bus modifier by providing a callback in CompilerOptions compiler options. Any computed value overrides the annotation or configuration default.

The following module declares a public event type that allows an application to send in events of that name:

@public @buseventtype create schema AccountQueryEvent (accountId string)

The information herein pertains to the routeEventType and EventSender as well.

15.3. Compiling a Module

The compile method takes two parameters. The first parameter is the module text or an module object model. The second parameter are compiler arguments.

The output of the compiler is an EPCompiled instance. You can deploy EPCompiled instances directly into a runtime as described in Section 16.4, “Deploying and Undeploying Using EPDeploymentService”.

15.4. Reading and Writing a Compiled Module

The EPCompiledIOUtil class is a utility for writing and reading EPCompiled instances to and from jar-files:

Write an EPCompiled instance to a jar file.
Read a jar file previously written by EPCompiledIOUtil and return an EPCompiled instance.

15.5. Reading Module Content

Read and parse module files using the readModule and parseModule methods, which return a Module instance to represent the module information.

This code snippet demonstrates reading and parsing a module given a file name:

Module module = EPCompilerProvider.getCompiler().read(new File("switchmonitor.epl"));

15.6. Compiler Arguments

The compiler arguments are:

The Configuration object can provide pre-configured event types and variables as well as other compiler settings.
The CompilerPath passes information that the compiler uses to determine the EPL-objects that the module may depend on.
The CompilerOptions are compiler instructions.

15.6.1. Compiler Configuration

Pass a Configuration instance to the compiler to configure the compiler. By default the compiler uses an empty configuration object.

The compiler only uses the common section and the compiler section of the configuration. The compiler ignores the runtime section of the configuration.

It is not necessary to pass a configuration object or to pre-configure event types. You may create event types by means of create schema.

A pre-configured event types is a convenience since the event type is already defined and ready to use. The common section of the configuration holds the pre-configured event types. The following sample adds a pre-configured WithdrawalEvent map-based event type:

Map<String, Object> columns = new LinkedHashMap<>();
columns.put("accountId", String.class);
columns.put("amount", double.class);

Configuration configuration = new Configuration();
configuration.getCommon().addEventType("WithdrawalEvent", columns);
CompilerArguments args = new CompilerArguments(configuration);

To obtain a configuration object from a runtime call getConfigurationDeepCopy on EPRuntime:

Configuration configuration = epRuntime.getConfigurationDeepCopy();
CompilerArguments args = new CompilerArguments(configuration);

More information on the common and compiler configuration can be found at Chapter 17, Configuration.

15.6.1.1. Configuring the Compiler for Subscribers

By default the compiler does not generate code for subscribers and the setSubscriber method on EPStatement throws an exception.

You may set the allowSubscriber option:

Configuration configuration = new Configuration();
configuration.getCompiler().getByteCode().setAllowSubscriber(true);
CompilerArguments args = new CompilerArguments(configuration);

15.6.2. Compiler Path

The compiler path provides EPL-objects that other modules may declare and that the current module may use.

For example, assume a module M₁ that declares a named window WithdrawalWindow:

@public create window WithdrawalWindow#time(10) as Withdrawal

A second module M₂ may query the named window like so:

select (select count(*) from WithdrawalWindow) as cnt from Withdrawal

Module M₂ depends on the EPL-object WithdrawalWindow (a named window) that module M₁ declares.

You can build a path from:

An existing runtime. This adds all EPL-objects that are currently deployed into the runtime to the path.
Compiled modules.

Assume that your application compiled module M₁ like so:

Map<String, Object> columns = new LinkedHashMap<>();
columns.put("accountId", String.class);
columns.put("amount", double.class);

Configuration configuration = new Configuration();
configuration.getCommon().addEventType("WithdrawalEvent", columns);

CompilerArguments arguments = new CompilerArguments(configuration);
EPCompiled compiledModuleM1 = EPCompilerProvider.getCompiler().compile("@public create window WithdrawalWindow#time(10) as Withdrawal", arguments);

The compiledModuleM1 instance holds the byte code of module M₁.

15.6.2.1. Compiling Against a Runtime

After deploying compiled modules to a runtime, the compiler can build the path from the runtime.

The getRuntimePath method of EPRuntime returns the path object for use by the compiler. The path object is an instance of EPCompilerPathable.

The add method of CompilerPath accepts a EPCompilerPathable instance provided by a runtime.

For example, as follows:

Map<String, Object> columns = new LinkedHashMap<>();
columns.put("accountId", String.class);
columns.put("amount", double.class);

Configuration configuration = new Configuration();
configuration.getCommon().addEventType("WithdrawalEvent", columns);

// Get a runtime
EPRuntime runtime = EPRuntimeProvider.getDefaultRuntime(configuration);
runtime.getDeploymentService().deploy(compiledModuleM1);

// Compile another module
CompilerArguments arguments = new CompilerArguments(configuration);
arguments.getPath().add(runtime.getRuntimePath());
EPCompiled compiledModuleM2 = EPCompilerProvider.getCompiler().compile("select (select count(*) from WithdrawalWindow) as cnt from Withdrawal", arguments);

15.6.2.2. Adding a Compiled Module to Path

Use the addPath method of CompilerPath to add a compiled module to path.

For example, as follows:

CompilerArguments arguments = new CompilerArguments(configuration);
arguments.getPath().add(compiledModuleM1);
EPCompiled compiledModuleM2 = EPCompilerProvider.getCompiler().compile("select (select count(*) from WithdrawalWindow) as cnt from Withdrawal", arguments);

Tip

Use a path cache when there are many compiled modules in the path, see Section 15.6.4, “Compiler Path Cache”.

15.6.3. Compiler Options

Compiler options provide compiler callbacks and other compile-time parameters:

Provide or override access modifiers and bus event type modifier.
Provide or override the statement name.
Provide a statement user object and that can be obtained from an EPStatement with getUserObjectCompileTime.
Provide or override the module name.
Provide or override module-uses information.
Receive Janino class-level compiler output for inlined-classes for example for use with black-listing or white-listing classes based on class constant pool information.

Please consult the JavaDoc for more information.

15.6.4. Compiler Path Cache

The path cache is not used by default and can optionally be set. It is only for use when your application adds compiled modules (EPCompiled instances) to the path as described in Section 15.6.2.2, “Adding a Compiled Module to Path”.

The compiler is a stateless service and does not retain any information between calls. It therefore does not know, for each of the EPCompiled instances in the path, the visible EPL objects that the EPCompiled in the path provides. The path cache retains this information so that subsequent calls to the compiler do not need to inspect each EPCompiled in the path.

Use CompilerPathCache.getInstance() and the setPathCache method of CompilerOptions to use a path cache. A path cache is thread-safe.

Obtain the path cache as follows:

// Obtain path cache for reuse when compiling modules
CompilerPathCache pathCache = CompilerPathCache.getInstance();

Set the path cache like so:

arguments.getOptions().setPathCache(pathCache);

The compiler automatically adds the current EPCompiled output and all EPCompiled instances in path to the cache.

15.7. Statement Object Model

The statement object model is a set of classes that provide an object-oriented representation of statement. The object model classes are found in package com.espertech.esper.common.client.soda. An instance of EPStatementObjectModel represents a statement's object model.

The statement object model classes are a full and complete specification of a statement. All EPL constructs including expressions and sub-queries are available in the statement object model.

The statement object model provides the means to building, changing or interrogating statements beyond the string representation. The object graph of the statement object model is fully navigable for easy querying by code, and is also serializable allowing applications to persist or transport statements in object form, when required.

The statement object model supports full round-trip from object model to statement string and back to object model: A statement object model can be rendered into a string representation via the toEPL method on EPStatementObjectModel. Further, the compiler API allows compiling a statement string into an object model representation via the eplToModel method on EPCompiler.

The statement object model is fully mutable. Mutating any list such as returned by getChildren(), for example, is acceptable and supported.

The following limitations apply:

Statement object model classes are not safe for sharing between threads other than for read access.
Between versions the serialized form of the object model is subject to change. There are no guarantees that the serialized object model of one version will be fully compatible with the serialized object model generated by another version. Please consider this issue when storing object models in persistent store.

15.7.1. Building an Object Model

A EPStatementObjectModel consists of an object graph representing all possible clauses that can be part of a statement.

Among all clauses, the SelectClause and FromClause objects are required clauses that must be present, in order to define what to select and where to select from.

Table 15.1. Required Statement Object Model Instances

Class	Description
EPStatementObjectModel	All statement clauses for a statement, such as the select-clause and the from-clause, are specified within the object graph of an instance of this class
SelectClause	A list of the selection properties or expressions, or a wildcard
FromClause	A list of one or more streams; A stream can be a filter-based, a pattern-based, SQL-based and other; Add data windows here.

Part of the statement object model package are convenient builder classes that make it easy to build a new object model or change an existing object model. The SelectClause and FromClause are such builder classes and provide convenient create methods.

Within the from-clause you have a choice of different streams to select on. The FilterStream class represents a stream that is filled by events of a certain type and that pass an optional filter expression.

We can use the classes introduced above to create a simple statement object model:

EPStatementObjectModel model = new EPStatementObjectModel();
model.setSelectClause(SelectClause.createWildcard());
model.setFromClause(FromClause.create(FilterStream.create("ReadyEvent")));

The model as above is equivalent to:

select * from ReadyEvent

Notes on usage:

Variable names can simply be treated as property names.
When selecting from named windows or tables, the name of the named window or table is the event type name for use in FilterStream instances or patterns.
To compile an arbitrary sub-expression text into an Expression object representation, simply add the expression text to a where clause, compile the EPL string into an object model via the eplToModel method on EPCompiler, and obtain the compiled where from the EPStatementObjectModel via the getWhereClause method.

15.7.2. Building Expressions

The EPStatementObjectModel includes an optional where-clause. The where-clause is a filter expression that the runtime applies to events in one or more streams. The key interface for all expressions is the Expression interface.

The Expressions class provides a convenient way of obtaining Expression instances for all possible expressions. Please consult the JavaDoc for detailed method information. The next example discusses sample where-clause expressions.

Use the Expressions class as a service for creating expression instances, and add additional expressions via the add method that most expressions provide.

The next example adds a simple where-clause to the EPL as shown earlier:

select * from ReadyEvent where line=8

And the code to add a where-clause to the object model is below.

model.setWhereClause(Expressions.eq("line", 8));

The following example considers a more complex where-clause. Assume you need to build an expression using logical-and and logical-or:

select * from ReadyEvent 
where (line=8) or (line=10 and age<5)

The code for building such a where-clause by means of the object model classes is:

model.setWhereClause(Expressions.or()
  .add(Expressions.eq("line", 8))
  .add(Expressions.and()
      .add(Expressions.eq("line", 10))
      .add(Expressions.lt("age", 5))
  ));

15.7.3. Building a Pattern Statement

The Patterns class is a factory for building pattern expressions. It provides convenient methods to create all pattern expressions of the pattern language.

Patterns in EPL are seen as a stream of events that consist of patterns matches. The PatternStream class represents a stream of pattern matches and contains a pattern expression within.

For instance, consider the following pattern statement.

select * from pattern [every a=MyAEvent and not b=MyBEvent]

The next code snippet outlines how to use the statement object model and specifically the Patterns class to create a statement object model that is equivalent to the pattern statement above.

EPStatementObjectModel model = new EPStatementObjectModel();
model.setSelectClause(SelectClause.createWildcard());
PatternExpr pattern = Patterns.and()
  .add(Patterns.everyFilter("MyAEvent", "a"))
  .add(Patterns.notFilter("MyBEvent", "b"));
model.setFromClause(FromClause.create(PatternStream.create(pattern)));

15.7.4. Building a Select Statement

This section builds a complete example statement and includes all optional clauses in one statement, to demonstrate the object model API.

A sample statement:

insert into ReadyStreamAvg(line, avgAge) 
select line, avg(age) as avgAge 
from ReadyEvent(line in (1, 8, 10))#time(10) as RE
where RE.waverId != null
group by line 
having avg(age) < 0
output every 10.0 seconds 
order by line

Finally, this code snippet builds the above statement from scratch:

EPStatementObjectModel model = new EPStatementObjectModel();
model.setInsertInto(InsertIntoClause.create("ReadyStreamAvg", "line", "avgAge"));
model.setSelectClause(SelectClause.create()
    .add("line")
    .add(Expressions.avg("age"), "avgAge"));
Filter filter = Filter.create("ReadyEvent", Expressions.in("line", 1, 8, 10));
model.setFromClause(FromClause.create(
    FilterStream.create(filter, "RE").addView("win", "time", 10)));
model.setWhereClause(Expressions.isNotNull("RE.waverId"));
model.setGroupByClause(GroupByClause.create("line"));
model.setHavingClause(Expressions.lt(Expressions.avg("age"), Expressions.constant(0)));
model.setOutputLimitClause(OutputLimitClause.create(OutputLimitSelector.DEFAULT, Expressions.timePeriod(null, null, null, 10.0, null)));
model.setOrderByClause(OrderByClause.create("line"));

15.7.5. Building a Create-Variable and On-Set Statement

This sample statement creates a variable:

create variable integer var_output_rate = 10

The code to build the above statement using the object model:

EPStatementObjectModel model = new EPStatementObjectModel();
model.setCreateVariable(CreateVariableClause.create("integer", "var_output_rate", 10));

A second statement sets the variable to a new value:

on NewValueEvent set var_output_rate = new_rate

The code to build the above statement using the object model:

EPStatementObjectModel model = new EPStatementObjectModel();
model.setOnExpr(OnClause.createOnSet("var_output_rate", Expressions.property("new_rate")));
model.setFromClause(FromClause.create(FilterStream.create("NewValueEvent")));

15.7.6. Building Create-Window, On-Delete and On-Select Statements

This sample statement creates a named window:

create window OrdersTimeWindow#time(30 sec) as select symbol as sym, volume as vol, price from OrderEvent

The is the code that builds the create-window statement as above:

EPStatementObjectModel model = new EPStatementObjectModel();
model.setCreateWindow(CreateWindowClause.create("OrdersTimeWindow").addView("win", "time", 30));
model.setSelectClause(SelectClause.create()
		.addWithName("symbol", "sym")
		.addWithName("volume", "vol")
		.add("price"));
model.setFromClause(FromClause.create(FilterStream.create("OrderEvent)));

A second statement deletes from the named window:

on NewOrderEvent as myNewOrders
delete from OrdersNamedWindow as myNamedWindow
where myNamedWindow.symbol = myNewOrders.symbol

The object model is built by:

EPStatementObjectModel model = new EPStatementObjectModel();
model.setOnExpr(OnClause.createOnDelete("OrdersNamedWindow", "myNamedWindow"));
model.setFromClause(FromClause.create(FilterStream.create("NewOrderEvent", "myNewOrders")));
model.setWhereClause(Expressions.eqProperty("myNamedWindow.symbol", "myNewOrders.symbol"));

A third statement selects from the named window using the non-continuous on-demand selection via on-select:

on QueryEvent(volume>0) as query
select count(*) from OrdersNamedWindow as win
where win.symbol = query.symbol

The on-select statement is built from scratch via the object model as follows:

EPStatementObjectModel model = new EPStatementObjectModel();
model.setOnExpr(OnClause.createOnSelect("OrdersNamedWindow", "win"));
model.setWhereClause(Expressions.eqProperty("win.symbol", "query.symbol"));
model.setFromClause(FromClause.create(FilterStream.create("QueryEvent", "query", 
  Expressions.gt("volume", 0))));
model.setSelectClause(SelectClause.create().add(Expressions.countStar()));

15.8. Substitution Parameters

Substitution parameters have the following syntax:

? [:[name] [:type]]

The name is optional. The absence of a name means the substitution parameter is only addressable by index.

The type is optional. The absence of the type means the type of the substitution parameter is java.lang.Object. Use cast or provide a type name when your expression requires a strongly-typed value. The type can also be a parameterized type such as java.util.List<String>.

Here are a few examples of valid substitution parameters:

Table 15.2. Valid Substitution Parameters

Value	Description
?	Unnamed and typed `Object`.
?::int	Unnamed and typed `int`.
?:param:string	Named and typed `string`.

All substitution parameters must either be unnamed or named. It is not possible to mix the two styles.

If not assigning a name to substitution parameters, the compiler assigns the first substitution parameter an index of 1 and subsequent parameters increment the index by one.

If assigning a name to each substitution parameter, the name can include slash (/) characters and can occur multiple times.

Substitution parameters can be inserted into any EPL construct that takes an expression. They are therefore valid in any clauses such as the select-clause, from-clause filters, where-clause, group-by-clause, having-clause or order-by-clause, including data window parameters and pattern observers and guards, for example. Substitution parameters cannot be used where a numeric constant is required rather than an expression.

You may use square brackets ([]) to denote array-types and [primitive] for array of primitive. For example int[primitive] for array of int-primitive and int[] for array of Integer.

All substitution parameters must be replaced by actual values at time of deployment.

15.9. OSGi, Class Loader, Class-For-Name

The configuration object (Configuration), in respect to classes, holds the fully-qualified class name and does not generally hold Class references. This is by design since the configuration object can be populated from XML.

The compiler may need to look up a class by name and may need to obtain a class loader. Your application has full control over class-for-name and classloader use. OSGi environments can provide a specific class-for-name and class loader. Please refer to Section 17.7, “Passing Services or Transient Objects”.

15.10. Authoring Tools

Enterprise Edition includes authoring tools for statements and modules by providing form-based dialogs, templates, an expression builder, simulation tool and other tools. Enterprise Edition also supports hot deployment and packaging options for EPL and related code.

Statements can be organized into modules as described above. Any text editor can edit statements and module text. A text editor or IDE that highlights SQL syntax or keywords works.

For authoring configuration files please consult the XSD schema files as provided with the distribution.

For information on authoring event classes or event definitions in general please see Chapter 3, Event Representations or Section 5.15, “Declaring an Event Type: Create Schema”.

15.11. Testing Tools

We recommend testing modules using a test framework such as JUnit or TestNG. Please consult the regression test suite for extensive examples, which can be downloaded from the distribution site.

Esper's API provides test framework classes to simplify automated testing of statements. Please see Section 16.18, “Test and Assertion Support” for more information.

We recommend performing latency and throughput tests early in the development lifecycle. Please consider the performance tips in Chapter 24, Performance for optimal performance.

Consider runtime and statement metrics reporting for identifying slow-performing statements, for example. See Section 16.12, “Runtime and Statement Metrics Reporting”.

15.12. Debugging

Enterprise Edition includes a debugger for module execution.

One important tool for debugging without Enterprise Edition is the parameterized @Audit annotation. This annotation allows to output, on statement-level, detailed information about many aspects of statement processing.

Another tool for logging runtime-level detail is Section 17.6.2.1, “Execution Path Debug Logging”.

Please see Section 17.9, “Logging Configuration” for information on configuring logging in general.

15.12.1. @Audit Annotation

Use the @Audit annotation to have the runtime output detailed information about statement processing. The runtime reports, at INFO level, the information under log name com.espertech.esper.audit. You may define an output format for audit information via configuration.

You may provide a comma-separated list of category names to @Audit to output information related to specific categories only. The table below lists all available categories. If no parameter is provided, the runtime outputs information for all categories. Category names are not case-sensitive.

For the next statement the runtime produces detailed processing information (all categories) for the statement:

@Name('All Order Events') @Audit select * from OrderEvent

For the next statement the runtime provides information about new events and also about event property values (2 categories are listed):

@Name('All Order Events') @Audit('stream,property') select price from OrderEvent

Here is a more complete example that uses the API to create the schema, create above statement and send an event:

try {
  String module =
    "@public @buseventtype create schema OrderEvent(price double);\n" +
    "@name('All-Order-Events') @Audit('stream,property') select price from OrderEvent;\n";
  EPCompiled compiled = EPCompilerProvider.getCompiler().compile(module, null);

  EPRuntime runtime = EPRuntimeProvider.getDefaultRuntime();
  EPDeployment deployment = runtime.getDeploymentService().deploy(compiled);
  deployment.getStatements()[0].addListener(new SupportUpdateListener());
  runtime.getEventService().sendEventMap(Collections.singletonMap("price", 100d), "OrderEvent");
} catch (Throwable t) {
  log.error(t.getMessage(), t);
}

The output is similar to the following:

INFO  [audit] Statement All-Order-Events stream OrderEvent inserted {price=100.0}
INFO  [audit] Statement All-Order-Events property price value 100.0

Table 15.3. @Audit Categories

Category	Description
ContextPartition	Each context partition allocation and de-allocation (only for statements that declare a context).
Dataflow-Source	Each data flow source operator providing an event.
Dataflow-Op	Each data flow operator processing an event.
Dataflow-Transition	Each data flow instance state transition.
Exprdef	Each expression declaration name and return value.
Expression	Each top-level expression and its return value.
Expression-nested	Each expression including child or nested expressions and their return value.
Insert	Each event inserted via insert-into.
Pattern	Each pattern sub-expression and its change in truth-value.
Pattern-instances	Each pattern sub-expression and its count of active instances.
Property	Each property name and the event's property value.
Schedule	Each schedule modification and trigger received by a statement.
Stream	Each new event received by a statement.
View	Each data window name and its insert and remove stream.

Note that the runtime only evaluates select-clause expressions if either a listener or subscriber is attached to the statement or if used with insert-into.

15.13. Ordering Multiple Modules

Since modules may have inter-dependencies as discussed under the uses declaration, there is a ModuleOrderUtil class that provides the getModuleOrder method to order a collection of modules before deployment.

Assuming your application reads multiple modules into a mymodules module list, this code snippet orders the modules for deployment and validates dependency declarations for each module:

List<Module> mymodules =  ... read modules...;  
ModuleOrder order = ModuleOrderUtil.getModuleOrder(mymodules, new ModuleOrderOptions());

15.14. Logging

You can log generated classes at INFO log level by setting the configuration flag for code logging as described in Section 17.5.3.1, “Byte Code Generation Logging”.

15.15. Debugging Generated Code

The information herein is for developers and is specific to the Janino compiler at the version provided with the distribution.

Set the system property org.codehaus.janino.source_debugging.enable to true to have Janino compile code with debug symbols.

Set the system property org.codehaus.janino.source_debugging.dir to a file system directory to have Janino generate classes into a given directory.

The IDE can debug into generated classes and show the source code provided that the IDE can access the source code. For example:

-Dorg.codehaus.janino.source_debugging.dir=/path/to/directory
-Dorg.codehaus.janino.source_debugging.enable=true

To include additional EPL-related comments in the generated code you can change the configuration as outlined in Section 17.5.1, “Compiler Settings Related to Byte Code Generation”.

15.16. Compiler Version and Runtime Version

The version format is major.minor.patch-level.

When deploying a compiled module to a runtime, or when executing a compiled fire-and-forget query, the runtime compares the compiler version that produced the compiled module or compiled query with the runtime version. If the major or minor version does not match, the runtime indicates a version mismatch by throwing an exception.

For example, an application may compile an EPL module using the version 8.0.0 compiler, i.e. the compiler major version is eight and the compiler minor version is zero and the compiler patch level is zero. Assume the application attempts to deploy the compiled module to a runtime of version 8.1.0, i.e. the runtime major version is eight and the runtime minor version is one and the runtime patch level is zero. The runtime throws an EPDeployDeploymentVersionException exception to indicate that the minor version mismatches.

15.17. Compiler Byte Code Optimizations

The compiler generates byte code that avoids down-casts and branching. It also removes many virtual calls as it transforms expression trees into byte code.

For aggregations the compiler produces a custom aggregation row class that has fields which represent the aggregation state. Therefore each aggregation row does not need additional objects to represent aggregations such as averages or sums and instead the aggregations are fields of the same class, reducing the number objects that the runtime manages per group-by key.

For any composite keys out of two or more expressions the compiler produces a class that represents the composite key and that implements equals and hashCode. This is applicable to the group-by clause including rollup, data windows with keys (such as #unique, #firstunique, #groupwin, #rank), partition-by for keyed segmented contexts, contexts with distinct, the select-clause distinct keyword, the query planner when planning implicit and explicit indexes, create index, every-distinct, the partition-by clause for match-recognize, table column keyed-access expressions, the for-clause for grouped delivery and the distinctOf enumeration method.

15.18. Compiler Filter Expression Analysis

The compiler analyzes filter expressions so that the runtime can build filter indexes. For more information on filter indexes please see Section 2.18.2, “Filter Indexes”.

By default the compiler uses advanced techniques for planning filter indexes. Your application may disable some or all of the advanced techniques as described in Section 17.5.8.2, “Filter Index Planning”.

To output the filter plans please set a compiler flag as described in Section 17.5.3.2, “Filter Plan Logging”.

Tip

Filter indexed planning, like all index planning, reflects a trade-off of amount of space needed for the index, the processing time required to build and maintain an index versus the performance gain of matching incoming events to statements and their partitions when there are many (same or different) filters.
The compiler does automatically plan all possible filter indexes. For performance tuning the ConfigurationCompilerExecution.FilterIndexPlanning.BASIC setting and ConfigurationCompilerExecution.FilterIndexPlanning.NONE setting and the hints are also available.

15.18.1. Constituent Analysis

This section applies in the default configuration (FilterIndexPlanning.ADVANCED) and the non-default FilterIndexPlanning.BASIC configuation.

The compiler analyzes filter expressions by first determining the constituents. Constituents are the individual expressions, connected by the and and or logical operator, that each return a boolean-type value and that make up the overall expression.

For instance, this EPL statement:

select * from Withdrawal(accountId = '123' and accountType = 'Checking')

The constituent expressions are accountId = '123' and accountType = 'Checking' (two constituent expressions).

The compiler analyzes each constituent expression separately. From a single constituent expression it attempts to form a lookupable-expression, filter-index-operator and value-expression triplet. For those constituent expressions that the compiler cannot form a triplet the compiler aggregates such expression into a common boolean expression. The remainder of the discussion focuses on a constituent expression and forming a triplet from it.

The lookupable-expression is the expression providing the filter index lookup value, for example the accountId expression in accountId = '123'.

The value-expression is the expression providing the indexed value, for instance the '123' expression in accountId = '123'.

The filter-index-operator means the type of index such as equals(=), relational (<,>,<=, >=) etc..

Therefore, in Withdrawal(accountId = '123'), the filter-index-operator is equals and the lookupable-expression is accountId and the value-expression is '123'.

The expressions that are left and right of a filter-index-operator are commutative, meaning they give the same result whatever the expression occurs on either side of the operator. It is equivalent to say accountId = '123' and '123' = accountId.

The order in which constituent expressions appear does matter. Place the most frequent constituent expressions first. Each filter-index-operator has a precedence level and the compiler orders by precedence level keeping the order as provided within the same precedence level. The precedence levels are, sorted by higher precedence level first, as follows: equal, is, in-list-of-values, advanced-index, range-open, range-half-open, range-half-closed, range-closed, less, less-or-equal, greater-or-equal, greater, not-range-closed, not-range-half-closed, not-range-half-open, not-range-open, not-in-list-of-values, not-equal, is-not.

The lookupable-expression in a triplet must be any of the following:

Event property of the arriving event
Plug-in single-row node with filter-optimizable setting enabled
Declared expression
The typeOf function
Other expressions; See hint Section 15.18.3, “Lookupable Composite Expression Analysis” for more information.

The following operators are the triplet filter-index-operator operators. A constituent expression that has one of these operators qualifies for a triplet:

equals =
not equals !=
comparison operators < , > , >=, <=
ranges
- use the between keyword for a closed range where both endpoints are included
- use the in keyword and round () or square brackets [] to control how endpoints are included
- for inverted ranges use the not keyword and the between or in keywords
list-of-values checks using the in keyword or the not in keywords followed by a comma-separated list of values
Other operators; See hint Section 15.18.4, “Boolean Reusable Expression Analysis” for more information.

The value-expression in a triplet can only be any of the expressions below:

Constant
Substitution parameter
Event property of a prior-matching event (not the currently arriving event)
Context property
The typeOf function
Plug-in single-row node with filter-optimizable setting enabled
Declared expression
Other expressions; See hint Section 15.18.2, “Value Composite Expression Analysis” for more information.

The or logical operator plays a special role in compiler analysis. The compiler rewrites the expression accountId = '123' or accountId = '456' to accountId in ('123', '456'). It also plans multiple filter index path which is further described at Section 17.5.8.1, “Filter Service Max Filter Width”.

The order of the triplets, which follows the ordering of the constituents and the ordering according to filter-index-operator precedence level (see above), defines the default nesting of filter indexes. The runtime, upon adding to the filter, may however encounter existing filter indexes and always prefers to add to existing filter indexes and following an existing path where possible, or creating new branches and leafs from the existing path.

The compiler unwinds logical and and or and may also rewind such expressions for planning as below. Therefore the expression a and (b and c) is the same as a and b and c and the expression a or (b or c) is the same as a or b or c.

15.18.2. Value Composite Expression Analysis

This section applies to the default configuration (FilterIndexPlanning.ADVANCED) and also when the configuration is FilterIndexPlanning.BASIC and the EPL contains the @Hint('filterindex(valuecomposite)') hint.

The compiler considers an expression that is meeting certain criteria to be a value-expression provided that there is one of the filter-index-operators as described above.

The compiler considers value expressions that are any expression and that do not have any of the following:

Subquery
Non-constant variable
Event property of the currently-arriving event
Expression returning the currently-arriving event i.e. wildcard or stream alias
Table access
Plug-in single-row node with filter-optimizable setting disabled
Lamda expression
Declared expression
current_timestamp Function

For example, consider the following EPL statements:

create constant variable string PREFIX = "CHK";
select * from Withdrawal(accountId = PREFIX || '123')

The expression PREFIX || '123' is a filter index value-expression and its return value is subject to entry into a filter index as a key.

The following example demonstrates the concept further:

create context TransactionContext initiated by Transaction as txn terminated after 10 minutes;
@Hint('filterindex(valuecomposite)')
context TransactionContext select * from Withdrawal(accountId = context.txn.transactionTarget || '-A')

Assume the following events arrive:

Transaction(transactionId=1, transactionTarget='123').
Transaction(transactionId=2, transactionTarget='456').
Transaction(transactionId=3, transactionTarget='123').

The below table is a sample filter index:

Table 15.4. Sample Filter Index

Value of `context.txn.transactionTarget \|\| '-A'`	Statements and their partitions
`'123-A'`	Statement Stmt-1 Partition `context.txn.transactionId`=1, Statement Stmt-1 Partition `context.txn.transactionId`=3
`'456-A'`	Statement Stmt-1 Partition `context.txn.transactionId`=2

When a Withdrawal event arrives, the runtime evaluates accountId just once and performs one keyed lookup into the filter index to determine which statements and their partitions should process the event.

The following example pattern results in equivalent filter index planning:

select * from pattern[txn=Transaction -> Withdrawal(accountId = txn.transactionTarget || '-A')]

15.18.3. Lookupable Composite Expression Analysis

The compiler considers an expression that is meeting certain criteria to be a lookupable-expression provided that there is one of the filter-index-operators as described above.

The lookupable-expression, in any subexpression, must reference at least one event property or the event itself and cannot have any of the following:

Subquery
Non-constant variable
Context property
Table access
Plug-in single-row node with filter-optimizable setting disabled
Lambda expression
Script expression
In a pattern, event property of a tagged event, or the tagged event itself (other than self)
Method invocation of non-public inlined class

This hint is usually used when:

Using a context and there are many context partitions
Using patterns and there are many active pattern subexpressions
There are many statements that have the same filter expression(s)

Consider the following EPL statement:

select * from Withdrawal(accountId || accountType = '123Checking')

The expression accountId || accountType can serve as a lookupable-expression. The compiler plans a filter index that indexes values such as '123Checking' and that uses the return value of accountId || accountType to perform a keyed lookup into the indexed values.

Extending the example above, please look at the following EPL:

create context TransactionContext initiated by Transaction as txn terminated after 10 minutes;
@Hint('filterindex(lkupcomposite)')
context TransactionContext select * from Withdrawal(accountId || accountType = context.txn.transactionTarget)

Assume the following events arrive:

Transaction(transactionId=1, transactionTarget='123A').
Transaction(transactionId=2, transactionTarget='456B').
Transaction(transactionId=3, transactionTarget='123A').

The below table is a sample filter index:

Table 15.5. Sample Filter Index

Value of `context.txn.transactionTarget`	Statements and their partitions
`'123A'`	Statement Stmt-1 Partition `context.txn.transactionId`=1, Statement Stmt-1 Partition `context.txn.transactionId`=3
`'456B'`	Statement Stmt-1 Partition `context.txn.transactionId`=2

When a Withdrawal event arrives, the runtime evaluates accountId || accountType just once and performs one keyed lookup into the filter index to determine which statements and their partitions should process the event.

15.18.4. Boolean Reusable Expression Analysis

The compiler considers any expression that is meeting certain criteria to be a reusable boolean expression (does not require any of the filter-index-operators).

The expression must not have any of the following:

Subquery
Non-constant variable
Table access
Plug-in single-row node with filter-optimizable setting disabled
Lambda expression
Method invocation of non-public inlined class
Declared expression
Chained expression

The expression must, in any subexpression, reference at least one event property or the event itself. All other expressions must be value-expressions that meet the same requirements as outlined for valuecomposite. The expression may not, in all subexpressions, have multiple value expressions that reference context properties or, in a pattern, event properties of prior events or prior events themselves.

Review the following EPL statement:

select * from Withdrawal(accountId regexp '.*123.*')

The compiler plans a filter index that holds the expression itself and all actual values (determined at runtime, such as '.*123.*') and their related statements and partitions.

Consider the following wherein Transaction events have a regexpFilter field that provides the regexp-right-hand-side value:

create context TransactionContext initiated by Transaction as txn terminated after 10 minutes;
@name('Stmt-1') context TransactionContext select * from Withdrawal(accountId regexp context.txn.regexpFilter)

Assume the following events arrive:

Transaction(transactionId=1, regexpFilter='.*123.*).
Transaction(transactionId=2, regexpFilter='.*456.*).
Transaction(transactionId=3, regexpFilter='.*123.*).

The below table is a sample filter index:

Table 15.6. Sample Filter Index

Value of `context.txn.regexpFilter`	Statements and their partitions
`'.123.'`	Statement Stmt-1 Partition `context.txn.transactionId`=1, Statement Stmt-1 Partition `context.txn.transactionId`=3
`'.456.'`	Statement Stmt-1 Partition `context.txn.transactionId`=2

When a Withdrawal event arrives, the runtime evaluates accountId regexp '.*123.*' and accountId regexp '.*456.*' both once.

15.18.5. Condition Analysis and Planning

The compiler identifies:

Any filter-negating-expression expressions.
Any filter-confirming-expression expressions.
Any path-negating-expression expressions.
Any triplet-confirming-expression expressions.

15.18.5.1. Filter-Negating-Expression

A filter-negating-expression is a value-composite-expression that, when false, negates the filter. An example follows.

create context TransactionContext initiated by Transaction as txn terminated after 10 minutes;
context TransactionContext select * from Withdrawal(accountId='123' and context.txn.counterparty='A')

The filter-negating-expression is context.txn.counterparty='A'. When false the runtime does not look for Withdrawal events.

15.18.5.2. Filter-Confirming-Expression

A filter-confirm-expression is a value-composite-expression that, when true, means there are no other filter criteria. Here is an example:

create context TransactionContext initiated by Transaction as txn terminated after 10 minutes;
context TransactionContext select * from Withdrawal(accountId='123' or context.txn.counterparty='A')

The filter-confirming-expression is context.txn.counterparty='A'. When true the runtime does not look at the accountId value of Withdrawal events.

15.18.5.3. Path-Negating-Expression

A path-negating-expression is a value-composite-expression that, when false, negates an alternative, for instance:

create context TransactionContext initiated by Transaction as txn terminated after 10 minutes;
context TransactionContext select * from Withdrawal(accountId='123' or (accountType='Checking' and context.txn.counterparty='A'))

The path-negating-expression is context.txn.counterparty='A'. When false the runtime does not look at the accountType value of Withdrawal events.

15.18.5.4. Triplet-Confirming-Expression

A triplet-confirming-expression is a value-composite-expression that, when true, skips a triplet. The example is:

create context TransactionContext initiated by Transaction as txn terminated after 10 minutes;
context TransactionContext select * from Withdrawal( (accountId='123' or context.txn.counterparty='A') and (accountType='Checking' or context.txn.counterparty='B'))

There are two triplet-confirming-expressions. When context.txn.counterparty='A' is true the runtime does not look at the accountId value of Withdrawal events. When context.txn.counterparty='B' is true the runtime does not look at the accountType value of Withdrawal events.

15.19. Limitations

The JVM platform limits the constant pool of classes and method size of methods. The EPL compiler minimizes the chance of stepping over JVM limits, see Section 17.5.1.1, “Byte Code General Settings”. This section provides information on known limitations of the EPL compiler.

The EPL compiler does not enforce hard limits and this documentation does not list exact limits. Below applies within a single given statement only. Below does not apply across statements and does not apply across modules or the runtime overall.

Per statement, there are however limits to compiler processing. Here is a non-comprehensive list of limits per statement:

Number of expressions in a group by clause.
Number of aggregations.
Number of expressions in a select-clause.
Number of property names in an insert into clause.
Number of uniqueness expressions provided to a #unique, #firstunique or #groupwin data window.
Number of expressions provided to for grouped_delivery clause.
Number of key expressions in a keyed context, number of hash functions in a hashed context, number of categories in a category context, number of contexts in a nested context.
Number of EPL objects consumed or provided by module.
Number of inlined-classes, declared expressions, scripts.
In patterns, the number of subexpressions to an and, or, -> (followed-by) and the overall number of subexpressions and the number of filter tags.
For filter index planning, the number of expressions considered or used by the compiler's filter index planner.
For query planning for joins and subqueries, the number of expressions and streams considered or used by the compiler's query planner.
Number of streams in the from-clause.
Number of inlined-classes, declared expressions, scripts.
For subqueries, the number of subqueries and the number of expressions in the select-clause of the subquery.
For data windows, the number of data windows that are placed in an intersection or union; and the number of additional parameters to statistics views.
Number of annotations for a given statement.
Number and serialized size of any application-provided user object that is associated to statements or deployments.
Number of expressions that are sort criteria in an order by-clause, or a #sort or #rank data window or the #sorted aggregation.
Number of rollup levels for rollup aggregation and the number of group expressions and group levels when provided as an aggregation group_by: parameter.
Number of parameters passed to static methods, instance methods, constructors, user-defined functions, inlined-classes, declared expressions, scripts, subscriber update methods or as parameters within the from-clause with joins for for accessing relational data via SQL and for accessing non-relational data.
Number of sub-expressions that are chained into one expression.
Number of event properties that appear chained into one nested event property.
For tables, the number of columns and number of aggregations and the number of table primary keys.
For named windows, the number of properties.
For case control flow (case), coalesce, new, and, or, {} (array expression), in-keyword, || (concat), arithmetic, all and any the number of sub-expressions.
Number of when matched-clauses and the number of actions in an on-merge-clause.
Number of crontabs wherever multiple crontabs are allowed.
For match-recognize, the number of aggregations and the number of variables and the number of pattern elements.
Number of set assignments.
Number of variables used by a given statement.
Number of properties and number of supertypes in create schema.
Number of columns in create index.
Number of JDBC fields returned by accessing relational data via SQL.
For dataflows, the number of types, operators and channels.

Generally the upper limit for above list is around 1000 for a single statement (this is an estimate). For example, the number of expressions in a group by clause is limited to somewhere around 1000 expressions or higher for a statement.

A statement that has, for example, 100000 expressions in a group by clause may experience a compiler exception.

As part of automated regression testing are tests such as below:

1000 aggregations including for tables.
1000 expressions in the select-clause (non-subquery) when the output event is map or objectarray.
create schema with 1000 properties for map and objectarray event types.
1000 substitution parameters.

Chapter 16. Runtime Reference

16.1. Introduction

16.2. Obtaining a Runtime From EPRuntimeProvider

16.3. The EPRuntime Runtime Interface

16.4. Deploying and Undeploying Using EPDeploymentService

16.4.1. Substitution Parameters
16.4.2. Roll-Out of Multiple Compiled Modules
16.4.3. Atomic Deployment Management

16.5. Obtaining Results Using EPStatement

16.5.1. Receiving Statement Results
16.5.2. Setting a Subscriber Object
16.5.3. Adding Listeners
16.5.4. Using Iterators
16.5.5. Event and Event Type
16.5.6. Type Information
16.5.7. Interrogating Annotations

16.6. Processing Events and Time Using EPEventService

16.6.1. Event Sender
16.6.2. Receiving Unmatched Events

16.7. Execute Fire-and-Forget Queries Using EPFireAndForgetService

16.7.1. Fire-and-forget Query Single Execution
16.7.2. Fire-and-forget Query Prepared Unparameterized Execution
16.7.3. Fire-and-forget Query Prepared Parameterized Execution
16.7.4. The From-Clause is Optional
16.7.5. The From-Clause can Access Relational Data via SQL

16.8. Runtime Threading and Concurrency

16.8.1. Advanced Threading
16.8.2. Processing Order

16.9. Controlling Time-Keeping

16.9.1. Controlling Time Using Time Span Events
16.9.2. Time Resolution and Time Unit
16.9.3. Internal Timer Based on JVM System Time

16.10. Exception Handling

16.11. Condition Handling

16.12. Runtime and Statement Metrics Reporting

16.12.1. Runtime Metrics
16.12.2. Statement Metrics

16.13. Monitoring and JMX

16.14. Event Rendering to XML and JSON

16.14.1. JSON Event Rendering Conventions and Options
16.14.2. XML Event Rendering Conventions and Options

16.15. Plug-In Loader

16.16. Context Partition Selection

16.16.1. Selectors

16.17. Context Partition Administration

16.18. Test and Assertion Support

16.18.1. EPAssertionUtil Summary
16.18.2. SupportUpdateListener Summary
16.18.3. Usage Example

16.19. OSGi, Class Loader, Class-For-Name

16.20. When Deploying with J2EE

16.20.1. J2EE Deployment Considerations
16.20.2. Servlet Context Listener

16.21. Stages

16.21.1. Overview
16.21.2. Stage APIs
16.21.3. Stage Example: Suspending and Resuming a Statement
16.21.4. Stage Other Considerations
16.21.5. Stage Limitations

16.1. Introduction

The runtime takes on these functions:

Provide an environment to execute compiled modules.
Provide an environment to run compiled fire-and-forget queries.
Process incoming events and time against deployed modules.

Your application obtains a runtime from EPRuntimeProvider. You may pass an arbitrary string-type runtime URI that uniquely identifies the runtime instance.

A runtime is an instance of EPRuntime. Use the runtime as follows:

The runtime is a stateful service.
You may obtain and use any number of runtime instances in parallel, each runtime instance uniquely identified by the runtime URI.
You may share a runtime instance between threads.
All runtime methods are thread-safe.
Each runtime is completely independent of other runtimes.

16.2. Obtaining a Runtime From `EPRuntimeProvider`

The EPRuntimeProvider class provides static methods that return EPRuntime runtimes.

Each runtime has a unique runtime URI which can be any string value. If your application does not pass a runtime URI then the default URI is default (as defined by EPRuntimeProvider.DEFAULT_RUNTIME_URI).

For the getRuntime methods, your application can pass a runtime URI to obtain different runtimes. The EPRuntimeProvider determines whether the provided runtime URI matches any existing runtime URIs and returns the existing runtime, or allocates a new runtime if none was found.

The getExistingRuntime method takes a runtime URI and returns the existing runtime for that URI or null if there is none.

The code snip below gets the default runtime. Subsequent calls to get the default runtime return the same runtime.

EPRuntime runtime = EPRuntimeProvider.getDefaultRuntime();

The next code gets a runtime for the runtime URI RFIDProcessor1. Subsequent calls to get a runtime with the same runtime URI return the same runtime instance.

EPRuntime runtime = EPRuntimeProvider.getRuntime("RFIDProcessor1");

Since the getRuntime methods return the same runtime for each URI there is no need to statically cache a runtime in your application.

You may also pass an optional Configuration. The next code snippet outlines a typical sequence of use:

// Configure the runtime, this is optional
Configuration config = new Configuration();
config.configure("configuration.xml");	// load a configuration from file

// Optionally set additional configuration values like so:
// config.getCommon().add....(...);

// Obtain a runtime
EPRuntime runtime = EPRuntimeProvider.getDefaultRuntime(config);

// Optionally, use initialize if the same runtime has been used before to start clean
runtime.initialize();

// Destroy the runtime when no longer needed, frees up resources, releases the runtime URI
runtime.destroy();

16.3. The `EPRuntime` Runtime Interface

The EPRuntime interface represents a runtime. Only the static methods of the EPRuntimeProvider class allocate new runtimes. A runtime is uniquely identified by runtime URI. The runtime URI is an arbitrary string. The default runtime has a runtime URI of default.

A runtime provides these services:

Table 16.1. Choices For Receiving Statement Results

Service	Runtime Method	Description
`EPDeploymentService`	`getDeploymentService`	For deploying and undeploying compiled modules.
`EPEventService`	`getEventService`	For processing events and advancing time.
`EPContextPartitionService`	`getContextPartitionService`	For information about context partitions.
`EPVariableService`	`getVariableService`	For access to variables.
`EPEventTypeService`	`getEventTypeService`	For obtaining event types.
`EPFireAndForgetService`	`getFireAndForgetService`	For executing fire-and-forget queries.
`EPDataFlowService`	`getDataFlowService`	For managing data flows.
`EPMetricsService`	`getMetricsService`	For control over metrics, and for reading current metrics.
`EPRenderEventService`	`getRenderEventService`	For rendering events.

You can reset a runtime by calling the initialize method. This operation resets the runtime to the configuration last provided to EPRuntimeProvider. If no configuration is provided, an empty (default) configuration applies. Your application must obtain new services from the initialized runtime as initialize marks existing services as invalid.

A runtime can be destroyed via the destroy method. This frees all resources held by the runtime. After a call to destroy the runtime can no longer be used.

You may register callbacks to receive notifications about runtime state. The runtime invokes any EPRuntimeStateListener callbacks when a runtime instance is about to be destroyed and after a runtime has been initialized. Use the addRuntimeStateListener methods to register interest.

When destroying a runtime your application must make sure that threads that are sending events into the runtime have completed their work. More generally, the runtime should not be currently in use during or after the destroy operation.

All runtime instances are completely independent. Your application may not send EventBean instances obtained from one runtime into a second runtime since the event type space between two runtimes is not shared.

16.4. Deploying and Undeploying Using `EPDeploymentService`

Your application must first compile a module or obtain a compiled module before it can deploy. The object representation of a compiled module is EPCompiled.

Call the deploy method and pass the compiled module. The runtime loads the byte code and adds the information contained in the byte code, causing all the compiled module's statements to begin receiving events and time.

Deploying is an atomic operation. At deployment completion all statements of the deployment begin to see events arriving and time passing consistently. In case the deployment fails the runtime rolls back all deployment changes.

The runtime resolves dependencies of the compiled module upon its deployment. The runtime does not validate that the information about EPL-object dependencies that existed at compile-time matches the runtime EPL-objects.

For example, assume there is a compiled module by name compiledModuleM1. Deploy as follows:

EPDeployment deployment = runtime.getDeploymentService().deploy(compiledModuleM1);

The runtime returns a EPDeployment instance that contains the deployment id, the EPStatement statement instances, module name and module properties. The deployment id is an arbitrary string-type identifier that uniquely identifies the deployment in the runtime.

The undeploy method takes the deployment id and undeploys the deployment. The undeployAll method undeploys all deployments.

A compiled module may be deployed any number of times. Substitution parameters can be handy for parameterizing deployed modules.

Your application may deploy and undeploy using any thread and also within listener or subscriber code. If using Bean-style class-based events your application may not invoke deploy or undeploy methods as part of getter or setter code. Extension API code and plug-in single-row methods also may not invoke deploy or undeploy methods.

You may pass a DeploymentOptions instance. Deployment options provide deployment callbacks and other deploy-time parameters:

Provide a deployment id. If none is provided the runtime generates a unique deployment id.
Provide substitution parameter values for parameterized modules.
Provide or override statement names.
Provide a runtime statement user object that gets associated to the statement and that can be obtained from an EPStatement with getUserObjectRuntime.

Please consult the JavaDoc for more information.

16.4.1. Substitution Parameters

The compiled module may have substitution parameters as explained in the compiler documentation.

All substitution parameters must be replaced by actual values before a compiled module with substitution parameters can be deployed. A compiled module may be deployed multiple times. Substitution parameters can be set to new values for every deployment.

To set substitution parameter values pass a Deployment Options object to the deploy method that provides a StatementSubstitutionParameterOption.

If not assigning a name to substitution parameters, replace the substitution parameter with an actual value using the setObject(int index, Object value) method for each index, starting from 1.

If assigning a name to each substitution parameter, replace the substitution parameter with an actual value using the setObject(String name, Object value) method for each name.

While the setObject method allows substitution parameters to assume any actual value including application Java objects or enumeration values, the application must provide the correct type of substitution parameter that matches the requirements of the expression the parameter resides in.

The below sample code compiles and deploys a parameterized module:

String stmt = "select * from PersonEvent(firstName=?::string)";
Configuration configuration = new Configuration();
configuration.getCommon().addEventType(PersonEvent.class);
CompilerArguments compilerArguments = new CompilerArguments(configuration);
EPCompiled compiled = EPCompilerProvider.getCompiler().compile(stmt, compilerArguments);

DeploymentOptions deploymentOptions = new DeploymentOptions();
deploymentOptions.setStatementSubstitutionParameter(prepared -> prepared.setObject(1, "Joe")); 
EPDeployment deployment = runtime.getDeploymentService().deploy(compiled, deploymentOptions);
EPStatement statement = deployment.getStatements()[0];

16.4.2. Roll-Out of Multiple Compiled Modules

Your application may deploy multiple compiled modules by calling the rollout method of EPDeploymentService. Roll-out deploys each compiled module, either deploying all compiled modules or deploying none of the compiled modules.

Internally to the runtime, a roll-out generally follows these steps:

For each compiled module, determine the deployment id or use the deployment id when provided in the deployment options; Check that all deployment ids do not already exist
For each compiled module, load the compilation unit via classloader and validate basic class-related information such as manifest information and version
For each compiled module, check deployment preconditions and resolve deployment dependencies on EPL objects
For each compiled module, initialize statement-internal objects
For each compiled module, perform internal deployment of each statement of each module

In case any of the above steps fail the runtime completely rolls back all changes.

Note

Roll-out does not re-order compiled modules and expects EPCompiled instances to be ordered according to module dependencies (if any).

For multiple rollouts or for atomically adding listeners and subscribers use Section 16.4.3, “Atomic Deployment Management”.

16.4.3. Atomic Deployment Management

Your application can concurrently send events into the runtime while deploying and undeploying statements and adding or removing listeners. It is safe to undeploy and deploy compiled modules while sending in events from other threads concurrently.

However in some cases your application may need more control over deployment, for example when deploying multiple modules or when attaching custom listener code.

Your application can use the API described below to obtain a lock and perform deployment actions as an atomic unit. For example, if your application would like to undeploy and re-deploy as a single atomic unit, while at the same time sending events into the runtime from different threads, it can obtain a lock to ensure that no events are concurrently processed while the operations take place.

Note

Deploying or undeploying a single compiled module is already an atomic operation by default and does not require taking an explicit lock. If your application would like to deploy multiple compiled modules or add custom listeners or subscribers during deployment it may obtain a lock as discussed below.

The below code sample obtains the runtime exclusive write lock to perform multiple management operations as a unit, excluding concurrent processing of events.

runtime.getRuntimeInstanceWideLock().writeLock().lock();
// Start atomic management unit. 
// Any events concurrently being processed by other threads must complete before the code completes obtaining the lock. 
// Any events sent in by other threads will await the release of the lock.
try {
  // Perform operations such as : 
  //   - deploy and/or undeploy multiple compiled modules  (deployment admin API)
  //   - set statement listeners and subscribers while deploying
  // There is no need to obtain this lock when deploying or undeploying a single module.
  // The lock is reentrant and can be safely taken multiple times by the same thread.
  // Make sure you use "try" and "finally" just like we have it here.
}
finally {
  // Complete atomic management unit. 
  // Any events sent in by other threads will now continue processing against the changed set of statements.
  runtime.getRuntimeInstanceWideLock().writeLock().unlock();
}

Note

There should always be a finally block in your code to ensure the lock is released in all cases.

16.5. Obtaining Results Using `EPStatement`

A compiled module contains zero, one or multiple statements. You can attach callbacks (listeners, subscribers) to statements to receive results (aka push, observer pattern). You can iterate statement current results (aka. poll).

Each statement is uniquely identified in the runtime by the combination of deployment id and statement name. The compiler or runtime always assign a statement name if none was provided.

The EPStatement instance represents the statement. Your application receives statements when deploying a module by calling getStatements on EPDeployment.

Your application may also look up a statement by it's deployment id and statement name using the getStatement method on EPDeploymentService.

16.5.1. Receiving Statement Results

For NEsper .NET also see Section J.15, “.NET API - Receiving Statement Results”.

Esper provides three choices for your application to receive statement results. Your application can use all three mechanisms alone or in any combination for each statement. The choices are:

Table 16.2. Choices For Receiving Statement Results

Name Methods on EPStatement Description

Listener Callbacks

addListener and removeListener

Your application provides implementations of the UpdateListener interface to the statement. Listeners receive EventBean instances containing statement results.

The runtime continuously indicates results to all listeners.

Subscriber Object

setSubscriber

Requires setting the allowSubscriber option on the compiler.

Your application provides a POJO (plain Java object) that exposes methods to receive statement results.

The name of the method that a subscriber object provides to receive results is update, unless your call to setSubscriber provides another method name.

The runtime continuously indicates results to the single subscriber.

This is the fastest method to receive statement results, as the runtime delivers strongly-typed results directly to your application objects without the need for building an EventBean result set.

There can be at most one subscriber object registered per statement. If you require more than one listener, use the listener instead (or in addition). The subscriber object is bound to the statement with a strongly typed support which ensure direct delivery of new events without type conversion. This optimization is made possible because there can only be zero or one subscriber object per statement.

Pull API

safeIterator and iterator

Your application asks the statement for results and receives a set of events via java.util.Iterator<EventBean>.

This is useful if your application does not need continuous indication of new results in real-time.

Tip

The runtime calls application-provided update listeners and subscribers for output. These commonly encapsulate the actions to take when there is output. This design decouples statements from actions and places actions outside of EPL. It allows actions to change independently from statements: A statement does not need to be updated when its associated action(s) change.

While action-taking, in respect to the code or script taking action, is not a part of the EPL language, here are a few noteworthy points. Through the use of EPL annotations you can attach information to EPL that can be used by applications to flexibly determine actions. The insert into-clause can be used to send results into a further stream and input and output adapters or data flows can exist to process output events from that stream. Also the data flow EPStatementSource operator can be used to hook up actions declaratively. The DeploymentStateListener can inform your application of newly-deployed statements and currently-undeployed statements.

Your application may attach one or more listeners, zero or one single subscriber and in addition use the pull API on the same statement. There are no limitations to the use of iterator, subscriber or listener alone or in combination to receive statement results.

The best delivery performance can generally be achieved by attaching a subscriber and by not attaching listeners. The runtime is aware of the listeners and subscriber attached to a statement. The runtime uses this information internally to reduce statement overhead. For example, if your statement does not have listeners or a subscriber attached, the runtime does not need to continuously generate results for delivery.

If your application attaches both a subscriber and one or more listeners then the subscriber receives the result first before any of the listeners.

If your application attaches more than one listener then the UpdateListener listeners receive results in the order they were added to the statement. To change the order of delivery among listeners your application can add and remove listeners at runtime.

If you have configured outbound threading, it means a thread from the outbound thread pool delivers results to the subscriber and listeners instead of the processing or event-sending thread.

If outbound threading is turned on, we recommend turning off the runtime setting preserving the order of events delivered to listeners as described in Section 17.6.1.1, “Preserving the Order of Events Delivered to Listeners”. If outbound threading is turned on statement execution is not blocked for the configured time in the case a subscriber or listener takes too much time.

16.5.2. Setting a Subscriber Object

Note

The compiler option allowSubscriber must be set at compile-time.

A subscriber object is a direct binding of statement results to an object. The object, receives statement results via method invocation. The subscriber class does not need to implement an interface or extend a superclass. Only one subscriber object may be set for a statement.

Subscriber objects have several advantages over listeners. First, they offer a substantial performance benefit: Statement results are delivered directly to your method(s) through Java virtual machine method calls, and there is no intermediate representation (EventBean). Second, as subscribers receive strongly-typed parameters, the subscriber code tends to be simpler.

This chapter describes the requirements towards the methods provided by your subscriber class.

The runtime can deliver results to your subscriber in two ways:

Each evert in the insert stream results in a method invocation, and each event in the remove stream results in further method invocations. This is termed row-by-row delivery.
A single method invocation that delivers all rows of the insert and remove stream. This is termed multi-row delivery.

16.5.2.1. Using the `EPStatement` Parameter

In the case that your subscriber object wishes to receive the EPStatement instance along with output data, please add EPStatement as the very first parameter of any of the delivery method footprints that are discussed next.

For example, your statement may be:

select count(*) from OrderEvent

Your subscriber class exposes the method:

public void update(EPStatement statement, long currentCount) {...}

16.5.2.2. Row-by-Row Delivery

Your subscriber class must provide a method by name update to receive insert stream events row-by-row. The number and types of parameters declared by the update method must match the number and types of columns as specified in the select clause, in the same order as in the select clause.

For example, if your statement is:

select orderId, price, count(*) from OrderEvent

Then your subscriber update method looks as follows:

public class MySubscriber {
  ...
  public void update(String orderId, double price, long count) {...}
  ...
}

Each method parameter declared by the update method must be assignable from the respective column type as listed in the select-clause, in the order selected. The assignability rules are:

Widening of types follows Java standards. For example, if your select clause selects an integer value, the method parameter for the same column can be typed int, long, float or double (or any equivalent boxed type).
Auto-boxing and unboxing follows Java standards. For example, if your select clause selects an java.lang.Integer value, the method parameter for the same column can be typed int. Note that if your select clause column may generate null values, an exception may occur at runtime unboxing the null value.
Interfaces and super-classes are honored in the test for assignability. Therefore java.lang.Object can be used to accept any select clause column type

In the case that your subscriber class offers multiple update method footprints, the runtime selects the closest-matching footprint by comparing the output types and method parameter types. The runtime prefers the update method that is an exact match of types, followed by an update method that requires boxing or unboxing, followed by an update method that requires widening and finally any other allowable update method.

Within the above criteria, in the case that your subscriber class offers multiple update method footprints with same method parameter types, the runtime prefers the update method that has EPStatement as the first parameter.

16.5.2.2.1. Wildcards

If your select clause contains one or more wildcards (*), then the equivalent parameter type is the underlying event type of the stream selected from.

For example, your statement may be:

select *, count(*) from OrderEvent

Then your subscriber update method looks as follows:

public void update(OrderEvent orderEvent, long count) {...}

In a join, the wildcard expands to the underlying event type of each stream in the join in the order the streams occur in the from clause. An example statement for a join is:

select *, count(*) from OrderEvent order, OrderHistory hist

Then your subscriber update method should be:

public void update(OrderEvent orderEvent, OrderHistory orderHistory, long count) {...}

The stream wildcard syntax and the stream name itself can also be used:

select hist.*, order from OrderEvent order, OrderHistory hist

The matching update method is:

public void update(OrderHistory orderHistory, OrderEvent orderEvent) {...}

16.5.2.2.2. Row Delivery as Map and Object Array

Alternatively, your update method may simply choose to accept java.util.Map as a representation for each row. Each column in the select clause is then made an entry in the resulting Map. The Map keys are the column name if supplied, or the expression string itself for columns without a name.

The update method for Map delivery is:

public void update(Map row) {...}

The runtime also supports delivery of select clause columns as an object array. Each item in the object array represents a column in the select clause. The update method then looks as follows:

public void update(Object[] row) {...}

16.5.2.2.3. Delivery of Remove Stream Events

Your subscriber receives remove stream events if it provides a method named updateRStream. The method must accept the same number and types of parameters as the update method (including EPStatement if present).

An example statement:

select orderId, count(*) from OrderEvent#time(20 sec) group by orderId

Then your subscriber update and updateRStream methods should be:

public void update(String, long count) {...}
public void updateRStream(String orderId, long count) {...}

16.5.2.2.4. Delivery of Begin and End Indications

If your subscriber requires a notification for begin and end of event delivery, it can expose methods by name updateStart and updateEnd.

The updateStart method must take two integer parameters that indicate the number of events of the insert stream and remove stream to be delivered. The runtime invokes the updateStart method immediately prior to delivering events to the update and updateRStream methods.

The updateEnd method must take no parameters. The runtime invokes the updateEnd method immediately after delivering events to the update and updateRStream methods.

An example set of delivery methods:

// Called by the runtime before delivering events to update methods
public void updateStart(int insertStreamLength, int removeStreamLength)

// To deliver insert stream events
public void update(String orderId, long count) {...}

// To deliver remove stream events
public void updateRStream(String orderId, long count) {...}

// Called by the runtime after delivering events
public void updateEnd() {...}

16.5.2.3. Multi-Row Delivery

In place of row-by-row delivery, your subscriber can receive all events in the insert and remove stream via a single method invocation. This is applicable when an EPL delivers multiple output rows for a given input event or time advancing, for example when multiple pattern matches occur for the same incoming event, for a join producing multiple output rows or with output rate limiting, for example.

The event delivery follow the scheme as described earlier in Section 16.5.2.2.2, “Row Delivery as Map and Object Array ”. The subscriber class must provide one of the following methods:

Table 16.3. Update Method for Multi-Row Delivery of Underlying Events

Method	Description
`update(Object[][] insertStream, Object[][] removeStream)`	The first dimension of each Object array is the event row, and the second dimension is the column matching the column order of the statement `select` clause
`update(Map[] insertStream, Map[] removeStream)`	Each map represents one event, and Map entries represent columns of the statement `select` clause

16.5.2.3.1. Wildcards

If your select clause contains a single wildcard (*) or wildcard stream selector, the subscriber object may also directly receive arrays of the underlying events. In this case, the subscriber class should provide a method update(Underlying[] insertStream, Underlying[] removeStream) , such that Underlying represents the class of the underlying event.

For example, your statement may be:

select * from OrderEvent#time(30 sec)

Your subscriber class exposes the method:

public void update(OrderEvent[] insertStream, OrderEvent[] removeStream) {...}

16.5.2.4. No-Parameter Update Method

In the case that your subscriber object wishes to receive no data from a statement please follow the instructions here.

You statement must select a single null value.

For example, your statement may be:

select null from OrderEvent(price > 100)

Your subscriber class exposes the method:

public void update() {...}

16.5.3. Adding Listeners

For NEsper .NET also see Section J.16, “.NET API - Adding Listeners”.

Your application can subscribe to updates posted by a statement via the addListener and removeListener methods on EPStatement . Your application must to provide an implementation of the UpdateListener interface to the statement:

UpdateListener myListener = new MyUpdateListener();
countStmt.addListener(myListener);

Statements publish old data and new data to registered UpdateListener listeners. New data published by statements is the events representing the new values of derived data held by the statement. Old data published by statements consists of the events representing the prior values of derived data held by the statement.

Important

UpdateListener listeners receive multiple result rows in one invocation by the runtime: the new data and old data parameters to your listener are array parameters. For example, if your application uses one of the batch data windows, or your application creates a pattern that matches multiple times when a single event arrives, then the runtime indicates such multiple result rows in one invocation and your new data array carries two or more rows.

To indicate results the runtime invokes the following method on UpdateListener listeners: update(EventBean[] newEvents, EventBean[] oldEvents, EPStatement statement, EPRuntime runtime)

16.5.3.1. Subscription Snapshot and Atomic Delivery

The addListenerWithReplay method provided by EPStatement makes it possible to send a snapshot of current statement results to a listener when the listener is added.

When using the addListenerWithReplay method to register a listener, the listener receives current statement results as the first call to the update method of the listener, passing in the newEvents parameter the current statement results as an array of zero or more events. Subsequent calls to the update method of the listener are statement results.

Current statement results are the events returned by the iterator or safeIterator methods.

Delivery is atomic: Events occurring during delivery of current results to the listener are guaranteed to be delivered in a separate call and not lost. The listener implementation should thus minimize long-running or blocking operations to reduce lock times held on statement-level resources.

16.5.4. Using Iterators

Subscribing to events posted by a statement is following a push model. The runtime pushes data to listeners when events are received that cause data to change or patterns to match. Alternatively, you need to know that statements serve up data that your application can obtain via the safeIterator and iterator methods on EPStatement. This is called the pull API and can come in handy if your application is not interested in all new updates, and only needs to perform a frequent or infrequent poll for the latest data.

The safeIterator method on EPStatement returns a concurrency-safe iterator returning current statement results, even while concurrent threads may send events into the runtime for processing. The runtime employs a read-write lock per context partition and obtains a read lock for iteration. Thus safe iterator guarantees correct results even as events are being processed by other threads and other context partitions. The cost is that the iterator obtains and holds zero, one or multiple context partition locks for that statement that must be released via the close method on the SafeIterator instance.

The iterator method on EPStatement returns a concurrency-unsafe iterator. This iterator is only useful for applications that are single-threaded, or applications that themselves perform coordination between the iterating thread and the threads that send events into the runtime for processing. The advantage to this iterator is that it does not hold a lock.

When statements are used with contexts and context partitions, the APIs to identify, filter and select context partitions for statement iteration are described in Section 16.16, “Context Partition Selection”.

The next code snippet shows a short example of use of safe iterators:

EPStatement statement = epAdmin.createEPL("select avg(price) as avgPrice from MyTick");
// .. send events into the runtime
// then use the pull API...
SafeIterator<EventBean> safeIter = statement.safeIterator();
try {
  for (;safeIter.hasNext();) {
     // .. process event ..
     EventBean event = safeIter.next();
     System.out.println("avg:" + event.get("avgPrice");
  }
}
finally {
  safeIter.close();	// Note: safe iterators must be closed
}

This is a short example of use of the regular iterator that is not safe for concurrent event processing:

double averagePrice = (Double) eplStatement.iterator().next().get("average");

The safeIterator and iterator methods can be used to pull results out of all statements, including statements that join streams, contain aggregation functions, pattern statements, and statements that contain a where clause, group by clause, having clause or order by clause.

For statements without an order by clause, the iterator method returns events in the order maintained by the data window. For statements that contain an order by clause, the iterator method returns events in the order indicated by the order by clause.

Consider using the on-select clause and a named window if your application requires iterating over a partial result set or requires indexed access for fast iteration; Note that on-select requires that you sent a trigger event, which may contain the key values for indexed access.

Esper places the following restrictions on the pull API and usage of the safeIterator and iterator methods:

In multithreaded applications, use the safeIterator method. Note: make sure your application closes the iterator via the close method when done, otherwise the iterated statement context partitions stay locked and event processing for statement context partitions does not resume.
In multithreaded applications, the iterator method does not hold any locks. The iterator returned by this method does not make any guarantees towards correctness of results and fail-behavior, if your application processes events into the runtime by multiple threads. Use the safeIterator method for concurrency-safe iteration instead.
Since the safeIterator and iterator methods return events to the application immediately, the iterator does not honor an output rate limiting clause, if present. That is, the iterator returns results as if there is no output-rate clause for the statement in statements without grouping or aggregation. For statements with grouping or aggregation, the iterator in combination with an output clause returns last output group and aggregation results. Use a separate statement and the insert into clause to control the output rate for iteration, if so required.
When iterating a statement that operates on an unbound stream (no data window declared), please note the following:
- When iterating a statement that groups and aggregates values from an unbound stream and that specifies output snapshot, the runtime retains groups and aggregations for output as iteration results or upon the output snapshot condition .
- When iterating a statement that groups and aggregates values from an unbound stream and that does not specify output snapshot, the runtime only retains the last aggregation values and the iterated result contains only the last updated group.
- When iterating a statement that operates on an unbound stream the iterator returns no rows. This behavior can be changed by specifying either the @IterableUnbound annotation or by changing the global view resources configuration.

16.5.5. Event and Event Type

An EventBean object represents a row (event) in your statement's result set. Each EventBean object has an associated EventType object providing event metadata.

An UpdateListener implementation receives one or more EventBean events with each invocation. Via the iterator method on EPStatement your application can poll or read data out of statements. Statement iterators also return EventBean instances.

Each statement provides the event type of the events it produces, available via the getEventType method on EPStatement.

16.5.5.1. Event Type Metadata

An EventType object encapsulates all the metadata about a certain type of events. As Esper supports an inheritance hierarchy for event types, it also provides information about super-types to an event type.

An EventType object provides the following information:

For each event property, it lists the property name and type as well as flags for indexed or mapped properties and whether a property is a fragment.
The direct and indirect super-types to the event type.
Value getters for property expressions.
Underlying class of the event representation.

For each property of an event type, there is an EventPropertyDescriptor object that describes the property. The EventPropertyDescriptor contains flags that indicate whether a property is an indexed (array) or a mapped property and whether access to property values require an integer index value (indexed properties only) or string key value (mapped properties only). The descriptor also contains a fragment flag that indicates whether a property value is available as a fragment.

The term fragment means an event property value that is itself an event, or a property value that can be represented as an event. The getFragmentType on EventType may be used to determine a fragment's event type in advance.

A fragment event type and thereby fragment events allow navigation over a statement's results even if the statement result contains nested events or a graph of events. There is no need to use the Java reflection API to navigate events, since fragments allow the querying of nested event properties or array values, including nested Java classes.

When using the Map or Object-array event representation, any named Map type or Object-array type nested within a Map or Object-array as a simple or array property is also available as a fragment. When using Java objects either directly or within Map or Object-array events, any object that is neither a primitive or boxed built-in type, and that is not an enumeration and does not implement the Map interface is also available as a fragment.

The nested, indexed and mapped property syntax can be combined to a property expression that may query an event property graph. Most of the methods on the EventType interface allow a property expression to be passed.

Your application may use an EventType object to obtain special getter-objects. A getter-object is a fast accessor to a property value of an event of a given type. All getter objects implement the EventPropertyGetter interface. Getter-objects work only for events of the same type or sub-types as the EventType that provides the EventPropertyGetter. The performance section provides additional information and samples on using getter-objects.

16.5.5.2. Event Object

An event object is an EventBean that provides:

The property value for a property given a property name or property expression that may include nested, indexed or mapped properties in any combination.
The event type of the event.
Access to the underlying event object.
The EventBean fragment or array of EventBean fragments given a property name or property expression.

The getFragment method on EventBean and EventPropertyGetter return the fragment EventBean or array of EventBean, if the property is itself an event or can be represented as an event. Your application may use EventPropertyDescriptor to determine which properties are also available as fragments.

The underlying event object of an EventBean can be obtained via the getUnderlying method. Please see Chapter 3, Event Representations for more information on different event representations.

From a threading perspective, it is safe to retain and query EventBean and EventType objects in multiple threads.

16.5.5.3. Query Example

Consider a statement that returns the symbol, count of events per symbol and average price per symbol for tick events. Our sample statement uses the event type: StockTickEvent. Assume that this event type was declared previously and exposes a symbol property of type String and a price property of type (Java primitive) double.

select symbol, avg(price) as avgprice, count(*) as mycount 
from StockTickEvent 
group by symbol

The next table summarizes the property names and types as posted by the statement above:

Table 16.4. Properties Offered by Sample Statement Aggregating Price

Name	Type	Description	Java code snippet
`symbol`	java.lang.String	Value of symbol event property	eventBean.get("symbol")
`avgprice`	java.lang.Double	Average price per symbol	eventBean.get("avgprice")
`mycount`	java.lang.Long	Number of events per symbol	eventBean.get("mycount")

A code snippet out of a possible UpdateListener implementation to this statement may look as below:

String symbol = (String) newEvents[0].get("symbol");
Double price= (Double) newEvents[0].get("avgprice");
Long count= (Long) newEvents[0].get("mycount");

The runtime supplies the boxed java.lang.Double and java.lang.Long types as property values rather than primitive Java types. This is because aggregated values can return a null value to indicate that no data is available for aggregation. Also, in a select statement that computes expressions, the underlying event objects to EventBean instances are either of type Object[] (object-array) or of type java.util.Map.

Use statement.getEventType().getUnderlyingType() to inspect the underlying type for all events delivered to listeners. Whether the runtime delivers Map or Object-array events to listeners can be specified as follows. If the statement provides the @EventRepresentation(objectarray) annotation the runtime delivers the output events as object array. If the statement provides the @EventRepresentation(map) annotation the runtime delivers output events as a Map. If neither annotation is provided, the runtime delivers the configured default event representation as discussed in Section 17.4.9.1, “Default Event Representation”.

Consider the next statement that specifies a wildcard selecting the same type of event:

select * from StockTickEvent where price > 100

The property names and types provided by an EventBean query result row, as posted by the statement above are as follows:

Table 16.5. Properties Offered by Sample Wildcard-Select Statement

Name	Type	Description	Java code snippet
`symbol`	java.lang.String	Value of symbol event property	eventBean.get("symbol")
`price`	double	Value of price event property	eventBean.get("price")

As an alternative to querying individual event properties via the get methods, the getUnderlying method on EventBean returns the underlying object representing the statement result. In the sample statement that features a wildcard-select, the underlying event object is of type org.sample.StockTickEvent:

StockTickEvent tick = (StockTickEvent) newEvents[0].getUnderlying();

16.5.5.4. Pattern Example

Composite events are events that aggregate one or more other events. Composite events are typically created by the runtime for statements that join two event streams, and for event patterns in which the causal events are retained and reported in a composite event. The example below shows such an event pattern.

// Look for a pattern where BEvent follows AEvent
select * from pattern [a=AEvent -> b=BEvent]

// Example listener code
public class MyUpdateListener implements UpdateListener {
  public void update(EventBean[] newData, EventBean[] oldData, EPStatement statement, EPRuntime runtime) {
    System.out.println("a event=" + newData[0].get("a"));
    System.out.println("b event=" + newData[0].get("b"));
  }
}

Note that the update method can receive multiple events at once as it accepts an array of EventBean instances. For example, a time batch window may post multiple events to listeners representing a batch of events received during a given time period.

Pattern statements can also produce multiple events delivered to update listeners in one invocation. The pattern statement below, for instance, delivers an event for each A event that was not followed by a B event with the same id property within 60 seconds of the A event. The runtime may deliver all matching A events as an array of events in a single invocation of the update method of each listener to the statement:

select * from pattern[every a=A -> (timer:interval(60 sec) and not B(id=a.id))]

A code snippet out of a possible UpdateListener implementation to this statement that retrives the events as fragments may look as below:

EventBean a = (EventBean) newEvents[0].getFragment("a");
// ... or using a nested property expression to get a value out of A event...
double value = (Double) newEvent[0].get("a.value");

Some pattern objects return an array of events. An example is the unbound repeat operator. Here is a sample pattern that collects all A events until a B event arrives:

select * from pattern [a=A until b=B]

A possible code to retrieve different fragments or property values:

EventBean[] a = (EventBean[]) newEvents[0].getFragment("a");
// ... or using a nested property expression to get a value out of A event...
double value = (Double) newEvent[0].get("a[0].value");

16.5.6. Type Information

The Esper compiler and runtime use the EPType interface for tracking all Java (or C# for .NET) type information. The EPType interface can be found in package com.espertech.esper.common.client.type.

EPL uses three-valued logic and thus null is a viable type. More information can be found at Section 2.19, “Basic Null”. The null-type is represented by EPTypeNull.

EPL supports un-parameterized types and a type that is not parameterized is represented by EPTypeClass.

EPL also supports parameterized types and a type that is parameterized is represented by EPTypeClassParameterized. Since an instance of java.lang.Class does not provide information about actual type parameters (aka. type erasure) and since there is no null-type class the compiler and runtime use EPType instead of java.lang.Class.

To obtain the event property type use the getPropertyEPType method of EventType or EventPropertyDescriptor. Please consult the JavaDoc for additional information.

16.5.7. Interrogating Annotations

As discussed in Section 5.2.7, “Annotation” an EPL annotation is an addition made to statement information. The API and examples to interrogate annotations are described here.

You may use the getAnnotations method of EPStatement to obtain annotations specified for a statement. Or when compiling an EPL expression to a EPStatementObjectModel statement object model you may also query, change or add annotations.

The following example code demonstrates iterating over an EPStatement statement's annotations and retrieving values:

String exampleEPL = "@Tag(name='direct-output', value='sink 1') select * from RootEvent";
Configuration configuration = new Configuration();
configuration.getCommon().addEventType("RootEvent", Collections.emptyMap()); // add an event type without properties
CompilerArguments compilerArguments = new CompilerArguments(configuration);
EPCompiled compiled = EPCompilerProvider.getCompiler().compile(stmt, compilerArguments);

EPDeployment deployment = runtime.getDeploymentService().deploy(compiled);
EPStatement stmt = deployment.getStatements()[0];
for (Annotation annotation : stmt.getAnnotations()) {
  if (annotation instanceof Tag) {
    Tag tag = (Tag) annotation;
    System.out.println("Tag name " + tag.name() + " value " + tag.value());
  }
}

The output of the sample code shown above is Tag name direct-output value sink 1.

16.6. Processing Events and Time Using `EPEventService`

The EPEventService interface is used to send events and advance time. Obtain the event service from a runtime by calling getEventService on EPRuntime.

This section focuses on processing events. For more information on controlling time using the event service please skip forward to Section 16.9, “Controlling Time-Keeping”.

Your application invokes any of the sendEventType methods listed below and must provide an event type name along with the actual event object:

Table 16.6. Send-Event Methods

Method	Description
sendEventBean(Object event, String eventTypeName)	Call when the event is a Bean-style event. The event type name should be associated to a class event representation.
sendEventMap(Map<String, Object> event, String eventTypeName)	Call when the event is a map. The event type name should be associated to a map event representation.
sendEventObjectArray(Object[] event, String eventTypeName);	Call when the event is an object-array. The event type name should be associated to an object-array event representation.
sendEventXMLDOM(Node node, String eventTypeName);	Call when the event is a DOM-Node. The event type name should be associated to an XML event representation.
void sendEventJson(String json, String jsonEventTypeName);	Call when the event is an JSON-formatted string-type document. The event type name should be associated to a JSON event representation.
void sendEventAvro(Object avroGenericDataDotRecord, String avroEventTypeName);	Call when the event is an Avro object. The event type name should be associated to an Avro event representation.

The Chapter 3, Event Representations section explains the types of event representations.

The below sample code assumes that the event type name MarketDataBean refers to a class event representation that matches the class MarketDataBean:

EPRuntime runtime = EPRuntimeProvider.getDefaultRuntime();
EPEventService eventService = runtime.getEventService();

// Send an example event containing stock market data
eventService.sendEventBean(new MarketDataBean("IBM", 75.0), "MarketDataBean");

Tip

Events, in theoretical terms, are observations of a state change that occurred in the past. Since you cannot change an event that happened in the past, events are best modelled as immutable objects.

If you find that your application requires processing events and control over time only for specific deployments and not for other deployments, please read up on Section 16.21, “Stages”.

Caution

The runtime relies on events that are sent into the runtime to not change their state. Typically, applications create a new event object for every new event, to represent that new event. Application should not modify an existing event that was sent into the runtime.

Important

Another important method in the runtime interface are the routeEventType methods. These methods are designed for use by UpdateListener and subscriber implementations as well as runtime extensions that need to send events into a runtime to avoid the possibility of a stack overflow due to nested calls to sendEvent and to ensure correct processing of the current and routed event. Note that if outbound-threading is enabled, listeners and subscribers should use sendEvent and not routeEvent.

16.6.1. Event Sender

The EventSender interface processes event objects that are of a known type. This facility can reduce the overhead of event object reflection and type lookup as an event sender is always associated to a single concrete event type.

Use the method getEventSender(String eventTypeName) to obtain an event sender for processing events of the named type:

EventSender sender = runtime.getEVentService().getEventSender("MyEvent");
sender.sendEvent(myEvent);

For events backed by a Java class (JavaBean events), the event sender ensures that the event object equals the underlying class, or implements or extends the underlying class for the given event type name.

For events backed by a java.util.Map (Map events), the event sender does not perform any checking other than checking that the event object implements Map.

For events backed by a Object[] (Object-array events), the event sender does not perform any checking other than checking that the event object implements Object[]. The array elements must be in the exact same order of properties as declared and array length must always be at least the number of properties declared.

For JSON events, the event sender checks that the event object is a string-type value or is an JsonEventUnderlying object returned by the parse method of EventSenderJson. The JSON document should match the fields defined in create schema.

For Avro events backed by a Apache Avro GenericData.Record, the event sender does not perform any checking other than checking that the event object is a GenericData.Record. The schema associated to the record should match the event type's Avro schema.

For events backed by a org.w3c.Node (XML DOM events), the event sender checks that the root element name equals the root element name for the event type.

16.6.2. Receiving Unmatched Events

Your application can register an implementation of the UnmatchedListener interface with the event service by calling the setUnmatchedListener method to receive events that were not matched by any statement.

Events that can be unmatched are all events that your application sends into the runtime via one of the sendEvent or routeEvent methods, or that have been generated via an insert into clause.

For an event to become unmatched by any statement, the event must not match any statement's event stream filter criteria. Note that the EPL where clause or having clause are not considered part of the filter criteria for a stream, as explained by example below.

In the following statement a MyEvent event with a 'quantity' property value of 5 or less does not match this statement's event stream filter criteria. The runtime delivers such an event to the registered UnmatchedListener instance provided no other statement matches on the event:

select * from MyEvent(quantity > 5)

For patterns, if no pattern sub-expression is active for an event type, an event of that type also counts as unmatched in regards to the pattern statement.

16.7. Execute Fire-and-Forget Queries Using `EPFireAndForgetService`

The EPFireAndForgetService interface offers methods to execute fire-and-forget queries. Obtain the fire-and-forget service from a runtime by calling getFireAndForgetService on EPRuntime.

As your application may not require streaming results and may not know each statement in advance, the fire-and-forget query facility provides for ad-hoc on-demand execution of an EPL query.

Fire-and-forget queries are not continuous in nature: The fire-and-forget query runtime executes the query once and returns all result rows to the application. Fire-and-forget query execution is very lightweight as the runtime performs no statement deployment and the query leaves no traces within the runtime.

Esper provides the facility to explicitly index named windows and tables to speed up fire-and-forget queries and statements. Please consult Section 6.9, “Explicitly Indexing Named Windows and Tables” for more information.

When named windows and tables are used with contexts and context partitions, the APIs to identify, filter and select context partitions for fire-and-forget queries can be found in Section 16.16, “Context Partition Selection”.

There are three ways to run fire-and-forget queries:

Use the executeQuery method to executes a given fire-and-forget query exactly once, see Section 16.7.1, “Fire-and-forget Query Single Execution”.
Use the prepareQuery method to prepare a given fire-and-forget query such that the same query can be executed multiple times, see Section 16.7.2, “Fire-and-forget Query Prepared Unparameterized Execution”.
Use the prepareQueryWithParameters method to prepare a given fire-and-forget query that may have substitution parameters such that the same query can be parameterized and executed multiple times without repeated parsing, see Section 16.7.3, “Fire-and-forget Query Prepared Parameterized Execution”

If your application must execute the same fire-and-forget query multiple times with different parameters use prepareQueryWithParameters.

If your application must execute the same fire-and-forget query multiple times without parameters use either prepareQuery or prepareQueryWithParameters and specify no substitution parameters.

By using any of the prepare... methods the runtime can load the byte code for the query once and reuse the byte code and thereby speed up repeated execution.

The following limitations apply:

A fire-and-forget only evaluates against the named windows and tables that your application creates. Fire-and-forget queries may not specify any other streams or application event types.
The following clauses are not allowed in fire-and-forget EPL queries: insert into and output.
Data windows and patterns are not allowed to appear in fire-and-forget queries.
Fire-and-forget EPL may not perform subqueries.
The previous and prior functions may not be used.

16.7.1. Fire-and-forget Query Single Execution

Use the executeQuery method for executing a fire-and-forget query once. For repeated execution, please consider any of the prepare... methods instead.

The next program listing runs a fire-and-forget query against a named window MyNamedWindow and prints a column of each row result of the query (this sample uses the compiler runtime-path):

String query = "select * from MyNamedWindow";
CompilerArguments compilerArguments = new CompilerArguments();
compilerArguments.getPath().add(runtime.getRuntimePath());
EPCompiled compiled = EPCompilerProvider.getCompiler().compileQuery(query, compilerArguments);

EPFireAndForgetQueryResult result = runtime.getFireAndForgetService().executeQuery(compiled);
for (EventBean row : result.getArray()) {
  System.out.println("name=" + row.get("name"));
}

For executing a fire-and-forget against a table please put the table name into the from-clause instead.

16.7.2. Fire-and-forget Query Prepared Unparameterized Execution

Prepared fire-and-forget queries are designed for repeated execution and may perform better then the dynamic single-execution method if running the same query multiple times. For use with parameter placeholders please see Section 16.7.3, “Fire-and-forget Query Prepared Parameterized Execution”.

The next code snippet demonstrates prepared fire-and-forget queries without parameter placeholder:

String query = "select * from MyNamedWindow where orderId = '123'";
CompilerArguments compilerArguments = new CompilerArguments();
compilerArguments.getPath().add(runtime.getRuntimePath());
EPCompiled compiled = EPCompilerProvider.getCompiler().compileQuery(query, compilerArguments);
						
EPFireAndForgetPreparedQuery prepared = runtime.getFireAndForgetService().prepareQuery(compiled);
EPFireAndForgetQueryResult result = prepared.execute();

// ...later on execute once more ...
prepared.execute();	// execute a second time

16.7.3. Fire-and-forget Query Prepared Parameterized Execution

Please see the compiler documentation for specifying substitution parameters.

All substitution parameters must be replaced by actual values before a fire-and-forget query with substitution parameters can be executed. Substitution parameters can be replaced with an actual value using the setObject method for each index or name. Substitution parameters can be set to new values and the query executed more than once.

While the setObject method allows substitution parameters to assume any actual value including application Java objects or enumeration values, the application must provide the correct type of substitution parameter that matches the type that was specified, if any, and the requirements of the expression the parameter resides in.

The next program listing runs a prepared and parameterized fire-and-forget query against a named window MyNamedWindow (this example does not assign names to substitution parameters):

String query = "select * from MyNamedWindow where orderId = ?::string";
CompilerArguments compilerArguments = new CompilerArguments();
compilerArguments.getPath().add(runtime.getRuntimePath());
EPCompiled compiled = EPCompilerProvider.getCompiler().compileQuery(query, compilerArguments);

EPFireAndForgetPreparedQueryParameterized prepared = runtime.getFireAndForgetService().prepareQueryWithParameters(query);

// Set the required parameter values before each execution
prepared.setObject(1, "123");
EPFireAndForgetQueryResult result = runtime.getFireAndForgetService().executeQuery(prepared);

// ...execute a second time with new parameter values...
prepared.setObject(1, "456");
result = runtime.getFireAndForgetService().executeQuery(prepared);

This second example uses the in operator and has multiple parameters:

String query = "select * from MyNamedWindow where orderId in (?::string[]) and price > ?::double";
CompilerArguments compilerArguments = new CompilerArguments();
compilerArguments.getPath().add(runtime.getRuntimePath());
EPCompiled compiled = EPCompilerProvider.getCompiler().compileQuery(query, compilerArguments);

EPFireAndForgetPreparedQueryParameterized prepared = runtime.getFireAndForgetService().prepareQueryWithParameters(compiled);
prepared.setObject(1, new String[] {"123", "456"});
prepared.setObject(2, 1000.0);

16.7.4. The From-Clause is Optional

The from-clause in EPL is optional and is only required for subqueries. You may execute fire-and-forget queries without a from-clause. For example:

select 1 as value

You may use fire-and-forget queries to obtain information about the current context partitions. More information can be found at Section 4.5, “Output When a Context Partition Starts (Non-Overlapping Context) or Initiates (Overlapping Context)”. For example:

context CtxPerOrder select context.orderEvent as orderEvent

16.7.5. The From-Clause can Access Relational Data via SQL

The from-clause can have an SQL query:

... from sql:database_name [" parameterized_sql_query "]

The Esper runtime executes such fire-and-forget queries with SQL using the JDBC API. Please also refer to Section 5.13, “Accessing Relational Data via SQL” for more information.

The sample EPL below returns the results of an SQL query against the database named MyCustomerDB and table Customer:

select * from sql:MyCustomerDB ['select cust_name from Customer where cust_id = 10']

Tip

Use prepared execution (unparameterized or parameterized) when executing the same SQL query multiple times with same or different parameters. This allows the Esper runtime to reuse the JDBC java.sql.Connection and JDBC PreparedStatement. EPL Expressions including expressions with (public) variables and substitution parameters can be placed within ${...} within the SQL text.
To execute a query once an application can use single execution (see above). In single execution the runtime obtains a new JDBC java.sql.Connection, creates a JDBC PreparedStatement and discards the JDBC connection and JDBC statement after execution. This is not recommended for best performance.

This code snippet illustrates executing the SQL query by fire-and-forget API prepared unparameterized execution:

String query = "select * from sql:MyCustomerDB ['select cust_name from Customer where cust_id = 10";
EPCompiled compiled = EPCompilerProvider.getCompiler().compileQuery(query, new CompilerArguments());
EPFireAndForgetPreparedQuery prepared = runtime.getFireAndForgetService().prepareQuery(compiled);
EPFireAndForgetQueryResult result = prepared.execute();

// ...close() ...
prepared.close();	// close when done executing

Warning

Call the close method of EPFireAndForgetPreparedQuery and EPFireAndForgetPreparedQueryParameterized to release JDBC resources held by the runtime.

The following limitations apply:

Joins are not allowed when using sql: SQL queries in the from-clause however the SQL itself can be any SQL including a join.
A context cannot be specified.

16.8. Runtime Threading and Concurrency

For NEsper .NET also see ???.

The runtime is designed from the ground up to operate as a component to multi-threaded, highly-concurrent applications that require efficient use of Java VM resources. In addition, multi-threaded execution requires guarantees in predictability of results and deterministic processing. This section discusses these concerns in detail.

In Esper, a runtime instance is a unit of separation. Applications can obtain and discard (initialize) one or more runtime instances within the same Java VM and can provide the same or different configurations to each instance. A runtime instance shares resources between statements by means of named windows, tables and variables.

Applications can use Esper APIs to concurrently, by multiple threads of execution, perform such functions as deploying modules, or sending events into the runtime for processing. Applications can use application-managed threads or thread pools or any set of same or different threads of execution with any of the public runtime APIs. There are no restrictions towards threading other than those noted in specific sections of this document.

The runtime does not prescribe a specific threading model. Applications using Esper retain full control over threading, allowing a runtime to be easily embedded and used as a component or library in your favorite Java container or process.

In the default configuration it is up to the application code to use multiple threads for processing events by the runtime, if so desired. All event processing takes places within your application thread call stack. The exception is timer-based processing if your runtime relies on the internal timer (default). If your application relies on external timer events instead of the internal timer then there need not be any runtime-managed internal threads.

The fact that event processing can take place within your application thread's call stack makes developing applications with the Esper runtime easier: Any common Java integrated development environment (IDE) can host a compiler and runtime instance. This allows developers to easily set up test cases, debug through listener code and inspect input or output events, or trace their call stack.

In the default configuration, each runtime maintains a single timer thread (internal timer) providing for time or schedule-based processing within the runtime. The default resolution at which the internal timer operates is 100 milliseconds. The internal timer thread can be disabled and applications can instead advance time to perform timer or scheduled processing at the resolution required by an application.

A runtime performs minimal locking to enable high levels of concurrency. A runtime locks on the combination of query and context partition to protect context partition resources. For example, two statements with three partitions each have a total of six locks. For stateless EPL select-statements the runtime does not use a context-partition lock and operates lock-free for the context partition. For stateful statements, the maximum (theoretical) degree of parallelism is 2^31-1 (2,147,483,647) parallel threads working to process a single statement under a hash segmented context.

For named windows and tables, on-select, on-merge, on-update and on-delete all execute under the same lock as the partition of the named window or table. Any insert into produces a new event for the work queue and does not lock the target of the insert-into.

Tip

For logging of lock activity please enable runtime lock activity logging as described in Section 17.6.2.3, “Lock Activity Logging”.

You may turn off context partition locking runtime-wide (also read the caution notice) as described in Section 17.6.10.4, “Disable Locking”. You may disable context partition locking for a given statement by providing the @NoLock annotation as part of your EPL. Note, Esper provides the @NoLock annotation for the purpose of identifying locking overhead, or when your application is single-threaded, or when using an external mechanism for concurrency control or for example with virtual data windows or plug-in data windows to allow customizing concurrency for a given statement or named window. Using this annotation may have unpredictable results unless your application is taking concurrency under consideration.

For a runtime to produce predictable results from the viewpoint of listeners to statements, a runtime by default ensures that it dispatches statement result events to listeners in the order in which a statement produced result events. Applications that require the highest possible concurrency and do not require predictable order of delivery of events to listeners, this feature can be turned off via configuration, see Section 17.6.1.1, “Preserving the Order of Events Delivered to Listeners”. For example, assume thread T1 processes an event applied to statement S producing output event O1. Assume thread T2 processes another event applied to statement S and produces output event O2. The runtime employs a configurable latch system to ensure that listeners to statement S receive and may complete processing of O1 before receiving O2. When using outbound threading (advanced threading options) or changing the configuration this guarantee is weakened or removed.

In multithreaded environments, when one or more statements make result events available via the insert into clause to further statements, the runtime preserves the order of events inserted into the generated insert-into stream, allowing statements that consume other statement's events to behave deterministic. This feature can also be turned off via configuration, see Section 17.6.1.2, “Preserving the Order of Events for Insert-Into Streams”. For example, assume thread T1 processes an event applied to statement S and thread T2 processes another event applied to statement S. Assume statement S inserts into stream ST. T1 produces an output event O1 for processing by consumers of ST1 and T2 produces an output event O2 for processing by consumers of ST. The runtime employs a configurable latch system such that O1 is processed before O2 by consumers of ST. When using route execution threading (advanced threading options) or changing the configuration this guarantee is weakened or removed.

We generally recommended that listener implementations block minimally or do not block at all. By implementing listener code as non-blocking code execution threads can often achieve higher levels of concurrency.

We recommended that, when using a single listener or subscriber instance to receive output from multiple statements, that the listener or subscriber code is multithread-safe. If your application has shared state between listener or subscriber instances then such shared state should be thread-safe.

16.8.1. Advanced Threading

In the default configuration the same application thread that invokes any of the sendEvent methods will process the event fully and also deliver output events to listeners and subscribers. By default the single internal timer thread based on system time performs time-based processing and delivery of time-based results.

This default configuration reduces the processing overhead associated with thread context switching, is lightweight and fast and works well in many environments such as J2EE, server or client. Latency and throughput requirements are largely use case dependent, and Esper provides runtime-level facilities for controlling concurrency that are described next.

Inbound Threading queues all incoming events: A pool of runtime-managed threads performs the event processing. The application thread that sends an event via any of the sendEvent methods returns without blocking.

Outbound Threading queues events for delivery to listeners and subscribers, such that slow or blocking listeners or subscribers do not block event processing.

Timer Execution Threading means time-based event processing is performed by a pool of runtime-managed threads. With this option the internal timer thread (or external timer event) serves only as a metronome, providing units-of-work to the runtime-managed threads in the timer execution pool, pushing threading to the level of each statement for time-based execution.

Route Execution Threading means that the thread sending in an event via any of the sendEvent methods (or the inbound threading pooled thread if inbound threading is enabled) only identifies and pre-processes an event, and a pool of runtime-managed threads handles the actual processing of the event for each statement, pushing threading to the level of each statement for event-arrival-based execution.

The runtime starts runtime-managed threads as daemon threads when the runtime instance is first obtained. The runtime stops runtime-managed threads when the runtime instance is destroyed via the destroy method. When the runtime is initialized via the initialize method the existing runtime-managed threads are stopped and new threads are created. When shutting down your application, use the destroy method to stop runtime-managed threads.

Note that the options discussed herein may introduce additional processing overhead into your system, as each option involves work queue management and thread context switching.

Note

If your use cases require ordered processing of events or do not tolerate disorder, the threading options described herein are not the right choice.

For enforcing a processing order within a given criteria, your application must enforce such processing order. Esper does not enforce order of processing if you enable inbound or route threading. Your application code could, for example, utilize a thread per group of criteria keys, a latch per criteria key, or a queue per criteria key, or use Java's completion service, all depending on your ordering requirements.

If your use cases require loss-less processing of events, wherein the threading options mean that events are held in an in-memory queue, the threading options described herein may not be the right choice.

Care should be taken to consider arrival rates and queue depth. Threading options utilize unbound queues or capacity-bound queues with blocking-put, depending on your configuration, and may therefore introduce an overload or blocking situation to your application. You may use the service provider interface as outlined below to manage queue sizes, if required, and to help tune the runtime to your application needs. Consider throttling down the event send rate when the API (see below) indicates that events are getting queued.

All threading options are on the level of a runtime. If you require different threading behavior for certain statements then consider using multiple runtimes, consider using the routeEvent method or consider using application threads instead.

Please consult Section 17.6.1, “Runtime Settings Related to Concurrency and Threading” for instructions on how to configure threading options. Threading options take effect at runtime initialization time.

16.8.1.1. Inbound Threading

With inbound threading a runtime places inbound events in a queue for processing by one or more runtime-managed threads other than the delivering application threads.

The delivering application thread uses one of the sendEventType methods on EPEventService to deliver events or may also use the sendEvent method on a EventSender. The runtime receives the event and places the event into a queue, allowing the delivering thread to continue and not block while the event is being processed and results are delivered.

Events that are sent into the runtime via one of the routeEvent methods are not placed into queue but processed by the same thread invoking the routeEvent operation.

16.8.1.2. Outbound Threading

With outbound threading a runtime places outbound events in a queue for delivery by one or more runtime-managed threads other than the processing thread originating the result.

With outbound threading your listener or subscriber class receives statement results from one of the runtime-managed threads in the outbound pool of threads. This is useful when you expect your listener or subscriber code to perform significantly blocking operations and you do not want to hold up event processing.

Note

If outbound-threading is enabled, listeners and subscribers that send events back into the runtime should use the sendEvent method and not the routeEvent method.

16.8.1.3. Timer Execution Threading

With timer execution threading an runtime places time-based work units into a queue for processing by one or more runtime-managed threads other than the internal timer thread or the application thread that sends an external timer event.

Using timer execution threading the internal timer thread (or thread delivering an external timer event) serves to evaluate which time-based work units must be processed. A pool of runtime-managed threads performs the actual processing of time-based work units and thereby offloads the work from the internal timer thread (or thread delivering an external timer event).

Enable this option as a tuning parameter when your statements utilize time-based patterns or data windows. Timer execution threading is fine grained and works on the level of a time-based schedule in combination with a statement.

16.8.1.4. Route Execution Threading

With route execution threading an runtime identifies event-processing work units based on the event and statement combination. It places such work units into a queue for processing by one or more runtime-managed threads other than the thread that originated the event.

While inbound threading works on the level of an event, route execution threading is fine grained and works on the level of an event in combination with a statement.

16.8.1.5. Threading Service Provider Interface

The service-provider interface EPRuntimeSPI is an extension API that allows to manage runtime-level queues and thread pools (Extension APIs are subject to change between release versions).

The following code snippet shows how to obtain the BlockingQueue<Runnable> and the ThreadPoolExecutor for the managing the queue and thread pool responsible for inbound threading:

EPRuntimeSPI spi = (EPRuntimeSPI) runtime;
int queueSize = spi.getThreadingService().getInboundQueue().size();
ThreadPoolExecutor threadpool = spi.getThreadingService().getInboundThreadPool();

16.8.2. Processing Order

16.8.2.1. Competing Statements

This section discusses the order in which N competing statements that all react to the same arriving event execute.

The runtime, by default, does not guarantee to execute competing statements in any particular order unless using @Priority. We therefore recommend that an application does not rely on the order of execution of statements by the runtime, since that best shields the behavior of an application from changes in the order that statements may get created by your application or by threading configurations that your application may change at will.

If your application requires a defined order of execution of competing statements, use the @Priority EPL syntax to make the order of execution between statements well-defined (requires that you set the prioritized-execution configuration setting). And the @Drop can make a statement preempt all other lowered priority ones that then won't get executed for any matching events.

16.8.2.2. Processing Principles of Events and Listener Updates

The runtime processes event by event. It only processes the next event when processing of the current event has completed.

For the purpose of processing event-by-event, the runtime maintains a thread-specific ordered work queue. In the default configuration the order of events in the work queue is dictated by the order of execution of insert-into.

New events get added to the end of the work queue (exceptions highlighted below) unless events have an event-precedence (see below). The runtime works off each event in the work queue completely, beginning with the oldest event, before processing the next newer event in the work queue.

The runtime processes the current event against all statements completely before delivering results to listeners and subscribers and before processing the next event. This is true regardless whether a given event was sent in via the API or whether the event was produced by a statement and insert-into. The order is:

Process a given event against only those statement context partitions that need to see the event (see information on filter indexes).
Dispatch statement results to listeners and subscribers.

For example, assume three statements named statement-1, statement-2 and statement-3:

@name('statement-1') select * from MyEvent;
@name('statement-2') insert into ABCStream select * from MyEvent;
@name('statement-3') select * from ABCStream;

When a MyEvent event arrives, the order of processing is:

The runtime processes statement-1 and statement-2 in any order (since there is no @priority set, see priority related documentation).
- For statement-1 the runtime determines whether listeners are attached and if yes buffers the dispatch for delivery to listeners.
- For statement-2 the runtime determines whether listeners are attached and if yes buffers the dispatch for delivery to listeners. The runtime also produces an ABCStream event and adds it to the work queue.
The runtime invokes the listeners to statement-1 and statement-2 (default configuration, not considing all threading configurations).
The runtime picks up the ABCStream event from the work queue and processes statement-3 .
The runtime invokes the listeners to statement-3.

Among all events generated by insert-into of statements and the events routed into the runtime via the routeEvent method, all events that insert-into a named window or table are processed first in the order generated. All other events are processed thereafter in the order they were generated (except when using event-precedence).

If your application requires a defined order of processing among the events that are generated by insert-into, use the event-precedence syntax. Please see Section 5.10.10, “Insert Into and Event Precedence” and Section 17.6.10.2, “Event-Precedence Execution” for more information.

When enabling timer or route execution threading as explained under advanced threading options then the runtime does not make any guarantee to the processing order except that is will prioritize events inserted into a named window.

16.9. Controlling Time-Keeping

There are two modes for a runtime to keep track of time: The internal timer based on JVM system time (the default), and externally-controlled (aka. event time) time giving your application full control over the concept of time within a runtime.

By default the internal timer provides time and evaluates schedules. External clocking i.e. event time can be used to supply time ticks to the runtime instead. The latter is useful for when events themselves provide the time to advance. External clocking also helps in testing time-based event sequences or for synchronizing the runtime with an external time source.

The internal timer relies on the java.util.concurrent.ScheduledThreadPoolExecutor class for time tick events. The next section describes timer resolution for the internal timer, by default set to 100 milliseconds but is configurable via the threading options. When using externally-controlled time the timer resolution is in your control.

To disable the internal timer and use externally-provided time instead, there are two options. The first option is to use the configuration API at runtime initialization time. The second option toggles on and off the internal timer at runtime, via special timer control events that are sent into the runtime like any other event.

If using a timer execution thread pool as discussed above, the internal timer or external time event provide the schedule evaluation however do not actually perform the time-based processing. The time-based processing is performed by the threads in the timer execution thread pool.

Tip

External and internal/system time is the same internally to the runtime thus the runtime behaves the same whether using external or internal timer.

This code snippet shows the use of the configuration API to disable the internal timer and thereby turn on externally-provided time (see the Configuration section for configuration via XML file):

Configuration config = new Configuration();
config.getRuntime().getThreading().setInternalTimerEnabled(false);
EPRuntime runtime = EPRuntimeProvider.getDefaultRuntime(config);

After disabling the internal timer, it is wise to set a defined time so that any statements created thereafter start relative to the time defined. Use the advanceTime method to indicate current time to the runtime and to move time forward for the runtime (a.k.a application-time model).

This code snippet obtains the current time and advances time:

long timeInMillis = System.currentTimeMillis();
runtime.getEventService().advanceTime(timeInMillis);

To enable or disable the internal timer by API call use the clockExternal and clockInternal methods of EPEventService.

The next code snippet demonstrates toggling to external time:

EPRuntime runtime = EPRuntimeProvider.getDefaultRuntime();
EPEventService eventService = runtime.getEventService();
// switch to external clocking
eventService.clockExternal();

The advanceTime method moves the time forward. All aspects of runtime current time related to statements and patterns are driven by the time that your application advances to.

The next example sequence of instructions sets time to zero, then creates a statement, then moves time forward to 1 seconds later and then 6 seconds later:

// Set start time at zero.
runtime.getEventService().advanceTime(0);

// deploy a module here
// sample EPL: select current_timestamp() as ct from pattern[every timer:interval(1 minute)]
runtime.getDeploymentService().deploy(compiled); // compiled is a module you compiled earlier

// move time forward 1 second
runtime.getEventService().advanceTime(1000);

// move time forward 5 seconds
runtime.getEventService().advanceTime(6000);

When advancing time your application should make sure values are ascending. That is, each time value should be either the same value or a larger value then the prior value provided.

Your application may use the getNextScheduledTime method in EPEventService to determine the earliest time a schedule for any statement requires evaluation.

The following code snippet sets the current time, creates a statement and prints the next scheduled time which is 1 minute later then the current time:

// set start time to the current time.
runtime.getEventService().advanceTime(System.currentTimeMillis());

// deploy a module
// sample EPL: select current_timestamp() as ct from pattern[every timer:interval(1 minute)]
runtime.getDeploymentService().deploy(compiled); // compiled is a module you compiled earlier

// print next schedule time
System.out.println("Next schedule at " + new Date(runtime.getEventService().getNextScheduledTime()));

Tip

If you find that your application requires control over time only for specific deployments and not for other deployments, please read up on Section 16.21, “Stages”.

Warning

You may not use advanceTime or advanceTimeSpan to control time when using internal timer since the internal timer tracks system time and must be the only source of time.

16.9.1. Controlling Time Using Time Span Events

The advanceTime method allows your application to advance runtime time to a given point in time. In addition, the getNextScheduledTime method in EPEventService returns the next scheduled time according to started statements. You would typically use advanceTime to advance time at a relatively high resolution i.e. milliseconds or microseconds.

To advance time for a span of time without individual calls to advanceTime the API provides the method advanceTimeSpan. The advanceTimeSpan method can accept a resolution parameter.

If your application provides the target end time of a time span to the advanceTimeSpan method and does not provide a resolution, the runtime advances time up to the target time by stepping through all relevant times according to started statements.

If your application provides the target end time of a time span and in addition a long-typed resolution to the advanceTimeSpan method the runtime advances time up to the target time by incrementing time according to the resolution (regardless of next scheduled time according to started statements).

Consider the following example:

// Set start time to Jan.1, 2010, 00:00 am for this example
SimpleDateFormat format = new SimpleDateFormat("yyyy MM dd HH:mm:ss SSS");
Date startTime = format.parse("2010 01 01 00:00:00 000");
runtime.getEventService().advanceTime(startTime.getTime());

// deploy a module
// sample EPL: select current_timestamp() as ct from pattern[every timer:interval(1 minute)]
runtime.getDeploymentService().deploy(compiled); // compiled is a module you compiled earlier
stmt.addListener(...);	// add a listener

// Advance time to 10 minutes after start time
runtime.getEventService().advanceTimeSpan(startTime.getTime() + 10*60*1000));

The above example advances time to 10 minutes after the start time using the advanceTimeSpan method. As the example does not pass a resolution, the runtime advances time according to statement schedules. Upon calling the advanceTimeSpan method the listener sees 10 invocations for minute 1 to minute 10.

To advance time according to a given resolution, you may provide the resolution as shown below:

// Advance time to 10 minutes after start time at 100 msec resolution
runtime.getEventService().advanceTimeSpan(startTime.getTime() + 10*60*1000, 100);

16.9.2. Time Resolution and Time Unit

Time can have a resolution of either milliseconds or microseconds.

The default time resolution is milliseconds. To configure the runtime for microsecond resolution, please see Section 17.4.14.1, “Time Unit”.

Table 16.7. Time Resolution

	Millisecond	Microsecond
Smallest unit for advancing time	1 millisecond	1 microsecond
Equivalent `java.util.concurrent.TimeUnit`	TimeUnit.MILLISECONDS	TimeUnit.MICROSECONDS
Default?	Default	Requires configuration change, see Section 17.4.14.1, “Time Unit”
Long-type runtime time represents	Milliseconds since Epoch	Microseconds since Epoch
Example: the date Tue, 01 Jan 1980 00:00:00 GMT	315532800000	315532800000000
Support for Internal System Time	Yes	No, requires external time (aka. event time) via `advanceTimeSpan` or `advanceTime`

A few notes on usage of microsecond time unit for time resolution:

The runtime automatically computes time periods into microseconds. For example 1 minute 2 seconds is 62000000 microseconds (62 * 1000000).
The runtime automatically computes time-in-second parameters into microseconds. For example 5.02 seconds is 5020000 microseconds.
The runtime automatically computes ISO schedules, crontabs and hints related to runtime time into microseconds.
The CurrentTimeSpanEvent or CurrentTimeEvent events must provide microsecond values.
Date-time methods with long-type input values assume microsecond values.
Date-time methods or other functions that take millisecond parameters or produce millisecond values still consume/produce millisecond values, such as the date-time method toMillisec.
The internal timer must be disabled (setInternalTimerEnabled(false)) and TimerControlEvent.ClockType.CLOCK_INTERNAL cannot be used.

16.9.3. Internal Timer Based on JVM System Time

By default the internal timer is enabled and that tracks VM system time. For many use cases your application may want to use event time or external time instead, as discussed above.

The internal timer thread, by default, uses the call System.currentTimeMillis() to obtain system time. Please see the JIRA issue ESPER-191 Support nano/microsecond resolution for more information on Java system time-call performance, accuracy and drift.

The internal timer thread can be configured to use nanosecond time as returned by System.nanoTime(). If configured for nanosecond time, the runtime computes an offset of the nanosecond ticks to wall clock time upon startup to present back an accurate millisecond wall clock time. Please see section Section 17.6.6, “Runtime Settings Related to Time Source” to configure the internal timer thread to use System.nanoTime().

The internal timer is based on java.util.concurrent.ScheduledThreadPoolExecutor and that generally provides high accuracy VM time (java.util.Timer does not support high accuracy VM time).

16.10. Exception Handling

You may register one or more exception handlers for the runtime to invoke in the case it encounters an exception processing a continuously-executing statement. By default and without exception handlers the runtime cancels execution of the current statement that encountered the exception, logs the exception and continues to the next statement, if any. The configuration is described in Section 17.6.11, “Runtime Settings Related to Exception Handling”.

If your application registers exception handlers as part of runtime configuration, the runtime invokes the exception handlers in the order they are registered passing relevant exception information such as statement name, expression and the exception itself.

Exception handlers receive any statement unchecked exception such as internal exceptions or exceptions thrown by plug-in aggregation functions or plug-in data windows. The runtime does not provide to exception handlers any exceptions thrown by static method invocations for function calls, method invocations in joins, methods on variables and event classes and listeners or subscriber exceptions.

An exception handler can itself throw a runtime exception to cancel execution of the current event against any further statements.

Note

Exceptions are meant to indicate an actual unexpected problem.

We do not recommend explicitly throwing exceptions for the purpose of flow control, preempting execution or other normal situations.

The runtime does not guarantee that throwing an exception has no other side effect and the runtime may not roll back changes that are already made to state.

For fire-and-forget queries the API indicates any exception directly back to the caller without the exception handlers being invoked, as exception handlers apply to statements only. The same applies to any API calls other than sendEvent and the EventSender methods.

As the configuration section describes, your application registers one or more classes that implement the ExceptionHandlerFactory interface in the runtime configuration. Upon runtime initialization the runtime obtains a factory instance from the class name that then provides the exception handler instance. The exception handler class must implement the ExceptionHandler interface.

16.11. Condition Handling

You may register one or more condition handlers for the runtime to invoke in the case it encounters certain conditions, as outlined below, when executing a statement. By default and without condition handlers the runtime logs the condition at informational level and continues processing. The configuration is described in Section 17.6.12, “Runtime Settings Related to Condition Handling”.

If your application registers condition handlers as part of runtime configuration, the runtime invokes the condition handlers in the order they are registered passing relevant condition information such as statement name, expression and the condition information itself.

Currently the only conditions indicated by this facility are raised by the pattern followed-by operator, see Section 7.5.8.1, “Limiting Sub-Expression Count” and see Section 7.5.8.2, “Limiting Runtime-Wide Sub-Expression Count”.

A condition handler may not itself throw a runtime exception or return any value.

As the configuration section describes, your application registers one or more classes that implement the ConditionHandlerFactory interface in the runtime configuration. Upon runtime initialization the runtime obtains a factory instance from the class name that then provides the condition handler instance. The condition handler class must implement the ConditionHandler interface.

16.12. Runtime and Statement Metrics Reporting

The runtime can report key processing metrics through the JMX platform mbean server by setting a single configuration flag described in Section 17.6.7, “Runtime Settings Related to JMX Metrics”. For additional detailed reporting and metrics events, please read on.

Metrics reporting is a feature that allows an application to receive ongoing reports about key runtime-level and statement-level metrics. Examples are the number of incoming events, the CPU time and wall time taken by statement executions or the number of output events per statement.

Metrics reporting is, by default, disabled. To enable reporting, please follow the steps as outlined in Section 17.6.8, “Runtime Settings Related to Metrics Reporting”. Metrics reporting must be enabled at runtime initialization time. The EPMetricsService interface, available from the runtime API, provides current metrics and provides control over reporting intervals.

Your application can receive metrics at configurable intervals via statement. A metric datapoint is simply a well-defined event. The events are RuntimeMetric and StatementMetric and the Java class representing the events can be found in the client API in package com.espertech.esper.common.client.metric.

Since metric events are processed by the runtime the same as application events, your EPL may use any construct on such events. For example, your application may select, filter, aggregate properties, sort or insert into a stream, named window or table all metric events the same as application events.

This example statement selects all runtime metric events:

select * from com.espertech.esper.common.client.metric.RuntimeMetric

The next statement selects all statement metric events:

select * from com.espertech.esper.common.client.metric.StatementMetric

The next statement selects all metric events:

select * from com.espertech.esper.common.client.metric.MetricEvent

Make sure to have metrics reporting enabled since only then do listeners or subscribers to a statement such as above receive metric events.

The runtime provides metric events after the configured interval of time has passed. By default, only started statements that have activity within an interval (in the form of event or timer processing) are reported upon.

The default configuration performs the publishing of metric events in an Esper daemon thread under the control of the runtime instance. Metrics reporting honors externally-supplied time, if using external timer events.

Via runtime configuration options provided by EPMetricsService, your application may enable and disable metrics reporting globally, provided that metrics reporting was enabled at initialization time. Your application may also enable and disable metrics reporting for individual statements by statement name.

Statement groups is a configuration feature that allows to assigning reporting intervals to statements. Statement groups are described further in the Section 17.6.8, “Runtime Settings Related to Metrics Reporting” section. Statement groups cannot be added or removed at runtime.

The following limitations apply:

If your Java VM version does not report current thread CPU time (most JVM do), then CPU time is reported as zero (use ManagementFactory.getThreadMXBean().isCurrentThreadCpuTimeSupported() to determine if your JVM supports this feature).
Note: In some JVM the accuracy of CPU time returned is very low (in the order of 10 milliseconds off) which can impact the usefulness of CPU metrics returned. Consider measuring CPU time in your application thread after sending a number of events in the same thread, external to the runtime as an alternative.
Your Java VM may not provide high resolution time via System.nanoTime. In such case wall time may be inaccurate and inprecise.
CPU time and wall time have nanosecond precision but not necessarily nanosecond accuracy, please check with your Java VM provider.
There is a performance cost to collecting and reporting metrics.
Not all statements may report metrics: The runtime performs certain runtime optimizations sharing resources between similar statements, thereby not reporting on certain statements.

16.12.1. Runtime Metrics

Runtime metrics are properties of RuntimeMetric events:

Table 16.8. RuntimeMetric Properties

Name	Description
runtimeURI	The URI of the runtime.
timestamp	The current runtime time.
inputCount	Cumulative number of input events since runtime initialization time. Input events are defined as events send in via application threads as well as `insert into` events.
inputCountDelta	Number of input events since last reporting period.
scheduleDepth	Number of outstanding schedules.

16.12.2. Statement Metrics

Statement metrics are properties of StatementMetric. The properties are:

Table 16.9. StatementMetric Properties

Name	Description
runtimeURI	The URI of the runtime.
timestamp	The current runtime time.
statementName	Statement name.
cpuTime	Statement processing CPU time (system and user) in nanoseconds (if available by Java VM, obtained from `ThreadMXBean.getCurrentThreadCpuTime`).
wallTime	Statement processing wall time in nanoseconds (based on `System.nanoTime`).
numInput	Number of input events to the statement.
numOutputIStream	Number of insert stream rows output to listeners or the subscriber, if any.
numOutputRStream	Number of remove stream rows output to listeners or the subscriber, if any.

The totals reported are cumulative relative to the last metric report.

16.13. Monitoring and JMX

Enterprise Edition has a library for measuring and reporting memory use for a runtime.

The runtime can report key processing metrics through the JMX platform mbean server by setting a single configuration flag described in Section 17.6.7, “Runtime Settings Related to JMX Metrics”.

Runtime and statement-level metrics reporting is described in Section 16.12, “Runtime and Statement Metrics Reporting”.

The easiest way to see thread contentions is by using VisualVM when Esper is under load and looking at the Threads tab. In the worst case you will see a lot of red color in VisualVM. The red line in VisualVM shows the threads that are either in a monitor region or waiting in an entry set for the monitor. The monitor is the mechanism that Java uses to support synchronization. When a statement is stateful the runtime manages the state using a monitor (lock) per context partition.

A JVM profiler can be handy to see how much CPU is spent in Esper by the sendEvent method.

The jconsole can provide information on the JVM heap. If memory gets tights the performance can drop significantly.

16.14. Event Rendering to XML and JSON

The EPRenderEventService interface offers methods to render events as XML or JSON. Obtain the service from a runtime by calling getRenderEventService on EPRuntime.

Your application may use the built-in XML and JSON formatters to render output events into a readable textual format, such as for integration or debugging purposes. This section introduces the utility classes in the client util package for rendering events to strings. Further API information can be found in the JavaDocs.

For repeated rendering of events of the same event type or subtypes, it is recommended to obtain a JSONEventRenderer or XMLEventRenderer instance and use the render method provided by the interface. This allows the renderer implementations to cache event type metadata for fast rendering.

This example shows how to obtain a renderer for repeated rendering of events of the same type, assuming that statement is an instance of EPStatement:

JSONEventRenderer jsonRenderer = runtime.getRenderEventService().getJSONRenderer(statement.getEventType());

Assuming that event is an instance of EventBean, this code snippet renders an event into the JSON format:

String jsonEventText = jsonRenderer.render("MyEvent", event);

The XML renderer works the same:

XMLEventRenderer xmlRenderer = runtime.getRenderEventService().getXMLRenderer(statement.getEventType());

...and...

String xmlEventText = xmlRenderer.render("MyEvent", event);

If the event type is not known in advance or if your application does not want to obtain a renderer instance per event type for fast rendering, your application can use one of the following methods to render an event to a XML or JSON textual format:

String json = runtime.getRenderEventService().renderJSON(event);
String xml = runtime.getRenderEventService().renderXML(event);

Use the JSONRenderingOptions or XMLRenderingOptions classes to control how events are rendered. To render specific event properties using a custom event property renderer, specify an EventPropertyRenderer as part of the options that renders event property values to strings. Please see the JavaDoc documentation for more information.

16.14.1. JSON Event Rendering Conventions and Options

The JSON renderer produces JSON text according to the standard documented at http://www.json.org.

The renderer formats simple properties as well as nested properties and indexed properties according to the JSON string encoding, array encoding and nested object encoding requirements.

The renderer does render indexed properties, it does not render indexed properties that require an index, i.e. if your event representation is backed by POJO objects and your getter method is getValue(int index), the indexed property values are not part of the JSON text. This is because the implementation has no way to determine how many index keys there are. A workaround is to have a method such as Object[] getValue() instead.

The same is true for mapped properties that the renderer also renders. If a property requires a Map key for access, i.e. your getter method is getValue(String key), such property values are not part of the result text as there is no way for the implementation to determine the key set.

16.14.2. XML Event Rendering Conventions and Options

The XML renderer produces well-formed XML text according to the XML standard.

The renderer can be configured to format simple properties as attributes or as elements. Nested properties and indexed properties are always represented as XML sub-elements to the root or parent element.

The root element name provided to the XML renderer must be the element name of the root in the XML document and may include namespace instructions.

The renderer does render indexed properties, it does not render indexed properties that require an index, i.e. if your event representation is backed by POJO objects and your getter method is getValue(int index), the indexed property values are not part of the XML text. This is because the implementation has no way to determine how many index keys there are. A workaround is to have a method such as Object[] getValue() instead.

16.15. Plug-In Loader

A plug-in loader is for general use with input adapters, output adapters or EPL code deployment or any other task that can benefits from being part of an Esper configuration file and that follows runtime lifecycle.

A plug-in loader implements the com.espertech.esper.runtime.client.plugin.PluginLoader interface and can be listed in the configuration.

Each configured plug-in loader follows the runtime instance lifecycle: When an runtime instance initializes, it instantiates each PluginLoader implementation class listed in the configuration. The runtime then invokes the lifecycle methods of the PluginLoader implementation class before and after the runtime is fully initialized and before an runtime instance is destroyed.

Declare a plug-in loader in your configuration XML as follows:

...
  <plugin-loader name="MyLoader" class-name="org.mypackage.MyLoader">
    <init-arg name="property1" value="val1"/>
  </plugin-loader>
...

Alternatively, add the plug-in loader via the configuration API:

Configuration config = new Configuration();
Properties props = new Properties();
props.put("property1", "value1");
config.getRuntime().addPluginLoader("MyLoader", "org.mypackage.MyLoader", props);

Implement the init method of your PluginLoader implementation to receive initialization parameters. The runtime invokes this method before the runtime is fully initialized, therefore your implementation should not yet rely on the runtime instance within the method body:

public class MyPluginLoader implements PluginLoader {
  public void init(String loaderName, Properties properties, EPRuntime runtime) {
     // save the configuration for later, perform checking
  }
  ...

The runtime calls the postInitialize method once the runtime completed initialization and to indicate the runtime is ready for traffic.

public void postInitialize() {
  // Start the actual interaction with external feeds or the runtime here
}
...

The runtime calls the destroy method once the runtime is destroyed or initialized for a second time.

public void destroy() {
  // Destroy resources allocated as the runtime instance is being destroyed
}

To access the plug-in at runtime, the getContext method provides access under the name plugin-loader/name:

runtime.getContext().getEnvironment().get("plugin-loader/MyLoader");

16.16. Context Partition Selection

This chapter discusses how to select context partitions. Contexts are discussed in Chapter 4, Context and Context Partitions and the reasons for context partition selection are introduced in Section 4.10, “Operations on Specific Context Partitions”.

The section is only relevant when you declare a context. It applies to all different types of hash, partitioned, category, overlapping or other temporal contexts. The section uses a category context for the purpose of illustration. The API discussed herein is general and handles all different types of contexts including nested contexts.

Consider a category context that separates bank transactions into small, medium and large:

// declare category context
create context TxnCategoryContext 
  group by amount < 100 as small, 
  group by amount between 100 and 1000 as medium, 
  group by amount > 1000 as large from BankTxn

// retain 1 minute of events of each category separately
context TxnCategoryContext select * from BankTxn#time(1 minute)

In order for your application to iterate one or more specific categories it is necessary to identify which category, i.e. which context partition, to iterate. Similarly for fire-and-forget queries, to execute fire-and-forget queries against one or more specific categories, it is necessary to identify which context partition to execute the fire-and-forget query against.

Your application may iterate one or more specific context partitions using either the iterate or safeIterate method of EPStatement by providing an implementation of the ContextPartitionSelector interface.

For example, assume your application must obtain all bank transactions for small amounts. It may use the API to identify the category and iterate the associated context partition:

ContextPartitionSelectorCategory categorySmall = new ContextPartitionSelectorCategory() {
    public Set<String> getLabels() {
      return Collections.singleton("small");
    }
  };
Iterator<EventBean> it = stmt.iterator(categorySmall);

Your application may execute fire-and-forget queries against one or more specific context partitions by using the executeQuery method on EPRuntime or the execute method on EPFireAndForgetPreparedQuery and by providing an implementation of ContextPartitionSelector.

Fire-and-forget queries execute against named windows and tables, therefore below statement creates a named window which the runtime manages separately for small, medium and large transactions according to the context declared earlier:

// Named window per category
context TxnCategoryContext create window BankTxnWindow#time(1 min) as BankTxn

The following code demonstrates how to fire a fire-and-forget query against the small and the medium category:

ContextPartitionSelectorCategory categorySmallMed = new ContextPartitionSelectorCategory() {
    public Set<String> getLabels() {
      return new HashSet<String>(Arrays.asList("small", "medium"));
    }
  };
runtime.getFireAndForgetService().executeQuery(
   "select count(*) from BankTxnWindow", 
   new ContextPartitionSelector[] {categorySmallMed});

The following limitations apply:

Fire-and-forget queries may not join named windows or tables that declare a context.

16.16.1. Selectors

This section summarizes the selector interfaces that are available for use to identify and interrogate context partitions. Please refer to the JavaDoc documentation for package com.espertech.esper.common.client.context and classes therein for additional information.

Use an implementation of ContextPartitionSelectorAll or the ContextPartitionSelectorAll.INSTANCE object to instruct the runtime to consider all context partitions.

Use an implementation of ContextPartitionSelectorById if your application knows the context partition ids to query. This selector instructs the runtime to consider only those provided context partitions based on their integer id value. The runtime outputs the context partition id in the built-in property context.id.

Use an implementation of ContextPartitionSelectorFiltered to receive and interrogate context partitions. Use the filter method that receives a ContextPartitionIdentifier to return a boolean indicator whether to include the context partition or not. The ContextPartitionIdentifier provides information about each context partition. Your application may not retain ContextPartitionIdentifier instances between filter method invocations as the runtime reuses the same instance. This selector is not supported with nested contexts.

Use an implementation of ContextPartitionSelectorCategory with category contexts.

Use an implementation of ContextPartitionSelectorSegmented with keyed segmented contexts.

Use an implementation of ContextPartitionSelectorHash with hash segmented contexts.

Use an implementation of ContextPartitionSelectorNested in combination with the selectors described above with nested contexts.

16.17. Context Partition Administration

This chapter briefly discusses the API to manage context partitions. Contexts are discussed in Chapter 4, Context and Context Partitions.

The section is only relevant when you declare a context. It applies to all different types of hash, partitioned, category, overlapping or other temporal contexts.

The EPContextPartitionService interface offers methods to manage context partitions. Obtain the service from a runtime by calling getContextPartitionService on EPRuntime.

The context partition admin API allows an application to:

Interrogate the state and identifiers for existing context partitions.
Determine statements associated to a context and context nesting level.
Receive a callback when new contexts get created and destroyed or when context partitions are allocated and de-allocated.
Obtain context properties.

Please see the JavaDoc documentation for more information.

16.18. Test and Assertion Support

Esper offers a listener and an assertions class to facilitate automated testing of EPL rules, for example when using a test framework such as JUnit or TestNG.

Esper does not require any specific test framework. If your application has the JUnit test framework in classpath Esper uses junit.framework.AssertionFailedError to indicate assertion errors, so as to integrate with continuous integration tools.

For detailed method-level information, please consult the JavaDoc of the package com.espertech.esper.common.client.scopetest and com.espertech.esper.runtime.client.scopetest.

The class com.espertech.esper.common.client.scopetest.EPAssertionUtil provides methods to assert or compare event property values as well as perform various array arthithmatic, sort events and convert events or iterators to arrays.

The class com.espertech.esper.runtime.client.scopetest.SupportUpdateListener provides an UpdateListener implementation that collects events and returns event data for assertion.

The class com.espertech.esper.runtime.client.scopetest.SupportSubscriber provides a subscriber implementation that collects events and returns event data for assertion. The SupportSubscriberMRD is a subscriber that accepts events multi-row delivery. The SupportSubscriber and SupportSubscriberMRD work similar to SupportUpdateListener that is introduced in more detail below.

16.18.1. `EPAssertionUtil` Summary

The below table only summarizes the most relevant assertion methods offered by EPAssertionUtil. Methods provide multiple footprints that are not listed in detail below. Please consult the JavaDoc for additional method-level information.

Table 16.10. Method Summary for EPAssertionUtil

Name	Description
`assertProps`	Methods that assert that property values of a single `EventBean`, POJO or Map matches compared to expected values.
`assertPropsPerRow`	Methods that assert that property values of multiple `EventBean`, POJOs or Maps match compared to expected values.
`assertPropsPerRowAnyOrder`	Same as above, but any row may match. Useful for unordered result sets.
`assertEqualsExactOrder`	Methods that compare arrays, allowing `null`. as parameters.
`assertEqualsAnyOrder`	Same as above, but any row may match. Useful for unordered result sets.

16.18.2. `SupportUpdateListener` Summary

The below table only summarizes the most relevant methods offered by SupportUpdateListener. Please consult the JavaDoc for additional information.

Table 16.11. Method Summary for SupportUpdateListener

Name	Description
`reset`	Initializes listener clearing current events and resetting the invoked flag.
`getAndClearIsInvoked`	Returns the "invoked" flag indicating the listener has been invoked, and clears the flag.
`getLastNewData`	Returns the last events received by the listener.
`getAndResetDataListsFlattened`	Returns all events received by the listener as a pair.
`assertOneGetNewAndReset`	Asserts that exactly one new event was received and no removed events, returns the event and resets the listener.
`assertOneGetNew`	Asserts that exactly one new event was received and returns the event.

16.18.3. Usage Example

The next code block is a short but complete programming example that asserts that the properties received from output events match expected value.

String epl = "select personName, count(*) as cnt from PersonEvent#length(3) group by personName";
Configuration configuration = new Configuration();
configuration.getCommon().addEventType(PersonEvent.class);
CompilerArguments compilerArguments = new CompilerArguments(configuration);
EPCompiled compiled = EPCompilerProvider.getCompiler().compile(stmt, compilerArguments);

EPRuntime runtime = EPRuntimeProvider.getDefaultRuntime(configuration);
EPStatement stmt = runtime.getDeploymentService().deploy(compiled).getStatements()[0];

SupportUpdateListener listener = new SupportUpdateListener();
stmt.addListener(listener);

runtime.getEventService().sendEventBean(new PersonEvent("Joe"), "PersonEvent");
EPAssertionUtil.assertProps(listener.assertOneGetNewAndReset(), "personName,cnt".split(","),
    new Object[]{"Joe", 1L});

A few additional examples are shown below:

String[] fields = new String[] {"property"};			
EPAssertionUtil.assertPropsPerRow(listener.getAndResetDataListsFlattened(), fields, 
    new Object[][]{{"E2"}}, new Object[][]{{"E1"}});

EPAssertionUtil.assertPropsPerRow(listener.getAndResetLastNewData(), fields, 
    new Object[][]{{"E1"}, {"E2"}, {"E3"}});

assertTrue(listener.getAndClearIsInvoked());

Please refer to the Esper codebase test sources for more examples using the assertion class and the listener class.

16.19. OSGi, Class Loader, Class-For-Name

When deploying compiled modules the runtime may use a class loader to find resources. Your application has full control over class-for-name and classloader use. OSGi environments can provide a specific class-for-name and class loader. Please refer to Section 17.7, “Passing Services or Transient Objects”.

16.20. When Deploying with J2EE

A compiler and runtime can well be deployed as part of a J2EE web or enterprise application archive to a web application server. When designing for deployment into a J2EE web application server, please consider the items discussed here.

We provide a sample servlet context listener in this section that uses the deployment API to deploy and undeploy modules as part of the servlet lifecycle.

The distribution provides a message-driven bean (MDB) example that you may find useful.

Esper does not have a dependency on any J2EE or Servlet APIs to allow the runtime to run in any environment or container.

16.20.1. J2EE Deployment Considerations

As multiple web applications deployed to a J2EE web application server typically have a separate classloader per application, you should consider whether runtime instances need to be shared between applications or can remain separate runtime instances. Consider the EPRuntimeProvider a Singleton. When deploying multiple web applications, your J2EE container classloader may provide a separate instance of the Singleton EPRuntimeProvider to each web application resulting in multiple independent runtime instances.

To share EPRuntime instances between web applications, one approach is to add the runtime jar files to the system classpath. A second approach can be to have multiple web applications share the same servet context and have your application place the EPRuntime instance into a servlet context attribute for sharing. Architecturally you may also consider a single archived application (such as an message-driven bean) that all your web applications communicate to via the JMS broker provided by your application server or an external JMS broker.

As per J2EE standards there are restrictions in regards to starting new threads in J2EE application code. Esper adheres to these restrictions: It allows to be driven entirely by external events. To remove all Esper threads, set the internal timer off and leave the advanced threading options turned off. To provide timer events when the internal timer is turned off, you should check with your J2EE application container for support of the Java system timer or for support of batch or work loading to send timer events to an runtime instance.

As per J2EE standards there are restrictions in regards to input and output by J2EE application code. Esper adheres to these restrictions: By itself it does not start socket listeners or performs any file IO.

16.20.2. Servlet Context Listener

When deploying a J2EE archive that contains EPL modules files below is sample code to read and deploy EPL modules files packaged with the enterprise or web application archive when the servlet initializes. The sample undeploys EPL modules when the servlet context gets destroyed.

A sample web.xml configuration extract is:

<?xml version="1.0" encoding="UTF-8"?>
<web-app>
  <listener>
    <listener-class>SampleServletListener</listener-class>
  </listener>
  <context-param>
    <param-name>eplmodules</param-name>
    <param-value>switchmonitor.epl</param-value>
</context-param>
</web-app>

A sample servet listener that deploys EPL module files packaged into the archive on context initialization and that undeploys when the application server destroys the context is shown here:

public class SampleServletListener implements ServletContextListener {

  private List<String> deploymentIds = new ArrayList<String>();
  
  public void contextInitialized(ServletContextEvent servletContextEvent) {
    try {
      String modulesList = servletContextEvent.getServletContext().getInitParameter("eplmodules");
      List<Module> modules = new ArrayList<Module>();
      if (modulesList != null) {
        String[] split = modulesList.split(",");
        for (int i = 0; i < split.length; i++) {
          String resourceName = split[i].trim();
          if (resourceName.length() == 0) {
            continue;
          }
          String realPath = servletContextEvent.getServletContext().getRealPath(resourceName);
  		Module module = EPCompilerProvider.getCompiler().readModule(new File(realPath));
          modules.add(module);
        }
      }
    
      // Determine deployment order
      ModuleOrder order = ModuleOrderUtil.getModuleOrder(modules, null);
  
      // Deploy
      for (Module module : order.getOrdered()) {
       // compile and deploy here (code not included), add deployment id
        deploymentIds.add(deployment.getDeploymentId());
      }
    }
    catch (Exception ex) {
      ex.printStackTrace();
    }
  }
  
  public void contextDestroyed(ServletContextEvent servletContextEvent) {
    EPRuntime runtime = EPRuntimeProvider.getDefaultRuntime();
    for (String deploymentId : deploymentIds) {
       runtime.getDeploymentService().undeploy(deploymentId);
    }
  }
}

16.21. Stages

16.21.1. Overview

An Esper stage is its own little container that can host deployments.

A stage processes only those events that an application sends into that stage. A stage does not see any events that an application sends into the runtime.

A stage has its own stage time. Time in a stage advances only when an application advances time for that stage. A stage advance times independently of runtime time.

Each stage is uniquely identified by a stage URI. Within different runtimes there can be stages of the same stage URI. A stage lives within the runtime instance and gets destroyed when the runtime gets destroyed. An application can allocate any number of stages. A stage URI can be any non-null string value.

These attributes make stages useful for:

Suspend and Resume. Suspend deployments so they don't receive events or don't advance time. Resume deployments when they are needed again.
Initialize, Load, or Catch-Up new Deployments to Existing Event and Time History. Stage deployments and replay events and time until deployments have caught up with history, and then unstage deployments to have them receive runtime events and time.
Replay. Replay events and time, receive results, destroy accumulated state, without affecting other deployments and in parallel to other activity.

In other words, a stage allows an application to control event visibility and the concept of time as desired on a deployment level: Events sent into a stage are visible only to those deployments that are staged and are not visible to deployments outside of that stage. Within a stage an application can control time independently, start time at a point in time and advance time at the resolution and pace suitable for the deployments added to that stage.

16.21.2. Stage APIs

The getStageService method on EPRuntime returns the service for managing stages:

EPStageService stageService = runtime.getStageService();

To allocate a stage or obtain an already-allocated stage, use getStage and pass the stage URI. The new stage current time is the runtime current time.

EPStage stage = stageService.getStage("myStage");

The EPStage instance represents the stage. The freshly-allocated stage is empty.

The stage method moves deployments from the runtime to the stage (assume deploymentId is a deployment id of an existing deployment):

stage.stage(Collections.singletonList(deploymentId));

A given deployment is either currently staged or un-staged. The same deployment is never both staged and un-staged.

The unstage method moves deployments from the stage to the runtime:

stage.unstage(Collections.singletonList(deploymentId));

To send events to the stage, use EPStageEventService which extends the EPEventService interface:

stage.getEventService().sendEventBean(new Order(...), "Order");

To advance time for a stage, also use EPStageEventService:

stage.getEventService().advanceTime(myTimeInMillis);

Finally, call destroy to destroy the stage. Destroy requires that the stage is empty, i.e. does not have any deployments:

stage.destroy();

16.21.3. Stage Example: Suspending and Resuming a Statement

By staging deployments, the deployments' time and event processing occurs only when the application explicitly sends events to the stage or advances time for that stage.

The example code below compiles and deploys EPL that reports order events:

String module =
    "@public @buseventtype create schema OrderEvent(price double);\n" +
    "@name('All-Order-Events') select * from OrderEvent;\n";
  EPCompiled compiled = EPCompilerProvider.getCompiler().compile(module, null);
  EPRuntime runtime = EPRuntimeProvider.getDefaultRuntime();
  EPDeployment deployment = runtime.getDeploymentService().deploy(compiled);
  deployment.getStatements()[0].addListener(new SupportUpdateListener());

The stage method stages the deployment:

EPStage stage = runtime.getStageService().getStage("myStage");
stage.stage(Collections.singletonList(deployment.getDeploymentId()));

The staged deployment only receives OrderEvent events that an application send into the stage. It does not receive OrderEvent events that an application sends into the runtime (the EPEventService returned by getEventService of EPRuntime). For example:

// The listener receives the following event
stage.getEventService().sendEventMap(Collections.singletonMap("price", 100d), "OrderEvent");

// The listener DOES NOT receive the following event
runtime.getEventService().sendEventMap(Collections.singletonMap("price", 200d), "OrderEvent");

The unstage method un-stages the deployment:

stage.unstage(Collections.singletonList(deployment.getDeploymentId()));

The un-staged deployment only receives OrderEvent events that an application sends into the runtime. It does not receive OrderEvent events that an application sends into the stage. For example:

// The listener DOES NOT receive the following event
stage.getEventService().sendEventMap(Collections.singletonMap("price", 100d), "OrderEvent");

// The listener receives the following event
runtime.getEventService().sendEventMap(Collections.singletonMap("price", 200d), "OrderEvent");

Finally, destroy the stage when it is no longer needed:

stage.destroy();

16.21.4. Stage Other Considerations

When staging and unstaging, existing schedules are adjusted for the time difference between stage and runtime, if any. For example, assume a pattern pattern[timer:interval(10 minutes)] deployed at 9:00:00, which would fire at 9:10:00. If the time of the target is 8:55:00 or 9:05:00 the pattern still fires at 9:10:00. If the time of the target is after, for example 9:15:00, the pattern fires when time advances again.

Staging and un-staging deployments is an inexpensive operation in general.

When using advanced threading options, each stage has its own threading i.e. its own thread pool and queues.

Each stage has its own metrics reporting, when enabled.

Stage use the some configuration values as provided for the runtime.

16.21.5. Stage Limitations

Deployments that have dependencies on other deployments require additional consideration. Such deployments may provide EPL objects to other deployments, and deployments may consume EPL objects from other deployments. For example, a deployment may create a named window and another deployment may query the named window.

When staging deployments that have dependencies on other deployments, such EPL-object-providing or EPL-object-consuming deployments must also be staged or unstaged in the same operation.

Chapter 17. Configuration

17.1. Overview

17.2. Programmatic Configuration

17.3. Configuration via XML File

17.4. Configuration Common

17.4.1. Annotation Class and Package Imports
17.4.2. Class and Package Imports
17.4.3. Events Represented by Classes
17.4.4. Events Represented by java.util.Map
17.4.5. Events Represented by Object[] (Object-array)
17.4.6. Events Represented by JSON
17.4.7. Events Represented by Avro GenericData.Record
17.4.8. Events Represented by org.w3c.dom.Node
17.4.9. Event Type Defaults
17.4.10. Event Type Import Package (Event Type Auto-Name)
17.4.11. From-Clause Method Invocation
17.4.12. Relational Database Access
17.4.13. Common Settings Related to Logging
17.4.14. Common Settings Related to Time Source
17.4.15. Variables
17.4.16. Variant Stream

17.5. Configuration Compiler

17.5.1. Compiler Settings Related to Byte Code Generation
17.5.2. Compiler Settings Related to View Resources
17.5.3. Compiler Settings Related to Logging
17.5.4. Compiler Settings Related to Stream Selection
17.5.5. Compiler Settings Related to Language and Locale
17.5.6. Compiler Settings Related to Expression Evaluation
17.5.7. Compiler Settings Related to Scripts
17.5.8. Compiler Settings Related to Execution of Statements
17.5.9. Compiler Settings Related to Serializers and Deserializers

17.6. Configuration Runtime

17.6.1. Runtime Settings Related to Concurrency and Threading
17.6.2. Runtime Settings Related to Logging
17.6.3. Runtime Settings Related to Variables
17.6.4. Runtime Settings Related to Patterns
17.6.5. Runtime Settings Related to Match-Recognize
17.6.6. Runtime Settings Related to Time Source
17.6.7. Runtime Settings Related to JMX Metrics
17.6.8. Runtime Settings Related to Metrics Reporting
17.6.9. Runtime Settings Related to Expression Evaluation
17.6.10. Runtime Settings Related to Execution of Statements
17.6.11. Runtime Settings Related to Exception Handling
17.6.12. Runtime Settings Related to Condition Handling

17.7. Passing Services or Transient Objects

17.7.1. Service Example
17.7.2. Class-for-Name
17.7.3. Class Loader

17.8. Type Names

17.9. Logging Configuration

17.9.1. Log4j Logging Configuration

17.1. Overview

Compile-time and runtime configuration is entirely optional. The compiler and runtime work out-of-the-box without configuration.

All configuration lives in the Configuration class (com.espertech.esper.common.client.configuration.Configuration).

The configuration class has configure methods that can read configuration XML and that add the information contained in the XML to the configuration. You can read multiple XML sources additively.

A configuration has three sections:

The common section with configuration that both the compiler and the runtime may use, represented by the ConfigurationCommon class.
The compiler section, which provides configuration for use only by the compiler, represented by the ConfigurationCompiler class.
The runtime section, which provides configuration for use only by the runtime, represented by the ConfigurationRuntime class.

Configuration is an initialization-time object. The compiler does not retain any association back to configuration. The runtime makes a deep copy of the configuration object available (see getConfigurationDeepCopy on EPRuntime) but the configuration object cannot be changed once provided to the runtime.

17.2. Programmatic Configuration

You may obtain a Configuration instance by instantiating it directly and adding or setting values on it.

The following example code adds a preconfigured event type and adds an import to the common section of the configuration.

Configuration configuration = new Configuration();
configuration.getCommon().addEventType("PriceLimit", PriceLimit.class.getName());
configuration.getCommon().addImport("org.mycompany.mypackage.MyUtility");

The above example adds a preconfigured event type. For adding an event type at runtime please use create schema.

17.3. Configuration via XML File

In addition to programmatic configuration, or as an alternative approach, you may specify configuration items in XML files.

The default name for the XML configuration file is esper.cfg.xml. The configuration class reads this file from the root of the CLASSPATH as an application resource via the configure method.

Configuration configuration = new Configuration();		
configuration.configure();

The Configuration class can read the XML configuration file from other sources as well. The configure method accepts URL, File and String filename parameters.

Configuration configuration = new Configuration();		
configuration.configure("myconfigfile.esper.cfg.xml");

The schema for the configuration file can be found in the etc folder and is named esper-configuration-majorversion-0.xsd. The schema is available online at http://www.espertech.com/schema/esper/esper-configuration-majorversion-0.xsd so that a tool can fetch it automatically. The namespace is http://www.espertech.com/schema/esper.

You can use the XML schema file to validate that your XML configuration file is valid.

Here is an example configuration file.

<?xml version="1.0" encoding="UTF-8"?>
<esper-configuration xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
    xmlns="http://www.espertech.com/schema/esper"
    xsi:schemaLocation="
http://www.espertech.com/schema/esper
http://www.espertech.com/schema/esper/esper-configuration-8-0.xsd">
  <common>
    <event-type name="PriceLimit" class="com.espertech.esper.example.stockticker.event.PriceLimit"/>
    <auto-import import-name="org.mycompany.mypackage.MyUtility"/>
  </common>
</esper-configuration>

17.4. Configuration Common

The common section of the configuration applies to the compiler and also applies to the runtime.

17.4.1. Annotation Class and Package Imports

If your application has certain classes or packages that should only be visible within an @-annotation, you may add these to the annotation imports list. Such classes are only visible when used in an annotation and not elsewhere.

In a XML configuration file the auto-import-annotation configuration may look as below:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <auto-import-annotations import-name="com.mycompany.mypackage.myannotations.*"/>
  </common>
</esper-configuration>

Here is an example of providing annotation-only imports via the API:

Configuration config = new Configuration();
// package import, only visible for annotation use
config.getCommon().addAnnotationImport("com.mycompany.mypackage.myannotations.*");

17.4.2. Class and Package Imports

EPL allows invocations of static Java library functions in expressions, as outlined in Section 10.1, “Single-Row Function Reference”. This configuration item can be set to allow a partial rather than a fully qualified class name in such invocations. The imports work in the same way as in Java files, so both packages and classes can be imported.

select Math.max(priceOne, PriceTwo)
// via configuration equivalent to
select java.lang.Math.max(priceOne, priceTwo)

EPL auto-imports the following Java library packages. Any additional imports that are specified in configuration files or through the API are added to the configuration in addition to the imports below.

java.lang.*
java.math.*
java.text.*
java.util.*

In a XML configuration file the auto-import configuration may look as below:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <auto-import import-name="com.mycompany.mypackage.*"/>
    <auto-import import-name="com.mycompany.myapp.MyUtilityClass"/>  
  </common>
</esper-configuration>

Here is an example of providing imports via the API:

Configuration config = new Configuration();
config.getCommon().addImport("com.mycompany.mypackage.*");	// package import
config.getCommon().addImport("com.mycompany.mypackage.MyLib");   // class import

17.4.3. Events Represented by Classes

This section is relevant if you want to use regular classes to represent events.

The runtime can process event objects via the sendEventBean(Object event, String eventTypeName) method on the EPEventService interface.

17.4.3.1. Bean-Style Classes

For JavaBean-style classes that have getter methods please specify an event type name and the class name or class. Interfaces and abstract classes are also supported.

The below sample XML configures an event type named StockTick and provides the fully-qualified class name.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <event-type name="StockTick" class="com.espertech.esper.example.stockticker.event.StockTick"/>
  </common>
</esper-configuration>

The sample code for the configuration is:

Configuration configuration = new Configuration();
configuration.getCommon().addEventType("StockTick", StockTick.class.getName());

Or alternatively:

configuration.getCommon().addEventType("StockTick", StockTick.class);

Warning

When using superclasses and interfaces, the order in which event types are added does matter. Please refer to Section D.6, “Superclasses and Interfaces”.

17.4.3.2. Non-JavaBean and Legacy Java Event Classes

You can use this setting herein when method and member variable names in your Java class do not adhere to the JavaBean convention - any public methods and public member variables can be exposed as event properties via the below configuration.

A Java class can optionally be configured with an accessor style attribute. This attribute instructs the compiler how it should expose methods and fields for use as event properties in statements.

Table 17.1. Accessor Styles

Style Name	Description
`javabean`	As the default setting, the compiler exposes an event property for each public method following the JavaBean getter-method conventions
`public`	The compiler exposes an event property for each public method and public member variable of the given class
`explicit`	The compiler exposes an event property only for the explicitly configured public methods and public member variables

For NEsper .NET accessor styles are NATIVE, PUBLIC and EXPLICIT.

Using the public setting for the accessor-style attribute instructs the compiler to expose an event property for each public method and public member variable of a Java class. The compiler assigns event property names of the same name as the name of the method or member variable in the Java class.

For example, assuming the class MyLegacyEvent exposes a method named readValue and a member variable named myField, you can then use properties as shown.

select readValue, myField from MyLegacyEvent

Using the explicit setting for the accessor-style attribute requires that event properties are declared via configuration. This is outlined in the next chapter.

When configuring a compiler or runtime from a XML configuration file, the XML snippet below demonstrates the use of the legacy-type element and the accessor-style attribute.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <event-type name="MyLegacyEvent" class="com.mycompany.mypackage.MyLegacyEventClass">
      <legacy-type accessor-style="public"/>
    </event-type>
  </common>
</esper-configuration>

When configuring an compiler or runtime via Configuration API, the sample code below shows how to set the accessor style.

Configuration configuration = new Configuration();
ConfigurationCommonEventTypeBean legacyDef = new ConfigurationCommonEventTypeBean();
legacyDef.setAccessorStyle(AccessorStyle.PUBLIC);
configuration.getCommon().addEventType("MyLegacyEvent", MyLegacyEventClass.class.getName(), legacyDef);

17.4.3.3. Specifying Event Properties for Java Classes

Sometimes it may be convenient to use event property names in pattern and statements that are backed up by a given public method or member variable (field) in a Java class. And it can be useful to declare multiple event properties that each map to the same method or member variable.

We can configure properties of events via method-property and field-property elements, as the next example shows.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
      <event-type name="StockTick" class="com.espertech.esper.example.stockticker.event.StockTickEvent">
        <legacy-type accessor-style="javabean" code-generation="enabled">
          <method-property name="price" accessor-method="getCurrentPrice" />
          <field-property name="volume" accessor-field="volumeField" />
       </legacy-type>
    </event-type>
  </common>
</esper-configuration>

The XML configuration snippet above declared an event property named price backed by a getter-method named getCurrentPrice, and a second event property named volume that is backed by a public member variable named volumeField. Thus the price and volume properties can be used in a statement:

select avg(price * volume) from StockTick

As with all configuration options, the API can also be used:

Configuration configuration = new Configuration();
ConfigurationCommonEventTypeBean legacyDef = new ConfigurationCommonEventTypeBean();
legacyDef.addMethodProperty("price", "getCurrentPrice");
legacyDef.addFieldProperty("volume", "volumeField");
configuration.getCommon().addEventType("StockTick", StockTickEvent.class.getName(), legacyDef);

17.4.3.4. Case Sensitivity and Property Names

By default the compiler resolves Java event properties case sensitive. That is, property names in statements must match JavaBean-convention property names in name and case. This option controls case sensitivity per Java class.

In the configuration XML, the optional property-resolution-style attribute in the legacy-type element can be set to any of these values:

Table 17.2. Property Resolution Case Sensitivity Styles

Style Name	Description
`case_sensitive (default)`	As the default setting, the compiler matches property names for the exact name and case only.
`case_insensitive`	Properties are matched if the names are identical. A case insensitive search is used and will choose the first property that matches the name exactly or the first property that matches case insensitively should no match be found.
`distinct_case_insensitive`	Properties are matched if the names are identical. A case insensitive search is used and will choose the first property that matches the name exactly case insensitively. If more than one 'name' can be mapped to the property an exception is thrown.

The sample below shows this option in XML configuration, however the setting can also be changed via API:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
      <event-type name="MyLegacyEvent" class="com.mycompany.package.MyLegacyEventClass">
        <legacy-type property-resolution-style="case_insensitive"/>
      </event-type>
  </common>
</esper-configuration>

17.4.3.5. Factory and Copy Method

The insert into clause and directly instantiate and populate your event object. By default the runtime invokes the default constructor to instantiate an event object. To change this behavior, you may configure a factory method. The factory method is a method name or a class name plus a method name (in the format class.method) that returns an instance of the class.

The update clause can change event properties on an event object. For the purpose of maintaining consistency, the runtime may have to copy your event object via serialization (implement the java.io.Serializable interface). If instead you do not want any copy operations to occur, or your application needs to control the copy operation, you may configure a copy method. The copy method is the name of a method on the event object that copies the event object.

The sample below shows this option in XML configuration, however the setting can also be changed via ConfigurationCommonEventTypeBean:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <event-type name="MyLegacyEvent" class="com.mycompany.package.MyLegacyEventClass">
      <legacy-type factory-method="com.mycompany.myapp.MySampleEventFactory.createMyLegacyTypeEvent" copy-method="myCopyMethod"/>
    </event-type>
  </common>
</esper-configuration>

The copy method should be a public method that takes no parameters and returns a new event object (it may not return this). The copy method may not be a static method and may not take parameters.

The Beacon data flow operator in connection with the Sun JVM can use sun.reflect.ReflectionFactory if the class has no default no-argument constructor.

17.4.3.6. Start and End Timestamp

For use with date-time interval methods, for example, you may let the compiler know which property of your class carries the start and end timestamp value.

The sample below shows this option in XML configuration, however the setting can also be changed via API. The sample sets the name of the property providing the start timestamp to startts and the name of the property providing the end timestamp endts:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <event-type name="MyLegacyEvent" class="com.mycompany.package.MyLegacyEventClass">
      <legacy-type start-timestamp-property-name="startts" end-timestamp-property-name="endts"/>
    </event-type>
  </common>
</esper-configuration>

17.4.4. Events Represented by `java.util.Map`

The runtime can process java.util.Map events via the sendEventMap(Map map, String eventTypeName) method on the EPEventService interface. Entries in the Map represent event properties. Please see the Appendix E, Event Representation: java.util.Map Events section for details on how to use Map events with the runtime.

You can provide an event type name for Map events.

The below snippet of XML configuration configures an event type named MyMapEvent.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <event-type name="MyMapEvent">
      <java-util-map>
        <map-property name="carId" class="int"/>
        <map-property name="carType" class="string"/>
        <map-property name="assembly" class="com.mycompany.Assembly"/>    
      </java-util-map>
    </event-type>
  </common>
</esper-configuration>

For NEsper .NET use util-map instead of java-util-map.

This configuration defines the carId property of MyMapEvent events to be of type int, and the carType property to be of type java.util.String. The assembly property of the Map event will contain instances of com.mycompany.Assembly for the runtime to query.

The valid types for the class attribute are listed in Section 17.8, “Type Names”. In addition, any fully-qualified Java class name that can be resolved via Class.forName is allowed.

You can also use the configuration API to configure Map event types, as the short code snippet below demonstrates:

Map<String, Object> properties = new LinkedHashMap<String, Object>();
properties.put("carId", "int");
properties.put("carType", "string");
properties.put("assembly", Assembly.class.getName());

Configuration configuration = new Configuration();
configuration.getCommon().addEventType("MyMapEvent", properties);

A Map event type may also become a subtype of one or more supertypes that must also be Map event types. The java-util-map element provides the optional attribute supertype-names that accepts a comma-separated list of names of Map event types that are supertypes to the type:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <event-type name="AccountUpdate">
      <java-util-map supertype-names="BaseUpdate, AccountEvent">
      </java-util-map>
    </event-type>
  </common>
</esper-configuration>

A Map event type may declare a start and end timestamp property name. The XML shown next instructs the compiler that the startts property carries the event start timestamp and the endts property carries the event end timestamp:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <event-type name="AccountUpdate">
      <java-util-map start-timestamp-property-name="startts" end-timestamp-property-name="endts">
      </java-util-map>
    </event-type>
  </common>
</esper-configuration>

For adding a type at runtime please use create map schema.

17.4.5. Events Represented by `Object[]` (Object-array)

The runtime can process Object-array (Object[]) events via the sendEventObjectArray(Object[] array, String eventTypeName) method on the EPEventService interface. Elements in the Object array represent event properties. Please see the Appendix F, Event Representation: Object-Array (Object[]) Events section for details on how to use Object[] events with the runtime.

The below snippet of XML configuration configures an event type named MyObjectArrayEvent.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <event-type name="MyObjectArrayEvent">
      <objectarray>
        <objectarray-property name="carId" class="int"/>
        <objectarray-property name="carType" class="string"/>
        <objectarray-property name="assembly" class="com.mycompany.Assembly"/>    
      </objectarray>
    </event-type>
  </common>
</esper-configuration>

This configuration defines the carId property of MyObjectArrayEvent events to be of type int and in the object array first element ([0]). The carType property to be of type java.util.String is expected in the second array element ([1]) . The assembly property of the object array event will contain instances of com.mycompany.Assembly for the runtime to query in element two ([2]).

Note that the runtime does not verify the length and property values of object array events when your application sends object-array events into the runtime. For the example above, the proper object array would look as follows: new Object[] {carId, carType, assembly}.

The valid types for the class attribute are listed in Section 17.8, “Type Names”. In addition, any fully-qualified Java class name that can be resolved via Class.forName is allowed.

You can also use the configuration API to configure Object[] event types, as the short code snippet below demonstrates:

String[] propertyNames = {"carId", "carType", "assembly"};
Object[] propertyTypes = {int.class, String.class, Assembly.class};

Configuration configuration = new Configuration();
configuration.getCommon().addEventType("MyObjectArrayEvent", propertyNames, propertyTypes);

An Object-array event type may also become a subtype of one supertype that must also be an Object-array event type. The objectarray element provides the optional attribute supertype-names that accepts a single name of an Object-array event type that is the supertype to the type:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <event-type name="AccountUpdate">
      <objectarray supertype-names="BaseUpdate">
      </objectarray>
    </event-type>
  </common>
</esper-configuration>

An Object-array event type may declare a start and end timestamp property name. The XML shown next instructs the compiler that the startts property carries the event start timestamp and the endts property carries the event end timestamp:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <event-type name="AccountUpdate">
      <objectarray start-timestamp-property-name="startts" end-timestamp-property-name="endts"/>
    </event-type>
  </common>
</esper-configuration>

For adding a type at runtime please use create objectarray schema.

17.4.6. Events Represented by JSON

Please use create json schema EPL to define a JSON event type. There is no configuration API to pre-define JSON event types.

17.4.7. Events Represented by Avro `GenericData.Record`

The runtime can process Avro GenericData.Record events via the sendEventAvro(GenericData.Record event, String eventTypeName) method on the EPEventService interface. Please see the Appendix H, Event Representation: Avro Events (org.apache.avro.generic.GenericData.Record) section for details on how to use Avro events with the compiler and runtime.

The below snippet of XML configuration configures an event type named MyAvroEvent.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <event-type name="MyAvroEvent">
      <avro schema-text='{"type":"record","name":"MyAvroEvent","fields":[{"name":"carId","type":"int"},{"name":"carType","type":{"type":"string","avro.java.string":"String"}}]}'/>
    </event-type>  
  </common>
</esper-configuration>

The sample Avro schema above in pretty-print is:

{
  "type" : "record",
  "name" : "MyAvroEvent",
  "fields" : [ {
    "name" : "carId",
    "type" : "int"
  }, {
    "name" : "carType",
    "type" : {
      "type" : "string",
      "avro.java.string" : "String"
    }
  } ]
}

This schema defines:

A carId property of type int.
A carType property of type string. Note:Wse the Avro-provided avro.java.string property to ensure is is a java.lang.String instance and not a java.lang.CharSequence) instance.

Note that the runtime does not verify that Avro events are valid or that they actually match the schema provided for the Avro event type.

You can also use the configuration API to configure Avro event types, as the short code snippet below demonstrates:

Configuration configuration = new Configuration();
ConfigurationCommonEventTypeAvro avroType = new ConfigurationCommonEventTypeAvro();
avroType.setAvroSchema(schema);
configuration.getCommon().addEventTypeAvro("MyAvroType", avroType);

For adding a type at runtime please use create avro schema.

An Avro event type may also become a subtype of one supertype that must also be an Avro event type. The avro element provides the optional attribute supertype-names that accepts a single name of an Avro event type that is the supertype to the type:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <event-type name="MyAvroEvent">
      <avro supertype-names="BaseUpdate"/>
    </event-type>  
  </common>
</esper-configuration>

An Avro event type may declare a start and end timestamp property name. The XML shown next instructs the compiler that the startts property carries the event start timestamp and the endts property carries the event end timestamp:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <event-type name="MyAvroEvent">
      <avro start-timestamp-property-name="startts" end-timestamp-property-name="endts"/>
    </event-type>  
  </common>
</esper-configuration>

17.4.8. Events Represented by `org.w3c.dom.Node`

Via this configuration item, or using create xml schema, the runtime can natively process org.w3c.dom.Node instances, i.e. XML document object model (DOM) nodes. Please see the Appendix I, Event Representation: org.w3c.dom.Node XML Events section for details on how to use Node events with the compiler and runtime.

EPL allows configuring XPath expressions as event properties. You can specify arbitrary XPath functions or expressions and provide a property name by which their result values will be available for use in expressions. Use the @XMLSchemaField annotation with create xml schema.

For XML documents that follow a XML schema, the compiler and runtime can load and interrogate your schema and validate event property names and types against the schema information.

Nested, mapped and indexed event properties are also supported in expressions against org.w3c.dom.Node events. Thus XML trees can conveniently be interrogated using the existing event property syntax for querying JavaBean objects, JavaBean object graphs or java.util.Map events.

In the simplest form, the compiler only requires a configuration entry containing the root element name and the event type name in order to process org.w3c.dom.Node events:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <event-type name="MyXMLNodeEvent">
      <xml-dom root-element-name="myevent" />
    </event-type>
  </common>
</esper-configuration>

You can also use the configuration API to configure XML event types, as the short example below demonstrates. In fact, all configuration options available through XML configuration can also be provided via setter methods on the ConfigurationEventTypeXMLDOM class.

Configuration configuration = new Configuration();
ConfigurationCommonEventTypeXMLDOM desc = new ConfigurationCommonEventTypeXMLDOM();
desc.setRootElementName("myevent");
desc.addXPathProperty("name1", "/element/@attribute", XPathConstants.STRING);
desc.addXPathProperty("name2", "/element/subelement", XPathConstants.NUMBER);
configuration.getCommon().addEventType("MyXMLNodeEvent", desc);

The equivalent to above configuration is declaring the schema via create xml schema:

@XMLSchema(rootElementName='myevent')
@XMLSchemaField(name='name1', xpath='/element/@attribute', type='string')
@XMLSchemaField(name='name2', xpath='/element/subelement', type='number')
create xml schema MyXMLNodeEvent()

The next example presents configuration options in a sample configuration entry.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <event-type name="AutoIdRFIDEvent">
      <xml-dom root-element-name="Sensor" schema-resource="data/AutoIdPmlCore.xsd" 
         default-namespace="urn:autoid:specification:interchange:PMLCore:xml:schema:1">
        <namespace-prefix prefix="pmlcore" 
           namespace="urn:autoid:specification:interchange:PMLCore:xml:schema:1"/>
        <xpath-property property-name="countTags" 
           xpath="count(/pmlcore:Sensor/pmlcore:Observation/pmlcore:Tag)" type="number"/>
      </xml-dom>
    </event-type>
  </common>
</esper-configuration>

The equivalent to above configuration is declaring the schema via create xml schema:

@XMLSchema(rootElementName='Sensor' schemaResource='data/AutoIdPmlCore.xsd', 
  defaultNamespace='urn:autoid:specification:interchange:PMLCore:xml:schema:1')
@XMLSchemaNamespacePrefix(prefix='pmlcore', namespace='urn:autoid:specification:interchange:PMLCore:xml:schema:1')" +
@XMLSchemaField(name='countTags', xpath='count(/pmlcore:Sensor/pmlcore:Observation/pmlcore:Tag)', type='number')
create xml schema AutoIdRFIDEvent()

This example configures an event property named countTags whose value is computed by an XPath expression. The namespace prefixes and default namespace are for use with XPath expressions and must also be made known to the compiler and runtime in order for the compiler/runtime to compile XPath expressions. Via the schema-resource attribute you can instruct the compiler/runtime to load a schema file. You may also use schema-text instead to provide the actual text of the schema.

17.4.8.1. Schema Resource

The schema-resource attribute or annotation field takes a schema resource URL or classpath-relative filename. The compiler and runtime attempts to resolve the schema resource as an URL. If the schema resource name is not a valid URL, the compiler and runtime attempts to resolve the resource from classpath via the ClassLoader.getResource method using the thread context class loader. If the name could not be resolved, the compiler and runtime uses the Configuration class classloader. Use the schema-text attribute instead when it is more practical to provide the actual text of the schema.

By providing a schema file for the compiler or runtime to load, the compiler performs these additional services:

Validates the event properties in a statement, ensuring the event property name matches an attribute or element in the XML
Determines the type of the event property allowing event properties to be used in type-sensitive expressions such as expressions involving arithmetic (Note: XPath properties are also typed)
Matches event property names to either element names or attributes

If no schema resource is specified, none of the event properties specified in statements are validated at compile-time and their type defaults to java.lang.String. Also, attributes are not supported if no schema resource is specified and must thus be declared via XPath expression.

17.4.8.2. Explicit XPath Property

The xpath-property element adds explicitly-names event properties to the event type that are computed via an XPath expression. In order for the XPath expression to compile, be sure to specify the default-namespace attribute and use the namespace-prefix to declare namespace prefixes.

XPath expression properties are strongly typed. The type attribute allows the following values. These values correspond to those declared by javax.xml.xpath.XPathConstants.

number (Note: resolves to a double)
string
boolean
node
nodeset

In case you need your XPath expression to return a type other than the types listed above, an optional cast-to type can be specified. If specified, the operation firsts obtains the result of the XPath expression as the defined type (number, string, boolean) and then casts or parses the returned type to the specified cast-to-type. At runtime, a warning message is logged if the XPath expression returns a result object that cannot be casted or parsed.

The next line shows how to return a long-type property for an XPath expression that returns a string:

desc.addXPathProperty("name", "/element/sub", XPathConstants.STRING, "long");

For use with create xml schema it is:

@XMLSchemaField(name='name', xpath='/element/sub', type='string', castToType='long')

The equivalent configuration XML is:

<xpath-property property-name="name"  xpath="/element/sub" type="string" cast="long"/>

See Section 17.8, “Type Names” for a list of cast-to type names.

17.4.8.3. Absolute or Deep Property Resolution

This setting indicates that when properties are compiled to XPath expressions that the compilation should generate an absolute XPath expression or a deep (find element) XPath expression.

For example, consider the following statement against an event type that is represented by a XML DOM document, assuming the event type GetQuote has been configured with the compiler as a XML DOM event type:

select request, request.symbol from GetQuote

By default, the compiler compiles the "request" property name to an XPath expression "/GetQuote/request". It compiles the nested property named "request.symbol" to an XPath expression "/GetQuote/request/symbol", wherein the root element node is "GetQuote".

By setting absolute property resolution to false, the compiler compiles the "request" property name to an XPath expression "//request". It compiles the nested property named "request.symbol" to an XPath expression "//request/symbol". This enables these elements to be located anywhere in the XML document.

The setting is available in XML via the attribute resolve-properties-absolute.

The configuration API provides the above settings as shown here in a sample code:

Configuration configuration = new Configuration();
ConfigurationCommonEventTypeXMLDOM desc = new ConfigurationCommonEventTypeXMLDOM();
desc.setRootElementName("GetQuote");
desc.setDefaultNamespace("http://services.samples/xsd");
desc.setRootElementNamespace("http://services.samples/xsd");
desc.addNamespacePrefix("m0", "http://services.samples/xsd");
desc.setXPathResolvePropertiesAbsolute(false);
configuration.getCommon().addEventType("GetQuote", desc);

The equivalent to above configuration is declaring the schema via create xml schema is:

@XMLSchema(rootElementName = 'GetQuote', defaultNamespace='http://services.samples/xsd',
  rootElementNamespace='http://services.samples/xsd', xpathResolvePropertiesAbsolute=false)
@XMLSchemaNamespacePrefix(prefix='m0', namespace='http://services.samples/xsd')
create xml schema GetQuote()

17.4.8.4. XPath Variable and Function Resolver

If your XPath expressions require variables or functions, your application may provide the class name of an XPathVariableResolver and XPathFunctionResolver. At type initialization time the compiler and runtime instantiates the resolver instances and provides these to the XPathFactory.

This example shows the API to set this configuration.

ConfigurationCommonEventTypeXMLDOM desc = new ConfigurationCommonEventTypeXMLDOM();
desc.setXPathFunctionResolver(MyXPathFunctionResolver.class.getName());
desc.setXPathVariableResolver(MyXPathVariableResolver.class.getName());

This option is for use when a XSD schema is provided and determines whether the compiler automatically creates an event type when a property expression transposes a property that is a complex type according to the schema.

@XMLSchema(xpathFunctionResolver='class name here', xpathVariableResolver='class name here')

17.4.8.5. Auto Fragment

An example:

ConfigurationCommonEventTypeXMLDOM desc = new ConfigurationCommonEventTypeXMLDOM();
desc.setAutoFragment(false);

By default the compiler and runtime employs the built-in DOM walker implementation to evaluate XPath expressions, which is not namespace-aware.

@XMLSchema(autoFragment=false

17.4.8.6. XPath Property Expression

This configuration setting, when set to true, instructs the compiler to rewrite property expressions into XPath.

An example:

ConfigurationCommonEventTypeXMLDOM desc = new ConfigurationCommonEventTypeXMLDOM();
desc.setXPathPropertyExpr(true);

By default an EventSender for a given XML event type validates the root element name for which the type has been declared against the one provided by the org.w3c.Node sent into the runtime.

@XMLSchema(xpathPpropertyExpr=true

17.4.8.7. Event Sender Setting

This configuration setting, when set to false, instructs an EventSender to not validate.

An example:

ConfigurationCommonEventTypeXMLDOM desc = new ConfigurationCommonEventTypeXMLDOM();
desc.setEventSenderValidatesRoot(false);

You may configure the name of the properties that provides the event start timestamp and the event end timestamp as part of the configuration.

@XMLSchema(eventSenderValidatesRoot=false

17.4.8.8. Start and End Timestamp

An example that configures startts as the property name providing the start timestamp and endts as the property name providing the end timestamp:

ConfigurationCommonEventTypeXMLDOM desc = new ConfigurationCommonEventTypeXMLDOM();
desc.setStartTimestampPropertyName("startts");
desc.setEndTimestampPropertyName("endts");

When using create xml schema the start and end timestamps are part of create schema.

17.4.9. Event Type Defaults

17.4.9.1. Default Event Representation

The default event representation is the Map event representation.

The default event representation is relevant when your query outputs individual properties to a listener and it does not specify a specific event representation in an annotation. The default event representation is also relevant for create schema and create window.

Note that the compiler may still use the Map representation for certain types of statements even when the default event representation is object array.

For example, consider the following statement:

select propertyOne, propertyTwo from MyEvent

Listeners to the statement above currently receive a Map-type event. By setting the configuration flag to object-array or Avro as described herein, listeners to the statement receive an Object-array-type event or an Avro-type event instead.

The XML snippet below is an example of setting the default event representation to Object-array:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <event-meta>
      <event-representation type="objectarray"/> <!-- use "json" for JSON, use "avro" for Avro -->
    </event-meta>
  </common>
</esper-configuration>

The code snippet shown next sets the default event representation to Object-array in the configuration object:

Configuration configuration = new Configuration();
configuration.getCommon().getEventMeta().
    setDefaultEventRepresentation(EventUnderlyingType.OBJECTARRAY);

17.4.9.2. Avro Settings

This configuration controls compiler settings in respect to Avro.

The enable-avro setting is boolean-typed and is false by default. It controls whether Avro is enabled or disabled. If disabled the compiler and runtime disallow registering Avro event types or using an Avro event representation.

The enable-native-string setting is boolean-typed and is true by default. It controls whether for String-type values, when the compiler generates an Avro schema, such field schema adds the property avro.java.string of value String.

The enable-schema-default-nonnull setting is boolean-typed and is true by default. It controls whether the compiler assembles non-null Avro schemas (true) or nullable (union) Avro schemas (false).

The objectvalue-typewidener-factory-class setting is a fully-qualified class name of the class implementing the com.espertech.esper.common.client.hook.type.ObjectValueTypeWidenerFactory interface and is null by default. If specified the factory can provide a type widener for widening, coercing or transforming any object value to a Avro field value.

The type-representation-mapper-class setting is a fully-qualified class name of the class implementing the com.espertech.esper.common.client.hook.type.TypeRepresentationMapper interface and is null by default. If specified the implementation can provide for a given class the Avro schema for the field.

The XML snippet below is an example of Avro settings that configures the same as the default values:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <event-meta>
      <avro-settings enable-avro="false" enable-native-string="true" enable-schema-default-nonnull="true"
          objectvalue-typewidener-factory-class=""
          type-representation-mapper-class=""/>
    </event-meta>
  </common>
</esper-configuration>

The code snippet shown next sets the default event representation to Object-array in the configuration object:

Configuration configuration = new Configuration();
configuration.getCommon().getEventMeta().getAvroSettings().setEnableAvro(false);
configuration.getCommon().getEventMeta().getAvroSettings().setEnableNativeString(true);
configuration.getCommon().getEventMeta().getAvroSettings().setEnableSchemaDefaultNonNull(true);
configuration.getCommon().getEventMeta().getAvroSettings().setObjectValueTypeWidenerFactoryClass(null);
configuration.getCommon().getEventMeta().getAvroSettings().setTypeRepresentationMapperClass(null);

17.4.9.3. XML XSD Schema Settings

This configuration controls compiler and runtime settings in respect to XML XSD Schemas. More information can be found at Section I.2.1, “Getting Started”.

The enable-xmlxsd setting is boolean-typed and is false by default. It controls whether XML XSD schemas using Apache Xerces 2 is enabled or disabled. If disabled the compiler and runtime disallow registering XML XSD schema event types.

The XML snippet below is an example of the settings that configures the same as the default values:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <event-meta enable-xmlxsd="false"\>
  </common>
</esper-configuration>

The code snippet shown next sets the default event representation to Object-array in the configuration object:

Configuration configuration = new Configuration();
configuration.getCommon().getEventMeta().setEnableXMLXSD(true);

17.4.9.4. Java Class Property Names, Case Sensitivity and Accessor Style

This allows to control case sensitivity or accessor style for all event classes as a default. The two settings are found under class-property-resolution under event-meta in the XML common configuration.

To control the case sensitivity as discussed in Section 17.4.3.4, “Case Sensitivity and Property Names”, add the style attribute in the XML configuration to set a default case sensitivity applicable to all event classes unless specifically overridden by class-specific configuration. The default case sensitivity is case_sensitive (case sensitivity turned on).

To control the accessor style as discussed in Section 17.4.3.2, “Non-JavaBean and Legacy Java Event Classes”, add the accessor-style attribute in the XML configuration to set a default accessor style applicable to all event classes unless specifically overridden by class-specific configuration. The default accessor style is javabean JavaBean accessor style.

The next code snippet shows how to control this feature via the API:

Configuration config = new Configuration();
config.getCommon().getEventMeta().setClassPropertyResolutionStyle(
    PropertyResolutionStyle.CASE_INSENSITIVE);
config.getCommon().getEventMeta().setDefaultAccessorStyle(
    AccessorStyle.PUBLIC);

17.4.10. Event Type Import Package (Event Type Auto-Name)

Via this configuration an application can make the Java package or packages that contain an application's Java event classes known. Thereby an application can use create schema name as simple-classname and the compiler can find the class.

The XML configuration for defining the Java packages that contain Java event classes is:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <event-type-auto-name package-name="com.mycompany.order.event"/>
  </common>
</esper-configuration>

The same configuration but using the Configuration class:

Configuration config = new Configuration();
config.getCommon().addEventTypeAutoName("com.mycompany.order.event");
// ... or ...
config.getCommon().addEventTypeAutoName(MyEvent.getPackage().getName());

17.4.11. From-Clause Method Invocation

Method invocations are allowed in the from clause in EPL, such that your application may join event streams to the data returned by a web service, or to data read from a distributed cache or object-oriented database, or obtain data by other means. A local cache may be placed in front of such method invocations through the configuration settings described herein.

The LRU cache is described in detail in Section 17.4.12.6.1, “LRU Cache”. The expiry-time cache documentation can be found in Section 17.4.12.6.2, “Expiry-Time Cache”

The next XML snippet is a sample cache configuration that applies to methods provided by the classes 'MyFromClauseLookupLib' and 'MyFromClauseWebServiceLib'. The XML and API configuration understand both the fully-qualified Java class name, as well as the simple class name:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <method-reference class-name="com.mycompany.MyFromClauseLookupLib">
      <expiry-time-cache max-age-seconds="10" purge-interval-seconds="10" ref-type="weak"/>
    </method-reference> 	
    <method-reference class-name="MyFromClauseWebServiceLib">
      <lru-cache size="1000"/>
    </method-reference>
  </common>
</esper-configuration>

17.4.12. Relational Database Access

For NEsper .NET also see Section J.17, “.NET Configurations - Relational Database Access”.

EPL has the capability to join event streams against historical data sources, such as a relational database. This section describes the configuration entries that the compiler or runtime require to access data stored in your database. Please see Section 5.13, “Accessing Relational Data via SQL” for information on the use of EPL queries that include historical data sources.

EPL queries that poll data from a relational database specify the name of the database as part of the statement. The compiler and runtime use the configuration information described here to resolve the database name in the statement to database settings. The required and optional database settings are summarized below.

Database connections can be obtained via JDBC javax.xml.DataSource, via java.sql.DriverManager and via data source factory. Either one of these methods to obtain database connections is a required configuration.
Optionally, JDBC connection-level settings such as auto-commit, transaction isolation level, read-only and the catalog name can be defined.
Optionally, a connection lifecycle can be set to indicate to the runtime whether the runtime must retain connections or must obtain a new connection for each lookup and close the connection when the lookup is done (pooled).
Optionally, define a cache policy to allow the runtime to retrieve data from a query cache, reducing the number of query executions.

Some of the settings can have important performance implications that need to be carefully considered in relationship to your database software, JDBC driver and runtime environment. This section attempts to outline such implications where appropriate.

The sample XML configuration file in the "etc" folder can be used as a template for configuring database settings. All settings are also available by means of the configuration API through the classes Configuration and ConfigurationDBRef.

17.4.12.1. Connections Obtained via DataSource

This configuration causes the compiler or runtime to obtain a database connection from a javax.sql.DataSource available from your JNDI provider.

The setting is most useful when running within an application server or when a JNDI directory is otherwise present in your Java VM. If your application environment does not provide an available DataSource, the next section outlines how to use Apache DBCP as a DataSource implementation with connection pooling options and outlines how to use a custom factory for DataSource implementations.

If your DataSource provides connections out of a connection pool, your configuration should set the collection lifecycle setting to pooled.

The snippet of XML below configures a database named mydb1 to obtain connections via a javax.sql.DataSource. The datasource-connection element instructs the runtime to obtain new connections to the database mydb1 by performing a lookup via javax.naming.InitialContext for the given object lookup name. Optional environment properties for the InitialContext are also shown in the example.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <database-reference name="mydb1">
      <datasource-connection context-lookup-name="java:comp/env/jdbc/mydb">
        <env-property name="java.naming.factory.initial" value ="com.myclass.CtxFactory"/>
        <env-property name="java.naming.provider.url" value ="iiop://localhost:1050"/>
      </datasource-connection>
    </database-reference>
  </common>
</esper-configuration>

To help you better understand how the runtime uses this information to obtain connections, please look at the logic below.

if (envProperties.size() > 0) {
  initialContext = new InitialContext(envProperties);
}
else {
  initialContext = new InitialContext();
}
DataSource dataSource = (DataSource) initialContext.lookup(lookupName);
Connection connection = dataSource.getConnection();

In order to plug-in your own implementation of the DataSource interface, your application may use an existing JNDI provider as provided by an application server if running in a J2EE environment.

In case your application does not have an existing JNDI implementation to register a DataSource to provide connections, you may set the java.naming.factory.initial property in the configuration to point to your application's own implementation of the javax.naming.spi.InitialContextFactory interface that can return your application DataSource though the javax.naming.Context provided by the factory implementation. Please see Java Naming and Directory Interface (JNDI) API documentation for further information.

17.4.12.2. Connections Obtained via DataSource Factory

This section describes how to use Apache Commons Database Connection Pooling (Apache DBCP). It explains how to provide a custom application-specific DataSource factory if not using Apache DBCP.

If your DataSource provides connections out of a connection pool, your configuration should set the collection lifecycle setting to pooled.

Apache DBCP provides comprehensive means to test for dead connections or grow and shrik a connection pool. Configuration properties for Apache DBCP can be found at Apache DBCP configuration. The listed properties are passed to Apache DBCP via the properties list provided as part of the configuration.

The snippet of XML below is an example that configures a database named mydb3 to obtain connections via the pooling DataSource provided by Apache DBCP BasicDataSourceFactory.

The listed properties are passed to DBCP to instruct DBCP how to manage the connection pool. The settings below initialize the connection pool to 2 connections and provide the validation query select 1 from dual for DBCP to validate a connection before providing a connection from the pool:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <database-reference name="mydb3">
      <!-- For a complete list of properties see Apache DBCP. -->
      <!-- NOTE: "dbcp2" applies to api-2.0 of DBCP, use "dbcp" otherwise. -->
      <datasourcefactory-connection class-name="org.apache.commons.dbcp2.BasicDataSourceFactory">	
        <env-property name="username" value ="myusername"/>
        <env-property name="password" value ="mypassword"/>
        <env-property name="driverClassName" value ="com.mysql.jdbc.Driver"/>
        <env-property name="url" value ="jdbc:mysql://localhost/test"/>
        <env-property name="initialSize" value ="2"/>
        <env-property name="validationQuery" value ="select 1 from dual"/>
      </datasourcefactory-connection>
      <connection-lifecycle value="pooled"/>
    </database-reference>
  </common>
</esper-configuration>

The same configuration options provided through the API:

Properties props = new Properties();
props.put("username", "myusername");
props.put("password", "mypassword");
props.put("driverClassName", "com.mysql.jdbc.Driver");
props.put("url", "jdbc:mysql://localhost/test");
props.put("initialSize", 2);
props.put("validationQuery", "select 1 from dual");

ConfigurationCommonDBRef configDB = new ConfigurationCommonDBRef();
// BasicDataSourceFactory is an Apache DBCP import
configDB.setDataSourceFactory(props, BasicDataSourceFactory.class.getName());
configDB.setConnectionLifecycleEnum(ConfigurationCommonDBRef.ConnectionLifecycleEnum.POOLED);

Configuration configuration = new Configuration();;
configuration.getCommon().addDatabaseReference("mydb3", configDB);

Apache Commons DBCP is a separate download and not provided as part of the distribution. The Apache Commons DBCP jar file requires the Apache Commons Pool jar file.

Your application can provide its own factory implementation for DataSource instances: Set the class name to the name of the application class that provides a public static method named createDataSource which takes a single Properties object as parameter and returns a DataSource implementation. For example:

configDB.setDataSourceFactory(props, MyOwnDataSourceFactory.class.getName());
...
class MyOwnDataSourceFactory {
  public static DataSource createDataSource(Properties properties) {
    return new MyDataSourceImpl(properties);
  }
}

17.4.12.3. Connections Obtained via DriverManager

The next snippet of XML configures a database named mydb2 to obtain connections via java.sql.DriverManager. The drivermanager-connection element instructs the runtime to obtain new connections to the database mydb2 by means of Class.forName and DriverManager.getConnection using the class name, URL and optional username, password and connection arguments.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <database-reference name="mydb2">
      <drivermanager-connection class-name="my.sql.Driver" 
            url="jdbc:mysql://localhost/test?user=root&amp;password=mypassword" 
            user="myuser" password="mypassword">
        <connection-arg name="user" value ="myuser"/>
        <connection-arg name="password" value ="mypassword"/>
        <connection-arg name="somearg" value ="someargvalue"/>
      </drivermanager-connection>
    </database-reference>
  </common>
</esper-configuration>

The username and password are shown in multiple places in the XML only as an example. Please check with your database software on the required information in URL and connection arguments.

17.4.12.4. Connections-Level Settings

Additional connection-level settings can optionally be provided to the runtime which the runtime will apply to new connections. When the runtime obtains a new connection, it applies only those settings to the connection that are explicitly configured. The runtime leaves all other connection settings at default values.

The below XML is a sample of all available configuration settings. Please refer to the Java API JavaDocs for java.sql.Connection for more information to each option or check the documentation of your JDBC driver and database software.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <database-reference name="mydb2">
    <!-- ... configure data source or driver manager settings... -->
      <connection-settings auto-commit="true" catalog="mycatalog" 
          read-only="true" transaction-isolation="1" />
    </database-reference>
  </common>
</esper-configuration>

The read-only setting can be used to indicate to your database runtime that SQL statements are read-only. The transaction-isolation and auto-commit help you database software perform the right level of locking and lock release. Consider setting these values to reduce transactional overhead in your database queries.

17.4.12.5. Connections Lifecycle Settings

By default the runtime retains a separate database connection for each started statement. However, it is possible to override this behavior and require the runtime to obtain a new database connection for each lookup, and to close that database connection after the lookup is completed. This often makes sense when you have a large number of statements and require pooling of connections via a connection pool.

In the pooled setting, the runtime obtains a database connection from the data source or driver manager for every query, and closes the connection when done, returning the database connection to the pool if using a pooling data source.

In the retain setting, the runtime retains a separate dedicated database connection for each statement and does not close the connection between uses.

The XML for this option is below. The connection lifecycle allows the following values: pooled and retain.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <database-reference name="mydb2">
    <!-- ... configure data source or driver manager settings... -->
        <connection-lifecycle value="pooled"/>
    </database-reference>
  </common>
</esper-configuration>

17.4.12.6. Cache Settings

Cache settings can dramatically reduce the number of database queries that the runtime executes for statements. If no cache setting is specified, the runtime does not cache query results and executes a separate database query for every event.

Caches store the results of database queries and make these results available to subsequent queries using the exact same query parameters as the query for which the result was stored. If your query returns one or more rows, the cache keep the result rows of the query keyed to the parameters of the query. If your query returns no rows, the cache also keeps the empty result. Query results are held by a cache until the cache entry is evicted. The strategies available for evicting cached query results are listed next.

17.4.12.6.1. LRU Cache

The least-recently-used (LRU) cache is configured by a maximum size. The cache discards the least recently used query results first once the cache reaches the maximum size.

The XML configuration entry for a LRU cache is as below. This entry configures an LRU cache holding up to 1000 query results.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <database-reference name="mydb">
    <!-- ... configure data source or driver manager settings... -->
      <lru-cache size="1000"/>
    </database-reference>  
  </common>
</esper-configuration>

17.4.12.6.2. Expiry-Time Cache

The expiry time cache is configured by a maximum age in seconds, a purge interval and an optional reference type. The cache discards (on the get operation) any query results that are older then the maximum age so that stale data is not used. If the cache is not empty, then every purge interval number of seconds the runtime purges any expired entries from the cache.

The XML configuration entry for an expiry-time cache is as follows. The example configures an expiry time cache in which prior query results are valid for 60 seconds and which the runtime inspects every 2 minutes to remove query results older then 60 seconds.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <database-reference name="mydb">
    <!-- ... configure data source or driver manager settings... -->
        <expiry-time-cache max-age-seconds="60" purge-interval-seconds="120" />
    </database-reference>
  </common>
</esper-configuration>

By default, the expiry-time cache is backed by a java.util.WeakHashMap and thus relies on weak references. That means that cached SQL results can be freed during garbage collection.

Via XML or using the configuration API the type of reference can be configured to not allow entries to be garbage collected, by setting the ref-type property to hard:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <database-reference name="mydb">
    <!-- ... configure data source or driver manager settings... -->
        <expiry-time-cache max-age-seconds="60" purge-interval-seconds="120" ref-type="hard"/>
    </database-reference>
  </common>
</esper-configuration>

The last setting for the cache reference type is soft: This strategy allows the garbage collection of cache entries only when all other weak references have been collected.

17.4.12.7. Column Change Case

This setting instructs the compiler to convert to lower- or uppercase any output column names returned by your database system. When using Oracle relational database software, for example, column names can be changed to lowercase via this setting.

A sample XML configuration entry for this setting is:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <database-reference name="mydb">
    <!-- ... configure data source or driver manager settings... -->
      <column-change-case value="lowercase"/>
    </database-reference>
  </common>
</esper-configuration>

17.4.12.8. SQL Types Mapping

For NEsper .NET this section is not applicable.

By providing a mapping of SQL types (java.sql.Types) to Java built-in types your code can avoid using sometimes awkward default database types and can easily change the way the compiler returns Java types for columns returned by a SQL query.

The mapping maps a constant as defined by java.sql.Types to a Java built-in type of any of the following Java type names: String, BigDecimal, Boolean, Byte, Short, Int, Long, Float, Double, ByteArray, SqlDate, SqlTime, SqlTimestamp. The Java type names are not case-sensitive.

A sample XML configuration entry for this setting is shown next. The sample maps Types.NUMERIC which is a constant value of 2 per JDBC API to the Java int type.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <database-reference name="mydb">
    <!-- ... configure data source or driver manager settings... -->
      <sql-types-mapping sql-type="2" java-type="int" />
    </database-reference>
  </common>
</esper-configuration>

17.4.12.9. Metadata Origin

This setting controls how the compiler retrieves SQL statement metadata from JDBC prepared statements.

Table 17.3. Syntax and Results of Aggregate Functions

Option	Description
default	By default, the compiler detects the driver name and queries prepared statement metadata if the driver is not an Oracle database driver. For Oracle drivers, the compiler uses lexical analysis of the SQL statement to construct a sample SQL statement and then fires that statement to retrieve statement metadata.
metadata	The compiler always queries prepared statement metadata regardless of the database driver used.
sample	The compiler always uses lexical analysis of the SQL statement to construct a sample SQL statement, and then fires that statement to retrieve statement metadata.

17.4.13. Common Settings Related to Logging

17.4.13.1. Query Plan Logging

By default, the compiler does not produce query plan output unless logging at debug-level. To enable query plan logging, set this option in the configuration. When enabled, the compiler reports, at INFO level, any query plans under the log name com.espertech.esper.queryplan.

Query plan logging is applicable to subqueries, joins (any type), named window and table on-actions (on-select, on-merge, on-insert, on-update, on-select) and fire-and-forget queries. It is not applicable and will not provide additional information for other types of constructs.

The API to use to enable query plan logging is shown here:

Configuration config = new Configuration();
config.getCommon().getLogging().setEnableQueryPlan(true);

By default, the compiler and runtime does not measure JDBC query execution times or report the number of rows returned from a JDBC query through logging. To enable JDBC logging, set this option in the configuration. When enabled, the compiler and runtime report, at INFO level, any JDBC query performance and number of rows returned under the log name com.espertech.esper.jdbc.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <logging>
      <query-plan enabled="true"/>
    </logging>
  </common>
</esper-configuration>

17.4.13.2. JDBC Logging

The API to use to enable JDBC query logging is shown here:

Configuration config = new Configuration();
config.getCommon().getLogging().setEnableJDBC(true);

The default time unit of time resolution is milliseconds. Your application may set the time resolution to microseconds instead.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
     <logging>
       <jdbc enabled="true"/>
    </logging>
  </common>
</esper-configuration>

17.4.14. Common Settings Related to Time Source

17.4.14.1. Time Unit

A sample XML configuration for millisecond time resolution is:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <time-source>
      <time-unit value="milliseconds"/>
    </time-source>
  </common>
</esper-configuration>

The equivalent code snippet using the configuration API is here:

Configuration config = new Configuration();
config.getCommon().getTimeSource().setTimeUnit(TimeUnit.MILLISECONDS);

17.4.15. Variables

Variables can be created dynamically in EPL via the create variable syntax but can also be configured.

A variable is declared by specifying a variable name, the variable type, an optional initialization value and an optional boolean-type flag indicating whether the variable is a constant (false by default). The initialization value can be of the same or compatible type as the variable type, or can also be a String value that, when parsed, is compatible to the type declared for the variable. Declare each variable a constant to achieve the best performance.

In a XML configuration file the variable configuration may look as below.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <variable name="var_threshold" type="long" initialization-value="100"/>
    <variable name="var_key" type="string"/>
    <variable name="test" type="int" constant="true"/>  
  </common>
</esper-configuration>

Please find the list of valid values for the type attribute in Section 17.8, “Type Names”.

17.4.16. Variant Stream

A variant stream is a predefined stream into which events of multiple disparate event types can be inserted, and which can be selected from in patterns and the from clause.

The name of the variant stream and, optionally, the type of events that the stream may accept, are part of the stream definition. By default, the variant stream accepts only the predefined event types. The compiler validates your insert into clause which inserts into the variant stream against the predefined types.

A variant stream can be set to accept any type of event, in which case all properties of the variant stream are effectively dynamic properties. Set the type variance flag to ANY to indicate the variant stream accepts any type of event.

The following XML configuration defines a variant stream by name OrderStream that carries only PartsOrder and ServiceOrder events:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <common>
    <variant-stream name="OrderStream">
      <variant-event-type name="PartsOrder"/>
      <variant-event-type name="ServiceOrder"/>
    </variant-stream>
  </common>
</esper-configuration>

This code snippet sets up a variant stream by name OutgoingEvent:

Configuration config = new Configuration();
ConfigurationCommonVariantStream variant = new ConfigurationCommonVariantStream();
variant.setTypeVariance(ConfigurationCommonVariantStream.TypeVariance.ANY);
config.getCommon().addVariantStream("OutgoingEvent", variant);

If specifying variant event type names, make sure such names have been configured for JavaBean, Map or XML events.

17.5. Configuration Compiler

17.5.1. Compiler Settings Related to Byte Code Generation

17.5.1.1. Byte Code General Settings

The setting include-debugsymbols is false by default. It controls whether the compiler generates debug symbols as part of the binary class.

The setting include-comments is false by default. It controls whether the compiler generates code that contains additional information to help tracing back generated code to the code that generated it.

The setting attach-epl is true by default. It controls whether the compiler adds the statement text of the statement to statement properties.

The setting attach-module-epl is false by default. It controls whether the compiler adds the EPL module text of the module to module properties.

The setting allow-subscriber is false by default. It controls whether the compiler adds code for handling subscribers. If this flag is false the setSubscriber method on the EPStatement class throws an exception.

The setting allow-inlined-class is true by default. It controls whether the compiler allows inlined-classes. If this flag is false the compiler throws an exception upon encountering an inlined-class.

The setting threadpool-compiler-num-threads sets the number of threads for compiling a statement to byte code and is eight (8) by default. Setting this value to zero disables multi-threading for compilation. When the number of threads is greater zero the calling thread generates classes for statements and the thread pool compiles statement classes to byte code. This setting improves compilation performance only when a module has multiple statements as the unit of parallelization is the statement. The setting threadpool-compiler-capacity defines the number of permits (capacity of the queue) for compiling statements to byte code and is unbound by default. Use null to represent unbound. The minimum value for capacity is one.

The setting max-methods-per-class sets the maximum number of methods in a class or inner class. This value defaults to 1024 (1k). This setting exists as the JVM limits the constant pool for each class to 64k. Among other things the JVM class constant pool contains method information. Therefore, for large EPL expressions, the compiler attempts to not exceed this JVM limit. Upon finding a class that exceeds the configured maximum number of methods the compiler moves methods into an additional class or multiple additional classes. The minimum value for this setting is 100. If this value is too large you may see a compilation message such as Constant pool for class has grown past JVM limit of 0xFFFF. Note that there can be other reasons for this message. Please see Section 15.19, “Limitations”.

The sample code below sets the same values as the default values:

Configuration configuration = new Configuration();
ConfigurationCompilerByteCode byteCode = configuration.getCompiler().getByteCode();
byteCode.setIncludeDebugSymbols(false);
byteCode.setIncludeComments(false);
byteCode.setAttachEPL(true);
byteCode.setAttachModuleEPL(false);
byteCode.setAllowSubscriber(false);
byteCode.setAllowInlinedClass(false);
byteCode.setInstrumented(false);
byteCode.setThreadPoolCompilerNumThreads(8);
byteCode.setThreadPoolCompilerCapacity(null);
byteCode.setMaxMethodsPerClass(16*1024);

The sample XML configuration below also sets default values:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <bytecode 
      include-comments="false" 
      include-debugsymbols="false"
      attach-epl="true" 
      attach-module-epl="false" 
      instrumented="false" 
      allow-subscriber="false"
      allow-inlined-class="true"
      threadpool-compiler-num-threads="8"
      max-methods-per-class="16384"/>
  </compiler>
</esper-configuration>

17.5.1.2. Byte Code Modifier Settings

Access modifiers default to private and are listed here. You may also use the @private, @protected and @public annotations or the CompilerOptions object to set access modifiers.

Table 17.4. Byte Code Access Modifiers

Name	Description
`access-modifier-context`	Whether contexts that may be declared by the module are visible to other modules.
`access-modifier-eventtype`	Whether event types that may be declared by the module are visible to other modules.
`access-modifier-expression`	Whether expressions that may be declared by the module are visible to other modules.
`access-modifier-inlined-class`	Whether inlined-classes that may be declared by the module are visible to other modules.
`access-modifier-namedwindow`	Whether named windows that may be declared by the module are visible to other modules.
`access-modifier-script`	Whether scripts that may be declared by the module are visible to other modules.
`access-modifier-table`	Whether tables that may be declared by the module are visible to other modules.
`access-modifier-variables`	Whether variables that may be declared by the module are visible to other modules.

The setting bus-modifier-event-type is set to nonbus by default. This means that any of the sendEventType method of EPEventService cannot be used to process events of that event type. Set this value to bus to indicate that the respective sendEventType method of EPEventService can process events of event types declared by the module (sendEventType throws an exception if it does not find a visible event type). You may also use the @buseventtype annotation or the CompilerOptions object to set bus event type visibility.

The sample code below sets the same values as the default values:

Configuration configuration = new Configuration();
ConfigurationCompilerByteCode byteCode = configuration.getCompiler().getByteCode();
byteCode.setAccessModifierContext(NameAccessModifier.PRIVATE);
byteCode.setAccessModifierEventType(NameAccessModifier.PRIVATE);
byteCode.setAccessModifierNamedWindow(NameAccessModifier.PRIVATE);
byteCode.setAccessModifierScript(NameAccessModifier.PRIVATE);
byteCode.setAccessModifierTable(NameAccessModifier.PRIVATE);
byteCode.setAccessModifierVariable(NameAccessModifier.PRIVATE);
byteCode.setBusModifierEventType(EventTypeBusModifier.NONBUS);

The sample XML configuration below also sets default values:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <bytecode 
      access-modifier-context="private" 
      access-modifier-event-type="private" 
      access-modifier-expression="private" 
      access-modifier-named-window="private" 
	  access-modifier-script="private" 
	  access-modifier-table="private" 
	  access-modifier-variable="private" 
	  bus-modifier-event-type="nonbus"
	  />
  </compiler>
</esper-configuration>

17.5.2. Compiler Settings Related to View Resources

17.5.2.1. Iterator Behavior For Unbound Streams

By default, when using the iterator API to iterate a statement with an unbound stream the runtime returns an empty iterator.

To have the runtime return the last event instead, please use the @IterableUnbound statement annotation or enable the compiler setting as described herein.

A code sample that turns iterable-unbound on is:

Configuration config = new Configuration();
config.getCompiler().getViewResources().setIterableUnbound(true);

17.5.2.2. Configuring Output Rate Limiting Options

This flag impacts output rate limiting as further outlined in Appendix B, Runtime Considerations for Output Rate Limiting. The flag serves to control the default behavior for output rate limiting for all statements that do not specify a hint.

If set to true (the default), all statements behave as if they hint @Hint('enable_outputlimit_opt').

If set to false, all statements behave as if they hint @Hint('disable_outputlimit_opt').

For example:

Configuration config = new Configuration();
config.getCompiler().getViewResources().setOutputLimitOpt(true);

17.5.3. Compiler Settings Related to Logging

17.5.3.1. Byte Code Generation Logging

By enabling this setting the compiler logs byte code generation information at INFO level. This setting is disabled by default.

The API to use to enable logging for generated code is shown here:

Configuration config = new Configuration();
config.getCompiler().getLogging().setEnableCode(true);

The compiler analyzes filter expressions and plans filter indexes. This setting instructs the compiler to log filter plans. The flag is false by default and if set to true the compiler outputs filter plans to log at INFO level.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
      <logging>
        <code enabled="true"/>
      </logging>
  </compiler>
</esper-configuration>

17.5.3.2. Filter Plan Logging

An API code snippet to turn on filter plan logging is:

Configuration config = new Configuration();
config.getCompiler().getLogging().setEnableFilterPlan(true);

Statements can produce both insert stream (new data) and remove stream (old data) results. Remember that insert stream refers to arriving events and new aggregation values, while remove stream refers to events leaving data windows and prior aggregation values. By default, the runtime delivers only the insert stream to listeners and observers of a statement.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
      <logging>
        <filter-plan enabled="true"/>
      </logging>
  </compiler>
</esper-configuration>

17.5.4. Compiler Settings Related to Stream Selection

17.5.4.1. Default Statement Stream Selection

There are keywords in the select clause that instruct the runtime to not generate insert stream and/or remove stream results if your application does not need either one of the streams. These keywords are the istream, rstream and the irstream keywords.

By default, the runtime only generates insert stream results equivalent to using the optional istream keyword in the select clause. If your application requires insert and remove stream results for many statements, your application can add the irstream keyword to the select clause of each statement, or you can set a new default stream selector via this setting.

The XML configuration for this setting is shown below:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <stream-selection>
      <stream-selector value="irstream" />
    </stream-selection>
  </compiler>
</esper-configuration>

The equivalent code snippet using the configuration API is here:

Configuration config = new Configuration();
config.getCompiler().getStreamSelection()
    .setDefaultStreamSelector(StreamSelector.RSTREAM_ISTREAM_BOTH);

17.5.5. Compiler Settings Related to Language and Locale

Locale-dependence in the compiler can be present in the sort order of string values by the order by clause and by the sort window.

By default, the runtime sorts string values using the compare method that is not locale dependent. To enable local dependent sorting you must set the configuration flag as described below.

The XML configuration sets the locale dependent sorting as shown below:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <language sort-using-collator="true"/>
  </compiler>
</esper-configuration>

By default the compiler returns double-typed values for divisions regardless of operand types. Division by zero returns positive or negative double infinity.

Configuration config = new Configuration();
config.getCompiler().getLanguage().setSortUsingCollator(true);

17.5.6. Compiler Settings Related to Expression Evaluation

17.5.6.1. Integer Division and Division by Zero

To have compiler use Java-standard integer division instead, use this setting as described here. In Java integer division, when dividing integer types, the result is an integer type. This means that if you divide an integer unevenly by another integer, it returns the whole number part of the result, does not perform any rounding and the fraction part is dropped. If Java-standard integer division is enabled, when dividing an integer numerator by an integer denominator, the result is an integer number. Thus the expression 1 / 4 results in an integer zero. Your EPL must then convert at least one of the numbers to a double value before the division, for example by specifying 1.0 / 4 or by using cast(myint, double).

When using Java integer division, division by zero for integer-typed operands always returns null. However division by zero for double-type operands still returns positive or negative double infinity. To also return null upon division by zero for double-type operands, set the flag to true as below (default is false).

The XML configuration is as follows:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <expression integer-division="false" division-by-zero-is-null="false"/>
  </compiler>
</esper-configuration>

By default runtime caches the result of an user-defined function if the parameter set to that function is empty or all parameters are constant values. Results of custom plug-in single-row functions are not cached according to the default configuration, unless the single-row function is explicitly configured with value cache enabled.

Configuration config = new Configuration();
config.getCompiler().getExpression().setIntegerDivision(true);
config.getCompiler().getExpression().setDivisionByZeroReturnsNull(true);

17.5.6.2. User-Defined Function or Static Method Cache

To have rntime evaluate the user-defined function regardless of constant parameters, set the flag to false as indicated herein.

The XML configuration as below sets the same as the default value:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <expression udf-cache="true"/>
  </compiler>
</esper-configuration>

17.5.6.3. Extended Built-in Aggregation Functions

By default EPL provides a number of additional aggregation functions over the SQL standards. To have the compiler only allow the standard SQL aggregation functions and not the additional ones, disable the setting as described here.

The XML configuration as below sets the same as the default value:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <expression extended-agg="true"/>
  </compiler>
</esper-configuration>

17.5.6.4. Duck Typing

By default the compiler validates method references when using the dot operator syntax at time of compilation. With duck typing, the compiler resolves method references at runtime.

The XML configuration as below sets the same as the default value:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <expression ducktyping="false"/>
  </compiler>
</esper-configuration>

17.5.6.5. Math Context

By default, when computing the average of BigDecimal values, the compiler does not pass a java.math.MathContext. Use the setting herein to specify a default math context.

The below XML configuration sets precision to 2 and rounding mode ceiling:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <expression math-context="precision=2 roundingMode=CEILING"/>
  </compiler>
</esper-configuration>

An example API configuration is shown next:

Configuration config = new Configuration();
config.getCompiler().getExpression().setMathContext(MathContext.UNLIMITED);

17.5.7. Compiler Settings Related to Scripts

You may configure a default script dialect as described herein. The default script dialect is js which stands for JavaScript, since most JVM ship with an integrated JavaScript execution runtime.

The default value for the enabled setting is true thus the compiler allows scripts. By setting enabled to false the compiler disallows script use entirely.

A sample XML configuration for this setting is shown below:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <scripts default-dialect="js" enabled="true"/>
  </compiler>
</esper-configuration>

A sample code snippet that sets a new script dialect is:

Configuration config = new Configuration();
config.getCompiler().getScripts().setDefaultDialect("js");
config.getCompiler().getScripts().setEnabled(true);

17.5.8. Compiler Settings Related to Execution of Statements

17.5.8.1. Filter Service Max Filter Width

The compiler analyzes filter expressions and determines whether the OR logical operator occurs with the filter expression(s). This setting is only relevant to those filter expression that have OR. Please see Section 15.18, “Compiler Filter Expression Analysis” for more information.

In the default configuration the setting is 16, which means that the filter expression analyzer can at most create 16 path expressions from a given filter expression. If the number of path expressions is over 16, the expression will instead be evaluated as non-path and not be subject to to be entered into filter indexes.

On the level of a statement, this setting can be controlled by providing a hint. For example:

// The compiler optimizes the filter expression to become:
//   "a=1, c=1" or "b=1, c=1" or "a=1, d=1" or "b=1, d=1".
//   This enables filter index sharing between filter expressions.
select * from Event((a=1 or b=1) and (c=1 or d=1))

// The compiler does not optimize filter expressions
@Hint('MAX_FILTER_WIDTH=0') select * from Event((a=1 or b=1) and (c=1 or d=1))

The XML configuration to set a new value is:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <execution filter-service-max-filter-width="100"/>
  </compiler>
</esper-configuration>

The API to change the setting:

Configuration config = new Configuration();
config.getCompiler().getExecution().
    setFilterServiceMaxFilterWidth(16);

17.5.8.2. Filter Index Planning

The compiler analyzes filter expressions and determines whether to use filter indexes. Please see Section 15.18, “Compiler Filter Expression Analysis” for more information.

By default the compiler performs advanced planning. The default setting is ConfigurationCompilerExecution.FilterIndexPlanning.ADVANCED.

This setting is an enumeration value that can be set to none, basic or advanced (the default).

By setting none the compiler simply builds a single non-reusable boolean composite expression for any filter expressions.

By setting basic the compiler analyzes filter expressions and builds basic triplets. The EPL can specify filter index planning hints to enable those advanced planning features that your application needs.

By setting advanced (the default) the compiler analyzes filter expressions fully. In this case the filter index planning hints are not considered by the compiler.

The XML configuration example is:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <execution filter-index-planning="advanced"/>
  </compiler>
</esper-configuration>

The API example to change the setting is:

Configuration config = new Configuration();
config.getCompiler().getExecution().setFilterIndexPlanning(
    ConfigurationCompilerExecution.FilterIndexPlanning.ADVANCED);

17.5.8.3. Declared Expression Value Cache

The enable-declared-expr-value-cache is true by default and the compile generates code such that it uses a declared-expression cache.

The XML configuration to sets the same value as the default:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <execution enable-declared-expr-value-cache="true"/>
  </compiler>
</esper-configuration>

In multithreaded environments, this setting controls whether dispatches of statement result events to listeners preserve the ordering in which a statement processes events. By default the runtime guarantees that it delivers a statement's result events to statement listeners in the order in which the result is generated. This behavior can be turned off via configuration as below. This behavior applies to stateful statements and not to stateless statements as stateless statements execute lock-free.

Configuration config = new Configuration();
config.getCompiler().getExecution().
    setEnabledDeclaredExprValueCache(true);

17.5.9. Compiler Settings Related to Serializers and Deserializers

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <serde-settings/>
  </compiler>
</esper-configuration>

17.6. Configuration Runtime

17.6.1. Runtime Settings Related to Concurrency and Threading

17.6.1.1. Preserving the Order of Events Delivered to Listeners

The next code snippet shows how to control this feature:

Configuration config = new Configuration();
config.getRuntime().getThreading().setListenerDispatchPreserveOrder(false);

And the XML configuration file can also control this feature by adding the following elements:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <threading>
      <listener-dispatch preserve-order="true" timeout-msec="1000" locking="spin"/>
    </threading>
  </runtime>
</esper-configuration>

As discussed, by default the runtime can temporarily block another processing thread when delivering result events to listeners in order to preserve the order in which results are delivered to a given statement. The maximum time the runtime blocks a thread can also be configured, and by default is set to 1 second.

As such delivery locks are typically held for a very short amount of time, the default blocking technique employs a spin lock (There are two techniques for implementing blocking; having the operating system suspend the thread until it is awakened later or using spin locks). While spin locks are CPU-intensive and appear inefficient, a spin lock can be more efficient than suspending the thread and subsequently waking it up, especially if the lock in question is held for a very short time. That is because there is significant overhead to suspending and rescheduling a thread.

The locking technique can be changed to use a blocking strategy that suspends the thread, by means of setting the locking property to 'suspend'.

17.6.1.2. Preserving the Order of Events for Insert-Into Streams

In multithreaded environments, this setting controls whether statements producing events for other statements via insert-into preserve the order of delivery within the producing and consuming statements, allowing statements that consume other statement's events to behave deterministic in multithreaded applications, if the consuming statement requires such determinism. By default, the runtime makes this guarantee (the setting is on). This behavior applies to stateful statements and not to stateless statements as stateless statements execute lock-free.

Take, for example, an application where a single statement (S1) inserts events into a stream that another statement (S2) further evaluates. A multithreaded application may have multiple threads processing events into statement S1. As statement S1 produces events for consumption by statement S2, such results may need to be delivered in the exact order produced as the consuming statement may rely on the order received. For example, if the first statement counts the number of events, the second statement may employ a pattern that inspects counts and thus expect the counts posted by statement S1 to continuously increase by 1 even though multiple threads process events.

The runtime may need to block a thread such that order of delivery is maintained, and statements that require order (such as pattern detection, previous and prior functions) receive a deterministic order of events. The settings available control the blocking technique and parameters. As described in the section immediately prior, the default blocking technique employs spin locks per statement inserting events for consumption, as the locks in questions are typically held a very short time. The 'suspend' blocking technique can be configured and a timeout value can also defined.

The XML configuration file may change settings via the following elements:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <threading>
      <insert-into-dispatch preserve-order="true" timeout-msec="100" locking="spin"/>
    </threading>
  </runtime>
</esper-configuration>

17.6.1.3. Preserving the Order of Named Window Dispatches to Named Window Consumer Statements

In multithreaded environments, this setting controls whether named windows producing insert and remove streams for other statements that consume the named window by means of from-clause preserve the order of delivery within the producing named window and the consuming statements, allowing statements that consume named window's insert and remove stream events to behave deterministic in multithreaded applications, if the consuming statement requires such determinism. By default, the runtime makes this guarantee (the setting is on) with spin locking and Long.MAX_VALUE as millisecond timeout.

Take, for example, an application where a named window (W1) produces inserts and remove stream events that a statement (S1) consumes. A multithreaded application may have multiple threads producing insert and remove stream events for consumption by statement S1. Such results may need to be delivered in the exact order produced by the named window as the consuming statement may rely on the order received.

The runtime may need to block a thread such that order of delivery is maintained, and statements that require order receive a deterministic order of events. The settings available control the blocking technique and parameters. As described in the section immediately prior, the default blocking technique employs spin locks per named window producing insert and removed stream events for consumption, as the locks in questions are typically held a very short time. The 'suspend' blocking technique can be configured and a timeout value can also defined.

The XML configuration file may change settings via the following elements:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <threading>
      <named-window-consumer-dispatch preserve-order="true" locking="spin"/>
    </threading>
  </runtime>
</esper-configuration>

17.6.1.4. Internal Timer Settings

This option can be used to disable the internal timer thread and such have the application supply external time events, as well as to set a timer resolution.

The next code snippet shows how to disable the internal timer thread via the configuration API:

Configuration config = new Configuration();
config.getRuntime().getThreading().setInternalTimerEnabled(false);

This snippet of XML configuration leaves the internal timer enabled (the default) and sets a resolution of 200 milliseconds (the default is 100 milliseconds):

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <threading>
      <internal-timer enabled="true" msec-resolution="200"/>
    </threading>
  </runtime>
</esper-configuration>

We recommend that when disabling the internal timer, applications send an external timer event setting the start time before creating statements, such that statement start time is well-defined.

17.6.1.5. Advanced Threading Options

The settings described herein are for enabling advanced threading options for inbound, outbound, timer and route executions.

Take the next snippet of XML configuration as an example. It configures all threading options to 2 threads, which may not be suitable to your application, however demonstrates the configuration:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <threading>
      <threadpool-inbound enabled="true" num-threads="2"/>
      <threadpool-outbound enabled="true" num-threads="2" capacity="1000"/>
      <threadpool-timerexec enabled="true" num-threads="2"/>
      <threadpool-routeexec enabled="true" num-threads="2"/>
    </threading>
  </runtime>
</esper-configuration>

By default, queues are unbound and backed by java.util.concurrent.LinkedBlockingQueue. The optional capacity attribute can be set to instruct the threading option to configure a capacity-bound queue with a sender-wait (blocking put) policy, backed ArrayBlockingQueue.

This example uses the API for configuring inbound threading :

Configuration config = new Configuration();
config.getRuntime().getThreading().setThreadPoolInbound(true);
config.getRuntime().getThreading().setThreadPoolInboundNumThreads(2);

With a bounded work queue, the queue size and pool size should be tuned together. A large queue coupled with a small pool can help reduce memory usage, CPU usage, and context switching, at the cost of potentially constraining throughput.

Note

If outbound-threading is enabled, listeners and subscribers that send events back into the runtime should use the sendEventType method and not the routeEvent method.

17.6.1.6. Runtime Fair Locking

By default the runtime configures the runtime-level lock without fair locking. The runtime-level lock coordinates event processing threads (threads that send events) with threads that perform administrative functions (threads that deploy and undeploy statements, for example). A fair lock is generally less performing that an unfair lock thus the default configuration is an unfair lock.

If your application is multi-threaded and multiple threads sends events without gaps and if the per-event processing time is significant, then configuring a fair lock can help prioritize administrative functions. Administrative functions exclude event-processing threads until the administrative function completed. You may need to set this flag to prevent lock starvation to perform an administrative function in the face of concurrent event processing. Please consult the Java API documentation under ReentrantReadWriteLock and Fair Mode for more information.

The XML configuration to enable fair locking, which is disabled by default, is as follows:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <threading runtime-fairlock="true"/>
  </runtime>
</esper-configuration>

By default, the runtime does not produce debug output for the event processing execution paths even when Log4j or Logger configurations have been set to output debug level logs. To enable debug level logging, set this option in the configuration as well as in your Log4j configuration file.

Configuration config = new Configuration();
config.getRuntime().getThreading().setRuntimeFairlock(true);

17.6.2. Runtime Settings Related to Logging

17.6.2.1. Execution Path Debug Logging

Statement-level processing information can be output via the @Audit annotation, please see Section 15.12.1, “@Audit Annotation”.

When debug-level logging is enabled by setting the flag as below and by setting DEBUG in the Log4j configuration file, then the timer processing may produce extensive debug output that you may not want to have in the log file. The timer-debug setting in the XML or via API as below disables timer debug output which is enabled by default.

The API to use to enable debug logging and disable timer event output is shown here:

Configuration config = new Configuration();
config.getRuntime().getLogging().setEnableExecutionDebug(true);
config.getRuntime().getLogging().setEnableTimerDebug(false);

Note: this is a configuration option that applies to all runtime instances of a given Java module or VM.

The XML snippet is:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
      <logging>
        <execution-path enabled="true"/>
        <timer-debug enabled="false"/>
    </logging>
  </runtime>
</esper-configuration>

17.6.2.2. Audit Logging

The settings herein control the output format of @Audit logs.

This setting applies to all runtime instances in the same JVM. Please also see the API documentation for information on pattern conversion characters.

Table 17.5. Audit Log Conversion Characters

Character	Description
`m`	Audit message
`s`	Statement name
`u`	Runtime URI
`d`	Deployment Id
`i`	Context partition id
`c`	Category
`tutc`	Runtime time formatted in ISO-8601 instance format as a UTC value
`tzone`	Runtime time formatted in ISO-8601 instance format as a zoned value according to the configured zone

The API to use to set am audit log format is shown here:

Configuration config = new Configuration();
config.getRuntime().getLogging().setAuditPattern("[%u] [%s] %m");

The XML snippet is:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <logging>
      <audit pattern="[%u] [%s]%m"/>
    </logging>
  </runtime>
</esper-configuration>

17.6.2.3. Lock Activity Logging

By default, lock activity logging is disabled. When lock activity logging is enabled the runtime outputs, at INFO level and under the package name com.espertech.esper.lock, information about read-write-lock activity. The output includes:

Acquire before the call to lock(), Got after the call to lock(), Release before the call to unlock() and Freed after the call to unlock()
The text write for the write-lock and the text read for the read-lock
The read-write lock's system identity hash code
The EPL statement name
The context partition id or -1 when unpartitioned
The tryLock timeout milliseconds or -1 when not applicable
The current read lock count
A boolean value indicating whether the lock is write-locked

Note

Note that certain EPL statements execute lock-free and would thus not produce lock activity output.

The API to use to enable debug logging and disable timer event output is shown here:

Configuration config = new Configuration();
config.getRuntime().getLogging().setEnableLockActivity(true);

This setting controls the length of time that the runtime retains variable versions for use by statements that use variables and that execute, within the same statement for the same event, longer then the time interval. By default, the runtime retains 15 seconds of variable versions.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
      <logging>
        <lock-activity enabled="true"/>
    </logging>
  </runtime>
</esper-configuration>

17.6.3. Runtime Settings Related to Variables

17.6.3.1. Variable Version Release Interval

For statements that use variables and that execute (in response to a single timer or other event) longer then the time period, the runtime returns the current variable version at the time the statement executes, thereby softening the guarantee of consistency of variable values within the long-running statement. Please see Section 5.17.3, “Using Variables” for more information.

The XML configuration for this setting is shown below:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <variables>
      <msec-version-release value="15000"/>
    </variables>
  </runtime>
</esper-configuration>

17.6.4. Runtime Settings Related to Patterns

17.6.4.1. Followed-By Operator Maximum Subexpression Count

You may use this setting to limit the total runtime-wide number of pattern sub-expressions that all followed-by operators may manage. When the limit is reached, a condition is raised by the runtime through the condition callback API.

By default, when the limit is reached, the runtime also prevents the start of new pattern sub-expressions, until pattern sub-expressions end and the limit is no longer reached. By setting the prevent-start flag to false you can instruct the runtime to only raise a condition and continue to allow the start of new pattern sub-expressions.

The implications of the settings described herein are also detailed in Section 7.5.8.2, “Limiting Runtime-Wide Sub-Expression Count”.

A sample XML configuration for this setting is shown below:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <patterns>
      <max-subexpression value="100" prevent-start="false"/>
    </patterns>
  </runtime>
</esper-configuration>

17.6.5. Runtime Settings Related to Match-Recognize

17.6.5.1. Maximum State Count

You may use this setting to limit the total runtime-wide number of states that all match-recognize constructs may manage. When the limit is reached, a condition is raised by the runtime through the condition callback API.

By default, when the limit is reached, the runtime also prevents the allocation of new states, until states get removed and the limit is no longer reached. By setting the prevent-start flag to false you can instruct the runtime to only raise a condition and continue to allow the allocation of new states.

The implications of the settings described herein are also detailed in Section 8.11, “Limiting Runtime-Wide State Count”.

A sample XML configuration for this setting is shown below:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <match-recognize>
      <max-state value="100" prevent-start="false"/>
    </match-recognize>
  </runtime>
</esper-configuration>

17.6.6. Runtime Settings Related to Time Source

17.6.6.1. Default Time Source

This setting only applies if internal timer events control runtime time (default). If external timer events provide runtime clocking, the setting does not apply.

By default, the internal timer uses the call System.currentTimeMillis() to determine runtime time in milliseconds. Via this setting the internal timer can be instructed to use System.nanoTime() instead. Please see Section 16.9.2, “Time Resolution and Time Unit” for more information.

Note: This is a Java VM global setting. If running multiple runtime instances in a Java VM, the timer setting is global and applies to all runtime instances in the same Java VM, for performance reasons.

A sample XML configuration for this setting is shown below, whereas the sample setting sets the time source to the nanosecond time provider:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <time-source>
      <time-source-type value="nano" />
    </time-source>
  </runtime>
</esper-configuration>

The equivalent code snippet using the configuration API is here:

Configuration config = new Configuration();
config.getRuntime().getTimeSource().setTimeSourceType(TimeSourceType.NANO);

17.6.7. Runtime Settings Related to JMX Metrics

Please set the flag as described herein to have the runtime report key counters and other processing information through the JMX mbean platform server. By default JMX is not enabled. For NEsper .NET this section does not apply and there is currently no equivalent.

A sample XML configuration is shown below:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <metrics-reporting jmx-runtime-metrics="true"/>
  </runtime>
</esper-configuration>

A sample code snippet to set this configuration via the API follows:

Configuration configuration = new Configuration();
configuration.getRuntime().getMetricsReporting().setJmxRuntimeMetrics(true);

17.6.8. Runtime Settings Related to Metrics Reporting

This section explains how to enable and configure metrics reporting, which is by default disabled. Please see Section 16.12, “Runtime and Statement Metrics Reporting” for more information on the metrics data reported to your application.

The flag that enables metrics reporting is global to a Java virtual machine. If metrics reporting is enabled, the overhead incurred for reporting metrics is carried by all runtime instances per Java VM.

Metrics reporting occurs by a runtime-controlled separate daemon thread that each runtime instance starts at runtime initialization time, if metrics reporting and threading is enabled (threading enabled is the default).

Runtime and statement metric intervals are in milliseconds. A negative or zero millisecond interval value may be provided to disable reporting.

To control statement metric reporting for individual statements or groups of statements, the runtime provides a facility that groups statements by statement name. Each such statement group may have different reporting intervals configured, and intervals can be changed at runtime through runtime configuration. A statement group is assigned a group name at configuration time to identify the group.

Metrics reporting configuration is part of the runtime settings. All configuration options are also available via the Configuration API.

A sample XML configuration is shown below:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <metrics-reporting enabled="true" runtime-interval="1000" statement-interval="1000" 
        threading="true"/>
  </runtime>
</esper-configuration>

The runtime-interval setting (defaults to 10 seconds) determines the frequency in milliseconds at which the runtime reports runtime metrics, in this example every 1 second. The statement-interval is for statement metrics. The threading flag is true by default since reporting takes place by a dedicated runtime thread and can be set to false to use the external or internal timer thread instead.

The next example XML declares a statement group: The statements that have statement names that fall within the group follow a different reporting frequency:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <metrics-reporting enabled="true" statement-interval="0">
      <stmtgroup name="MyStmtGroup" interval="2000" default-include="true" num-stmts="100" 
           report-inactive="true">
        <exclude-regex>.*test.*</exclude-regex>
      </stmtgroup>
    </metrics-reporting>
  </runtime>
</esper-configuration>

The above example configuration sets the statement-interval to zero to disable reporting for all statements. It defines a statement group by name MyStmtGroup and specifies a 2-second interval. The example sets the default-include flag to true (by default false) to include all statements in the statement group. The example also sets report-inactive to true (by default false) to report inactive statements.

The exclude-regex element may be used to specify a regular expression that serves to exclude statements from the group. Any statement whose statement name matches the exclude regular expression is not included in the group. In the above example, all statements with the characters 'test' inside their statement name are excluded from the group.

Any statement not belonging to any of the statement groups follow the configured statement interval.

There are additional elements available to include and exclude statements: include-regex, include-like and exclude-like. The latter two apply SQL-like matching. All patterns are case-sensitive.

Here is a further example of a possible statement group definition, which includes statements whose statement name have the characters @REPORT or @STREAM, and excludes statements whose statement name have the characters @IGNORE or @METRICS inside.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <metrics-reporting enabled="true">
      <stmtgroup name="MyStmtGroup" interval="1000">
        <include-like>%@REPORT%</include-like>
        <include-regex>.*@STREAM.*</include-regex>
        <exclude-like>%@IGNORE%</exclude-like>
        <exclude-regex>.*@METRICS.*</exclude-regex>
      </stmtgroup>
    </metrics-reporting>
  </runtime>
</esper-configuration>

17.6.9. Runtime Settings Related to Expression Evaluation

17.6.9.1. Subselect Evaluation Order

By default the runtime updates sub-selects with new events before evaluating the enclosing statement. This is relevant for statements that look for the same event in both the from clause and subselects.

To have runtime evaluate the enclosing clauses before updating the subselect in a subselect expression, set the flag as indicated herein.

The XML configuration as below sets the same as the default value:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <expression self-subselect-preeval="true"/>
  </runtime>
</esper-configuration>

Here is a sample statement that utilitzes a sub-select against the same-events:

select * from MyEvent where prop not in (select prop from MyEvent#unique(otherProp))

By default the subselect data window updates first before the where clause is evaluated, thereby above statement never returns results.

Changing the setting described here causes the where clause to evaluate before the subselect data window updates, thereby the statement does post results.

17.6.9.2. Time Zone

By default, when performing calendar operations, the runtime uses the default time zone obtained by java.util.TimeZone.getDefault(). Use the setting herein to specify a time zone other then the default time zone.

The below XML configuration sets a time zone 'GMT-4:00':

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <expression time-zone="GMT-4:00"/>
  </runtime>
</esper-configuration>

An example API configuration is shown next:

Configuration config = new Configuration();
config.getRuntime().getExpression().setTimeZone(TimeZone.getTimeZone("GMT-4:00"));

17.6.10. Runtime Settings Related to Execution of Statements

17.6.10.1. Prioritized Execution

By default the runtime ignores @Priority and @Drop annotations and executes unprioritized, that is the runtime does not attempt to interpret assigned priorities and reorder executions based on priority. Use this setting if your application requires prioritized execution.

By setting this configuration, the runtime executes statements, when an event or schedule matches multiple statements, according to the assigned priority, starting from the highest priority value. See built-in EPL annotations in Section 5.2.7.7, “@Priority”.

The XML configuration to enable the flag, which is disabled by default, is as follows:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <execution prioritized="true"/>
  </runtime>
</esper-configuration>

The API to change the setting:

Configuration config = new Configuration();
config.getRuntime().getExecution().setPrioritized(true);

17.6.10.2. Event-Precedence Execution

By default, the runtime ignores event-precedence. This is because there can be a small performance cost for tracking event precedence.

Use this setting if your application requires a processing order of insert-into events according to event precedence. Enabling this setting means that the runtime makes sure that the events in the work queue are ordered by event precedence.

For more information on the work queue and order of event processing, see Section 16.8.2.2, “Processing Principles of Events and Listener Updates”.

For more information of the event-precedence keyword, please refer to Section 5.10.10, “Insert Into and Event Precedence”.

The XML configuration to enable the flag, which is disabled by default, is as follows:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <execution precedence-enabled="true"/>
  </runtime>
</esper-configuration>

The API to change the setting:

Configuration config = new Configuration();
config.getRuntime().getExecution().setPrecedenceEnabled(true);

17.6.10.3. Context Partition Fair Locking

By default the runtime configures context partition locks without fair locking. If your application is multi-threaded and performs very frequent reads via iterator or fire-and-forget queries, you may need to set this flag to prevent lock starvation in the face of concurrent reads and writes. Please consult the Java API documentation under ReentrantReadWriteLock and Fair Mode for more information.

The XML configuration to enable fair locking, which is disabled by default, is as follows:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <execution fairlock="true"/>
  </runtime>
</esper-configuration>

By default the runtime configures context partition locks as required after analyzing your statements. You may disable context partition locks using the setting described here. Use the @NoLock annotation instead to disable locking for a given statement or named window only.

Configuration config = new Configuration();
config.getRuntime().getExecution().setFairlock(true);

17.6.10.4. Disable Locking

Warning

The runtime provides this setting for the purpose of identifying locking overhead, or when your application is single-threaded, or when using an external mechanism for concurrency control. Setting disable-locking to true may have unpredictable results unless your application is taking concurrency under consideration.

The XML configuration to disable context level locking is as follows:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <execution disable-locking="true"/>
  </runtime>
</esper-configuration>

This setting is for performance tuning of filter service which handles matching incoming events to context partitions and statements.

Configuration config = new Configuration();
config.getRuntime().getExecution().setDisableLocking(true);

17.6.10.5. Filter Service Profile

In the default configuration termed readmostly, filter service locking is coarse-grained assuming a large number of reads and comparatively few writes. "Reads" are evaluations of events, while with "writes" we mean filter service changes such as new statements, a new pattern subexpression becoming active or a pattern subexpression being deactivated.

Set the configuration to readwrite if you have multiple threads and your statements very frequently add and remove filters using pattern subexpressions, for example. This setting instructs the runtime to maintain fine-grained locks instead generally allowing for higher concurrency but possibly incurring additional overhead.

The XML configuration to set a new filter service profile is as follows:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <execution filter-service-profile="readwrite"/>
  </runtime>
</esper-configuration>

In the default configuration the setting is 1, which means that for each declared expression the runtime retains a cache of only the last computed value, for use for the duration of an evaluation of an event or time against a context partition. You may set the value to zero to disable caching. You may set the value to N to instruct the runtime to retain a cache of the last N computed values. This setting is not applicable to stateful declared expressions such as declared expressions with aggregation functions, for example.

Configuration config = new Configuration();
config.getRuntime().getExecution().
    setFilterServiceProfile(FilterServiceProfile.READWRITE);

17.6.10.6. Declared Expression Value Cache Size

The runtime only uses the declared expression cache for expressions that receive events as parameters. For expressions that receive other values as parameters the runtime does not use the declared expression cache.

The XML configuration to sets the same value as the default:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <execution declared-expr-value-cache-size="1"/>
  </runtime>
</esper-configuration>

Use the settings as described here to register a condition handler factory class that provides a condition handler. The runtime invokes condition handlers in the order they are listed to indicate conditions, which is the term used for notification when certain predefined limits are reached, as further described in Section 16.11, “Condition Handling”.

Configuration config = new Configuration();
config.getRuntime().getExecution().
    setDeclaredExprValueCacheSize(1);

17.6.11. Runtime Settings Related to Exception Handling

Use the settings as described here to register an exception handler factory class that provides an exception handler. The runtime invokes exception handlers in the order they are listed to handle a continues-query unchecked exception, as further described in Section 16.10, “Exception Handling”. Please provide the full-qualified class name of each class that implements the com.espertech.esper.common.client.hook.exception.ExceptionHandlerFactory interface in the runtime configuration as below.

By default, during a module undeploy when the runtime encounters a runtime exception for any of the statements it logs such exceptions as warnings. You can set the undeploy-rethrow-policy flag to rethrow_first instead have the runtime rethrow the first runtime exception.

The XML configuration is as follows:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <exceptionHandling undeploy-rethrow-policy="warn">
      <handlerFactory class="my.company.cep.MyCEPRuntimeExceptionHandlerFactory"/>
    </exceptionHandling>
  </runtime>
</esper-configuration>

The API calls to register an exception handler factory are as follows:

Configuration config = new Configuration();
config.getRuntime().getExceptionHandling().addClass(MyCEPRuntimeExceptionHandlerFactory.class);
config.getRuntime().getExceptionHandling().setUndeployRethrowPolicy(UndeployRethrowPolicy.RETHROW_FIRST);

Tip

The application-provided exception handler receives an ExceptionHandlerContext instance that contains the exception detail. The application code that performs logging should log detailed information such as deployment and statement information. The information can be useful to troubleshoot exceptions.

For inspiration, there is a code extract of the runtime's default exception handling code below.

For reference, we include the default exception handling code here:

if (exceptionHandlers.isEmpty()) {
  StringWriter writer = new StringWriter();
  if (type == ExceptionHandlerExceptionType.PROCESS) {
    writer.append("Exception encountered processing ");
  } else {
    writer.append("Exception encountered performing instance stop for ");
  }
  writer.append("deployment-id '");
  writer.append(deploymentId);
  writer.append("' ");
  writer.append("statement '");
  writer.append(statementName);
  writer.append("'");
  if (optionalEPL != null) {
    writer.append(" expression '");
    writer.append(optionalEPL);
    writer.append("'");
  }
  writer.append(" : ");
  writer.append(t.getMessage());
  String message = writer.toString();

  if (type == ExceptionHandlerExceptionType.PROCESS) {
    log.error(message, t);
  } else {
    log.warn(message, t);
  }
  return;
}

ExceptionHandlerContext context = new ExceptionHandlerContext(runtimeURI, t, deploymentId, statementName, optionalEPL, type, optionalCurrentEvent);
for (ExceptionHandler handler : exceptionHandlers) {
  handler.handle(context);
}

17.6.12. Runtime Settings Related to Condition Handling

Please provide the full-qualified class name of each class that implements the com.espertech.esper.common.client.hook.condition.ConditionHandlerFactory interface in the runtime configuration as below.

The XML configuration is as follows:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <runtime>
    <conditionHandling>
      <handlerFactory class="my.company.cep.MyCEPRuntimeConditionHandlerFactory"/>
    </conditionHandling>
  </runtime>
</esper-configuration>

The API calls to register a condition handler factory are as follows:

Configuration config = new Configuration();
config.getRuntime().getConditionHandling().addClass(MyCEPRuntimeConditionHandlerFactory.class);

17.7. Passing Services or Transient Objects

The Configuration object allows passing application objects such as services or other transient objects. This information can be used by extensions, listeners or subscribers, for example, to obtain application objects from the runtime. Your application may provide a custom class loader or class-for-name service.

Use setTransientConfiguration and provide a Map<String, Object> that contains the application objects. The runtime retains and makes available the same Map instance available via API. Its contents including services can be changed by an application at runtime. The API methods to retrieve transient configuration are:

The getConfigurationTransients method of EPRuntime
The getConfigurationDeepCopy method of EPRuntime

17.7.1. Service Example

Assuming your application has a service myLocalService instance, the example code is:

Configuration configuration = new Configuration();
HashMap<String, Object> transients = new HashMap<String, Object>();
transients.put(SERVICE_NAME, myLocalService); // SERVICE_NAME is a well-known string value defined elsewhere
configuration.getCommon().setTransientConfiguration(transients);

EPRuntime runtime = EPRuntimeProvider.getDefaultRuntime(configuration);

A sample listener that receives a service from transient configuation is:

public class MyListener implements UpdateListener {
  public void update(EventBean[] newEvents, EventBean[] oldEvents, EPStatement statement, EPRuntime runtime) {
    MyLocalService service = (MyLocalService) runtime.getConfigurationTransient().get(SERVICE_NAME);
    // use the service here
  }
}

An alternative means to obtain application services is to define a constant variable.

17.7.2. Class-for-Name

By default, when resolving a fully-qualified class name to a Class, the com.espertech.esper.common.client.util.ClassForNameProviderDefault uses:

ClassLoader cl = Thread.currentThread().getContextClassLoader();
return Class.forName(className, true, cl);

Your application can implement the com.espertech.esper.common.client.util.ClassForNameProvider interface to provide an alternate implementation.

For example, this provider prevents the System class from being available in EPL:

runtime.getConfigurationTransient().put(ClassForNameProvider.NAME,
  new ClassForNameProvider() {
    public Class classForName(String className) throws ClassNotFoundException {
      if (className.equals(System.class.getName())) { // prevent the System class from loading
        return null;
      }
      return Class.forName(className, true, Thread.currentThread().getContextClassLoader());
   }
});

17.7.3. Class Loader

By default, to obtain a class loader, the com.espertech.esper.common.client.util.ClassLoaderProviderDefault uses Thread.currentThread().getContextClassLoader().

Your application can implement the com.espertech.esper.common.client.util.ClassLoaderProvider interface to provide an alternate implementation.

For example, this provider returns a pre-determined classloader:

ClassLoader classLoader = new CustomClassLoader();
runtime.getConfigurationTransient().put(ClassLoaderProvider.NAME,
  new ClassLoaderProvider() {
    public ClassLoader classloader() {
      return classLoader;
    }
});

17.8. Type Names

Certain configuration values accept type names. Type names can occur in the configuration of variable types, Map-event property types as well as XPath cast types, for example. Types names are not case-sensitive.

The table below outlines all possible type names:

Table 17.6. Variable Type Names

Type Name	Type
`string`, `varchar`, `varchar2` or `java.lang.String`	A string value
`int`, `integer` or `java.lang.Integer`	An integer value
`long` or `java.lang.Long`	A long value
`bool`, `boolean` or `java.lang.Boolean`	A boolean value
`double` or `java.lang.Double`	A double value
`float` or `java.lang.Float`	A float value
`short` or `java.lang.Short`	A short value
`char`, `character` or `java.lang.Character`	A character value
`byte` or `java.lang.Byte`	A byte value

17.9. Logging Configuration

For NEsper .NET also see Section J.18, “.NET Configurations - Logging Configuration”.

The compiler and runtime log all messages to SLF4J under an appropriate log level. To output log messages you can add Log4j and SLF4J-Log4j (1.2) to classpath and configure Log4j as below.

The only direct dependency for logging is the SLF4J interfaces (slf4j-api-x.y.z.jar). Please see the SLF4J documentation on redirecting logs to other logging frameworks.

Statement-level processing information can be output, please see Section 15.12.1, “@Audit Annotation”.

For performance reasons, the runtime does not log any debug-level or informational-level messages for event execution unless explicitly configured via Section 17.6.2.1, “Execution Path Debug Logging”.

A callback API for receiving certain critical runtime reports is available as described in Section 16.10, “Exception Handling”.

More information on configuring runtime-level settings for logging are at Section 17.6.2, “Runtime Settings Related to Logging”.

The next table explains the log levels:

Table 17.7. Log Levels

Log Level	Use
Debug	Displays detailed runtime-internal information that may not be easy to understand for application developers but are useful for runtime support.
Info	Used for a few critical runtime-level log messages.
Warn	Certain important warning or informational messages are displayed under the warning level.
Error	Exceptions reported within the runtime or by plug-in components are reported under the error level.

17.9.1. Log4j Logging Configuration

Log4j is the default logging component. Please find additional information for Log4j configuration and extension in http://logging.apache.org/log4j.

The easiest way to configure Log4j is by providing a Log4J configuration file, similar to the log4j.xml file shipped in the etc folder of the distribution.

Add the log4j.configuration system property to the java command line and provide the file name of the Log4j configuration file, making sure your classpath also includes the directory of the file:

java -Dlog4j.configuration=log4j.xml ...

18.1. Overview

Your application can add Java class code (or C# class code for NEsper) directly into the EPL. The EPL compiler compiles the classes your application provides and makes the classes available for use within EPL. Classes can be local i.e. visible to the current module only, or global i.e. visible to other modules according to EPL-object visibility rules. The typical use of classes is to perform imperative programming or provide utility, integration or extension functionality.

For declaring classes that are visible across multiple statements i.e. globally visible classes please consult Section 18.3, “Declaring a Global Class” that explains the create inlined_class clause. Inlined-classes are EPL-objects following visibility rules according to Section 15.2.2, “EPL-objects”.

Inlined classes can provide single-row functions as described in Section 22.2.1, “Using an Inlined Class to Provide a Single-Row Function”.

Inlined classes can provide aggregation single-functions as described in Section 22.5.1.4, “Using an Inlined Class to Provide an Aggregation Single-Function”.

Inlined classes can provide aggregation multi-functions as described in Section 22.5.2.8, “Using an Inlined Class to Provide an Aggregation Multi-Function”.

Inlined classes can serve as a variable type. The create variable clause allows the class name of an inlined class as a variable type.

18.2. Declaring a Local Class

A local inlined class is a class that is only visible to the same EPL statement that it is part of. The class is not visible to other EPL statements in the same module and is not visible to other modules.

The syntax for inlined classes is:

inlined_class """ class-text """

Use the inlined_class keyword to declare a class. Place the class-text class code within three double-quotes (""").

The class-text class code is a Java class, i.e. public class class-name{...} or public enum class-name{...}.

The class text may specify a package name and imports. The class text is subject to limitations as listed below.

The next example shows a statement that calls a static method of an inlined-class by name MyUtility which computes the Fibonacci total for a given number:

inlined_class """
  public class MyUtility {
    public static double fib(int n) {
      if (n <= 1)
        return n;
      return fib(n-1) + fib(n-2);
    }
  }
"""
select MyUtility.fib(intValue) from SomeEvent

18.3. Declaring a Global Class

Use the create inlined_class syntax to declare an inlined class. The compiler remembers the class and allows the class to be referenced by other statements of the same module and by other modules.

The below EPL declares a globally visible class by name MyUtility that provides a method midPrice that computes a mid-price and that a buy and sell parameter:

create inlined_class """ 
  public class MyUtility {
    public static double midPrice(double buy, double sell) {
      return (buy + sell) / 2;
    }
  }
"""

The next EPL returns mid-price for each event:

select MyUtility.midPrice(buy, sell) from MarketDataEvent

The class name must be unique for global inlined classes. It is not possible to declare the same global inlined-class twice with the same class name and the same module name.

The runtime, at the time your application deploys a module that references global inlined classes, provides a classloader that adds the imported inlined classes that are provided by the deployment that contains the inlined classes. Thus the compiler and runtime never copy or otherwise duplicate the inlined class or class byte code.

18.4. Performance Notes

The EPL compiler compiles the inlined classes to byte code and the runtime loads the byte code using a classloader that is aware of the inlined classes.

18.5. Known Limitations

The compiler for inlined classes is Janino. Janino does not support all of Java 17 and higher. Please see the Janino documentation for more information.

The following limitations apply:

Annotations; an inlined class cannot be an annotation
Listeners or subscribers; an inlined class cannot be a listener or subscriber
Insert-into; for insert into, your application cannot populate an instance of an inlined class
Event type; for create schema, an inlined class cannot be a property type and cannot be an event type
Named windows and tables; for create window and for create table, an inlined class cannot be a column type
The @type annotation cannot be used with inlined classes
Inlined classes cannot be view, enumeration method, virtual data window or pattern extension classes
Script may not use inlined classes

Chapter 19. Script Support

19.1. Overview
19.2. Syntax
19.3. Examples
19.4. Built-In EPL Script Attributes
19.5. Performance Notes
19.6. Additional Notes

19.1. Overview

EPL allows the use scripting languages within EPL. You may use scripts for imperative programming to execute certain code as part of EPL processing by the runtime.

The syntax and examples outlined below discuss how to declare a script that is visible to the same statement that listed the script.

For declaring scripts that are visible across multiple statements i.e. globally visible scripts please consult Section 5.18.3, “Global Scripts” that explains the create expression clause.

Any scripting language that supports JSR 223 and also the MVEL scripting language can be specified in EPL. This section provides MVEL and JavaScript examples.

For more information on the MVEL scripting language and its syntax, please refer to the MVEL documentation. MVEL is an expression language that has a natural syntax for Java-based applications and compiles to provide fast execution times. To use MVEL with the runtime, please make sure to add the MVEL jar file to the application classpath.

For more information on JSR 223 scripting languages, please refer to external resources. As JSR 223 defines a standard API for script execution, your application may use any script execution that implements the API. Current JVM versions ship with a JavaScript script execution. Other script executors such as Groovy, Ruby and Python scripts can be used as implementations of JSR 223.

As an alternative to a script consider providing a custom single row function as described in Section 22.2, “Single-Row Function”.

19.2. Syntax

The syntax for scripts is:

expression [return_type] [@type(eventtype_name)] [dialect_identifier:] script_name [ (parameters) ] [ script_body ]

Use the expression keyword to declare a script.

The return_type is optional. If the script declaration provides a return type the compiler can perform strong type checking: Any expressions that invoke the script and use the return value are aware of the return type. If no return type is provided the compiler assumes the script returns java.lang.Object. A parameterized type such as java.util.Collection<Integer> is also allowed as the return type.

If the return type of the script is EventBean[] you must provide the @type(name) annotation after the return type to name the event type of events returned by the script. The @type is allowed only when the return type is EventBean instances.

The dialect_identifier is optional and identifies the scripting language. Use mvel for MVEL , js for JavaScript and python for Python and similar for other JSR 223 scripting languages. If no dialect identifier is specified, the default dialect that is configured applies, which is js unless your application changes the default configuration.

It follows the script name. You may use the same script name multiple times and thus overload providing multiple signatures under the same script name. The combination of script name and number of parameters must be unique however.

If you have script parameters, specify the parameter names for the script as a comma-separated list of identifiers in parenthesis. It is not necessary to list parameter types.

The script body is the actual MVEL or JavaScript or other scripting language script and is placed in square brackets: [ ... script body ...].

19.3. Examples

The next example shows a statement that calls a JavaScript script which computes the Fibonacci total for a given number:

expression double js:fib(num) [
fib(num);
function fib(n) {
  if(n <= 1)
    return n;
  return fib(n-1) + fib(n-2);
}
]
select fib(intPrimitive) from SupportBean;

The expression keyword is followed by the return type (double), the dialect (js) and the script name (fib) that declares a single parameter (num). The JavaScript code that computes the Fibonacci total is between square brackets [].

The following example shows a statement that calls a MVEL script which outputs all the different colors that are listed in the colors property of each ColorEvent:

expression mvel:printColors(colors) [
String c = null;
for (c : colors) {
   System.out.println(c);
}
]
select printColors(colors) from ColorEvent;

This example instead uses JavaScript to print colors and passes the event itself as a script parameter (this example is for use with the Nashorn JavaScript runtime):

expression js:printColors(colorEvent) [
  print(java.util.Arrays.toString(colorEvent.getColors()));
]
select printColors(colorEvent) from ColorEvent as colorEvent

The next example creates a globally-visible script that returns ItemEvent events, assuming that the ItemEvent event type is an event type defined by create schema ItemEvent(id string):

create expression EventBean[] @type(ItemEvent) js:myScriptReturnsEvents() [
myScriptReturnsEvents();
function myScriptReturnsEvents() {
  var EventBeanArray = Java.type(\"com.espertech.esper.common.client.EventBean[]\");
  var events = new EventBeanArray(1);
  events[0] = epl.getEventBeanService().adapterForMap(java.util.Collections.singletonMap(\"id\", \"id1\"), \"ItemEvent\");
  return events;
}

// sample EPL:
// select myScriptReturnsEvents().where(v => v.id in ('id1', 'id3')) from MyEvent]

19.4. Built-In EPL Script Attributes

The compiler provides a built-in script object under the variable name epl to all scripts. Your scripts may use this script object to share and retain state by setting and reading script attributes. The runtime maintains a separate script object per context partition or per statement if not declaring a context. Therefore script attributes are not shared between statements, however multiple scripts executed by the same context partition receive the same script object.

The epl script object implements the interface com.espertech.esper.common.client.hook.expr.EPLScriptContext. Please see the JavaDoc for services provided by EPLScriptContext.

For script state management, the EPLScriptContext interface has two methods: The void setScriptAttribute(String attribute, Object value) method to set an attribute value and the Object getScriptAttribute(String attribute) method to read an attribute value.

The next example demonstrates the use of the epl script object. It outputs a flag value true when an RFID event matched because the location is A, and outputs a flag value false when an RFID event matched because the location is B. The example works the same for either MVEL or JavaScript dialects: You may simple replace the js dialect with mvel.

expression boolean js:setFlag(name, value, returnValue) [
  if (returnValue) epl.setScriptAttribute(name, value);
  returnValue;
]
expression js:getFlag(name) [
  epl.getScriptAttribute(name);
]
select getFlag('locA') as flag from RFIDEvent(zone = 'Z1' and
  (setFlag('locA', true, location = 'A') or setFlag('locA', false, location = 'B')) )

The example above utilizes two scripts: The setFlag script receives an attribute name, attribute value and a return value. The script sets the script attribute only when the return value is true. The getFlag script simply returns the script attribute value.

19.5. Performance Notes

Upon statement compilation, the compiler resolves script parameter types and performs script compilation. At runtime the runtime evaluates the script in its compiled form.

As the compiler cannot inspect scripts if is not possible for the compiler to perform query planning or many optimizations based on the information in scripts. It is thus recommended to structure EPL such that basic filter and join expressions are EPL expressions and not script expressions.

19.6. Additional Notes

Your EPL may declare a return type for the script. If no return type is declared and when using the MVEL dialect, the compiler will infer the return type from the MVEL expression analysis result. If the return type is not provided and cannot be inferred or the dialect is not MVEL, the return type is Object.

If the EPL declares a numeric return type then the compiler performs coercion of the numeric result to the return type that is specified.

In the case that the EPL declares a return type that does not match the type of the actual script return value, the compiler does not check return value type.

Chapter 20. EPL Reference: Spatial Methods and Indexes

20.1. Overview

20.2. Spatial Methods

20.2.1. Point-Inside-Rectangle
20.2.2. Rectangle-Intersects-Rectangle

20.3. Spatial Index - Quadtree

20.3.1. Overview
20.3.2. Declaring a Point-Region Quadtree Index
20.3.3. Using a Point-Region Quadtree as a Filter Index
20.3.4. Using a Point-Region Quadtree as an Event Index
20.3.5. Declaring a MX-CIF Quadtree Index
20.3.6. Using a MX-CIF Quadtree as a Filter Index
20.3.7. Using a MX-CIF Quadtree as an Event Index

20.4. Spatial Types, Functions and Methods from External Libraries

20.1. Overview

EPL provides spatial methods and spatial indexes.

The compiler analyzes filter criteria and the where-clause and considers spatial methods, utilizing spatial filter indexes or spatial event indexes for efficient matching and lookup.

For general information on the dot-operator please consult Section 9.7, “Dot Operator”.

20.2. Spatial Methods

The below table summarizes the built-in spatial methods available:

Table 20.1. Spatial Methods

Method

Result

point(x,y).inside(rectangle(x,y,width,height))

Returns true if the point is inside the rectangle.

Section 20.2.1, “Point-Inside-Rectangle”.

rectangle(x,y,width,height).intersects(rectangle(x,y,width,height))

Returns true if the rectangle intersects with the rectangle.

Section 20.2.2, “Rectangle-Intersects-Rectangle”.

20.2.1. Point-Inside-Rectangle

The method compares a point to a rectangle and returns true if the point falls inside the rectangle.

The method takes a point as input and a rectangle as a parameter:

point(point_x, point_y [, filterindex:configexpression]).inside(rectangle(rect_x, rect_y, width, height))

For the point, please provide the point_x and point_y expressions that return the (x, y)-coordinates of the point. The filterindex named parameter is for use with filter indexes as described below. The left-hand side point can be subject to point-region quadtree indexing (MX-CIF quadtrees do not apply).

For the rectangle, the rect_x expression and rect_y expressions return the (x, y)-coordinates of the rectangle and the width expression and height expressions return the width and height of the rectangle.

All expressions must return a number-type and the implementation compares the double-values returned by the expressions.

A point is considered inside the rectangle if (point_x >= rect_x) and (point_x < rect_x + width) and (point_y >= rect_y) and (point_y < rect_y + height).

Table 20.2. Point-Inside-Rectangle Examples

Expression	Result
point(10, 20).inside(rectangle(0, 0, 50, 50))	true
point(10, 20).inside(rectangle(20, 20, 50, 50))	false
point(10, 20).inside(rectangle(9, 19, 1, 1))	false
point(10, 20).inside(rectangle(9, 19, 1.0001, 1.0001))	true

20.2.2. Rectangle-Intersects-Rectangle

The method compares a rectangle to a rectangle and returns true if the rectangles intersect.

The method takes a rectangle as input and a rectangle as a parameter:

rectangle(rect_x, rect_y, rect_width, rect_height [, filterindex:configexpression]).intersects(rectangle(other_x, other_y, other_width, other_height))

The left-hand side is the rectangle's rect_x, rects_y, rect_width and rect_height expressions that return the (x, y)-coordinates and the size of the rectangle. The filterindex named parameter is for use with filter indexes as described below. The left-hand side rectangle can be subject to MX-CIF quadtree indexing (point-region quadtrees do not apply).

For the compared-to rectangle on the right-hand side, the other_x, other_y, other_width and other_height expressions return the (x, y)-coordinates and size of the compared-to rectangle.

All expressions must return a number-type and the implementation compares the double-values returned by the expressions.

A rectangle is considered to intersect another rectangle if:

rect_x + rect_width >= other_x (a is not left of b) and
rect_x <= other_x + other_width (a is not right of b) and
rect_y + rect_height >= other_y (a is not above b) and
rect_y <= other_y + other_height (a is not below b).

Table 20.3. Rectangle-Intersects-Rectangle Examples

Expression	Result
rectangle(10, 20, 5, 5).intersects(rectangle(0, 0, 50, 50))	true
rectangle(10, 20, 5, 5).intersects(rectangle(20, 20, 50, 50))	false
rectangle(10, 20, 5, 5).intersects(rectangle(9, 19, 1, 1))	true
rectangle(10, 20, 5, 5).intersects(rectangle(9, 19, 0.999, 0.999))	false
rectangle(10, 20, 5, 5).intersects(rectangle(15, 25, 1, 1))	true
rectangle(10, 20, 5, 5).intersects(rectangle(15.001, 25.001, 1, 1))	false

20.3. Spatial Index - Quadtree

20.3.1. Overview

A quadtree is a tree data structure in which each branch node has exactly four children. Quadtrees are often used to partition a two-dimensional space by recursively subdividing it into four quadrants or regions (source:WikiPedia).

Quadtree indexes can be used for:

Filter indexes, which organize active filters so that they can be searched efficiently. When the runtime receives an event, it consults the filter indexes to determine which statements, if any, must process the event.
Event indexes, which organize properties of events so that they can be searched efficiently. When the runtime performs statement processing it may use event indexes to find correlated events efficiently.

The point-region quadtree is a quadtree for the efficient finding of points that fall inside a given rectangle. Use this index with the point-inside-rectangle method described above.

The MX-CIF quadtree is a quadtree for the efficient finding of rectangles that intersect with a given rectangle. Use this index with the rectangle-intersects-rectangle method described above.

While point-region quadtree and MX-CIF quadtree are similar, they are not compatible and are not the same. In point-region quadtree, only leaf nodes have data. In MX-CIF quadtrees both branch and leaf nodes have data as branches hold rectangles that don't fit any given quadrant. The runtime expands and shrinks both types of trees dynamically based on data by promoting or subdividing a leaf node to branch nodes when adding data and by demoting or merging branches to a leaf node when removing data.

20.3.2. Declaring a Point-Region Quadtree Index

Declaring a point-region quadtree index is the same for both filter indexes and event indexes. Point-region quadtrees are suitable for efficiently finding points inside a rectangle, when there are many points.

The synopsis to declare a point-region quadtree index, as part of a statement, is:

pointregionquadtree(min_x_expression, min_y_expression, 
  width, height [, leaf_capacity_expression [, max_tree_height_expression]])

The min_x_expression, min_y_expression, width, height are index parameter expressions that return the range of the index. The width and height must be greater zero. The index range rectangle is represented by double-type values internally. A point is inside the index range if x >= minX and y >= minY and x < minX+width and y < minY+height.

Note

An attempt to insert points into the index that are outside of the declared index range causes an exception.

The leaf_capacity_expression is optional and must return a positive integer. It defines the number of coordinates a node may contain before it gets split into regions. The default value is 4.

The max_tree_height_expression is optional and must return an integer value of 2 or more. It defines the maximum depth of the tree. Upon the tree reaching the maximum depth a leaf node does not get split into regions. The default value is 20.

20.3.3. Using a Point-Region Quadtree as a Filter Index

The section that summarizes filter indexes is Section 2.18.2, “Filter Indexes”. As there could be many point(...).inside(rectangle) filters active, having a filter index allows the runtime to efficiently match incoming events to statements.

For use of a point-region quadtree index within filter criteria you must:

Define an expression that returns the point-region quadtree configuration, making sure it specifies pointregionquadtree.
Add the filterindex named parameter providing the expression name.

For defining a local or global expression, please consult Section 5.2.9, “Expression Declaration”.

This sample statement defines the point-region quadtree filter index to have a bounding box of (0,0,100,100):

expression myPointRegionQuadtreeSettings { pointregionquadtree(0, 0, 100, 100) } 
select * from RectangleEvent(point(0, 0, filterindex:myPointRegionQuadtreeSettings).inside(rectangle(x, y, width, height)))

The filterindex named parameter instructs the runtime that the settings for the point-region quadtree filter index are provided by the expression myPointRegionQuadtreeSettings, a local expression in this example. For sharing point-region quadtree settings across statements you may use a global expression instead. Please see Section 5.18, “Declaring Global Expressions, Aliases and Scripts: Create Expression”.

If your EPL does not specify filterindex the runtime does not build a point-region quadtree filter index.

If your EPL specifies filterindex the runtime always builds and uses a point-region quadtree filter index. In the case the compiler analyses filter criteria and determines that it cannot use the point-region quadtree filter index, the compiler fails statement validation.

If your EPL specifies filterindex and the compiler determines that it cannot use the point-region quadtree filter index it fails statement validation.

The runtime shares point-region quadtree filter indexes across the runtime within the same event type given that:

Filters have the same rectangle expressions.
Filters use the same filterindex parameter i.e. the text myPointRegionQuadtreeSettings in above example.
Filters use the same point-region quadtree index configuration i.e. pointregionquadtree(0,0,100,100) in above example.

For use with the filterindex named parameter, the following requirements apply towards point expressions:

Point expressions must be a constant, a context-provided built-in property or an event property provided by a previous pattern match within the same pattern.

For use with the filterindex named parameter, the following requirements apply towards rectangle expressions:

Rectangle expressions must be event properties.

20.3.4. Using a Point-Region Quadtree as an Event Index

The section that summarizes event indexes is Section 2.18.3, “Event Indexes”. The create index clause is described in Section 6.9, “Explicitly Indexing Named Windows and Tables”.

Declare a point-region quadtree event index as follows:

create index ... on ... (
  (x_expression, y_expression) pointregionquadtree(pointregion_quadtree_configuration)
)

The x_expression and y_expression expressions form the index columns. The expressions return the (x, y)-coordinates and must return numeric values. Coordinates are represented as double-type values internally. See above for the pointregion_quadtree_configuration point-region quadtree configuration.

For example, assume a table that contains points:

create table PointTable(pointId string primary key, px double, py double)

This example EPL declares an index on the points, with px and py becoming index columns that determine (x, y)-coordinates:

create index PointIndex on PointTable((px, py) pointregionquadtree(0, 0, 100, 100))

The above sample quadtree index expects (x, y)-coordinates that are in the range 0 <= px <= 100 and 0 <= py <= 100.

The example schema for events providing rectangles is:

create schema RectangleEvent(rx double, ry double, w double, h double)

This EPL outputs, upon arrival of a RectangleEvent, all points that fall inside the rectangle:

on RectangleEvent
select pointId from PointTable
where point(px, py).inside(rectangle(rx, ry, w, h))

Internally the runtime does not instantiate point or rectangle objects at all but instead optimizes the expression to comparison between double-type values.

20.3.4.1. Point-Region Quadtree Event Index Usage Notes

Point-Region quadtree indexes allow computed values for both index columns and index parameters. For example, the following EPL declares an index wherein (x, y)-coordinates are (px/100, py/100)-values. The sample EPL assumes that context.frame is a built-in property as provided by context FramedCtx:

context FramedCtx create index PointIndex on PointTable((Math.round(px/100), Math.round(py/100)) pointregionquadtree(context.frame.startx, context.frame.starty, context.frame.w, context.frame.h))

The compiler compares the index column expressions to the point-inside-rectangle left-hand-side expressions to determine which index to use. For example, if the expression is point(px+1, py+1).inside(rectangle(rx, ry, w, h)) as (px+1, py+1) does not match (Math.round(px/100), Math.round(py/100)) the query planner does not use the index. If the expression is point(Math.round(px/100), Math.round(py/100)).inside(rectangle(rx, ry, w, h)) the query planner does use the index as index column expressions match.

The query planner prefers point-region quadtree over other index types. Index hints are not yet available for query planning with quadtree indexes.

20.3.5. Declaring a MX-CIF Quadtree Index

Declaring a MX-CIF quadtree index is the same for both filter indexes and event indexes. MX-CIF quadtrees are suitable for efficiently finding rectangles that intersect with a rectangle, when there are many rectangles.

The synopsis to declare a MX-CIF quadtree index, as part of a statement, is:

mxcifquadtree(min_x_expression, min_y_expression, 
  width, height [, leaf_capacity_expression [, max_tree_height_expression]])

The min_x_expression, min_y_expression, width, height are index parameter expressions that return the range of the index. The width and height must be greater zero. The index range rectangle is represented by double-type values internally. A given rectangle must intersect with the index range.

Note

An attempt to insert rectangles into the index that do not intersect with the declared index range causes an exception.

The leaf_capacity_expression is optional and must return a positive integer. It defines the number of coordinates a node may contain before it gets split into regions. The default value is 4.

20.3.6. Using a MX-CIF Quadtree as a Filter Index

The section that summarizes filter indexes is Section 2.18.2, “Filter Indexes”. As there could be many rectangle(...).intersects(rectangle) filters active, having a filter index allows the runtime to efficiently match incoming events to statements.

For use of a MX-CIF quadtree index within filter criteria you must:

Define an expression that returns the MX-CIF quadtree configuration, making sure it specifies mxcifquadtree.
Add the filterindex named parameter providing the expression name.

For defining a local or global expression, please consult Section 5.2.9, “Expression Declaration”.

This sample statement defines the MX-CIF quadtree filter index to have a bounding box of (0,0,100,100):

expression myMXCIFQuadtreeSettings { mxcifquadtree(0, 0, 100, 100) } 
select * from RectangleEvent(rectangle(10, 20, 5, 5, filterindex:myMXCIFQuadtreeSettings).intersects(rectangle(x, y, width, height)))

The filterindex named parameter instructs the compiler that the settings for the MX-CIF quadtree filter index are provided by the expression myMXCIFQuadtreeSettings, a local expression in this example. For sharing MX-CIF quadtree settings across statements you may use a global expression instead. Please see Section 5.18, “Declaring Global Expressions, Aliases and Scripts: Create Expression”.

If your EPL does not specify filterindex the runtime does not build a MX-CIF quadtree filter index.

If your EPL specifies filterindex the runtime always builds and uses a MX-CIF quadtree filter index. In the case the compiler analyses filter criteria and determines that it cannot use the MX-CIF quadtree filter index, the compiler fails statement validation.

If your EPL specifies filterindex and the compiler determines that it cannot use the MX-CIF quadtree filter index it fails statement validation.

The runtime shares MX-CIF quadtree filter indexes across the runtime within the same event type given that:

Filters have the same rectangle expressions.
Filters use the same filterindex parameter i.e. the text myMXCIFQuadtreeSettings in above example.
Filters use the same MX-CIF quadtree index configuration i.e. mxcifquadtree(0,0,100,100) in above example.

For use with the filterindex named parameter, the following requirements apply towards left-hand side rectangle expressions:

Left-hand side rectangle expressions must be a constant, a context-provided built-in property or an event property provided by a previous pattern match within the same pattern.

For use with the filterindex named parameter, the following requirements apply towards right-hand side rectangle expressions:

Right-hand side rectangle expressions must be event properties.

20.3.7. Using a MX-CIF Quadtree as an Event Index

The section that summarizes event indexes is Section 2.18.3, “Event Indexes”. The create index clause is described in Section 6.9, “Explicitly Indexing Named Windows and Tables”.

Declare a MX-CIF quadtree event index as follows:

create index ... on ... (
  (x_expression, y_expression, width_expression, height_expression) mxcifquadtree(mxcif_quadtree_configuration)
)

The x_expression, y_expression, width_expression and height_expression expressions form the index columns. The expressions return the (x, y)-coordinates and rectangle size and must return numeric values. Coordinates and sizes are represented as double-type values internally. See above for the mxcif_quadtree_configuration MX-CIF quadtree configuration.

For example, assume a table that contains rectangles:

create table RectangleTable(rectangleId string primary key, rx double, ry double, rwidth double, rheight double)

This example EPL declares an index on the rectangles, with rx, ry, rwidth and rheight becoming index columns that determine the (x, y)-coordinates and the sizes:

create index RectangleIndex on RectangleTable((rx, ry, rwidth, rheight) mxcifquadtree(0, 0, 100, 100))

The above sample quadtree index expects rectangles to intersect the rectangle (0, 0, 100, 100).

The example schema for arriving events is:

create schema OtherRectangleEvent(otherX double, otherY double, otherWidth double, otherHeight double)

This EPL outputs, upon arrival of a OtherRectangleEvent, all rectangles stored in the table that intersect the arriving-events rectangle:

on OtherRectangleEvent
select rectangleId from RectangleTable
where rectangle(rx, ry, rwidth, rheight).intersects(rectangle(otherX, otherY, otherWidth, otherHeight))

Internally the runtime does not instantiate rectangle objects at all but instead optimizes the expression to comparison between double-type values.

20.3.7.1. MX-CIF Quadtree Event Index Usage Notes

MX-CIF quadtree indexes allow computed values for both index columns and index parameters. For example, the following EPL declares an index wherein (x, y)-coordinates are (px/100, py/100)-values. The sample EPL assumes that context.frame is a built-in property as provided by context FramedCtx:

context FramedCtx create index RectangleIndex on RectangleTable((Math.round(rx/100), Math.round(ry/100), Math.round(rwidth/100), Math.round(rheight/100)) mxcifquadtree(context.frame.startx, context.frame.starty, context.frame.w, context.frame.h))

The compiler compares the index column expressions to the rectangle-interwsects-rectangle left-hand-side expressions to determine which index to use.

The query planner prefers MX-CIF quadtree over other index types. Index hints are not yet available for query planning with quadtree indexes.

20.4. Spatial Types, Functions and Methods from External Libraries

The scope of the compiler and runtime does not include addressing all geographical, topological or spatial processing. We encourage using external libraries for library calls. EPL makes it easy to use and extend EPL, using functions, methods, data types and data structures provided by external libraries.

For example, assume you would like to use a geometric data type and the geographical distance function. Please consider using the Java Topology Suite (JTS) (https://www.locationtech.org) which provides a pretty complete set of geo computing functionality.

To pick an example data type, the compiler and runtime allow any class such as the JTS Geometry class (org.locationtech.jts.geom.Geometry) to become an event type, an event property type or a column type in a named window, table. The compiler and runtime also allow the use of such class anywhere within EPL expressions.

The EPL snippet below declares an event type that has a Geometry property:

create schema ShapeArrivalEvent(shapeId string, geometry org.locationtech.jts.geom.Geometry) // use imports to remove the need to have a package name

EPL can call methods and your application can declare its own functions. Registering an own EPL function is described in Section 22.2, “Single-Row Function”.

This sample EPL outputs events that have a distance of more than 100 comparing the current event's geometry to the last 1 minute of previous event's geometry:

select * from ShapeArrivalEvent as e1 unidirectional, ShapeArrivalEvent.time(1 minute) as e2
where e1.geometry.distance(e2.geometry) > 100

Chapter 21. EPL Reference: Data Flow

21.1. Introduction

Data flows in EPL have the following purposes:

Support for data flow programming and flow-based programming.
Declarative and runtime manageable integration of input and output adapters that may be provided by EsperIO or by an application.
Remove the need to use an event bus achieving dataflow-only visibility of events and event types for performance gains.

Data flow operators communicate via streams of either underlying event objects or wrapped events. Underlying event objects are POJO, Map, Object-array or DOM/XML. Wrapped events are represented by EventBean instances that associate type information to underlying event objects.

For more information on data flow programming or flow-based programming please consult the Wikipedia FBP Article.

EPL offers a number of useful built-in operators that can be combined in a graph to program a data flow. In addition EsperIO offers prebuilt operators that act as sources or sinks of events. An application can easily create and use its own data flow operators.

Using data flows an application can provide events to the data flow operators directly without using an runtime's event bus. Not using an event bus (as represented by the sendEventType methods of EPEventService) can achieve performance gains as the runtime does not need to match events to statements and the runtime does not need to wrap underlying event objects in EventBean instances.

Data flows also allow for finer-grained control over threading, synchronous and asynchronous operation.

21.2. Usage

21.2.1. Overview

Your application declares a data flow using create dataflow dataflow-name. Declaring the data flow causes the EPL compiler to validate the syntax and some aspects of the data flow graph of operators. Declaring the data flow does not actually instantiate or execute a data flow. Resolving event types and instantiating operators (as required) takes place at time of data flow instantiation.

After your application has declared a data flow, it can instantiate the data flow and execute it. A data flow can be instantiated as many times as needed and each data flow instance can only be executed once.

The example EPL below creates a data flow that, upon execution, outputs the text Hello World to console and then ends.

create dataflow HelloWorldDataFlow
  BeaconSource -> helloworld.stream { text: 'hello world' , iterations: 1}
  LogSink(helloworld.stream) {}

The sample data flow above declares a BeaconSource operator parameterized by the "hello world" text and 1 iteration. The -> keyword reads as produces streams. The BeaconSource operator produces a single stream named helloworld.stream. The LogSink operator receives this stream and prints it unformatted.

The next program code snippet declares the data flow to the runtime:

String epl = "create dataflow HelloWorldDataFlow\n" +
  "BeaconSource -> helloworldStream { text: 'hello world' , iterations: 1}\n" +
  "LogSink(helloworldStream) {}";

Configuration configuration = new Configuration();
CompilerArguments compilerArguments = new CompilerArguments(configuration);
EPCompiled compiled = EPCompilerProvider.getCompiler().compile(epl, compilerArguments);
EPDeployment deployment = runtime.getDeploymentService().deploy(compiled);

After declaring a data flow to a runtime, your application can then instantiate and execute the data flow.

The following program code snippet instantiates the data flow:

EPDataFlowInstance instance =
  runtime.getDataFlowService().instantiate(deployment.getDeploymentId(), "HelloWorldDataFlow");

A data flow instance is represented by an EPDataFlowInstance object.

The next code snippet executes the data flow instance:

instance.run();

By using the run method of EPDataFlowInstance the runtime executes the data flow using the same thread (blocking execute) and returns when the data flow completes. A data flow completes when all operators receive final markers.

The hello world data flow simply prints an unformatted Hello World string to console. Please check the built-in operator reference for BeaconSource and LogSink for more options.

21.2.2. Syntax

The synopsis for declaring a data flow is:

create dataflow name
	[schema_declarations]
	[operator_declarations]

After create dataflow follows the data flow name and a mixed list of event type (schema) declarations and operator declarations.

Schema declarations define an event type. Specify any number of create schema clauses as part of the data flow declaration followed by a comma character to end each schema declaration. The syntax for create schema is described in Section 5.15, “Declaring an Event Type: Create Schema”.

All event types that are defined as part of a data flow are private to the data flow and not available to other statements. To define event types that are available across data flows and other statements, use a create schema statement, runtime or static configuration.

Annotations as well as expression declarations and scripts can also be pre-pended to the data flow declaration.

21.2.2.1. Operator Declaration

For each operator, declare the operator name, input streams, output streams and operator parameters.

The syntax for declaring a data flow operator is:

operator_name [(input_streams)]  [-> output_streams] {
  [parameter_name : parameter_value_expr] [, ...]
}

The operator name is an identifier that identifies an operator.

If the operator accepts input streams then those may be listed in parenthesis after the operator name, see Section 21.2.2.2, “Declaring Input Streams”.

If the operator can produce output streams then specify -> followed by a list of output stream names and types. See Section 21.2.2.3, “Declaring Output Streams”.

Following the input and output stream declaration provide curly brackets ({}) containing operator parameters. See Section 21.2.2.4, “Declaring Operator Parameters”.

An operator that receives no input streams, produces no output streams and has no parameters assigned to it is shown in this EPL example data flow:

create dataflow MyDataFlow
  MyOperatorSimple {}

The next EPL shows a data flow that consists of an operator MyOperator that receives a single input stream myInStream and produces a single output stream myOutStream holding MyEvent events. The EPL configures the operator parameter myParameter with a value of 10:

create dataflow MyDataFlow
  create schema MyEvent as (id string, price double),
  MyOperator(myInStream) -> myOutStream<MyEvent> {
    myParameter : 10
  }

The next sections outline input stream, output stream and parameter assignment in greater detail.

21.2.2.2. Declaring Input Streams

In case the operator receives input streams, list the input stream names within parenthesis following the operator name. As part of the input stream declaration you may use the as keyword to assign an alias short name to one or multiple input streams.

The EPL shown next declares myInStream and assigns the alias mis:

create dataflow MyDataFlow
  MyOperator(myInStream as mis) {}

Multiple input streams can be listed separated by comma. We use the term input port to mean the ordinal number of the input stream in the order the input streams are listed.

The EPL as below declares two input streams and assigns an alias to each. The runtime assigns streamOne to input port 0 (zero) and streamTwo to port 1.

create dataflow MyDataFlow
  MyOperator(streamOne as one, streamTwo as two) {}

You may assign multiple input streams to the same port and alias by placing the stream names into parenthesis. All input streams for the same port must have the same event type associated.

The next statement declares an operator that receives input streams streamA and streamB both assigned to port 0 (zero) and alias streamsAB:

create dataflow MyDataFlow
  MyOperator( (streamA, streamB) as streamsAB) {}

Input and output stream names can have the dot-character in their name.

The following is also valid EPL:

create dataflow MyDataFlow
  MyOperator(my.in.stream) -> my.out.stream {}

Note

Reserved keywords may not appear in the stream name.

21.2.2.3. Declaring Output Streams

In case the operator produces output streams, list the output streams after the -> keyword. Multiple output streams can be listed separated by comma. We use the term output port to mean the ordinal number of the output stream in the order the output streams are listed.

The sample EPL below declares an operator that produces two output streams my.out.one and my.out.two.

create dataflow MyDataFlow
  MyOperator -> my.out.one, my.out.two {}

Each output stream can be assigned optional type information within less/greater-then (<>). Type information is required if the operator cannot deduce the output type from the input type and the operator does not declare explicit output type(s). The event type name can either be an event type defined within the same data flow or an event type defined in the runtime.

This EPL example declares an RFIDSchema event type based on an object-array event representation and associates the output stream rfid.stream with the RFIDSchema type. The stream rfid.stream therefore carries object-array (Object[]) typed objects according to schema RFIDSchema:

create dataflow MyDataFlow
  create objectarray schema RFIDSchema (tagId string, locX double, locY double),
  MyOperator -> rfid.stream<RFIDSchema> {}

The keyword eventbean is reserved: Use eventbean<type-name> to indicate that a stream carries EventBean instances of the given type instead of the underlying event object.

This EPL example declares an RFIDSchema event type based on an object-array event representation and associates the output stream rfid.stream with the event type, such that the stream rfid.stream carries EventBean objects:

create dataflow MyDataFlow
  create objectarray schema RFIDSchema (tagId string, locX double, locy double),
  MyOperator -> rfid.stream<eventbean<RFIDSchema>> {}

Use questionmark (?) to indicate that the type of events is not known in advance.

In the next EPL the stream my.stream carries EventBean instances of any type:

create dataflow MyDataFlow
  MyOperator -> my.stream<eventbean<?>> {}

21.2.2.4. Declaring Operator Parameters

Operators can receive constants, objects, EPL expressions and complete statements as parameters. All parameters are listed within curly brackets ({}) after input and output stream declarations. Curly brackets are required as a separator even if the operator has no parameters.

The syntax for parameters is:

name : value_expr [,...]

The parameter name is an identifier that is followed by the colon (:) or equals (=) character and a value expression. A value expression can be any expression, system property, JSON notation object or statement. Parameters are separated by comma character.

The next EPL demonstrates operator parameters that are scalar values:

create dataflow MyDataFlow
  MyOperator {
    stringParam : 'sample',
    secondString : "double-quotes are fine",
    intParam : 10
  }

Operator parameters can be any EPL expression including expressions that use variables. Subqueries, aggregations and the prev and prior functions cannot be applied here.

The EPL shown below lists operator parameters that are expressions:

create dataflow MyDataFlow
  MyOperator {
    intParam : 24*60*60,
    threshold : var_threshold	// a variable defined in the runtime
  }

To obtain the value of a system property, the special systemProperties property name is reserved for access to system properties.

The following EPL sets operator parameters to a value obtained from a system property:

create dataflow MyDataFlow
  MyOperator {
    someSystemProperty : systemProperties('mySystemProperty') 
  }

Any JSON value can also be used as a value. Use square brackets [] for JSON arrays. Use curly brackets {} to hold nested Map or other object values. Provide the special class property to instantiate a given instance by class name. The runtime populates the respective array, Map or Object as specified in the JSON parameter value.

The below EPL demonstrates operator parameters that are JSON values:

create dataflow MyDataFlow
  MyOperator {
    myStringArray: ['a', "b"],
    myMapOrObject: {
      a : 10,
      b : 'xyz',
    },
    myInstance: {
      class: 'com.myorg.myapp.MyImplementation',
      myValue : 'sample'
    }
  }

The special parameter name select is reserved for use with EPL select statements. Please see the Select built-in operator for an example.

21.3. Built-In Operators

The below table summarizes the built-in data flow operators available:

Table 21.1. Built-in Operators

Operator	Description
BeaconSource	Utility source that generates events. See Section 21.3.1, “BeaconSource”.
Emitter	Special operator for injecting events into a stream. See Section 21.4.5, “Start Captive”.
EPStatementSource	One or more statements act as event sources. See Section 21.3.2, “EPStatementSource”.
EventBusSink	The event bus is the sink: Sends events from the data flow into the event bus. See Section 21.3.3, “EventBusSink”.
EventBusSource	The event bus is the source: Receives events from the event bus into the data flow. See Section 21.3.4, “EventBusSource”.
Filter	Filters an input stream and produces an output stream containing the events passing the filter criteria. See Section 21.3.5, “Filter”.
LogSink	Utility sink that outputs events to console or log. See Section 21.3.6, “LogSink”.
Select	An EPL select statement that executes on the input stream events. See Section 21.3.7, “Select”.

The below table summarizes the built-in EsperIO data flow operators. Please see the EsperIO documentation and source for more information.

Table 21.2. EsperIO Built-in Operators

Operator	Description
AMQPSource	Attaches to AMQP broker to receive messages to process.
AMQPSink	Attaches to AMQP broker to send messages.
FileSource	Reads one or more files and produces events from file data.
FileSink	Write one or more files from events received.

21.3.1. BeaconSource

The BeaconSource operator generates events and populates event properties.

The BeaconSource operator does not accept any input streams and has no input ports.

The BeaconSource operator must have a single output stream. When the BeaconSource operator completed generating events according to the number of iterations provided or when it is cancelled it outputs a final marker to the output stream.

Parameters for the BeaconSource operator are all optional parameters:

Table 21.3. BeaconSource Parameters

Name	Description
initialDelay	Specifies the number of seconds delay before producing events.
interval	Time interval between events. Takes a integer or double-typed value for the number of seconds. The interval is zero when not provided.
iterations	Number of events produced. Takes an integer value. When not provided the operator produces tuples until the data flow instance gets cancelled.

Event properties to be populated can simply be added to the parameters.

If your declaration provides an event type for the output stream then BeaconSource will populate event properties of the underlying events. If no event type is specified, BeaconSource creates an anonymous object-array event type to carry the event properties that are generated and associates this type with its output stream.

Examples are:

create dataflow MyDataFlow
  create schema SampleSchema(tagId string, locX double),	// sample type			
			
  // BeaconSource that produces empty object-array events without delay 
  // or interval until cancelled.
  BeaconSource -> stream.one {}
  
  // BeaconSource that produces one RFIDSchema event populating event properties
  // from a user-defined function "generateTagId" and the provided values.
  BeaconSource -> stream.two<SampleSchema> {
    iterations : 1,
    tagId : generateTagId(),
    locX : 10
  }
  
  // BeaconSource that produces 10 object-array events populating
  // the price property with a random value.
  BeaconSource -> stream.three {
    iterations : 10,
    interval : 10, // every 10 seconds
    initialDelay : 5, // start after 5 seconds
    price : Math.random() * 100
  }

21.3.2. EPStatementSource

The EPStatementSource operator maintains a subscription to the results of one or more statements. The operator produces the statement output events.

The EPStatementSource operator does not accept any input streams and has no input ports.

The EPStatementSource operator must have a single output stream. It does not generate a final or other marker.

Either the statement name or the statement filter parameter is required:

Table 21.4. EPStatementSource Parameters

Name	Description
collector	Optional parameter, used to transform statement output events to submitted events.
statementName	Name of the statement that produces events. The statement does not need to exist at the time of data flow instantiation.
statementFilter	Implementation of the `EPDataFlowEPStatementFilter` that returns true for each statement that produces events. Statements do not need to exist at the time of data flow instantiation.

If a statement name is provided, the operator subscribes to output events of the statement if the statement exists or when it gets created at a later point in time.

If a statement filter is provided instead, the operator subscribes to output events of all statements that currently exist and pass the filter pass method or that get created at a later point in time and pass the filter pass method.

The collector can be specified to transform output events. If no collector is specified the operator submits the underlying events of the insert stream received from the statement. The collector object must implement the interface EPDataFlowIRStreamCollector.

Examples are:

create dataflow MyDataFlow
  create schema SampleSchema(tagId string, locX double),	// sample type			
			
  // Consider only the statement named MySelectStatement when it exists.
  // No transformation.
  EPStatementSource -> stream.one<eventbean<?>> {
    statementName : 'MySelectStatement'
  }
  
  // Consider all statements that match the filter object provided.
  // No transformation.
  EPStatementSource -> stream.two<eventbean<?>> {
    statementFilter : {
      class : 'com.mycompany.filters.MyStatementFilter'
    }
  }
  
  // Consider all statements that match the filter object provided.
  // With collector that performs transformation.
  EPStatementSource -> stream.two<SampleSchema> {
    collector : {
      class : 'com.mycompany.filters.MyCollector'
    },
    statementFilter : {
      class : 'com.mycompany.filters.MyStatementFilter'
    }
  }

21.3.3. EventBusSink

The EventBusSink operator send events received from a data flow into the event bus. Any statement that looks for any of the events gets triggered, equivalent to the sendEventType methods on EPEventService or the insert into clause.

The EventBusSink operator accepts any number of input streams. The operator forwards all events arriving on any input ports to the event bus, equivalent to the sendEventType methods on EPEventService.

The EventBusSink operator cannot declare any output streams.

Parameters for the EventBusSink operator are all optional parameters:

Table 21.5. EventBusSink Parameters

Name	Description
collector	Optional parameter, used to transform data flow events to event bus events.

The collector can be specified to transform data flow events to event bus events. If no collector is specified the operator submits the events directly to the event bus. The collector object must implement the interface EPDataFlowEventCollector.

Examples are:

create dataflow MyDataFlow
  BeaconSource -> instream<SampleSchema> {}  // produces a sample stream
  
  // Send SampleSchema events produced by beacon to the event bus.
  EventBusSink(instream) {}
  
  // Send SampleSchema events produced by beacon to the event bus.
  // With collector that performs transformation.
  EventBusSink(instream) {
    collector : {
      class : 'com.mycompany.filters.MyCollector'
    }
  }

21.3.4. EventBusSource

The EventBusSource operator receives events from the event bus and produces an output stream of the events received. With the term event bus we mean any event visible to the runtime either because the application send the event via any of the sendEventType methods on EPEventService or because statements populated streams as a result of insert into.

The EventBusSource operator does not accept any input streams and has no input ports.

The EventBusSource operator must have a single output stream. It does not generate a final or other marker. The event type declared for the output stream is the event type of events received from the event bus.

All parameters to EventBusSource are optional:

Table 21.6. EventBusSource Parameters

Name	Description
collector	Optional parameter and used to transform event bus events to submitted events.
filter	Filter expression for event bus matching.

The collector can be specified to transform output events. If no collector is specified the operator submits the underlying events of the stream received from the event bus. The collector object must implement the interface EPDataFlowEventBeanCollector.

The filter is an expression that the event bus compiles and efficiently matches even in the presence of a large number of event bus sources. The filter expression must return a boolean-typed value, returning true for those events that the event bus passes to the operator.

Examples are:

create dataflow MyDataFlow

  // Receive all SampleSchema events from the event bus.
  // No transformation.
  EventBusSource -> stream.one<SampleSchema> {}
  
  // Receive all SampleSchema events with tag id '001' from the event bus.
  // No transformation.
  EventBusSource -> stream.one<SampleSchema> {
    filter : tagId = '001'
  }

  // Receive all SampleSchema events from the event bus.
  // With collector that performs transformation.
  EventBusSource -> stream.two<SampleSchema> {
    collector : {
      class : 'com.mycompany.filters.MyCollector'
    },
  }

21.3.5. Filter

The Filter operator filters an input stream and produces an output stream containing the events passing the filter criteria. If a second output stream is provided, the operator sends events not passing filter criteria to that output stream.

The Filter operator accepts a single input stream.

The Filter operator requires one or two output streams. The event type of the input and output stream(s) must be the same. The first output stream receives the matching events according to the filter expression. If declaring two output streams, the second stream receives non-matching events.

The Filter operator has a single required parameter:

Table 21.7. Filter Parameters

Name	Description
filter	The filter criteria expression.

Examples are:

create dataflow MyDataFlow
  create schema SampleSchema(tagId string, locX double),	// sample type
  BeaconSource -> samplestream<SampleSchema> {}  // sample source
  
  // Filter all events that have a tag id of '001'
  Filter(samplestream) -> tags_001 {
    filter : tagId = '001' 
  }
  
  // Filter all events that have a tag id of '001', 
  // putting all other events into the second stream
  Filter(samplestream) -> tags_001, tags_other {
    filter : tagId = '001' 
  }

21.3.6. LogSink

The LogSink operator outputs events to console or log file in either a JSON, XML or built-in format (the default).

The LogSink operator accepts any number of input streams. All events arriving on any input ports are logged.

The LogSink operator cannot declare any output streams.

Parameters for the LogSink operator are all optional parameters:

Table 21.8. LogSink Parameters

Name	Description
format	Specify format as a string value: `json` for JSON-formatted output, `xml` for XML-formatted output and `summary` (default) for a built-in format.
layout	Pattern string according to which output is formatted. Place `%df` for data flow name, `%p` for port number, `%i` for data flow instance id, `%t` for title, `%e` for event data.
log	Boolean true (default) for log output, false for console output.
linefeed	Boolean true (default) for line feed, false for no line feed.
title	String title text pre-pended to output.

Examples are:

create dataflow MyDataFlow
  BeaconSource -> instream {}  // produces sample stream to use below
  
  // Output textual event to log using defaults.
  LogSink(instream) {}
  
  // Output JSON-formatted to console.
  LogSink(instream) {
    format : 'json',
    layout : '%t [%e]',
    log : false,
    linefeed : true,
    title : 'My Custom Title:'
  }

21.3.7. Select

The Select operator is configured with an EPL select statement. It applies events from input streams to the select statement and outputs results either continuously or when the final marker arrives.

The Select operator accepts one or more input streams.

The Select operator requires a single output stream.

The Select operator requires the select parameter, all other parameters are optional:

Table 21.9. Select Operator Parameters

Name	Description
iterate	Boolean indicator whether results should be output continuously or only upon arrival of the final marker.
select	EPL `select` statement in parenthesis.

Set the optional iterate flag to false (the default) to have the operator output results continuously. Set the iterate flag to true to indicate that the operator outputs results only when the final marker arrives. If iterate is true then output rate limiting clauses are not supported.

The select parameter is required and provides an EPL select statement within parenthesis. For each input port the statement should list the input stream name or the alias name in the from clause. Only filter-based streams are allowed in the from clause and patterns or named windows are not supported. Also not allowed are the insert into clause, the irstream keyword and subselects.

The Select operator determines the event type of output events based on the select clause. It is not necessary to declare an event type for the output stream.

Examples are:

create dataflow MyDataFlow
  create schema SampleSchema(tagId string, locX double),	// sample type			
  BeaconSource -> instream<SampleSchema> {}  // sample stream
  BeaconSource -> secondstream<SampleSchema> {}  // sample stream
  
  // Simple continuous count of events
  Select(instream) -> outstream {
    select: (select count(*) from instream)
  }
  
  // Demonstrate use of alias
  Select(instream as myalias) -> outstream {
    select: (select count(*) from myalias)
  }
  
  // Output only when the final marker arrives
  Select(instream as myalias) -> outstream {
    select: (select count(*) from myalias),
    iterate: true
  }

  // Same input port for the two sample streams
  Select( (instream, secondstream) as myalias) -> outstream {
    select: (select count(*) from myalias)
  }

  // A join with multiple input streams,
  // joining the last event per stream forming pairs
  Select(instream, secondstream) -> outstream {
    select: (select a.tagId, b.tagId 
        from instream#lastevent as a, secondstream#lastevent as b)
  }
  
  // A join with multiple input streams and using aliases.
  Select(instream as S1, secondstream as S2) -> outstream {
    select: (select a.tagId, b.tagId 
        from S1#lastevent as a, S2#lastevent as b)
  }

21.4. API

This section outlines the steps to declare, instantiate, execute and cancel or complete data flows.

21.4.1. Declaring a Data Flow

Compile data flow the same as any other statement and deploy the compiled module. The EPStatementObjectModel statement object model can also be used to compile a data flow.

Annotations that are listed at the top of the EPL text are applied to all statements and operators in the data flow. Annotations listed for a specific operator apply to that operator only.

The next program code snippet declares a data flow to the runtime:

String epl = "@Name('MyStatementName') create dataflow HelloWorldDataFlow\n" +
  "BeaconSource -> helloworldStream { text: 'hello world' , iterations: 1}\n" +
  "LogSink(helloworldStream) {}";

Configuration configuration = new Configuration();
CompilerArguments compilerArguments = new CompilerArguments(configuration);
EPCompiled compiled = EPCompilerProvider.getCompiler().compile(epl, compilerArguments);
EPDeployment deployment = runtime.getDeploymentService().deploy(compiled);

The statement name that can be assigned to the statement is used only for statement management. Your application may undeploy the statement declaring the data flow thereby making the data flow unavailable for instantiation. Existing instances of the data flow are not affected by an undeploy of the statement that declares the data flow.

Listeners or the subscriber to the statement declaring a data flow receive no events or other output. The statement declaring a data flow returns no rows when iterated.

21.4.2. Instantiating a Data Flow

The com.espertech.esper.common.client.dataflow.core.EPDataFlowService available via getDataFlowService on EPRuntime manages declared data flows.

Use the instantiate method on EPDataFlowRuntime to instantiate a data flow after it has been declared. Pass the data flow name and optional instantiation options to the method. A data flow can be instantiated any number of times.

A data flow instance is represented by an instance of EPDataFlowInstance. Each instance has a state as well as methods to start, run, join and cancel as well as methods to obtain execution statistics.

Various optional arguments including operator parameters can be passed to instantiate via the EPDataFlowInstantiationOptions object as explained in more detail below.

The following code snippet instantiates the data flow:

EPDataFlowInstance instance =
  runtime.getDataFlowService().instantiate(deployment.getDeploymentId(), "HelloWorldDataFlow");

The runtime does not track or otherwise retain data flow instances in memory. It is up to your application to retain data flow instances as needed.

Each data flow instance associates to a state. The start state is EPDataFlowState.INSTANTIATED. The end state is either COMPLETED or CANCELLED.

The following table outlines all states:

Table 21.10. Data Flow Instance States

State	Description
INSTANTIATED	Start state, applies when a data flow instance has been instantiated and has not executed.
RUNNING	A data flow instance transitions from instantiated to running when any of the `start`, `run` or `startCaptive` methods are invoked.
COMPLETED	A data flow instance transitions from running to completed when all final markers have been processed by all operators.
CANCELLED	A data flow instance transitions from running to cancelled when your application invokes the `cancel` method on the data flow instance.

21.4.3. Executing a Data Flow

After your application instantiated a data flow instance it can execute the data flow instance using either the start, run or startCaptive methods.

Use the start method to have the runtime allocate a thread for each source operator. Execution is non-blocking. Use the join method to have one or more threads join a data flow instance execution.

Use the run method to have the runtime use the current thread to execute the single source operator. Multiple source operators are not allowed when using run.

Use the startCaptive method to have the runtime return all Runnable instances and emitters, for the purpose of having complete control over execution. The runtime allocates no threads and does not perform any logic for the data flow unless your application employs the Runnable instances and emitters returned by the method.

The next code snippet executes the data flow instance as a blocking call:

instance.run();

By using the run method of EPDataFlowInstance the runtime executes the data flow instance using the same thread (blocking execute) and returns when the data flow instance completes. A data flow instance completes when all operators receive final markers.

The hello world data flow simply prints an unformatted Hello World string to console. The BeaconSource operator generates a final marker when it finishes the 1 iteration. The data flow instance thus transitions to complete after the LogSink operator receives the final marker, and the thread invoking the run method returns.

The next code snippet executes the data flow instance as a non-blocking call:

instance.start();

Use the cancel method to cancel execution of a running data flow instance:

instance.cancel();

Use the join method to join execution of a running data flow instance, causing the joining thread to block until the data flow instance either completes or is cancelled:

instance.join();

21.4.4. Instantiation Options

The EPDataFlowInstantiationOptions object that can be passed to the instantiate method may be used to customize the operator graph, operator parameters and execution of the data flow instance.

Passing runtime parameters to data flow operators is easiest using the addParameterURI method. The first parameter is the data flow operator name and the operator parameter name separated by the slash character. The second parameter is the value object.

For example, in order to pass the file name to the FileSource operator at runtime, use the following code:

EPDataFlowInstantiationOptions options = new EPDataFlowInstantiationOptions();
options.addParameterURI("FileSource/file", filename);
EPDataFlowInstance instance = runtime.getDataFlowService().instantiate(deployment.getDeploymentId(), "MyFileReaderDataFlow",options);
instance.run();

The optional operatorProvider member takes an implementation of the EPDataFlowOperatorProvider interface. The runtime invokes this provider to obtain operator instances.

The optional parameterProvider member takes an implementation of the EPDataFlowOperatorParameterProvider interface. The runtime invokes this provider to obtain operator parameter values. The values override the values provided via parameter URI above.

The optional exceptionHandler member takes an implementation of the EPDataFlowExceptionHandler interface. The runtime invokes this provider to when exceptions occur.

The optional dataFlowInstanceId can be assigned any string value for the purpose of identifying the data flow instance.

The optional dataFlowInstanceUserObject can be assigned any object value for the purpose of associating a user object to the data flow instance.

Set the operatorStatistics flag to true to obtain statistics for operator execution.

Set the cpuStatistics flag to true to obtain CPU statistics for operator execution.

21.4.5. Start Captive

Use the startCaptive method on a EPDataFlowInstance data flow instance when your application requires full control over threading. This method returns an EPDataFlowInstanceCaptive instance that contains a list of java.lang.Runnable instances that represent each source operator.

The special Emitter operator can occur in a data flow. This emitter can be used to inject events into the data flow without writing a new operator. Emitter takes a single name parameter that provides the name of the emitter and that is returned in a map of emitters by EPDataFlowInstanceCaptive.

The example EPL below creates a data flow that uses emitter.

create dataflow HelloWorldDataFlow
  create objectarray schema SampleSchema(text string),	// sample type		
	
  Emitter -> helloworld.stream<SampleSchema> { name: 'myemitter' }
  LogSink(helloworld.stream) {}

Your application may obtain the Emitter instance and sends events directly into the output stream. This feature is only supported in relationship with startCaptive since the runtime does not allocate any threads or run source operators.

The example code snippet below obtains the emitter instance and send events directly into the data flow instance:

EPDataFlowInstance instance =
      runtime.getDataFlowService().instantiate(deployment.getDeploymentId(), "HelloWorldDataFlow", options);
EPDataFlowInstanceCaptive captiveStart = instance.startCaptive();
Emitter emitter = captiveStart.getEmitters().get("myemitter");
emitter.submit(new Object[] {"this is some text"});

When emitting DOM XML events please emit the root element obtained from document.getDocumentElement().

21.4.6. Data Flow Punctuation With Markers

When your application executes a data flow instance by means of the start (non-blocking) or run (blocking) methods, the data flow instance stays running until either completed or cancelled. While cancellation is always via the cancel method, completion occurs when all source operators provide final markers.

The final marker is an object that implements the EPDataFlowSignalFinalMarker interface. Some operators may also provide or process data window markers which implement the EPDataFlowSignalWindowMarker interface. All such signals implement the EPDataFlowSignal interface.

Some source operators such as EventBusSource and EPStatementSource do not generate final markers as they act continuously.

21.4.7. Exception Handling

All exceptions during the execution of a data flow are logged and reported to the EPDataFlowExceptionHandler instance if one was provided.

If no exception handler is provided or the provided exception handler re-throws or generates a new runtime exception, the source operator handles the exception and completes (ends). When all source operators complete then the data flow instance transitions to complete.

21.5. Examples

The following example is a rolling top words count implemented as a data flow, over a 30 second time window and providing the top 3 words every 2 seconds:

create dataflow RollingTopWords
  create objectarray schema WordEvent (word string),
  
  Emitter -> wordstream<WordEvent> {name:'a'} {} // Produces word stream
  
  Select(wordstream) -> wordcount { // Sliding time window count per word
    select: (select word, count(*) as wordcount 
          from wordstream#time(30) group by word)
  }

  Select(wordcount) -> wordranks { // Rank of words
    select: (select window(*) as rankedWords 
          from wordcount#sort(3, wordcount desc) 
          output snapshot every 2 seconds)
  }
  
  LogSink(wordranks) {}

The next example implements a bargain index computation that separates a mixed trade and quote event stream into a trade and a quote stream, computes a vwap and joins the two streams to compute an index:

create dataflow VWAPSample
  create objectarray schema TradeQuoteType as (type string, ticker string, price double, volume long, askprice double, asksize long),
  
  MyObjectArrayGraphSource -> TradeQuoteStream<TradeQuoteType> {}
  
  Filter(TradeQuoteStream) -> TradeStream {
    filter: type = "trade"
  }
  
  Filter(TradeQuoteStream) -> QuoteStream {
    filter: type = "quote"
  }
  
  Select(TradeStream) -> VwapTrades {
    select: (select ticker, sum(price * volume) / sum(volume) as vwap, 
          min(price) as minprice
          from TradeStream#groupwin(ticker)#length(4) group by ticker)
  }
  
  Select(VwapTrades as T, QuoteStream as Q) -> BargainIndex {
    select: 
      (select case when vwap > askprice then asksize * (Math.exp(vwap - askprice)) else 0.0d end as index
      from T#unique(ticker) as t, Q#lastevent as q
      where t.ticker = q.ticker)
  }
  
  LogSink(BargainIndex) {}

The final example is a word count data flow, in which three custom operators tokenize, word count and aggregate. The custom operators in this example are discussed next.

create dataflow WordCount
  MyLineFeedSource -> LineOfTextStream {}
  MyTokenizerCounter(LineOfTextStream) -> SingleLineCountStream {}
  MyWordCountAggregator(SingleLineCountStream) -> WordCountStream {}
  LogSink(WordCountStream) {}

21.6. Operator Implementation

Note

Implementing an operator requires the use of extension and internal APIs that are not considered stable and may change between versions.

This section discusses how to implement classes that serve as operators in a data flow. The section employs the example data flow as shown earlier.

This example data flow has operators MyLineFeedSource, MyTokenizerCounter and MyWordCountAggregator that are application provided operators:

create dataflow WordCount
  MyLineFeedSource -> LineOfTextStream {}
  MyTokenizerCounter(LineOfTextStream) -> SingleLineCountStream {}
  MyWordCountAggregator(SingleLineCountStream) -> WordCountStream {}
  LogSink(WordCountStream) {}

Each operator requires implementing the following interfaces:

Implement the DataFlowOperatorForge interface for the compiler to use.
Implement the DataFlowOperatorFactory interface for the runtime to instantiate operator instances.
Implement either the DataFlowOperator interface, the DataFlowOperatorLifecycle or the DataFlowSourceOperator interface.

The compiler must be able to find the class implementing DataFlowOperatorForge. Add the forge package or forge class to imports:

// Sample code adds 'package.*' to simply import the package.
Configuration configuration = new Configuration();
configuration.getCommon().addImport(MyLineFeedSourceForge.class.getName());

21.6.1. Sample Operator Acting as Source

Every operator has a forge class that implements the DataFlowOperatorForge interface and is only used at compile-time. The compiler provides the operator parameter expressions to the forge instance and invokes the initializeForge method. When it is time to compile the compiler generates code by invoking the make method.

// The OutputTypes annotation can be used to specify the type of events
// that are output by the operator.
// If provided, it is not necessary to declare output types in the data flow.
// The event representation is object-array.
@OutputTypes(value = {
        @OutputType(name = "line", typeName = "String")
})

// Provide the DataFlowOpProvideSignal annotation to indicate that
// the source operator provides a final marker.
@DataFlowOpProvideSignal
public class MyLineFeedSourceForge implements DataFlowOperatorForge {

    public DataFlowOpForgeInitializeResult initializeForge(DataFlowOpForgeInitializeContext context) throws ExprValidationException {
        return null;
    }

    public CodegenExpression make(CodegenMethodScope parent, SAIFFInitializeSymbol symbols, CodegenClassScope classScope) {
        return newInstance(MyLineFeedSourceFactory.class);
    }
}

The operator factory class must implement the DataFlowOperatorFactory interface. At deployment time the operator factory initializes using the code generated in the forge make method. Upon instantiating a data flow the factory must return an operator instance.

The implementation for the sample MyLineFeedSourceFactory is:

public class MyLineFeedSourceFactory implements DataFlowOperatorFactory {

    public void initializeFactory(DataFlowOpFactoryInitializeContext context) {
    }

    public DataFlowOperator operator(DataFlowOpInitializeContext context) {
        return new MyLineFeedSource(Collections.emptyIterator());
    }
}

The operator implementation for the sample MyLineFeedSource is:

public class MyLineFeedSource implements DataFlowSourceOperator {

    @DataFlowContext
    private EPDataFlowEmitter dataFlowEmitter;

    private final Iterator<String> lines;

    public MyLineFeedSource(Iterator<String> lines) {
        this.lines = lines;
    }

    public void open(DataFlowOpOpenContext openContext) {
    }

    public void next() {
        if (lines.hasNext()) {
            dataFlowEmitter.submit(new Object[]{lines.next()});
        } else {
            dataFlowEmitter.submitSignal(new EPDataFlowSignalFinalMarker() {
            });
        }
    }

    public void close(DataFlowOpCloseContext openContext) {
    }
}

21.6.2. Sample Tokenizer Operator

The implementation for the sample MyTokenizerCounter is a forge, factory and operator in one class:

@OutputTypes({
        @OutputType(name = "line", type = int.class),
        @OutputType(name = "wordCount", type = int.class),
        @OutputType(name = "charCount", type = int.class)
})
public class MyTokenizerCounter implements DataFlowOperatorForge, DataFlowOperatorFactory, DataFlowOperator {
    private static final Logger log = LoggerFactory.getLogger(MyTokenizerCounter.class);

    @DataFlowContext
    private EPDataFlowEmitter graphContext;

    public DataFlowOpForgeInitializeResult initializeForge(DataFlowOpForgeInitializeContext context) throws ExprValidationException {
        return null;
    }

    public CodegenExpression make(CodegenMethodScope parent, SAIFFInitializeSymbol symbols, CodegenClassScope classScope) {
        return newInstance(MyTokenizerCounter.class);
    }

    public void initializeFactory(DataFlowOpFactoryInitializeContext context) {
    }

    public DataFlowOperator operator(DataFlowOpInitializeContext context) {
        return new MyTokenizerCounter();
    }

    public void onInput(String line) {
        StringTokenizer tokenizer = new StringTokenizer(line, " \t");
        int wordCount = tokenizer.countTokens();
        int charCount = 0;
        while (tokenizer.hasMoreTokens()) {
            String token = tokenizer.nextToken();
            charCount += token.length();
        }
        log.debug("Submitting stat words[" + wordCount + "] chars[" + charCount + "] for line '" + line + "'");
        graphContext.submit(new Object[]{1, wordCount, charCount});
    }
}

21.6.3. Sample Aggregator Operator

The implementation for the sample MyWordCountAggregator with comments is:

@OutputTypes(value = {
        @OutputType(name = "stats", type = MyWordCountStats.class)
})
public class MyWordCountAggregator implements DataFlowOperatorForge, DataFlowOperatorFactory, DataFlowOperator {
    private static final Logger log = LoggerFactory.getLogger(MyWordCountAggregator.class);

    @DataFlowContext
    private EPDataFlowEmitter graphContext;

    private final MyWordCountStats aggregate = new MyWordCountStats();

    public DataFlowOpForgeInitializeResult initializeForge(DataFlowOpForgeInitializeContext context) throws ExprValidationException {
        return null;
    }

    public CodegenExpression make(CodegenMethodScope parent, SAIFFInitializeSymbol symbols, CodegenClassScope classScope) {
        return newInstance(MyWordCountAggregator.class);
    }

    public void initializeFactory(DataFlowOpFactoryInitializeContext context) {
    }

    public DataFlowOperator operator(DataFlowOpInitializeContext context) {
        return new MyWordCountAggregator();
    }

    public void onInput(int lines, int words, int chars) {
        aggregate.add(lines, words, chars);
        log.debug("Aggregated: " + aggregate);
    }

    public void onSignal(EPDataFlowSignal signal) {
        log.debug("Received punctuation, submitting totals: " + aggregate);
        graphContext.submit(aggregate);
    }
}

21.6.4. Passing Operator Parameters

The forge instance receives parameters expressions. A forge can declare parameters like so:

// Expose a parameter named "file" that takes any expression as parameter
@DataFlowOpParameter
private ExprNode file;

// Expose a parameter named "adapterInputSource" that will be an instance of some interface
// Interface implementations as parameters are declare a Map<String, Object>
@DataFlowOpParameter
private Map<String, Object> adapterInputSource;

// Expose a paramerer named "propertyNames" that is an array of string constants
@DataFlowOpParameter
private String[] propertyNames;

The forge class can obtain the output event type if needed. It should also validate the expression parameters and throw ExprValidationException if the parameter expression does not return the expected type. The utility class DataFlowParameterValidation has validate utility methods that return a validated expression: For example:

public DataFlowOpForgeInitializeResult initializeForge(DataFlowOpForgeInitializeContext context) throws ExprValidationException {
  // Obtain the declared output event type
  outputEventType = context.getOutputPorts().get(0).getOptionalDeclaredType() != null ? context.getOutputPorts().get(0).getOptionalDeclaredType().getEventType() : null;
  if (outputEventType == null) {
    throw new ExprValidationException("No event type provided for output, please provide an event type name");
  }

  // validate the "file" parameter expression expected to return a String-typed value
  file = DataFlowParameterValidation.validate("file", file, String.class, context);
  return null;
}

The forge class passes parameters to the factory. We use SAIFFInitializeBuilder that is a builder utility for building the factory. For example:

public CodegenExpression make(CodegenMethodScope parent, SAIFFInitializeSymbol symbols, CodegenClassScope classScope) {
  return new SAIFFInitializeBuilder(FileSourceFactory.class, this.getClass(), "factory", parent, symbols, classScope)
    .exprnode("file", file)
    .constant("propertyNames", propertyNames)
    .map("adapterInputSource", adapterInputSource)
    .build();
}

The factory class must have setter-methods of the same name that receive the parameters:

private ExprEvaluator file;
private String[] propertyNames;
private Map<String, Object> adapterInputSource;

    public void setFile(ExprEvaluator file) {
        this.file = file;
    }
    
    public void setPropertyNames(String[] propertyNames) {
        this.propertyNames = propertyNames;
    }

    public void setAdapterInputSource(Map<String, Object> adapterInputSource) {
        this.adapterInputSource = adapterInputSource;
    }

The factory class can resolve parameter values by evaluating expressions and by determining whether parameters were passed as options. The DataFlowParameterResolution class provides convenience methods. For example:

public DataFlowOperator operator(DataFlowOpInitializeContext context) {
  String fileName = DataFlowParameterResolution.resolveWithDefault("file", file, null, String.class, context);
  AdapterInputSource adapterInputSourceInstance = DataFlowParameterResolution.resolveOptionalInstance("adapterInputSource", adapterInputSource, AdapterInputSource.class, context);
  return new MyOperator(fileName, adapterInputSourceInstance);
}

Chapter 22. Integration and Extension

22.1. Overview

22.2. Single-Row Function

22.2.1. Using an Inlined Class to Provide a Single-Row Function
22.2.2. Using an Application Class to Provide a Single-Row Function
22.2.3. Value Cache
22.2.4. Single-Row Functions in Filter Predicate Expressions
22.2.5. Single-Row Functions Taking Events as Parameters
22.2.6. Single-Row Functions Returning Events
22.2.7. Receiving a Context Object
22.2.8. Exception Handling

22.3. Virtual Data Window

22.3.1. How to Use
22.3.2. Implementing the Forge
22.3.3. Implementing the Factory-Factory
22.3.4. Implementing the Factory
22.3.5. Implementing the Virtual Data Window

22.4. Data Window View and Derived-Value View

22.4.1. Implementing a View Forge
22.4.2. Implementing a View Factory
22.4.3. Implementing a View
22.4.4. View Contract
22.4.5. Configuring View Namespace and Name
22.4.6. Requirement for Data Window Views
22.4.7. Requirement for Derived-Value Views

22.5. Aggregation Function

22.5.1. Aggregation Single-Function Development
22.5.2. Aggregation Multi-Function Development

22.6. Pattern Guard

22.6.1. Implementing a Guard Forge
22.6.2. Implementing a Guard Factory
22.6.3. Implementing a Guard Class
22.6.4. Configuring Guard Namespace and Name

22.7. Pattern Observer

22.7.1. Implementing an Observer Forge
22.7.2. Implementing an Observer Factory
22.7.3. Implementing an Observer Class
22.7.4. Configuring Observer Namespace and Name

22.8. Date-Time Method

22.8.1. Implement the DateTimeMethodForgeFactory Interface
22.8.2. Implement the Static Methods
22.8.3. Add the Date-Time Method Extension to the Compiler Configuration
22.8.4. Use the new Date-Time Methods

22.9. Enumeration Method

22.9.1. Implement the EnumMethodForgeFactory Interface
22.9.2. Implement the EnumMethodState Interface
22.9.3. Implement the Static Method for Processing
22.9.4. Add the Enumeration Method Extension to the Compiler Configuration
22.9.5. Use the new Enumeration Method
22.9.6. Further Information to Lambda Parameters

22.1. Overview

This chapter summarizes integration and describes in detail each of the extension APIs that allow integrating external data and/or extend runtime functionality.

For information on calling external services via instance method invocation, for instance to integrate with dependency injection frameworks such as Spring or Guice, please see Section 5.17.5, “Class and Event-Type Variables”.

For information on input and output adapters that connect to an event transport and perform event transformation for incoming and outgoing on-the-wire event data, for use with streaming data, please see the EsperIO reference documentation. The data flow instances as described in Chapter 21, EPL Reference: Data Flow are an easy way to plug in operators that perform input and output. Data flows allow providing parameters and managing individual flows independent of runtime lifecycle. Also consider using the Plug-in Loader API for creating a new adapter that starts or stops as part of the CEP runtime initialization and destroy lifecycle, see Section 16.15, “Plug-In Loader”.

To join data that resides in a relational database and that is accessible via JDBC driver and SQL statement the runtime offers syntax for using SQL within EPL, see Section 5.13, “Accessing Relational Data via SQL”. A relational database input and output adapter for streaming input from and output to a relational database also exists (EsperIO).

To join data that resides in a non-relational store the runtime offers a two means: First, the virtual data window, as described below, for transparently integrating the external store as a named window. The second mechanism is a special join syntax based on static method invocation; see Section 5.14, “Accessing Non-Relational Data via Method, Script or UDF Invocation”.

Tip

The best way to test that your extension code works correctly is to write unit tests against a statement that utilizes the extension code. Samples can be obtained from Esper regression test code base.

Note

For all extension code and similar to listeners and subscribers, to send events into the runtime from extension code the routeEvent method should be used (and not sendEvent) to avoid the possibility of stack overflow due to event-callback looping and ensure correct processing of the current and routed event. Note that if outbound-threading is enabled, listeners and subscribers should use sendEvent and not routeEvent.

Note

For all extension code it is not safe to deploy and undeploy within the extension code. For example, it is not safe to implement a data window that deploys compiled modules and that undeploys deployments.

22.2. Single-Row Function

Single-row functions return a single value. They are not expected to aggregate rows but instead should be stateless non-blocking functions. These functions can appear in any expressions and can be passed any number of parameters.

You may not override a built-in function with a single-row function provided by you. The single-row function you register must have a different name then any of the built-in functions.

The EPL compiler provides two ways to provide plug-in single-row functions:

An inlined class that is part of the same EPL module or another EPL module, see next under Section 22.2.1, “Using an Inlined Class to Provide a Single-Row Function” .
An external class that your application provides as part of the application, see below under Section 22.2.2, “Using an Application Class to Provide a Single-Row Function” .

An example single-row function can also be found in the examples under the runtime configuration example.

22.2.1. Using an Inlined Class to Provide a Single-Row Function

You may use an inline class that is part of the EPL modules to provide a single-row function. For more information on inline classes see Chapter 18, Inlined Classes. Using an inline class does not require any compiler configuration.

Specify the ExtensionSingleRowFunction annotation on the class level and the name (the EPL function name) and methodName (the name of the exposed method). This annotation instructs the compiler that the inlined class exposes a single-row function.

This sample EPL includes an inlined class by name MyUtilityClass that provides a public static method by name computePercent to return a percentage value:

inlined_class """
  @com.espertech.esper.common.client.hook.singlerowfunc.ExtensionSingleRowFunction(name="computePercent", methodName="computePercent")
  public class MyUtilityClass {
    public static double computePercent(double amount, double total) {
      return amount / total * 100;
    }
  }
""" 
select computePercent(itemQuantity,totalQuantity) from OrderEvent

Only one ExtensionSingleRowFunction annotation can be specified per class and the annotation is only for use with inlined classes. The method can be overloaded. Using an inline class does not require any compiler configuration.

When using create inlined_class the runtime resolves dependencies on EPL objects at time of deployment (the same as for all EPL objects).

22.2.2. Using an Application Class to Provide a Single-Row Function

Your application may provide a class that exposes a single-row function. In this case you must configure the class name, name of the function and function method name as part of compiler configuration. This is not necessary when using inlined classes as discussed before.

22.2.2.1. Implementing a Single-Row Function

Single-row function classes have no further requirement then provide a public static method.

The following sample single-row function simply computes a percentage value based on two number values.

This sample class provides a public static method by name computePercent to return a percentage value:

public class MyUtilityClass {
  public static double computePercent(double amount, double total) {
    return amount / total * 100;
  }
}

22.2.2.2. Configuring the Single-Row Function Name

The class name of the class, the method name and the function name of the new single-row function must be added to the compiler configuration. The configuration shown below is XML however the same options are available through the configuration API:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <plugin-singlerow-function name="percent" function-class="mycompany.MyUtilityClass" function-method="computePercent" />
  </compiler>
</esper-configuration>

Note that the function name and method name need not be the same.

The new single-row function is now ready to use in a statement:

select percent(fulfilled,total) from MyEvent

When selecting from a single stream, you may also pass wildcard to the single-row function and the function receives the underlying event:

select percent(*) from MyEvent

If the single-row function returns an object that provides further functions, you may chain function calls.

The following demonstrates a chained single-row function. The example assumes that a single-row function by name calculator returns an object that provides the add function which accepts two parameters:

select calculator().add(5, amount) from MyEvent

22.2.3. Value Cache

When a single-row function receives parameters that are all constant values or expressions that themselves receive only constant values, the runtime can pre-evaluate the result of the single-row function at time of statement. By default, the runtime does not pre-evaluate the single-row function unless you configure the value cache as enabled.

If using inlined classes and the ExtensionSingleRowFunction annotation, to enable specify the value cache as follows (default is disabled):

ExtensionSingleRowFunction(..., valueCache=com.espertech.esper.common.client.configuration.compiler.ConfigurationCompilerPlugInSingleRowFunction.ValueCache.ENABLED)

If using application classes and the compiler configuration, the following configuration XML enables the value cache for the single-row function:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <plugin-singlerow-function name="getDate" 
      function-class="mycompany.DateUtil" function-method="parseDate"
      value-cache="enabled" />
  </compiler>
</esper-configuration>

When the single-row function receives constants as parameters, the runtime computes the result once and returns the cached result for each evaluation:

select getDate('2002-05-30T9:00:00.000') from MyEvent

22.2.4. Single-Row Functions in Filter Predicate Expressions

Your EPL may use plug-in single row functions among the predicate expressions as part of the filters in a stream or pattern.

For example, the EPL below uses the function computeHash as part of a predicate expression:

select * from MyEvent(computeHash(field) = 100)

When you have many statements or many context partitions that refer to the same function, event type and parameters in a predicate expression, the compiler may optimize evaluation: The function gets evaluated only once per event.

While the optimization is enabled by default for all plug-in single row functions, you can also disable the optimization for a specific single-row function. By disabling the optimization for a single-row function the runtime may use less memory to identify reusable function footprints but may cause the runtime to evaluate each function more frequently than necessary.

If using inlined classes and the ExtensionSingleRowFunction annotation, specify the filter optimizable flag as follows (default is enabled):

ExtensionSingleRowFunction(..., filterOptimizable=com.espertech.esper.common.client.configuration.compiler.ConfigurationCompilerPlugInSingleRowFunction.FilterOptimizable.DISABLED)

If using application classes and the compiler configuration, the following configuration XML disables the filter optimization for a single-row function (by default it is enabled):

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <plugin-singlerow-function name="computeHash" 
      function-class="mycompany.HashUtil" function-method="computeHash"
      filter-optimizable="disabled" />
  </compiler>
</esper-configuration>

22.2.5. Single-Row Functions Taking Events as Parameters

EPL allows parameters to a single-row function to be events. In this case, declare the method parameter type to either take EventBean, Collection<EventBean> or the underlying class as a parameter.

Sample method footprints are:

public static double doCompute(EventBean eventBean) {...}
public static boolean doCheck(MyEvent myEvent, String text) {...}
public static String doSearch(Collection<EventBean> events) {...}

To pass the event, specify the stream alias, or wildcard (*) or the tag name when used in a pattern.

The EPL below shows example uses:

select * from MyEvent(doCompute(me) = 100) as me

select * from MyEvent where doCompute(*) = 100

select * from pattern[a=MyEvent -> MyEvent(doCheck(a, 'sometext'))]

select * from MyEvent#time(1 min) having doCompute(last(*))]

select * from MyEvent#time(1 min) having doSearch(window(*))]

Declare the method parameter as Collection<EventBean> if the method expects an expression result that returns multiple events.

Declare the method parameter as EventBean if the method expects an expression result that returns a single event.

22.2.6. Single-Row Functions Returning Events

A single-row function may return events. Please declare your single-row function method to return Collection<EventBean> or EventBean[] and configure the event type name.

For example, assuming there is an MyItem event type such as created via create schema MyItem(id string):

public static EventBean[] myItemProducer(String string, EPLMethodInvocationContext context) {
  String[] split = string.split(",");
  EventBean[] events = new EventBean[split.length];
  for (int i = 0; i < split.length; i++) {
    events[i] = context.getEventBeanService().adapterForMap(Collections.singletonMap("id", split[i]), "MyItem");
  }
  return events;
}

The sample EPL queries items filtering those items that have a given value for the id field:

select myItemProducer(ordertext).where(v => v.id in ('id1', 'id3')) as c0 from Order

If using inlined classes and the ExtensionSingleRowFunction annotation, specify the event type name as follows:

ExtensionSingleRowFunction(..., eventTypeName="MyItem")

If using application classes and the compiler configuration, this sample code register the myItemProducer function as a single-row function with an event type name:

ConfigurationCompilerPlugInSingleRowFunction entry = new ConfigurationCompilerPlugInSingleRowFunction();
entry.setName("myItemProducer");
entry.setFunctionClassName(...);
entry.setFunctionMethodName(...);
entry.setEventTypeName("MyItem");
Configuration configuration = new Configuration();
configuration.getCompiler().addPlugInSingleRowFunction(entry);

If your single row function returns EventBean[] and is used with enumeration methods the configuration must provide an event type name.

22.2.7. Receiving a Context Object

A sample method footprint and EPL are shown below:

public static double computeSomething(double number, EPLMethodInvocationContext context) {...}

select computeSomething(10) from MyEvent

22.2.8. Exception Handling

By default the runtime logs any exceptions thrown by the single row function and returns a null value. To have exceptions be re-thrown instead, which makes exceptions visible to any registered exception handler, please configure as discussed herein.

If using inlined classes and the ExtensionSingleRowFunction annotation, you may set rethrow as follows:

ExtensionSingleRowFunction(..., rethrowExceptions=true)

If using application classes and the compiler configuration, set the rethrow-exceptions flag in the XML configuration or the rethrowExceptions flag in the API when registering the single row function to have the runtime re-throw any exceptions that the single row function may throw.

22.3. Virtual Data Window

Use a virtual data window if you have a (large) external data store that you want to access as a named window. The access is transparent: There is no need to use special syntax or join syntax. All regular queries including subqueries, joins, on-merge, on-select, on-insert, on-delete, on-update and fire-and-forget are supported with virtual data windows.

There is no need to keep any data or events in memory with virtual data windows. The only requirement for virtual data windows is that all data rows returned are EventBean instances.

When implementing a virtual data window it is not necessary to send any events into the runtime or to use insert-into. The event content is simply assumed to exist and accessible to the runtime via the API implementation you provide.

The distribution ships with a sample virtual data window in the examples folder under the name virtualdw. The code snippets below are extracts from the example.

We use the term store here to mean a source set of data that is managed by the virtual data window. We use the term store row or just row to mean a single data item provided by the store. We use the term lookup to mean a read operation against the store returning zero, one or many rows.

Virtual data windows allow high-performance low-latency lookup by exposing all relevant statement access path information. This makes it possible for the virtual data window to choose the desired access method into its store.

The following steps are required to develop and use a virtual data window:

Implement the interface com.espertech.esper.common.client.hook.vdw.VirtualDataWindowForge. This class is used by the compiler.
Implement the interface com.espertech.esper.common.client.hook.vdw.VirtualDataWindowFactoryFactory. This class is referred to, by class name, by the compiler. It is used at runtime.
Implement the interface com.espertech.esper.common.client.hook.vdw.VirtualDataWindowFactory (used at runtime only).
Implement the interface com.espertech.esper.common.client.hook.vdw.VirtualDataWindow (used at runtime only).
Implement the interface com.espertech.esper.common.client.hook.vdw.VirtualDataWindowLookup (used at runtime only).
Register the factory class in the configuration.

Once you have completed above steps, the virtual data window is ready to use in statements.

From a threading perspective, virtual data window implementation classes must be thread-safe if objects are shared between multiple named windows. If no objects are shared between multiple different named windows, thereby each object is only used for the same named window and other named windows receive a separate instance, it is no necessary that the implementation classes are thread-safe.

22.3.1. How to Use

Your application must first register the virtual data window factory as part of configuration:

Configuration config = new Configuration();
config.getCompiler().addPlugInVirtualDataWindow("sample", "samplevdw", 
    SampleVirtualDataWindowForge.class.getName());

Your application may then create a named window backed by a virtual data window.

For example, assume that the SampleEvent event type is declared as follows:

create schema SampleEvent as (key1 string, key2 string, value1 int, value2 double)

The next statement creates a named window MySampleWindow that provides SampleEvent events and is backed by a virtual data window:

create window MySampleWindow.sample:samplevdw() as SampleEvent

You may then access the named window, same as any other named window, for example by subquery, join, on-action, fire-and-forget query or by consuming its insert and remove stream. While this example uses Map-type events, the example code is the same for POJO or other events.

Your application may obtain a reference to the virtual data window from the runtime context.

This code snippet looks up the virtual data window by the named window name:

try {
  return (VirtualDataWindow) runtime.getContext().lookup("/virtualdw/MySampleWindow");
}
catch (NamingException e) {
  throw new RuntimeException("Failed to look up virtual data window, is it created yet?");
}

22.3.1.1. Query Access Path

When your application registers a subquery, join or on-action query or executes a fire-and-forget query against a virtual data window the runtime interacts with the virtual data window. The interaction is a two-step process.

At time of deployment (once), the runtime uses the information the compiler collected by analyzing the EPL where-clause, if present. It then creates a list of hash-index and binary tree (btree, i.e. sorted) index properties. It passes the property names that are queried as well as the operators (i.e. =, >, range etc.) to the virtual data window. The virtual data window returns a lookup strategy object to the runtime.

At time of statement execution (repeatedly as triggered), the runtime uses that lookup strategy object to execute a lookup. It passes to the lookup all actual key values (hash, btree including ranges) to make fast and efficient lookup achievable.

To explain in detail, assume that your application creates a statement with a subquery as follows:

select (select * from MySampleWindow where key1 = 'A1') from OtherEvent

At the time of compilation of the statement above the compiler analyzes the statement. It determines that the subquery queries a virtual data window. It determines from the where-clause that the lookup uses property key1 and hash-equals semantics. The runtime then provides this information as part of VirtualDataWindowLookupContext passed to the getLookup method. Your application may inspect hash and btree properties and may determine the appropriate store access method to use.

The hash and btree property lookup information is for informational purposes, to enable fast and performant queries that return the smallest number of rows possible. Your implementation classes may use some or none of the information provided and may also instead return some or perhaps even all rows, as is practical to your implementation. The where-clause still remains in effect and gets evaluated on all rows that are returned by the lookup strategy.

Following the above example, the sub-query executes once when a OtherEvent event arrives. At time of execution the runtime delivers the string value A1 to the VirtualDataWindowLookup lookup implementation provided by your application. The lookup object queries the store and returns store rows as EventBean instances.

As a second example, consider an EPL join statement as follows:

select * from MySampleWindow, MyTriggerEvent where key1 = trigger1 and key2 = trigger2

The compiler analyzes the statement and the runtime passes to the virtual data window the information that the lookup occurs on properties key1 and key2 under hash-equals semantics. When a MyTriggerEvent arrives, it passes the actual value of the trigger1 and trigger2 properties of the current MyTriggerEvent to the lookup.

As a last example, consider a fire-and-forget query as follows:

select * from MySampleWindow key1 = 'A2' and value1 between 0 and 1000

The compiler analyzes the statement and the runtime passes to the virtual data window the lookup information. The lookup occurs on property key1 under hash-equals semantics and on property value1 under btree-open-range semantics. When your application executes the fire-and-forget query the runtime passes A2 and the range endpoints 0 and 1000 to the lookup.

For more information, please consult the JavaDoc API documentation for class VirtualDataWindow, VirtualDataWindowLookupContext or VirtualDataWindowLookupFieldDesc.

22.3.2. Implementing the Forge

For each named window that refers to the virtual data window, the runtime instantiates one instance of the forge at compile-time.

A virtual data window forge class is responsible for the following functions:

Implement the initialize method that accepts a virtual data window forge context object as a parameter.
Implement the getFactoryMode method that information how to initialize the factory-factory class (the class that acts as a factory for virtual data window factories).
Implement the getUniqueKeyPropertyNames method that can return the set of property names that are unique keys, for the purpose of query planning.

The compiler instantiates a VirtualDataWindowForge instance for each named window created by create window. The compiler invokes the initialize method once in respect to the named window being created passing a VirtualDataWindowForgeContext context object.

The sample code shown here can be found among the examples in the distribution under virtualdw:

public class SampleVirtualDataWindowForge implements VirtualDataWindowForge {

    public void initialize(VirtualDataWindowForgeContext initializeContext) {
    }

    public VirtualDataWindowFactoryMode getFactoryMode() {
        // The injection strategy defines how to obtain and configure the factory-factory.
        InjectionStrategy injectionStrategy = new InjectionStrategyClassNewInstance(SampleVirtualDataWindowFactoryFactory.class);
        
        // The managed-mode is the default. It uses the provided injection strategy.
        VirtualDataWindowFactoryModeManaged managed = new VirtualDataWindowFactoryModeManaged();
        managed.setInjectionStrategyFactoryFactory(injectionStrategy);
        
        return managed;
    }

    public Set<String> getUniqueKeyPropertyNames() {
        // lets assume there is no unique key property names
        return null;
    }
}

Your forge class must implement the getFactoryMode method which instructs the compiler how to obtain a factory class that returns a factory for creating virtual data window instances (a factory-factory). The class acting as the factory-factory will be SampleVirtualDataWindowFactoryFactory.

22.3.3. Implementing the Factory-Factory

At deployment time, the runtime instantiates the factory-factory and obtains a factory for virtual data windows.

A virtual data window factory-factory class is responsible for the following functions:

Implement the createFactory method that accepts a factory-factory context and that returns the virtual data window factory.

The sample code shown here can be found among the examples in the distribution under virtualdw:

public class SampleVirtualDataWindowFactoryFactory implements VirtualDataWindowFactoryFactory {

    public VirtualDataWindowFactory createFactory(VirtualDataWindowFactoryFactoryContext ctx) {
        return new SampleVirtualDataWindowFactory();
    }
}

22.3.4. Implementing the Factory

For each named window that refers to the virtual data window, the runtime instantiates one instance of the factory.

A virtual data window factory class is responsible for the following functions:

Implement the initialize method that accepts a virtual data window factory context object as a parameter.
Implement the create method that accepts a virtual data window context object as a parameter and returns a VirtualDataWindow implementation.
Implement the destroy method that gets called once when the named window is undeployed.

The runtime instantiates a VirtualDataWindowFactory instance for each named window created via create window. The runtime invokes the initialize method once in respect to the named window being created passing a VirtualDataWindowFactoryContext context object.

If not using contexts, the runtime calls the create method once after calling the initialize method. If using contexts, the runtime calls the create method every time it allocates a context partition. If using contexts and your virtual data window implementation operates thread-safe, you may return the same virtual data window implementation object for each context partition. If using contexts and your implementation object is not thread safe, return a separate thread-safe implementation object for each context partition.

The runtime invokes the destroy method once when the named window is undeployed. If not using contexts, the runtime calls the destroy method of the virtual data window implementation object before calling the destroy method on the factory object. If using contexts, the runtime calls the destroy method on each instance associates to a context partition at the time the associated context partition terminates.

The sample code shown here can be found among the examples in the distribution under virtualdw:

public class SampleVirtualDataWindowFactory implements VirtualDataWindowFactory {

    public void initialize(VirtualDataWindowFactoryContext factoryContext) {
    }

    public VirtualDataWindow create(VirtualDataWindowContext context) {
        return new SampleVirtualDataWindow(context);
    }

    public void destroy() {
        // cleanup can be performed here
    }

    public Set<String> getUniqueKeyPropertyNames() {
        // lets assume there is no unique key property names
        return null;
    }
}

Your factory class must implement the create method which receives a VirtualDataWindowContext object. This method is called once for each EPL that creates a virtual data window (see example create window above).

The VirtualDataWindowContext provides to your application:

String namedWindowName;	// Name of named window being created.
Object[] parameters;  // Any optional parameters provided as part of create-window.
EventType eventType;  // The event type of events.
EventBeanFactory eventFactory;  // A factory for creating EventBean instances from store rows.
VirtualDataWindowOutStream outputStream;  // For stream output to consuming statements.
AgentInstanceContext agentInstanceContext;  // Other statement information in statement context.

When using contexts you can decide whether your factory returns a new virtual data window for each context partition or returns the same virtual data window instance for all context partitions. Your extension code may refer to the named window name to identify the named window and may refer to the agent instance context that holds the agent instance id which is the id of the context partition.

22.3.5. Implementing the Virtual Data Window

A virtual data window implementation is responsible for the following functions:

Accept the lookup context object as a parameter and return the VirtualDataWindowLookup implementation.
Optionally, post insert and remove stream data.
Implement the destroy method, which the runtime calls for each context partition when the named window is stopped or destroyed, or once when a context partition is ended/terminated.

The sample code shown here can be found among the examples in the distribution under virtualdw.

The implementation class must implement the VirtualDataWindow interface like so:

public class SampleVirtualDataWindow implements VirtualDataWindow {

  private final VirtualDataWindowContext context;
  
  public SampleVirtualDataWindow(VirtualDataWindowContext context) {
    this.context = context;
  } ...

When the compiler compiles a statement and detects a virtual data window, the compiler compiles access path information and the runtime invokes the getLookup method indicating hash and btree access path information by passing a VirtualDataWindowLookupContext context. The lookup method must return a VirtualDataWindowLookup implementation that the statement uses for all lookups until the statement is stopped or destroyed.

The sample implementation does not use the hash and btree access path information and simply returns a lookup object:

public VirtualDataWindowLookup getLookup(VirtualDataWindowLookupContext desc) {

  // Place any code that interrogates the hash-index and btree-index fields here.

  // Return the lookup strategy.
  return new SampleVirtualDataWindowLookup(context);
}

The runtime calls the update method when data changes because of on-merge, on-delete, on-update or insert-into. For example, if you have an on-merge statement that is triggered and that updates the virtual data window, the newData parameter receives the new (updated) event and the oldData parameter receives the event prior to the update. Your code may use these events to update the store or delete from the store, if needed.

If your application plans to consume data from the virtual data window, for example via select * from MySampleWindow, then the code must implement the update method to forward insert and remove stream events, as shown below, to receive the events in consuming statements. To post insert and remove stream data, use the VirtualDataWindowOutStream provided by the context object as follows.

public void update(EventBean[] newData, EventBean[] oldData) {
  // This sample simply posts into the insert and remove stream what is received.
  context.getOutputStream().update(newData, oldData);
}

Your application should not use VirtualDataWindowOutStream to post new events that originate from the store. The object is intended for use with on-action statements. Use insert-into instead for any new events that originate from the store.

22.4. Data Window View and Derived-Value View

Views in EPL are used to derive information from an event stream, and to represent data windows onto an event stream. This chapter describes how to plug-in a new, custom view.

The following steps are required to develop and use a custom view.

Implement a view forge class. View forges are compile-time classes that accept and check view parameters and refer to the appropriate view factory for the runtime.
Implement a view factory class. View factories are classes that instantiate the appropriate view class at runtime.
Implement a view class. A view class commonly represents a data window or derives new information from a stream at runtime.
Configure the view factory class supplying a view namespace and name in the compiler configuration.

The example view factory and view class that are used in this chapter can be found in the examples source folder in the OHLC (open-high-low-close) example. The class names are OHLCBarPlugInViewForge, OHLCBarPlugInViewFactory and OHLCBarPlugInView.

Views can make use of the runtime services available via StatementContext, for example:

The SchedulingService interface allows views to schedule timer callbacks to a view

Section 22.4.4, “View Contract” outlines the requirements for correct behavior of your custom view within the runtime.

Note that custom views may use runtime services and APIs that can be subject to change between major releases. The runtime services discussed above and view APIs are considered part of the runtime internal API and are only limited stable. Please also consider contributing your custom view to the project by submitting the view code.

22.4.1. Implementing a View Forge

A view forge class is a compile-time class and is responsible for the following functions:

Accept zero, one or more view parameters. View parameters are themselves expressions. The view forge must validate the expressions.
Build the view factory class. At deployment-time this code executes and builds the view factory.
Provide information about the event type of events posted by the view.

View forge classes must implement the ViewFactoryForge interface. Additionally a view forge class must implement the DataWindowViewForge interface if the view is a data window (retains events provided to it).

public class OHLCBarPlugInViewForge implements ViewFactoryForge { ...

Your view forge class must implement the setViewParameters method to accept view parameters and the attach method to attach the view to a stream:

public class OHLCBarPlugInViewForge implements ViewFactoryForge {
    private List<ExprNode> viewParameters;
    private ExprNode timestampExpression;
    private ExprNode valueExpression;
    private EventType eventType;

    public void setViewParameters(List<ExprNode> parameters, ViewForgeEnv viewForgeEnv, int streamNumber) throws ViewParameterException {
        this.viewParameters = parameters;
    }

    public void attach(EventType parentEventType, int streamNumber, ViewForgeEnv env, boolean grouped) throws ViewParameterException {
        if (viewParameters.size() != 2) {
            throw new ViewParameterException("View requires a two parameters: the expression returning timestamps and the expression supplying OHLC data points");
        }
        ExprNode[] validatedNodes = ViewForgeSupport.validate("OHLC view", parentEventType, viewParameters, false, env, streamNumber);

        timestampExpression = validatedNodes[0];
        valueExpression = validatedNodes[1];

        if (!JavaClassHelper.isTypeLong(timestampExpression.getForge().getEvaluationType())) {
            throw new ViewParameterException("View requires long-typed timestamp values in parameter 1");
        }
        if (!JavaClassHelper.isTypeDouble(valueExpression.getForge().getEvaluationType())) {
            throw new ViewParameterException("View requires double-typed values for in parameter 2");
        }
        ....

After the compiler supplied view parameters to the forge, the compiler will ask the view to attach to its parent and validate any parameter expressions against the parent view's event type. If the view will be generating events of a different type then the events generated by the parent view, then the view factory can allocate the new event type.

Finally, the compiler asks the view forge to generate code that initializes the view factory:

public CodegenExpression make(CodegenMethodScope parent, SAIFFInitializeSymbol symbols, CodegenClassScope classScope) {
    return new SAIFFInitializeBuilder(new EPTypeClass(OHLCBarPlugInViewFactory.class), this.getClass(), "factory", parent, symbols, classScope)
                .exprnode("timestampExpression", timestampExpression)
                .exprnode("valueExpression", valueExpression)
                .build();
}

Use the internal SAIFFInitializeBuilder to build your view factory providing it the expressions and other values it needs.

22.4.2. Implementing a View Factory

A view factory class is responsible for the following functions:

Implement initialization code when required.
Instantiate the actual view class.
Provide information about the event type of events posted by the view.

View factory classes implement the ViewFactory interface. Additionally a view factory class must implement the DataWindowViewFactory interface if the view is a data window (retains events provided to it).

public class OHLCBarPlugInViewFactory implements ViewFactory { ...

The runtime initializes a view factory by calling its init method.

The runtime asks the view factory to create a view instance, and asks for the type of event generated by the view:

public View makeView(AgentInstanceViewFactoryChainContext agentInstanceViewFactoryContext) {
    return new OHLCBarPlugInView(this, agentInstanceViewFactoryContext);
}

public EventType getEventType() {
    return eventType;
}

22.4.3. Implementing a View

A view class is responsible for:

The update method receives insert streams and remove stream events from its parent view
The iterator method supplies an (optional) iterator to allow an application to pull or request results from an EPStatement

View classes subclass ViewSupport. Additionally a view class must implement the DataWindowView interface if the view is a data window (retains events provided to it).

public class OHLCBarPlugInView extends ViewSupport { ...

Your view's update method will be processing incoming (insert stream) and outgoing (remove stream) events posted by the parent view (if any), as well as providing incoming and outgoing events to child views. The convention required of your update method implementation is that the view releases any insert stream events (EventBean object references) which the view generates as reference-equal remove stream events (EventBean object references) at a later time.

The view implementation must call child.update(...) to post outgoing insert and remove stream events. Similar to the update method, the child.update takes insert and remove stream events as parameters.

A sample update method implementation is provided in the OHLC example.

22.4.4. View Contract

The update method must adhere to the following conventions, to prevent memory leaks and to enable correct behavior within the runtime:

A view implementation that posts events to the insert stream must post unique EventBean object references as insert stream events, and cannot post the same EventBean object reference multiple times. The underlying event to the EventBean object reference can be the same object reference, however the EventBean object reference posted by the view into the insert stream must be a new instance for each insert stream event.
If the custom view posts a continuous insert stream, then the views must also post a continuous remove stream (second parameter to the updateChildren method). If the view does not post remove stream events, it assumes unbound keep-all semantics.
EventBean events posted as remove stream events must be the same object reference as the EventBean events posted as insert stream by the view. Thus remove stream events posted by the view (the EventBean instances, does not affect the underlying representation) must be reference-equal to insert stream events posted by the view as part of an earlier invocation of the update method, or the same invocation of the update method.
EventBean events represent a unique observation. The values of the observation can be the same, thus the underlying representation of an EventBean event can be reused, however event property values must be kept immutable and not be subject to change.
Array elements of the insert and remove stream events must not carry null values. Array size must match the number of EventBean instances posted. It is recommended to use a null value for no insert or remove stream events rather then an empty zero-size array.

Your view implementation must implement the AgentInstanceStopCallback interface to receive a callback when the view gets destroyed.

Please refer to the sample views for a code sample on how to implement the iterator method.

In terms of multiple threads accessing view state, there is no need for your custom view factory or view implementation to perform any synchronization to protect internal state. The iterator of the custom view implementation does also not need to be thread-safe. The runtime ensures the custom view executes in the context of a single thread at a time. If your view uses shared external state, such external state must be still considered for synchronization when using multiple threads.

22.4.5. Configuring View Namespace and Name

The view factory class name as well as the view namespace and name for the new view must be added to the compiler configuration. The configuration shown below is XML however the same options are available through configuration:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <plugin-view namespace="custom" name="ohlc" 
        forge-class="com.espertech.esper.example.ohlc.OHLCBarPlugInViewFactory" /> 
  </compiler>
</esper-configuration>

The new view is now ready to use in a statement:

select * from StockTick.custom:ohlc(timestamp, price)

Note that the view must implement additional interfaces if it acts as a data window view, or works in a grouping context, as discussed in detail below.

22.4.6. Requirement for Data Window Views

Your custom view may represent an expiry policy and may retain events and thus act as a data window view. In order to allow the compiler to validate that your view can be used with named windows, which allow only data window views, this section documents any additional requirement that your classes must fulfill.

Your view forge class must implement the DataWindowViewForge interface. This marker interface (no methods required) indicates that your views are data window views.

Your view factory class must implement the DataWindowViewFactory interface. This marker interface (no methods required) indicates that your views are data window views.

Your view class must implement the DataWindowView interface. This interface indicates that your view is a data window view and therefore eligible to be used in any construct that requires a data window view. The DataWindowView interface extends the ViewDataVisitable interface. Please provide an empty implementation method for the visitView method as required by ViewDataVisitable (the default behavior is sufficient).

22.4.7. Requirement for Derived-Value Views

Your custom view may compute derived information from the arriving stream, instead of retaining events, and thus act as a derived-value view.

Your view class should implement the DerivedValueView interface. This marker interface indicates that your view is a derived-value view, affecting correct behavior of the view when used in joins.

22.5. Aggregation Function

Aggregation functions are stateful functions that aggregate events, event property values or expression results. Examples for built-in aggregation functions are count(*), sum(price * volume), window(*) or maxby(volume).

EPL allows two different ways for your application to provide aggregation functions. We use the name aggregation single-function and aggregation multi-function for the two independent extension APIs for aggregation functions.

The aggregation single-function API is simple to use however it imposes certain restrictions on how expressions that contain aggregation functions share state and how they are evaluated.

The aggregation multi-function API is more powerful and provides control over how expressions that contain aggregation functions share state and are evaluated.

The next table compares the two aggregation function extension API's:

Table 22.1. Aggregation Function Extension API's

	Single-Function	Multi-Function
Return Value	Can only return a single value or object. Cannot return an `EventBean` event, collection of `EventBean` events or collection or array of values for use with enumeration methods, for example.	Can return an `EventBean` event, a collection of `EventBean` events or a collection or array of objects for use with enumeration methods or to access event properties.
Complexity of API	Simple (consists of 2 interfaces).	More complex (consists of 6 interfaces).
State Sharing	State and parameter evaluation shared if multiple aggregation functions of the same name in the same statement (and context partition) take the exact same parameter expressions.	State and parameter evaluation sharable when multiple aggregation functions of a related name (related thru configuration) for the same statement (and context partition) exist, according to a sharing-key provided by your API implementation.
Function Name	Each aggregation function expression receives its own factory object.	Multiple related aggregation function expressions share a single factory object.
Distinct Keyword	Handled by the runtime transparently depending on mode.	Indicated to the API implementation only.

The following sections discuss developing an aggregation single-function first, followed by the subject of developing an aggregation multi-function.

Note

The aggregation multi-function API is a powerful and lower-level API to extend the runtime. Any classes that are not part of the client package should be considered unstable and are subject to change between minor and major releases.

22.5.1. Aggregation Single-Function Development

This section describes the aggregation single-function extension API for providing aggregation functions.

The EPL compiler provides two ways to provide aggregation single-functions:

An inlined class that is part of the same EPL module or another EPL module, see next under Section 22.5.1.4, “Using an Inlined Class to Provide an Aggregation Single-Function” .
An external class that your application provides as part of the application, see below under Section 22.5.1.5, “Using an Application Class to Provide the Aggregation Single-Function” .

In either case, the following steps are required to develop and use a custom aggregation single-function.

Implement an aggregation function forge by implementing the interface com.espertech.esper.common.client.hook.aggfunc.AggregationFunctionForge. This class provides compile-time information.
Implement an aggregation function factory by implementing the interface com.espertech.esper.common.client.hook.aggfunc.AggregationFunctionFactory (used at runtime).
Implement an aggregation function by implementing the interface com.espertech.esper.common.client.hook.aggfunc.AggregationFunction (used at runtime).

Custom aggregation functions can also be passed multiple parameters, as further described in Section 22.5.1.6, “Aggregation Single-Function: Accepting Multiple Parameters”. In the example below the aggregation function accepts a single parameter.

The code for the example aggregation function as shown in this chapter can be found in the runtime configuration example in the package com.espertech.esper.example.runtimeconfig by the name MyConcatAggregationFunction. The sample function simply concatenates string-type values.

22.5.1.1. Implementing an Aggregation Single-Function Forge

An aggregation function forge class is only used at compile-time and is responsible for the following functions:

Implement a setFunctionName method that receives the function name (optional, the default implementation is empty).
Implement a validate method that validates the value type of the data points that the function must process.
Implement a getValueType method that returns the type of the aggregation value generated by the aggregation function instances. For example, the built-in count aggregation function returns Long.class as it generates long -typed values.
Implement a getAggregationFunctionMode which provided information about the factory class to the compiler.

Aggregation forge classes implement the interface AggregationFunctionForge:

public class MyConcatAggregationFunctionForge implements AggregationFunctionForge { ...

The compiler constructs one instance of the aggregation function forge class for each time the function is listed in a statement, however the compiler may decide to reduce the number of aggregation forge instances if it finds equivalent aggregations.

The aggregation function forge instance receives the aggregation function name via set setFunctionName method.

The sample concatenation function forge provides an empty setFunctionName method:

public void setFunctionName(String functionName) {
  // no action taken
}

An aggregation function forge must provide an implementation of the validate method that is passed a AggregationFunctionValidationContext validation context object. Within the validation context you find the result type of each of the parameters expressions to the aggregation function as well as information about constant values and data window use. Please see the JavaDoc API documentation for a comprehensive list of validation context information.

Since the example concatenation function requires string types it implements a type check:

public void validate(AggregationValidationContext validationContext) {
  if (validationContext.getParameterTypes().length != 1 || !JavaClassHelper.isTypeString(validationContext.getParameterTypes()[0])) {
    throw new IllegalArgumentException("Concat aggregation requires a single parameter of type String");
  }
}

In order for the compiler to validate the type returned by the aggregation function against the types expected by enclosing expressions, the getValueType must return the result type of any values produced by the aggregation function:

public EPTypeClass getValueType() {
  return EPTypePremade.STRING.getEPType();
}

Finally the forge implementation must provide a getAggregationFunctionMode method that returns information about the factory. The compiler uses this information to build the aggregation function factory.

public AggregationFunctionMode getAggregationFunctionMode() {
    // Inject a factory by using "new"
    InjectionStrategy injectionStrategy = new InjectionStrategyClassNewInstance(MyConcatAggregationFunctionFactory.class);
    
    // The managed mode means there is no need to write code that generates code
    AggregationFunctionModeManaged mode = new AggregationFunctionModeManaged();
    mode.setInjectionStrategyAggregationFunctionFactory(injectionStrategy);
        
    return mode;
}

22.5.1.2. Implementing an Aggregation Single-Function Factory

An aggregation function factory class is responsible for the following functions:

Implement a newAggregator method that instantiates and returns an aggregation function instance.

Aggregation function factory classes implement the interface AggregationFunctionFactory:

public class MyConcatAggregationFunctionFactory implements AggregationFunctionFactory { ...

The runtime constructs the aggregation function factory at time of deployment.

The factory must provide a newAggregator method that returns instances of AggregationFunction. The runtime invokes this method for each new aggregation state to be allocated.

public AggregationFunction newAggregator() {
  return new MyConcatAggregationFunction();
}

22.5.1.3. Implementing an Aggregation Single-Function

An aggregation function class is responsible for the following functions:

Implement an enter method that the runtime invokes to add a data point into the aggregation, when an event enters a data window
Implement a leave method that the runtime invokes to remove a data point from the aggregation, when an event leaves a data window
Implement a getValue method that returns the current value of the aggregation.
Implement a clear method that resets the current value.

Aggregation function classes implement the interface AggregationFunction:

public class MyConcatAggregationFunction implements AggregationFunction { ...

The class that provides the aggregation and implements AggregationFunction does not have to be threadsafe.

The constructor initializes the aggregation function:

public class MyConcatAggregationFunction implements AggregationFunction {
  private final static char DELIMITER = ' ';
  private StringBuilder builder;
  private String delimiter;

  public MyConcatAggregationFunction() {
    builder = new StringBuilder();
    delimiter = "";
  }
  ...

The enter method adds a datapoint to the current aggregation value. The example enter method shown below adds a delimiter and the string value to a string buffer:

public void enter(Object value) {
  if (value != null) {
    builder.append(delimiter);
    builder.append(value.toString());
    delimiter = String.valueOf(DELIMITER);
  }
}

Conversly, the leave method removes a datapoint from the current aggregation value. The example leave method removes from the string buffer:

public void leave(Object value) {
  if (value != null) {
    builder.delete(0, value.toString().length() + 1);
  }
}

Finally, the runtime obtains the current aggregation value by means of the getValue method:

public Object getValue() {
  return builder.toString();
}

For on-demand queries the aggregation function must support resetting its value to empty or start values. Implement the clear function to reset the value as shown below:

public void clear() {
  builder = new StringBuilder();
  delimiter = "";
}

22.5.1.4. Using an Inlined Class to Provide an Aggregation Single-Function

You may use an inline class that is part of the EPL modules to provide an aggregation function. For more information on inline classes see Chapter 18, Inlined Classes. Using an inline class does not require any compiler configuration.

Specify the ExtensionAggregationFunction annotation on the class level of the AggregationFunctionForge implementation class and the name (the EPL aggregation function name). This annotation instructs the compiler that the inlined class exposes an aggregation function.

This sample EPL includes an inlined class by name ConcatAggForge that provides a concat aggregation function that concatenates:

inlined_class """
import com.espertech.esper.common.client.hook.aggfunc.*;
import com.espertech.esper.common.client.hook.forgeinject.*;
import com.espertech.esper.common.client.serde.*;
import java.io.*;
import com.espertech.esper.common.internal.epl.expression.core.*;
@ExtensionAggregationFunction(name="concat")
public class ConcatAggForge implements AggregationFunctionForge {
  public void validate(AggregationFunctionValidationContext validationContext) throws ExprValidationException {
    EPType paramType = validationContext.getParameterTypes()[0];
    if (paramType == EPTypeNull.INSTANCE || ((EPTypeClass) paramType).getType() != String.class) {
      throw new ExprValidationException("Invalid parameter type '" + paramType + "'");
    }
  }

  public EPTypeClass getValueType() {
    return new EPTypeClass(String.class);
  }

  public AggregationFunctionMode getAggregationFunctionMode() {
    AggregationFunctionModeManaged mode = new AggregationFunctionModeManaged();
    mode.setHasHA(true);
    mode.setSerde(ConcatAggSerde.class);
    mode.setInjectionStrategyAggregationFunctionFactory(new InjectionStrategyClassNewInstance(ConcatAggFactory.class.getName()));
    return mode;
  }

  public static class ConcatAggFactory implements AggregationFunctionFactory {
    public AggregationFunction newAggregator(AggregationFunctionFactoryContext ctx) {
      return new ConcatAggFunction();
    }
  }

  public static class ConcatAggFunction implements AggregationFunction {
    private final static String DELIMITER = ",";
    private StringBuilder builder;
    private String delimiter;

    public ConcatAggFunction() {
      super();
      builder = new StringBuilder();
      delimiter = "";
    }

    public void enter(Object value) {
      if (value != null) {
        builder.append(delimiter);
        builder.append(value.toString());
        delimiter = DELIMITER;
      }
    }

    public void leave(Object value) {
      if (value != null) {
        builder.delete(0, value.toString().length() + 1);
      }
    }
  
    public String getValue() {
      return builder.toString();
    }
  
    public void clear() {
      builder = new StringBuilder();
      delimiter = "";
    }
  }
  
  // the serializer-deserializer is only for high availability and is not required otherwise
  public static class ConcatAggSerde {
    public static void write(DataOutput output, AggregationFunction value) throws IOException {
      ConcatAggFunction agg = (ConcatAggFunction) value;
      output.writeUTF(agg.getValue());
    }

    public static AggregationFunction read(DataInput input) throws IOException {
      ConcatAggFunction concatAggFunction = new ConcatAggFunction();
      String current = input.readUTF();
      if (!current.isEmpty()) {
        concatAggFunction.enter(current);
      }
      return concatAggFunction;
    }
  }
}
""" 
select concat(personName) from PersonEvent

Only one ExtensionAggregationFunction annotation can be specified per class and the annotation is only for use with inlined classes. Using an inline class does not require any compiler configuration.

When using create inlined_class the runtime resolves dependencies on EPL objects at time of deployment (the same as for all EPL objects).

22.5.1.5. Using an Application Class to Provide the Aggregation Single-Function

The aggregation function class name as well as the function name for the new aggregation function must be added to the compiler configuration. The configuration shown below is XML however the same options are available through the configuration API. Configuring the function is not necessary when using inlined classes as discussed before.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <plugin-aggregation-function name="concat" 
      forge-class="com.espertech.esper.example.runtimeconfig.MyConcatAggregationFunctionFactory" />
  </compiler>
</esper-configuration>

The new aggregation function is now ready to use in a statement:

select concat(symbol) from StockTick#length(3)

22.5.1.6. Aggregation Single-Function: Accepting Multiple Parameters

Your plug-in aggregation function may accept multiple parameters. You must provide a different mode however:

    public AggregationFunctionMode getAggregationFunctionMode() {
        InjectionStrategy injectionStrategy = new InjectionStrategyClassNewInstance(SupportCountBackAggregationFunctionFactory.class);

        AggregationFunctionModeMultiParam multiParam = new AggregationFunctionModeMultiParam();
        multiParam.setInjectionStrategyAggregationFunctionFactory(injectionStrategy);
        
        return multiParam;
    }

For instance, assume an aggregation function rangeCount that counts all values that fall into a range of values. The EPL that calls this function and provides a lower and upper bounds of 1 and 10 is:

select rangeCount(1, 10, myValue) from MyEvent

The enter method of the plug-in aggregation function may look as follows:

public void enter(Object value)  {
  Object[] params = (Object[]) value;
  int lower = (Integer) params[0];
  int upper = (Integer) params[1];
  int val = (Integer) params[2];
  if ((val >= lower) && (val <= upper)) {
    count++;
  }
}

Your plug-in aggregation function may want to validate parameter types or may want to know which parameters are constant-value expressions. Constant-value expressions are evaluated only once by the runtime and could therefore be cached by your aggregation function for performance reasons. The runtime provides constant-value information as part of the AggregationValidationContext passed to the validate method.

22.5.1.7. Aggregation Single-Function: The Filter Parameter

When using AggregationFunctionModeManaged the runtime already takes care of filters.

When using AggregationFunctionModeMultiParam, the compiler takes the filter named parameter filter expression as a boolean-type value and the runtime provides the value to your enter method as the last value in the parameter array.

For instance, assume an aggregation function concat that receives a word value and that has a filter expression as parameters:

select concat(word, filter: word not like '%jim%') from MyWordEvent

The enter method of the plug-in aggregation function may look as follows:

public void enter(Object value)  {
  Object[] arr = (Object[]) value;
  Boolean pass = (Boolean) arr[1];
  if (pass != null && pass) {
    buffer.append(arr[0].toString());
  }
}

Your code can obtain the actual filter expression from the AggregationValidationContext that is passed to the validate method and that returns the named parameters via getNamedParameters.

22.5.1.8. Aggregation Single-Function: Distinct

When using AggregationFunctionModeManaged the runtime already takes care of distinct.

When using AggregationFunctionModeMultiParam your application code must determine and process distinct.

22.5.1.9. Aggregation Single-Function: Dot-Operator Use

When the custom aggregation function returns an object as a return value, the EPL can use parenthesis and the dot-operator to invoke methods on the return value.

The following example assumes that the myAggregation custom aggregation function returns an object that has getValueOne and getValueTwo methods:

select (myAggregation(myValue)).getValueOne(),  (myAggregation(myValue)).getValueTwo() from MyEvent

Since the above EPL aggregates the same value, the runtime internally uses a single aggregation to represent the current value of myAggregation (and not two instances of the aggregation, even though myAggregation is listed twice).

22.5.2. Aggregation Multi-Function Development

This section introduces the aggregation multi-function API. Aggregation multi-functions can offer multiple functions, methods, modifiers or views onto sharable aggregation state. Please refer to the JavaDoc for more complete class and method-level documentation.

The EPL compiler provides two ways to provide aggregation multi-functions:

An inlined class that is part of the same EPL module or another EPL module, see next under Section 22.5.2.8, “Using an Inlined Class to Provide an Aggregation Multi-Function”.
An external class that your application provides as part of the application, see below under Section 22.5.2.7, “Using an Application Class to Provide the Aggregation Multi-Function”.

In either case, the following steps are required to develop and use a custom aggregation multi-function.

Implement an aggregation multi-function forge by implementing the interface com.espertech.esper.common.client.hook.aggmultifunc.AggregationMultiFunctionForge.
Implement one or more handlers for aggregation functions by implementing the interface com.espertech.esper.common.client.hook.aggmultifunc.AggregationMultiFunctionHandler.
Implement an aggregation state key by implementing the interface com.espertech.esper.common.client.hook.aggmultifunc.AggregationMultiFunctionStateKey.
Implement an aggregation state factory by implementing the interface com.espertech.esper.common.client.hook.aggmultifunc.AggregationMultiFunctionStateFactory.
Implement an aggregation state holder by implementing the interface com.espertech.esper.common.client.hook.aggmultifunc.AggregationMultiFunctionState.
Implement a state accessor factory by implementing the interface com.espertech.esper.common.client.hook.aggmultifunc.AggregationMultiFunctionAccessorFactory.
Implement a state accessor by implementing the interface com.espertech.esper.common.client.hook.aggmultifunc.AggregationMultiFunctionAccessor.
For use with tables, implement an agent factory by implementing the interface com.espertech.esper.common.client.hook.aggmultifunc.AggregationMultiFunctionAgentFactory.
For use with tables, implement an agent by implementing the interface com.espertech.esper.common.client.hook.aggmultifunc.AggregationMultiFunctionAgent.
For use with aggregation methods, implement an aggregation method factory by implementing the interface com.espertech.esper.common.client.hook.aggmultifunc.AggregationMultiFunctionAggregationMethodFactory.
For use with aggregation methods, implement an aggregation method by implementing the interface com.espertech.esper.common.client.hook.aggmultifunc.AggregationMultiFunctionAggregationMethod.
When using an inlined class, use the ExtensionAggregationMultiFunction annotation and by providing the function names. When providing external application classes, register the aggregation multi-function forge class with the compiler by supplying one or more function names, via the compiler configuration file or the runtime and static configuration API.

There are two examples for aggregation multi-function. The first example uses inlined classes and can be found at Section 22.5.2.8, “Using an Inlined Class to Provide an Aggregation Multi-Function”. The second example for aggregation multi-function is called Cycle-Detect and is used for the step-by-step below.

Cycle-Detect takes incoming transaction events that have from-account and to-account fields. The example detects a cycle in the transactions between accounts in order to detect a possible transaction fraud. Please note that the graph and cycle detection logic of the example is not part of the distribution: The example utilizes the jgrapht library.

In the Cycle-Detect example, the vertices of a graph are the account numbers. For example the account numbers Acct-1, Acct-2 and Acct-3. In the graph the edges are transaction events that identify a from-account and a to-account. An example edge is {from:Acct-1, to:Acct-2}. An example cycle is therefore in the three transactions {from:Acct-1, to:Acct-2}, {from:Acct-2, to:Acct-3} and {from:Acct-3, to:Acct-1}.

The code for the example aggregation multi-function as shown in this chapter can be found in the Cycle-Detect example in the package com.espertech.esper.example.cycledetect. The example provides two aggregation functions named cycledetected and cycleoutput:

The cycledetected function returns a boolean value whether a graph cycle is found or not.
The cycleoutput function outputs the vertices (account numbers) that are part of the graph cycle.

In the Cycle-Detect example, the following statement utilizes the two functions cycledetected and cycleoutput that share the same graph state to detect a cycle among the last 1000 events:

@Name('CycleDetector') select cycleoutput() as cyclevertices
from TransactionEvent#length(1000)
having cycledetected(fromAcct, toAcct)

If instead the goal is to run graph cycle detection every 1 second (and not upon arrival of a new event), this sample statement uses a pattern to trigger cycle detection:

@Name('CycleDetector')
select (select cycleoutput(fromAcct, toAcct) from TransactionEvent#length(1000)) as cyclevertices
from pattern [every timer:interval(1)]

22.5.2.1. Implementing an Aggregation Multi-Function Forge

An aggregation multi-function forge class is a compile-time class responsible for the following functions:

Implement the addAggregationFunction method that receives an invocation for each aggregation function declared in the statement that matches any of the function names provided at configuration time.
Implement the validateGetHandler method that receives an invocation for each aggregation function to be validated in the statement that matches any of the function names provided at configuration time.

Aggregation multi-function factory classes implement the interface AggregationMultiFunctionForge:

public class CycleDetectorAggregationForge implements AggregationMultiFunctionForge { ...

The compiler constructs a single instance of the aggregation multi-function forge class that is shared for all aggregation function expressions in a statement that have one of the function names provided in the configuration object.

The compiler invokes the addAggregationFunction method at the time it compiles a statement. The method receives a declaration-time context object that provides the function name as well as additional information.

The sample Cycle-Detect factory class provides an empty addAggregationFunction method:

public void addAggregationFunction(AggregationMultiFunctionDeclarationContext declarationContext) {
    // provides an opportunity to inspect where used
}

The compiler invokes the validateGetHandler method at the time of expression validation. It passes a AggregationMultiFunctionValidationContext validation context object that contains actual parameters expressions. Please see the JavaDoc API documentation for a comprehensive list of validation context information.

The validateGetHandler method must return a handler object the implements the AggregationMultiFunctionHandler interface. Return a handler object for each aggregation function expression according to the aggregation function name and its parameters that are provided in the validation context.

The example cycledetect function takes two parameters that provide the cycle edge (from-account and to-account):

public AggregationMultiFunctionHandler validateGetHandler(AggregationMultiFunctionValidationContext validationContext) {
  if (validationContext.getParameterExpressions().length == 2) {
    fromExpression = validationContext.getParameterExpressions()[0];
    toExpression = validationContext.getParameterExpressions()[1];
  }
  return new CycleDetectorAggregationHandler(this, validationContext);
}

22.5.2.2. Implementing an Aggregation Multi-Function Handler

An aggregation multi-function handler class is a compile-time class that must implement the AggregationMultiFunctionHandler interface and is responsible for the following functions:

Implement the getReturnType method that returns information about the type of return values provided.
Implement the getAggregationStateUniqueKey method that provides a key object used by the compiler to determine which aggregation functions share state.
Implement the getStateMode method that returns information to the compiler that the compiler uses to initialize the state factory at deployment time.
Implement the getAccessorMode method that returns information to the compiler that the compiler uses to initialize the accessor factory at deployment time.
Implement the getAgentMode method that returns information to the compiler that the compiler uses to initialize the agent factory at deployment time, for use with tables.
Implement the getTableReaderMode method that returns information to the compiler that the compiler uses to initialize the table reader factory at deployment time, for use with tables.

In the Cycle-Detect example, the class CycleDetectorAggregationHandler is the handler for all aggregation functions.

public class CycleDetectorAggregationHandler implements AggregationMultiFunctionHandler { ...

The getReturnType method provided by the handler instructs the compiler about the return type of each aggregation accessor. The class EPChainableType holds return type information.

In the Cycle-Detect example the cycledetected function returns a single boolean value. The cycleoutput returns a collection of vertices:

public EPChainableType getReturnType() {
    if (validationContext.getFunctionName().toLowerCase(Locale.ENGLISH).equals(CycleDetectorConstant.CYCLEOUTPUT_NAME)) {
        return EPChainableTypeHelper.collectionOfSingleValue((EPTypeClass) forge.getFromExpression().getForge().getEvaluationType());
    }
    return EPChainableTypeHelper.singleValue(Boolean.class);
}

The compiler invokes the getAggregationStateUniqueKey method to determine whether multiple aggregation function expressions in the same statement can share the same aggregation state or should receive different aggregation state instances.

The getAggregationStateUniqueKey method must return an instance of AggregationMultiFunctionStateKey. The compiler uses equals-semantics (the hashCode and equals methods) to determine whether multiple aggregation function share the state object. If the key object returned for each aggregation function by the handler is an equal key object then the compiler shares aggregation state between such aggregation functions for the same statement and context partition.

In the Cycle-Detect example the state is shared, which it achieves by simply returning the same key instance:

private static final AggregationMultiFunctionStateKey CYCLE_KEY = new AggregationMultiFunctionStateKey() {};

public AggregationMultiFunctionStateKey getAggregationStateUniqueKey() {
    return CYCLE_KEY;
}

The compiler invokes the getStateMode method to obtain an instance of AggregationMultiFunctionStateMode. The state mode is responsible to obtaining and configuring an aggregation state factory instance at time of deployment.

In the Cycle-Detect example the method passes the expression evaluators providing the from-account and to-account expressions to the state factory:

public AggregationMultiFunctionStateMode getStateMode() {
    AggregationMultiFunctionStateModeManaged managed = new AggregationMultiFunctionStateModeManaged();
    InjectionStrategyClassNewInstance injection = new InjectionStrategyClassNewInstance(CycleDetectorAggregationStateFactory.class);
    injection.addExpression("from", forge.getFromExpression());
    injection.addExpression("to", forge.getToExpression());
    managed.setInjectionStrategyAggregationStateFactory(injection);
    return managed;
}

The compiler invokes the getAccessorMode method to obtain an instance of AggregationMultiFunctionAccessorMode. The accessor mode is responsible to obtaining and configuring an accessor factory instance at time of deployment.

The getAccessorMode method provides information about the accessor factories according to whether the aggregation function name is cycledetected or cycleoutput:

public AggregationMultiFunctionAccessorMode getAccessorMode() {
    Class accessor;
    if (validationContext.getFunctionName().toLowerCase(Locale.ENGLISH).equals(CycleDetectorConstant.CYCLEOUTPUT_NAME)) {
        accessor = CycleDetectorAggregationAccessorOutputFactory.class;
    }
    else {
        accessor = CycleDetectorAggregationAccessorDetectFactory.class;
    }
    AggregationMultiFunctionAccessorModeManaged managed = new AggregationMultiFunctionAccessorModeManaged();
    InjectionStrategyClassNewInstance injection = new InjectionStrategyClassNewInstance(accessor);
    managed.setInjectionStrategyAggregationAccessorFactory(injection);
    return managed;
}

22.5.2.3. Implementing an Aggregation Multi-Function State Factory

An aggregation multi-function state factory class must implement the AggregationMultiFunctionStateFactory interface and is responsible for the following functions:

Implement the newState method that returns an aggregation state holder.

The runtime invokes the newState method to obtain a new aggregation state instance before applying aggregation state. If using group by in your statement, the runtime invokes the newState method to obtain a state holder for each group.

In the Cycle-Detect example, the class CycleDetectorAggregationStateFactory is the state factory for all aggregation functions:

public class CycleDetectorAggregationStateFactory implements AggregationMultiFunctionStateFactory {

    private ExprEvaluator from;
    private ExprEvaluator to;

    public AggregationMultiFunctionState newState(AggregationMultiFunctionStateFactoryContext ctx) {
        return new CycleDetectorAggregationState(this);
    }

    public void setFrom(ExprEvaluator from) {
        this.from = from;
    }

    public void setTo(ExprEvaluator to) {
        this.to = to;
    }

    public ExprEvaluator getFrom() {
        return from;
    }

    public ExprEvaluator getTo() {
        return to;
    }
}

22.5.2.4. Implementing an Aggregation Multi-Function State

An aggregation multi-function state class must implement the AggregationMultiFunctionState interface and is responsible for the following functions:

Implement the applyEnter method that enters events, event properties or computed values.
Implement the applyLeave method that can remove events or computed values.
Implement the clear method to clear state.

In the Cycle-Detect example, the class CycleDetectorAggregationState is the state for all aggregation functions. Please review the example for more information.

22.5.2.5. Implementing an Aggregation Multi-Function Accessor Factory

An aggregation multi-function accessor factory class must implement the AggregationMultiFunctionAccessorFactory interface and is responsible for the following functions:

Implement the newAccessor method that returns a new accessor.

In the Cycle-Detect example, the class CycleDetectorAggregationAccessorDetectFactory returns the accessor like so:

public class CycleDetectorAggregationAccessorDetectFactory implements AggregationMultiFunctionAccessorFactory {
    public AggregationMultiFunctionAccessor newAccessor(AggregationMultiFunctionAccessorFactoryContext ctx) {
        return new CycleDetectorAggregationAccessorDetect();
    }
}

22.5.2.6. Implementing an Aggregation Multi-Function Accessor

An aggregation multi-function accessor class must implement the AggregationMultiFunctionAccessor interface and is responsible for the following functions:

Implement the Object getValue(AggregationMultiFunctionState state, ...) method that returns a result object for the aggregation state.
Implement the Collection<EventBean> getEnumerableEvents(AggregationMultiFunctionState state, ...) method that returns a collection of events for enumeration, if applicable (or null).
Implement the EventBean getEnumerableEvent(AggregationMultiFunctionState state, ...) method that returns an event, if applicable (or null).
Implement the Collection getEnumerableScalar(AggregationMultiFunctionState state, ...) method that returns an event, if applicable (or null).

In the Cycle-Detect example, the class CycleDetectorAggregationAccessorDetect returns state for the cycledetected aggregation function and the CycleDetectorAggregationAccessorOutput returns the state for the cycleoutput aggregation function.

22.5.2.7. Using an Application Class to Provide the Aggregation Multi-Function

An aggregation multi-function configuration can receive one or multiple function names. You must also set a factory class name.

The sample XML snippet below configures an aggregation multi-function that is associated with the function names func1 and func2.

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <plugin-aggregation-multifunction 
        function-names="cycledetected,cycleoutput"
        forge-class="com.espertech.esper.example.cycledetect.CycleDetectorAggregationFactory"/>
  </compiler>
</esper-configuration>

The next example uses the configuration API to register the same:

String[] functionNames = new String[] {"cycledetected", "cycleoutput"};
ConfigurationPlugInAggregationMultiFunction config = new ConfigurationPlugInAggregationMultiFunction(functionNames, CycleDetectorAggregationFactory.class.getName());
Configuration configuration = new Configuration();
configuration.getCompiler().addPlugInAggregationMultiFunction(config);

22.5.2.8. Using an Inlined Class to Provide an Aggregation Multi-Function

You may use an inline class that is part of the EPL modules to provide an aggregation multi-function. For more information on inline classes see Chapter 18, Inlined Classes. Using an inline class does not require any compiler configuration.

Specify the ExtensionAggregationMultiFunction annotation on the class level of the AggregationMultiFunctionForge implementation class and the function names (names of states and aggregation methods). This annotation instructs the compiler that the inlined class exposes an aggregation multi-function.

This sample EPL includes an inlined class by name TrieAggForge that uses a Trie data structures to store person names. A trie is a tree-like data structure whose nodes store the letters of an alphabet. Here, the Trie stores person names. The implementation uses the Trie provided by the Apache Commons Collections library.

The following sample EPL uses the Trie to store persons by person name and return a prefix-map, which is a sorted map of all persons that have the same prefix:

// The PersonEvent provides a person name and person id			
@public @buseventtype create schema PersonEvent(name string, id string);

// This provides the aggregation multi-function 
create inlined_class """
import com.espertech.esper.common.client.*;
import com.espertech.esper.common.client.type.*;
import com.espertech.esper.common.client.hook.aggmultifunc.*;
import com.espertech.esper.common.client.hook.forgeinject.*;
import com.espertech.esper.common.internal.epl.expression.core.*;
import com.espertech.esper.common.internal.rettype.*;
import com.espertech.esper.common.internal.epl.agg.core.*;
import org.apache.commons.collections4.Trie;
import org.apache.commons.collections4.trie.PatriciaTrie;
import java.util.*;
import java.util.function.*;

// We have 3 function names: 
// - "trieState" for use with create-table to hold the Trie
// - "trieEnter" for entering person events into the Trie by person name, for use with into-table aggregation
// - "triePrefixMap" for returning a prefix map of all persons with the same prefix as provided by its parameter
@ExtensionAggregationMultiFunction(names="trieState,trieEnter,triePrefixMap")
/**
 * The trie aggregation forge is the entry point for providing the multi-function aggregation.
 */
public class TrieAggForge implements AggregationMultiFunctionForge {
  public AggregationMultiFunctionHandler validateGetHandler(AggregationMultiFunctionValidationContext validationContext) {
    String name = validationContext.getFunctionName();
    if (name.equals("trieState")) {
      return new TrieAggHandlerTrieState();
    } else if (name.equals("trieEnter")) {
      return new TrieAggHandlerTrieEnter(validationContext.getParameterExpressions());
    } else if (name.equals("triePrefixMap")) {
      return new TrieAggHandlerTriePrefixMap();
    }
    throw new IllegalStateException("Unrecognized name '" + name + "' for use with trie");
  }
  
  /**
   * This handler handles the "trieState"-type table column, for use with create-table.
   */
  public static class TrieAggHandlerTrieState implements AggregationMultiFunctionHandler {
    public EPChainableType getReturnType() {
      return EPChainableTypeHelper.singleValue(Trie.class);
    }

    public AggregationMultiFunctionStateKey getAggregationStateUniqueKey() {
      return new AggregationMultiFunctionStateKey() {};
    }

    public AggregationMultiFunctionStateMode getStateMode() {
      InjectionStrategyClassNewInstance injection = new InjectionStrategyClassNewInstance(TrieAggStateFactory.class);
      return new AggregationMultiFunctionStateModeManaged(injection);
    }

    public AggregationMultiFunctionAccessorMode getAccessorMode() {
      // accessor that returns the trie itself
      InjectionStrategyClassNewInstance injection = new InjectionStrategyClassNewInstance(TrieAggAccessorFactory.class);
      return new AggregationMultiFunctionAccessorModeManaged(injection);
    }

    public AggregationMultiFunctionAgentMode getAgentMode() {
      throw new UnsupportedOperationException("Trie aggregation access is only by the 'triePrefixMap' method");
    }

    public AggregationMultiFunctionAggregationMethodMode getAggregationMethodMode(AggregationMultiFunctionAggregationMethodContext ctx) {
      throw new UnsupportedOperationException("Trie aggregation access is only by the 'triePrefixMap' method");
    }
  }
  
  /**
   * This handler handles the "trieEnter"-operation that updates trie state, for use with into-table aggregation
   */
  public static class TrieAggHandlerTrieEnter implements AggregationMultiFunctionHandler {
    private final ExprNode[] parameters;
    
    public TrieAggHandlerTrieEnter(ExprNode[] parameters) {
      this.parameters = parameters;
    }

    public EPChainableType getReturnType() {
      return EPChainableTypeHelper.singleValue(Trie.class); // we return the Trie itself
    }

    public AggregationMultiFunctionStateKey getAggregationStateUniqueKey() {
      throw new UnsupportedOperationException("Not a trie state");
    }

    public AggregationMultiFunctionStateMode getStateMode() {
      throw new UnsupportedOperationException("Not a trie state");
    }

    public AggregationMultiFunctionAccessorMode getAccessorMode() {
      // accessor that returns the trie itself
      InjectionStrategyClassNewInstance injection = new InjectionStrategyClassNewInstance(TrieAggAccessorFactory.class);
      return new AggregationMultiFunctionAccessorModeManaged(injection);
    }

    public AggregationMultiFunctionAgentMode getAgentMode() {
      if (parameters.length != 1 || ((EPTypeClass) parameters[0].getForge().getEvaluationType()).getType() != String.class) {
        throw new IllegalArgumentException("Requires a single parameter returing a string value");
      }
      InjectionStrategyClassNewInstance injection = new InjectionStrategyClassNewInstance(TrieAggAgentFactory.class);
      injection.addExpression("keyExpression", parameters[0]);
      return new AggregationMultiFunctionAgentModeManaged(injection);
    }

    public AggregationMultiFunctionAggregationMethodMode getAggregationMethodMode(AggregationMultiFunctionAggregationMethodContext ctx) {
      throw new UnsupportedOperationException("Trie aggregation access is only by the 'triePrefixMap' method");
    }
  }
  
  /**
   * This handler handles the "triePrefixMap" accessor for returning a prefix map of same-prefix person events
   */
  public static class TrieAggHandlerTriePrefixMap implements AggregationMultiFunctionHandler {
    public EPChainableType getReturnType() {
      return EPChainableTypeHelper.singleValue(Map.class);
    }
    
    public AggregationMultiFunctionStateKey getAggregationStateUniqueKey() {
      throw new UnsupportedOperationException("Not implemented for 'triePrefixMap' trie method");
    }

    public AggregationMultiFunctionStateMode getStateMode() {
      throw new UnsupportedOperationException("Not implemented for 'triePrefixMap' trie method");
    }

    public AggregationMultiFunctionAccessorMode getAccessorMode() {
      throw new UnsupportedOperationException("Not implemented for 'triePrefixMap' trie method");
    }

    public AggregationMultiFunctionAgentMode getAgentMode() {
      throw new UnsupportedOperationException("Not implemented for 'triePrefixMap' trie method");
    }

    public AggregationMultiFunctionAggregationMethodMode getAggregationMethodMode(AggregationMultiFunctionAggregationMethodContext ctx) {
      if (ctx.getParameters().length != 1 || ((EPTypeClass) ctx.getParameters()[0].getForge().getEvaluationType()).getType() != String.class) {
        throw new IllegalArgumentException("Requires a single parameter returning a string value");
      }
      InjectionStrategyClassNewInstance injection = new InjectionStrategyClassNewInstance(TrieAggMethodFactoryPrefixMap.class);
      injection.addExpression("keyExpression", ctx.getParameters()[0]);
      return new AggregationMultiFunctionAggregationMethodModeManaged(injection);
    }
  }
  
  /**
   * The agent state factory is responsible for producing a state holder that holds the trie state
   */
  public static class TrieAggStateFactory implements AggregationMultiFunctionStateFactory {
    public AggregationMultiFunctionState newState(AggregationMultiFunctionStateFactoryContext ctx) {
      return new TrieAggState();
    }
  }
  
  /**
   * The agent state is the state holder that holds the trie state
   */
  public static class TrieAggState implements AggregationMultiFunctionState {
    private final Trie<String, List<Object>> trie = new PatriciaTrie<>();
    
    public void applyEnter(EventBean[] eventsPerStream, ExprEvaluatorContext exprEvaluatorContext) {
      throw new UnsupportedOperationException("Not used since the agent updates the table");
    }
    
    public void applyLeave(EventBean[] eventsPerStream, ExprEvaluatorContext exprEvaluatorContext) {
      throw new UnsupportedOperationException("Not used since the agent updates the table");
    }
    
    public void clear() {
      trie.clear();
    }

    public void add(String key, Object underlying) {
      List<Object> existing = (List<Object>) trie.get(key);
      if (existing != null) {
        existing.add(underlying);
        return;
      }
      List<Object> events = new ArrayList<>(2);
      events.add(underlying);
      trie.put(key, events);
    }
    
    public void remove(String key, Object underlying) {
      List<Object> existing = (List<Object>) trie.get(key);
      if (existing != null) {
        existing.remove(underlying);
        if (existing.isEmpty()) {
          trie.remove(key);
        }
      }
    }
  }
  
  /**
   * The accessor factory is responsible for producing an accessor that returns the result of the trie table column when accessed without an aggregation method
   */
  public static class TrieAggAccessorFactory implements AggregationMultiFunctionAccessorFactory {
    public AggregationMultiFunctionAccessor newAccessor(AggregationMultiFunctionAccessorFactoryContext ctx) {
      return new TrieAggAccessor();
    }
  }
  
  /**
   * The accessor returns the result of the trie table column when accessed without an aggregation method
   */
  public static class TrieAggAccessor implements AggregationMultiFunctionAccessor {
    // This is the value return when just referring to the trie table column by itself without a method name such as "prefixMap".
    public Object getValue(AggregationMultiFunctionState state, EventBean[] eventsPerStream, boolean isNewData, ExprEvaluatorContext exprEvaluatorContext) {
      TrieAggState trie = (TrieAggState) state;
      return trie.trie;
    }
  }
  
  /**
   * The agent factory is responsible for producing an agent that handles all changes to the trie table column.
   */
  public static class TrieAggAgentFactory implements AggregationMultiFunctionAgentFactory {
    private ExprEvaluator keyExpression;
    
    public void setKeyExpression(ExprEvaluator keyExpression) {
      this.keyExpression = keyExpression;
    }
    
    public AggregationMultiFunctionAgent newAgent(AggregationMultiFunctionAgentFactoryContext ctx) {
      return new TrieAggAgent(this);
    }
  }
  
  /**
   * The agent is responsible for all changes to the trie table column.
   */
  public static class TrieAggAgent implements AggregationMultiFunctionAgent {
    private final TrieAggAgentFactory factory;
    
    public TrieAggAgent(TrieAggAgentFactory factory) {
      this.factory = factory;
    }
    
    public void applyEnter(EventBean[] eventsPerStream, ExprEvaluatorContext exprEvaluatorContext, AggregationRow row, int column) {
      String key = (String) factory.keyExpression.evaluate(eventsPerStream, true, exprEvaluatorContext);
      TrieAggState trie = (TrieAggState) row.getAccessState(column);
      trie.add(key, eventsPerStream[0].getUnderlying());
    }

    public void applyLeave(EventBean[] eventsPerStream, ExprEvaluatorContext exprEvaluatorContext, AggregationRow row, int column) {
      String key = (String) factory.keyExpression.evaluate(eventsPerStream, false, exprEvaluatorContext);
      TrieAggState trie = (TrieAggState) row.getAccessState(column);
      trie.remove(key, eventsPerStream[0].getUnderlying());
    }
  }
  
  /**
   * The aggregation method factory is responsible for producing an aggregation method for the "trie" return result of the trie table column.
   */
  public static class TrieAggMethodFactoryTrieColumn implements AggregationMultiFunctionAggregationMethodFactory {
    public AggregationMultiFunctionAggregationMethod newMethod(AggregationMultiFunctionAggregationMethodFactoryContext context) {
      return new AggregationMultiFunctionAggregationMethod() {
        public Object getValue(int aggColNum, AggregationRow row, EventBean[] eventsPerStream, boolean isNewData, ExprEvaluatorContext exprEvaluatorContext) {
          TrieAggState trie = (TrieAggState) row.getAccessState(aggColNum);
          return trie.trie;
        }
      };
    }
  }
  
  /**
   * The aggregation method factory is responsible for producing an aggregation method for the "triePrefixMap" expression of the trie table column.
   */
  public static class TrieAggMethodFactoryPrefixMap implements AggregationMultiFunctionAggregationMethodFactory {
    private ExprEvaluator keyExpression;
    
    public void setKeyExpression(ExprEvaluator keyExpression) {
      this.keyExpression = keyExpression;
    }

    public AggregationMultiFunctionAggregationMethod newMethod(AggregationMultiFunctionAggregationMethodFactoryContext context) {
      return new TrieAggMethodPrefixMap(this);
    }
  }
  
  /**
   * The aggregation method is responsible for the "triePrefixMap" expression result of the trie table column.
   */
  public static class TrieAggMethodPrefixMap implements AggregationMultiFunctionAggregationMethod {
    private final TrieAggMethodFactoryPrefixMap factory;
    
    public TrieAggMethodPrefixMap(TrieAggMethodFactoryPrefixMap factory) {
      this.factory = factory;
    }
    
    public Object getValue(int aggColNum, AggregationRow row, EventBean[] eventsPerStream, boolean isNewData, ExprEvaluatorContext exprEvaluatorContext) {
      String key = (String) factory.keyExpression.evaluate(eventsPerStream, false, exprEvaluatorContext);
      TrieAggState trie = (TrieAggState) row.getAccessState(aggColNum);
      return trie.trie.prefixMap(key);
    }
  }
}
""";

// We use a table to store the Trie. The Trie is effectively a Trie<String, List<PersonEvent>> holding a list of person events in branch and leaf nodes.
@name('table') create table TableWithTrie(nameTrie trieState(string));

// We aggregate directly into the table using the person name as the Trie key and the event as value
@Priority(1) into table TableWithTrie select trieEnter(name) as nameTrie from PersonEvent;

// For each person output the prefix map, a sorted map (SortedMap<String, List<PersonEvent>>) with the same prefixes as the person name
@Priority(0) @name('s0') select TableWithTrie.nameTrie.triePrefixMap(name) from PersonEvent;

Only one ExtensionAggregationMultiFunction annotation can be specified per class and the annotation is only for use with inlined classes. Using an inline class does not require any compiler configuration.

When using create inlined_class the runtime resolves dependencies on EPL objects at time of deployment (the same as for all EPL objects).

22.5.2.9. Aggregation Multi-Function Thread Safety

The runtime shares an AggregationAccessor instance between threads. The accessor should be designed stateless and should not use any locking of any kind in the AggregationAccessor implementation unless your implementation uses other state. Since the runtime passes an aggregation state instance to the accessor it is thread-safe as long as it relies only on the aggregation state passed to it.

The runtime does not share an AggregationState instance between threads. There is no need to use locking of any kind in the AggregationState implementation unless your implementation uses other state.

22.5.2.10. Aggregation Multi-Function Use With Tables

Tables allow columns to hold aggregation state including the state for multi-function aggregations. This section provides API pointers.

When a statement accesses a table column that declares aggregation state of a multi-function aggregation, the AggregationMultiFunctionValidationContext contains an optionalTableColumnRead field that provides information about the table column.

To find out the statement type, such as to determine whether the current statement is a create-table statement, use context.getValidationContext().getExprEvaluatorContext().getStatementType().

To find out whether the statement aggregates into a table, use context.getValidationContext().getIntoTableName() that returns the table name or null if not aggregating into a table.

The compiler uses AggregationMultiFunctionStateKey to determine whether an aggregation function listed with into table is compatible with the aggregation type that a table column declares. The equals method of the object must return true for compatible and false for incompatible.

Your handler may provide a agent and aggregation method modes. Please follow the JavaDoc or inspect the regression test suite.

22.5.2.11. Aggregation Multi-Function Use Filter Expression

The filter expression is passed to you in PlugInAggregationMultiFunctionValidationContext as part of getNamedParameters under the name filter. When use with tables the filter expression is part of PlugInAggregationMultiFunctionAgentContext.

Your application must invoke the filter expression as the runtime does not evaluate the filter expression for you. For example:

ExprEvaluator filterEval = validationContext.getNamedParameters().get("filter").get(0).getExprEvaluator();

public void applyEnter(EventBean[] eventsPerStream, ExprEvaluatorContext exprEvaluatorContext) {
  Boolean pass = (Boolean) filterEval.evaluate(eventsPerStream, true, exprEvaluatorContext); // note: pass "false" for applyLeave
  if (pass != null && pass) {
    Object value = valueEval.evaluate(eventsPerStream, true, exprEvaluatorContext); // note: pass "false" for applyLeave
    // do something
  }
}

22.6. Pattern Guard

Pattern guards are pattern objects that control the lifecycle of the guarded sub-expression, and can filter the events fired by the subexpression.

The following steps are required to develop and use a custom guard object.

Implement a guard forge class, responsible for compile-time guard information.
Implement a guard factory class, responsible for creating guard object instances at runtime.
Implement a guard class (used at runtime).
Register the guard forge class with the compiler by supplying a namespace and name, via the compiler configuration.

The code for the example guard object as shown in this chapter can be found in the test source folder in the package com.espertech.esper.regressionlib.support.extend.pattern by the name MyCountToPatternGuardForge. The sample guard discussed here counts the number of events occurring up to a maximum number of events, and end the sub-expression when that maximum is reached.

Some of the APIs that you use to implement a pattern guard are internal APIs and are not stable and may change between releases. The client package contains all the stable interface classes.

22.6.1. Implementing a Guard Forge

A guard forge class is only used by the compiler and is responsible for the following functions:

Implement a setGuardParameters method that takes guard parameters, which are themselves expressions.
Implement a collectSchedule method that collects guard schedule objects if any.
Implement a makeCodegen method that provides the code to construct a guard factory at time of deployment.

Guard forge classes implement the GuardForge:

public class MyCountToPatternGuardForge implements GuardForge { ...

The compiler constructs one instance of the guard forge class for each time the guard is listed in a statement.

The guard forge class implements the setGuardParameters method that is passed the parameters to the guard as supplied by the statement. It verifies the guard parameters, similar to the code snippet shown next. Our example counter guard takes a single numeric parameter:

public void setGuardParameters(List<ExprNode> guardParameters, MatchedEventConvertorForge convertor, StatementCompileTimeServices services) throws GuardParameterException {
    String message = "Count-to guard takes a single integer-value expression as parameter";
    if (guardParameters.size() != 1) {
        throw new GuardParameterException(message);
    }

    Class paramType = guardParameters.get(0).getForge().getEvaluationType();
    if (paramType != Integer.class && paramType != int.class) {
        throw new GuardParameterException(message);
    }
        
    this.numCountToExpr = guardParameters.get(0);
    this.convertor = convertor;
}

The makeCodegen method is called by the compiler to receive the code that builds a guard factory. Use the SAIFFInitializeBuilder to build factory initialization code:

public CodegenExpression makeCodegen(CodegenMethodScope parent, SAIFFInitializeSymbol symbols, CodegenClassScope classScope) {
    SAIFFInitializeBuilder builder = new SAIFFInitializeBuilder(MyCountToPatternGuardFactory.class, this.getClass(), "guardFactory", parent, symbols, classScope);
    return builder.exprnode("numCountToExpr", numCountToExpr)
                .expression("convertor", convertor.makeAnonymous(builder.getMethod(), classScope))
                .build();
}

22.6.2. Implementing a Guard Factory

A guard factory class is responsible for the following functions:

Implement a makeGuard method that constructs a new guard instance.

Guard factory classes implements the GuardFactory:

public class MyCountToPatternGuardFactory implements GuardFactory { ...

The runtime obtains an instance of the guard factory class at time of deployment.

The makeGuard method is called by the runtime to create a new guard instance. The example makeGuard method shown below passes the maximum count of events to the guard instance. It also passes a Quitable implementation to the guard instance. The guard uses Quitable to indicate that the sub-expression contained within must stop (quit) listening for events.

public Guard makeGuard(PatternAgentInstanceContext context, MatchedEventMap beginState, Quitable quitable, Object guardState) {
    EventBean[] events = convertor == null ? null : convertor.convert(beginState);
    Object parameter = PatternExpressionUtil.evaluateChecked("Count-to guard", numCountToExpr, events, context.getAgentInstanceContext());
    if (parameter == null) {
        throw new EPException("Count-to guard parameter evaluated to a null value");
    }

    Integer numCountTo = (Integer) parameter;
    return new MyCountToPatternGuard(numCountTo, quitable);
}

22.6.3. Implementing a Guard Class

A guard class has the following responsibilities:

Provides a startGuard method that initalizes the guard.
Provides a stopGuard method that stops the guard, called by the runtime when the whole pattern is stopped, or the sub-expression containing the guard is stopped.
Provides an inspect method that the pattern runtime invokes to determine if the guard lets matching events pass for further evaluation by the containing expression.

Guard classes implement the GuardSupport interface as shown here:

public class MyCountToPatternGuard implements Guard {

The compiler invokes the guard factory class to construct an instance of the guard class for each new sub-expression instance within a statement.

A guard class must provide an implementation of the startGuard method that the runtime invokes to start a guard instance. In our example, the method resets the guard's counter to zero:

public void startGuard() {
  counter = 0;
}

The runtime invokes the inspect method for each time the sub-expression indicates a new event result. Our example guard needs to count the number of events matched, and quit if the maximum number is reached:

public boolean inspect(MatchedEventMap matchEvent) {
  counter++;
  if (counter > numCountTo) {
    quitable.guardQuit();
    return false;
  }
  return true;
}

The inspect method returns true for events that pass the guard, and false for events that should not pass the guard.

22.6.4. Configuring Guard Namespace and Name

The guard factory class name as well as the namespace and name for the new guard must be added to the compiler configuration. The configuration shown below is XML however the same options are available through the configuration API:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <plugin-pattern-guard namespace="myplugin" name="count_to" 
        forge-class="com.espertech.esper.regressionlib.support.extend.pattern.MyCountToPatternGuardForge"/>
  </compiler>
</esper-configuration>

The new guard is now ready to use in a statement. The next pattern statement detects the first 10 MyEvent events:

select * from pattern [(every MyEvent) where myplugin:count_to(10)]

Note that the every keyword was placed within parentheses to ensure the guard controls the repeated matching of events.

22.7. Pattern Observer

Pattern observers are pattern objects that are executed as part of a pattern expression and can observe events or test conditions. Examples for built-in observers are timer:at and timer:interval. Some suggested uses of observer objects are:

Implement custom scheduling logic using the runtime's own scheduling and timer services
Test conditions related to prior events matching an expression

The following steps are required to develop and use a custom observer object within pattern statements:

Implement an observer forge class, which is used by the compiler only and is responsible for validating parameters and for initializing an observer factory.
Implement an observer factory class, responsible for creating observer object instances.
Implement an observer class.
Register an observer factory class with the compiler by supplying a namespace and name, via the compiler configuration file or the configuration API.

The code for the example observer object as shown in this chapter can be found in the test source folder in package com.espertech.esper.regression.client by the name MyFileExistsObserver. The sample observer discussed here very simply checks if a file exists, using the filename supplied by the pattern statement, and via the java.io.File class.

Some of the APIs that you use to implement a pattern observer are internal APIs and are not stable and may change between releases. The client package contains all the stable interface classes.

22.7.1. Implementing an Observer Forge

An observer forge class is responsible for the following functions:

Implement a setObserverParameters method that takes observer parameters, which are themselves expressions.
Implement a collectSchedule method that collects observer schedule objects if any.
Implement a makeCodegen method that provides the code to construct an observer factory at time of deployment.

Observer forge classes implement the ObserverForge interface:

public class MyFileExistsObserverForge implements ObserverForge { ...

The compiler constructs one instance of the observer forge class for each time the observer is listed in a statement.

The observer forge class implements the setObserverParameters method that is passed the parameters to the observer as supplied by the statement. It verifies the observer parameters, similar to the code snippet shown next. Our example file-exists observer takes a single string parameter:

public void setObserverParameters(List<ExprNode> observerParameters, MatchedEventConvertorForge convertor, ExprValidationContext validationContext) throws ObserverParameterException {
    String message = "File exists observer takes a single string filename parameter";
    if (observerParameters.size() != 1) {
        throw new ObserverParameterException(message);
    }
    if (!(observerParameters.get(0).getForge().getEvaluationType() == String.class)) {
        throw new ObserverParameterException(message);
    }

    this.filenameExpression = observerParameters.get(0);
    this.convertor = convertor;
}

The compiler calls the makeCodegen method to provide code that initializes the observer factory at time of deployment. It uses the SAIFFInitializeBuilder to build the code.

public CodegenExpression makeCodegen(CodegenMethodScope parent, SAIFFInitializeSymbol symbols, CodegenClassScope classScope) {
    SAIFFInitializeBuilder builder = new SAIFFInitializeBuilder(MyFileExistsObserverFactory.class, this.getClass(), "observerFactory", parent, symbols, classScope);
    return builder.exprnode("filenameExpression", filenameExpression)
            .expression("convertor", convertor.makeAnonymous(builder.getMethod(), classScope))
            .build();
}

22.7.2. Implementing an Observer Factory

An observer factory class is responsible for the following functions:

Implement a makeObserver method that returns a new observer instance.

Observer factory classes implement the ObserverFactory:

public class MyFileExistsObserverFactory implements ObserverFactory { ...

The runtime obtains an instance of the observer factory class at time of deployment.

The runtime calls the makeObserver method to create a new observer instance. The example makeObserver method shown below passes parameters to the observer instance:

public EventObserver makeObserver(PatternAgentInstanceContext context, MatchedEventMap beginState, ObserverEventEvaluator observerEventEvaluator, Object observerState, boolean isFilterChildNonQuitting) {
    EventBean[] events = convertor == null ? null : convertor.convert(beginState);
    Object filename = PatternExpressionUtil.evaluateChecked("File-exists observer ", filenameExpression, events, context.getAgentInstanceContext());
    if (filename == null) {
        throw new EPException("Filename evaluated to null");
    }
    return new MyFileExistsObserver(beginState, observerEventEvaluator, filename.toString());
}

The ObserverEventEvaluator parameter allows an observer to indicate events, and to indicate change of truth value to permanently false. Use this interface to indicate when your observer has received or witnessed an event, or changed it's truth value to true or permanently false.

The MatchedEventMap parameter provides a Map of all matching events for the expression prior to the observer's start. For example, consider a pattern as below:

a=MyEvent -> myplugin:my_observer(...)

The above pattern tagged the MyEvent instance with the tag "a". The runtime starts an instance of my_observer when it receives the first MyEvent. The observer can query the MatchedEventMap using "a" as a key and obtain the tagged event.

22.7.3. Implementing an Observer Class

An observer class has the following responsibilities:

Provides a startObserve method that starts the observer.
Provides a stopObserve method that stops the observer, called by the runtime when the whole pattern is stopped, or the sub-expression containing the observer is stopped.

Observer classes subclass com.espertech.esper.pattern.observer.ObserverSupport as shown here:

public class MyFileExistsObserver implements EventObserver { ...

The runtime invokes the observer factory class to construct an instance of the observer class for each new sub-expression instance within a statement.

An observer class must provide an implementation of the startObserve method that the runtime invokes to start an observer instance. In our example, the observer checks for the presence of a file and indicates the truth value to the remainder of the expression:

public void startObserve() {
  File file = new File(filename);
  if (file.exists()) {
    observerEventEvaluator.observerEvaluateTrue(beginState);
  } 
  else {
    observerEventEvaluator.observerEvaluateFalse(); 
  }
}

Note the observer passes the ObserverEventEvaluator an instance of MatchedEventMap. The observer can also create one or more new events and pass these events through the Map to the remaining expressions in the pattern.

22.7.4. Configuring Observer Namespace and Name

The observer factory class name as well as the namespace and name for the new observer must be added to the compiler configuration via the configuration API or using the XML configuration file. The configuration shown below is XML however the same options are available through the configuration API:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <plugin-pattern-observer namespace="myplugin" name="file_exists" 
      forge-class="com.espertech.esper.regressionlib.support.extend.pattern.MyFileExistsObserverForge" />
  </compiler>
</esper-configuration>

The new observer is now ready to use in a statement. The next pattern statement checks every 10 seconds if the given file exists, and indicates to the listener when the file is found.

select * from pattern [every timer:interval(10 sec) -> myplugin:file_exists("myfile.txt")]

22.8. Date-Time Method

Your application can provide additional date-time methods by implementing the extension API as shown below. The steps are as follows:

Implement the DateTimeMethodForgeFactory interface (package com.espertech.esper.common.client.hook.datetimemethod) that the compiler invokes for validation and to receive information on the public static methods that your application exposes that provide the date-time method logic.
Implement a static method (or multiple static methods) that receives the respective date-time value and additional parameters, if any, and that may return a new value, following the rules outlined below.
Add the class name of the DateTimeMethodForgeFactory implementation to the compiler configuration.
Use the new date-time method(s).

The EPL compiler distinguishes between two types of date-time methods:

A modifying date-time method modifies the date-time value, while the type of the result is the same as the type of the date-time value (i.e. input and result is a Calendar or LocalDateTime or other value).
A reformatting date-time method transforms the date-time value into a result that has a different return type.

The example herein builds a value-changing date-time method by name roll that rolls a field forward or backward and that receives the name of the field and an up/down flag as parameters.

The second example herein builds a reformatting date-time method by name asArrayOfString that returns an array of strings containing the day, month and year values of the date-time value.

22.8.1. Implement the `DateTimeMethodForgeFactory` Interface

The implementation of the DateTimeMethodForgeFactory interface is responsible for:

The initialize method returns the available footprints.
The validate method may validate actual provided parameters and always returns either a DateTimeMethodOpsModify (for modify) or a DateTimeMethodOpsReformat (for reformat).

The EPL compiler calls the initialize method to obtain the allowed footprints. It compares the footprints to the number and type of parameters actually provided.

For example, if there is a single footprint with no parameters, use this:

DotMethodFP[] footprints = new DotMethodFP[] {
    new DotMethodFP(DotMethodFPInputEnum.SCALAR_ANY)
  };
return new DateTimeMethodDescriptor(footprints);

For example, if there is a single footprint with a single string-type parameter, use this:

DotMethodFP[] footprints = new DotMethodFP[] {
  new DotMethodFP(DotMethodFPInputEnum.SCALAR_ANY,
      new DotMethodFPParam("provide a descriptive name of parameter", EPLExpressionParamType.SPECIFIC, String.class)
    )
  };

In the case that the date-time method modifies the date-time value, make the validate method returns an instance of DateTimeMethodModifyOps.

In the case that the date-time method reformats the date-time value, make the validate method returns an instance of DateTimeMethodReformatOps.

Use the DateTimeMethodModeStaticMethod class to provide the class and the name of the public static method providing the respective operation.

It is not required to provide a static method for each of the different types of date-time values. You may leave an operation at a null-value to indicate it is not provided for that date-time value type.

The following class handles the new roll date-time method, which has single footprint that has a string-type and a boolean-type parameter:

public class MyLocalDTMForgeFactoryRoll implements DateTimeMethodForgeFactory {
  private final static DotMethodFP[] FOOTPRINTS = new DotMethodFP[]{
    new DotMethodFP(DotMethodFPInputEnum.SCALAR_ANY,
        new DotMethodFPParam("an string-type calendar field name", EPLExpressionParamType.SPECIFIC, String.class),
        new DotMethodFPParam("a boolean-type up/down indicator", EPLExpressionParamType.SPECIFIC, boolean.class))
    };

  public DateTimeMethodDescriptor initialize(DateTimeMethodInitializeContext context) {
    return new DateTimeMethodDescriptor(FOOTPRINTS);
  }

  public DateTimeMethodOps validate(DateTimeMethodValidateContext context) {
    // this is an opportunity to do additional validation or evaluation when desired
    // however the footprint is already validated
    DateTimeMethodOpsModify roll = new DateTimeMethodOpsModify();
    
    // see below for MyLocalDTMRollUtility
    roll.setCalendarOp(new DateTimeMethodModeStaticMethod(MyLocalDTMRollUtility.class, "roll"));
    roll.setLdtOp(new DateTimeMethodModeStaticMethod(MyLocalDTMRollUtility.class, "roll"));
    roll.setZdtOp(new DateTimeMethodModeStaticMethod(MyLocalDTMRollUtility.class, "roll"));
    return roll;
 }
}

The following class handles the new asArrayOfString date-time method, which has single footprint that has a no parameters:

public class MyLocalDTMForgeFactoryArrayOfString implements DateTimeMethodForgeFactory {
  private final static DotMethodFP[] FOOTPRINTS = new DotMethodFP[]{
    new DotMethodFP(DotMethodFPInputEnum.SCALAR_ANY)
  };

  public DateTimeMethodDescriptor initialize(DateTimeMethodInitializeContext context) {
    return new DateTimeMethodDescriptor(FOOTPRINTS);
  }

  public DateTimeMethodOps validate(DateTimeMethodValidateContext context) {
    DateTimeMethodOpsReformat asArrayOfString = new DateTimeMethodOpsReformat();
    asArrayOfString.setReturnType(String[].class);
    
    // see below for MyLocalDTMArrayOfStringUtility
    asArrayOfString.setLongOp(new DateTimeMethodModeStaticMethod(MyLocalDTMArrayOfStringUtility.class, "asArrayOfString"));
    asArrayOfString.setDateOp(new DateTimeMethodModeStaticMethod(MyLocalDTMArrayOfStringUtility.class, "asArrayOfString"));
    asArrayOfString.setCalendarOp(new DateTimeMethodModeStaticMethod(MyLocalDTMArrayOfStringUtility.class, "asArrayOfString"));
    asArrayOfString.setLdtOp(new DateTimeMethodModeStaticMethod(MyLocalDTMArrayOfStringUtility.class, "asArrayOfString"));
    asArrayOfString.setZdtOp(new DateTimeMethodModeStaticMethod(MyLocalDTMArrayOfStringUtility.class, "asArrayOfString"));
    return asArrayOfString;    
  }
}

22.8.2. Implement the Static Methods

For value-changing date-time methods that operate on long, Date and Calendar, the static method must return void, its first parameter must be Calendar and the remaining parameters much match the expression parameters, such as:

public static void roll(Calendar calendar, String fieldName, boolean flagValue) {

For value-changing date-time methods that operate on LocalDateTime, the static method must return LocalDateTime, its first parameter must be LocalDateTime and the remaining parameters much match the expression parameters, such as:

public static LocalDateTime roll(LocalDateTime ldt, String fieldName, boolean flagValue) {

For value-changing date-time methods that operate on ZonedDateTime, the static method must return ZonedDateTime, its first parameter must be ZonedDateTime and the remaining parameters much match the expression parameters, such as:

public static ZonedDateTime roll(ZonedDateTime zdt, String fieldName, boolean flagValue) {

For reformatting date-time methods, the static method must return the same type as provided by the getReturnType method of DateTimeMethodReformatMode, its first parameter must be any of the below and the remaining parameters much match the expression parameters (see example below).

long
Date (from java.util)
Calendar
LocalDateTime
ZonedDateTime

The class providing the static methods for the roll date-time method is shown next.

public class MyLocalDTMRollUtility {
  public static void roll(Calendar calendar, String fieldName, boolean flagValue) {
    switch (fieldName) {
      case "date": calendar.roll(Calendar.DATE, flagValue); break;
        default: throw new EPException("Invalid field name '" + fieldName + "'");
    }
  }

  public static LocalDateTime roll(LocalDateTime ldt, String fieldName, boolean flagValue) {
    switch (fieldName) {
      case "date": return ldt.plusDays(1);
        default: throw new EPException("Invalid field name '" + fieldName + "'");
    }
  }

  public static ZonedDateTime roll(ZonedDateTime zdt, String fieldName, boolean flagValue) {
    switch (fieldName) {
      case "date": return zdt.plusDays(1);
        default: throw new EPException("Invalid field name '" + fieldName + "'");
    }
  }
}

The class providing the static methods for the asArrayOfString date-time method is shown next.

public class MyLocalDTMArrayOfStringUtility {
  public static String[] asArrayOfString(long date) {
    Calendar calendar = Calendar.getInstance();
    calendar.setTimeInMillis(date);
    return asArrayOfString(calendar);
  }

  public static String[] asArrayOfString(Date date) {
    Calendar calendar = Calendar.getInstance();
    calendar.setTime(date);
    return asArrayOfString(calendar);
  }

  public static String[] asArrayOfString(Calendar calendar) {
    return new String[] {Integer.toString(calendar.get(Calendar.DAY_OF_MONTH)),
      Integer.toString(calendar.get(Calendar.MONTH) + 1),
      Integer.toString(calendar.get(Calendar.YEAR))};
  }

  public static String[] asArrayOfString(LocalDateTime ldt) {
    return new String[] {Integer.toString(ldt.getDayOfMonth()),
      Integer.toString(ldt.getMonthValue()),
      Integer.toString(ldt.getYear())};
  }

  public static String[] asArrayOfString(ZonedDateTime zdt) {
    return new String[] {Integer.toString(zdt.getDayOfMonth()),
      Integer.toString(zdt.getMonthValue()),
      Integer.toString(zdt.getYear())};
    }
  }
}

22.8.3. Add the Date-Time Method Extension to the Compiler Configuration

You must provide the date-time method name and the forge factory class name to the compiler configuration.

The sample XML snippet below configures the two example date-time methods:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <plugin-method-datetime method-name="roll" forge-class="packagename.MyLocalDTMForgeFactoryRoll"/>
    <plugin-method-datetime method-name="asArrayOfString" forge-class="packagename.MyLocalDTMForgeFactoryArrayOfString"/>
  </compiler>
</esper-configuration>

The next example uses the configuration API to register the same:

Configuration configuration = new Configuration();
configuration.getCompiler().addPlugInDateTimeMethod("roll", MyLocalDTMForgeFactoryRoll.class.getName());
configuration.getCompiler().addPlugInDateTimeMethod("asArrayOfString", MyLocalDTMForgeFactoryArrayOfString.class.getName());

22.8.4. Use the new Date-Time Methods

The sample EPL below uses the new methods:

select caldate.roll('date', true) as calroll,
  longdate.roll('date', true) as longroll,
  utildate.roll('date', true) as utilroll,
  localdate.roll('date', true) as localdateroll,
  zoneddate.roll('date', true) as zoneddateroll,
  caldate.asArrayOfString() as arraystring_from_cal,
  longdate.asArrayOfString() as arraystring_from_long,
  utildate.asArrayOfString() as arraystring_from_util,
  localdate.asArrayOfString() as arraystring_from_local,
  zoneddate.asArrayOfString() as arraystring_from_zoned
 from MyDateTimeEvent

22.9. Enumeration Method

Your application can provide additional enumeration methods by implementing the extension API as shown below. The steps are as follows:

Implement the EnumMethodForgeFactory interface (package com.espertech.esper.common.client.hook.enummethod) that the compiler invokes for validation and to receive information on the state class and the public static methods that your application exposes that provide the enumeration method logic.
Implement the EnumMethodState that holds the state for enumerating over input values.
Implement a static method (the processing method) that receives the state and each of the items of the collection of events, scalar values or object values and the result of lambda parameter expression evalutions.
Add the class name of the EnumMethodForgeFactory implementation to the compiler configuration.
Use the new enumeration method(s).

The example herein builds a simple enumeration method by name median that computes the median for a set of integer-typed input values and that returns a double-type median.

22.9.1. Implement the `EnumMethodForgeFactory` Interface

The implementation of the EnumMethodForgeFactory interface is responsible for:

The initialize method returns the available footprints.
The validate method may validate actual provided parameters and returns an EnumMethodModeStaticMethod descriptor.

The EPL compiler calls the initialize method to obtain the allowed footprints. It compares the footprints to the number and type of parameters actually provided.

The example median enumeration method takes a scalar numeric values as input and has no parameters.

DotMethodFP[] footprints = new DotMethodFP[] {
    new DotMethodFP(DotMethodFPInputEnum.SCALAR_NUMERIC)
  };
return new EnumMethodDescriptor(footprints);

Additional examples for footprint are:

Table 22.2. Enumeration Method Footprint Examples

Sample Footprint and Processing Method	Comment
new DotMethodFP(DotMethodFPInputEnum.EVENTCOLL, new DotMethodFPParam(1, "predicate", EPLExpressionParamType.BOOLEAN)) process(State_class state, EventBean event, Boolean pass)	Enumeration method taking events as input and that has a single lambda expression that is a predicate and that returns a boolean value
new DotMethodFP(DotMethodFPInputEnum.SCALAR_ANY, new DotMethodFPParam(1, "value-selector", EPLExpressionParamType.NUMERIC)) process(State_class state, Object value, Object lambdaResult)	Enumeration method taking any type of scalar values as input and that has a single lambda expression that is a value-selector and that returns a numeric value
new DotMethodFP(DotMethodFPInputEnum.SCALAR_NUMERIC, new DotMethodFPParam("from", EPLExpressionParamType.NUMERIC), new DotMethodFPParam("to", EPLExpressionParamType.NUMERIC)) process(State_class state, Object value)	Enumeration method taking any type of numeric scalar values as input and that has no lambda expressions as parameter but two non-lambda expressions as parameter both returning a numeric value
new DotMethodFP(DotMethodFPInputEnum.EVENTCOLL, new DotMethodFPParam(1, "v1", EPLExpressionParamType.ANY), new DotMethodFPParam(1, "v2", EPLExpressionParamType.ANY)) process(State_class state, EventBean event, Object v1, Object v2)	Enumeration method taking events as input and that has two lambda expressions as parameter both returning any object value
new DotMethodFP(DotMethodFPInputEnum.ANY, new DotMethodFPParam(2, "value, index", EPLExpressionParamType.BOOLEAN)) process(State_class state, EventBean event, Boolean pass)	Enumeration method taking any type of scalar values as input and that a single lambda expression that has 2 parameters (value and index, similar to `takeWhile` with index) and that returns a boolean value

Use the EnumMethodModeStaticMethod class to provide the class of the state object and the class and the name of the public static method that is the processing method.

The following class handles the new median enumeration method, which has single footprint that has no parameters:

public static class MyLocalEnumMethodForgeMedian implements EnumMethodForgeFactory {
  private static final DotMethodFP[] FOOTPRINTS = new DotMethodFP[]{
    new DotMethodFP(DotMethodFPInputEnum.SCALAR_NUMERIC)
  };

  public EnumMethodDescriptor initialize(EnumMethodInitializeContext context) {
    return new EnumMethodDescriptor(FOOTPRINTS);
  }

  public EnumMethodModeStaticMethod validate(EnumMethodValidateContext context) {
    Class stateClass = MyLocalEnumMethodMedianState.class; // the class providing state, must implement EnumMethodState
    Class serviceClass = MyLocalEnumMethodMedianService.class; // the class providing the processing method (any class)
    String methodName = "next"; // the name of the method for processing an item of input values (any method name)
    EPChainableType returnType = EPChainableTypeHelper.singleValue(Double.class); // indicate that we are returning a Double-type value
    boolean earlyExit = false;
    return new EnumMethodModeStaticMethod(stateClass, serviceClass, methodName, returnType, earlyExit);
  }
}

The EnumMethodModeStaticMethod provides multiple settings to the EPL compiler:

The class that implements the EnumMethodState interface which holds enumeration state.
The class and method name of the method that processes values and that receives the result of lambda parameter evaluation.
The return type of the enumeration method which is an EPChainableType value.
An indicator whether the state requires early-access checking.

The EPChainableType return type has the following choices:

For a method returning a collection of events, always use EPChainableTypeHelper.collectionOfEvents(context.getInputEventType()).
For a method returning a collection of objects, use EPChainableTypeHelper.collectionOfSingleValue(class_of_value).
For a method returning a single scalar value, use EPChainableTypeHelper.singleValue(class_of_value).

22.9.2. Implement the `EnumMethodState` Interface

The implementation of the EnumMethodState interface is responsible for holding the transient state of one pass over input values to the enumeration method. The runtime allocates a new instance of the provided class for each execution of the enumeration method. The implementation class must have a default constructor.

The state class does this:

The state method returns the final result.
(Optional) Override the setParameter method to receive non-lambda expression parameter results.
(Optional) Override the completed method to indicate early-exit.

The following class handles the state for the median enumeration method and computes the median:

public class MyLocalEnumMethodMedianState implements EnumMethodState {
  private List<Integer> list = new ArrayList<>();

  public Object state() {
    Collections.sort(list);
    // get count of scores
    int totalElements = list.size();
    if (totalElements < 2) {
      return null;
    }
    // check if total number of scores is even
    if (totalElements % 2 == 0) {
      int sumOfMiddleElements = list.get(totalElements / 2) + list.get(totalElements / 2 - 1);
      // calculate average of middle elements
      return ((double) sumOfMiddleElements) / 2;
    }
  return (double) list.get(totalElements / 2);
  }

  public void add(Integer value) {
    list.add(value);
  }
}

The example does not have additional non-lambda parameters and therefore does not override setParameter. Your application can receive any non-lambda expression values by overriding setParameter.

The example does not have early-exit and therefore does not override completed. Your application can override completed to indicate an early exit. Please make sure EnumMethodModeStaticMethod has the early-exit flag set.

22.9.3. Implement the Static Method for Processing

The processing method is the method that the runtime invokes for each input element. There is no interface for the class providing the method and the method must be public and static.

The processing method, that your application specified as part of EnumMethodModeStaticMethod, receives these values:

The first parameter is always the state class as provided in the previous step.
The second parameter is always the item value of type EventBean when the input is events, or type Object when the input is not events.
The remaining parameters are the result of the runtime evaluating the expression bodies of lambda expression parameters to the enumeration method.

The example median enumeration method takes scalar values as input and has no lambda expression parameters:

public static class MyLocalEnumMethodMedianService {
  public static void next(MyLocalEnumMethodMedianState state, Object element) {
    state.add((Integer) element);
  }
}

22.9.4. Add the Enumeration Method Extension to the Compiler Configuration

You must provide the enumeration method name and the forge factory class name to the compiler configuration.

The sample XML snippet below configures the two example date-time methods:

<esper-configuration xmlns="http://www.espertech.com/schema/esper">
  <compiler>
    <plugin-method-enum method-name="median" forge-class="packagename.MyLocalEnumMethodForgeMedian"/>
  </compiler>
</esper-configuration>

The next example uses the configuration API to register the same:

Configuration configuration = new Configuration();
configuration.getCompiler().addPlugInEnumerationMethod("median", MyLocalEnumMethodForgeMedian.class.getName());

22.9.5. Use the new Enumeration Method

The sample EPL below uses the new method, assuming that the MyIntegerValuesEvent has an event property by name intvalues that returns an array or collection of int-type values:

select intvalues.median() as median from MyIntegerValuesEvent

22.9.6. Further Information to Lambda Parameters

There are three types of lambda parameters supported.

When a lambda parameter is the value itself it is represented by EnumMethodLambdaParameterTypeValue. For example, in orderItems.where(v => v.price > 0) the v parameter is the input item value itself, i.e. the event or scalar value depending on input. This is always the default and no additional code is required.

When a lambda parameter is the index of the value it is represented by EnumMethodLambdaParameterTypeIndex. For example, in orderItems.takeWhile( (v, ind) => ind < 10) the ind parameter is the numeric index of the item starting at zero.

The below code snippet sets the lambda parameter types for value and index:

mode.setLambdaParameters(descriptor -> {
  if (descriptor.getLambdaParameterNumber() == 0) {
    return EnumMethodLambdaParameterTypeValue.INSTANCE;
  }
  return EnumMethodLambdaParameterTypeIndex.INSTANCE;
});

When a lambda parameter is provided by the state class itself it is represented by EnumMethodLambdaParameterTypeStateGetter. For example, in orderItems.aggregate(0, (result, v) => result + v.price)) the result parameter is provided by the state itself.

The below code snippet sets the lambda parameter types for state-provided and index:

mode.setLambdaParameters(descriptor -> {
  if (descriptor.getLambdaParameterNumber() == 0) {
    // the state class has a getResult method returning string
    return new EnumMethodLambdaParameterTypeStateGetter(int.class, "getResult");
  }
  return EnumMethodLambdaParameterTypeValue.INSTANCE;
});

Note that the example above assumes that the state class has a getResult method returning an int-type value.

For additional information and examples please consult the JavaDoc and regression testing code.

Chapter 23. Examples, Tutorials, Case Studies

23.1. Examples Overview

23.2. Running the Examples

23.3. AutoID RFID Reader

23.4. Runtime Configuration

23.5. JMS Server Shell and Client

23.5.1. Overview
23.5.2. JMS Messages as Events
23.5.3. JMX for Remote Dynamic Statement Management

23.6. Market Data Feed Monitor

23.6.1. Input Events
23.6.2. Computing Rates per Feed
23.6.3. Detecting a Fall-Off
23.6.4. Event generator

23.7. OHLC Plug-In Data Window

23.8. Transaction 3-Event Challenge

23.8.1. The Events
23.8.2. Combined Event
23.8.3. Real-Time Summary Data
23.8.4. Find Problems
23.8.5. Event Generator

23.9. Self-Service Terminal

23.9.1. Events
23.9.2. Detecting Customer Check-In Issues
23.9.3. Absence of Status Events
23.9.4. Activity Summary Data
23.9.5. Sample Application for J2EE Application Server

23.10. Assets Moving Across Zones - An RFID Example

23.11. StockTicker

23.12. MatchMaker

23.13. Named Window Query

23.14. Sample Virtual Data Window

23.15. Sample Cycle Detection

23.16. Quality of Service

23.17. Trivia Geeks Club

23.1. Examples Overview

This chapter outlines the examples that come with the distribution in the examples folder of the distribution. Each sample is in a separate folder that contains all files needed by the example, excluding jar files.

Here is an overview over the examples in alphabetical order:

Table 23.1. Examples

Name	Description
Section 23.3, “AutoID RFID Reader”	An array of RFID readers sense RFID tags as pallets are coming within the range of one of the readers. Shows the use of an XSD schema and XML event representation. A single statement shows a rolling time window, a where-clause filter on a nested property and a group-by.
Section 23.6, “Market Data Feed Monitor”	Processes a raw market data feed and reports throughput statistics and detects when the data rate of a feed falls off unexpectedly. Demonstrates a batch time window and a rolling time window with a having-clause. Multi-threaded example with a configurable number of threads and a simulator for generating feed data.
Section 23.12, “MatchMaker”	In the MatchMaker example every mobile user has an X and Y location and the task of the event patterns created by this example is to detect mobile users that are within proximity given a certain range, and for which certain properties match preferences. Uses an overlapping context to find matching mobile users based on mobile user events.
Section 23.13, “Named Window Query”	A mini-benchmark that handles temperature sensor events. The sample creates a named window and fills it with a large number of events. It then executes a large number of pre-compiled statements as well as fire-and-forget queries and reports times. Study this example if you are interested in named windows, Map event type representation, fire-and-forget queries as well as pre-defined statements via on-select, and the performance aspects.
Section 23.14, “Sample Virtual Data Window”	This example demonstrates the use of virtual data window to expose a (large) external data store, without any need to keep events in memory, and without sacrificing statement performance.
Section 23.15, “Sample Cycle Detection”	This example showcases the aggregation multi-function extension API for use with a cycle-detection problem detecting cycles in transactions between accounts.
Section 23.7, “OHLC Plug-In Data Window”	A plug-in custom data window addressing a problem in the financial space: Computes open-high-low-close bars for minute-intervals of events that may arrive late, based on each event's timestamp. A custom plug-in data window based on the extension API can be a convenient and reusable way to express a domain-specific analysis problem as a unit, and this example includes the code for the OHLC data window factory and window as well as simulator to test the window.
Section 24.3, “Using the Performance Kit”	A benchmark that is further described in the performance section of this document under performance kit.
Section 23.16, “Quality of Service”	This example develops some code for measuring quality-of-service levels such as for a service-level agreement (SLA). This example combines patterns with select-statements, shows the use of the timer `'at'` operator and followed-by operator `->`, and uses the iterator API to poll for current results.
Section 23.10, “Assets Moving Across Zones - An RFID Example”	An example out of the RFID domain processes location report events. The example includes a simple Swing-based GUI for visualization allows moving tags from zone to zone visually. It also a contains comprehensive simulator to generate data for a large number of asset groups and their tracking. The example uses non-overlapping context to detect patterns in the aggregated data to determine when an asset group constraint is violated.
Section 23.4, “Runtime Configuration”	Example code to demonstrate various key compile-time and runtime actions such as adding event types on-the-fly, adding new variables, adding plug-in single-row and aggregation functions and adding variant streams.
Section 23.5, “JMS Server Shell and Client”	The server shell is a Java Messaging Service (JMS) -based server and client that send and listens to messages on a JMS destination. It also demonstrates a simple Java Management Extension (JMX) MBean for remote statement management. A single statement computes an average duration for each IP address on a rolling time window and outputs a snapshot every 2 seconds.
Section 23.11, “StockTicker”	An example from the financial domain that features event patterns to filter stock tick events based on price and symbol. The example is designed to provide a high volume of events and includes multithreaded unit test code as well as a simulting data generator. The example uses overlapping context to find when price spikes happen based on price limit events received.
Section 23.9, “Self-Service Terminal”	A J2EE-based self-service terminal managing system in an airport that gets a lot of events from connected terminals. Contains a message-driven bean (EJB-MDB) for use in a J2EE container, a client and a simulator, as well as statements for detecting various conditions. A version that runs outside of a J2EE container is also available.
Section 23.17, “Trivia Geeks Club”	Trivia Geeks Club demonstrates EPL for a scoring system computing scores in a trivia game.

23.2. Running the Examples

In order to compile and run the samples please follow the below instructions:

Make sure Java 1.6 or greater is installed and the JAVA_HOME environment variable is set.
Open a console window and change directory to examples/example_name/etc.
Run "setenv.bat" (Windows) or "setenv.sh" (Unix) to verify your environment settings.
Run "compile.bat" (Windows) or "compile.sh" (Unix) to compile an example.
Now you are ready to run an example. Some examples require mandatory parameters that are also described in the file "readme.txt" in the "etc" folder.
Modify the logger logging level in the "log4j.xml" configuration file changing DEBUG to INFO on a class or package level to control the volume of text output.

Each example also provides Eclipse project .classpath and .project files. The Eclipse projects expect an esper_runtime user library that includes the runtime dependencies.

JUnit tests exist for the example code. The JUnit test source code for the examples can be found in each example's src/test folder. To build and run the example JUnit tests, use the Maven 2 goal test.

23.3. AutoID RFID Reader

In this example an array of RFID readers sense RFID tags as pallets are coming within the range of one of the readers. A reader generates XML documents with observation information such as reader sensor ID, observation time and tags observed. A statement computes the total number of tags per reader sensor ID within the last 60 seconds.

This example demonstrates how XML documents unmarshalled to org.w3c.dom.Node DOM document nodes can natively be processed by the runtime without requiring Java object event representations. The example uses an XPath expression for an event property counting the number of tags observed by a sensor. The XML documents follow the AutoID (http://www.autoid.org/) organization standard.

The classes for this example can be found in package com.espertech.esper.example.autoid. As events are XML documents with no Java object representation, the example does not have event classes.

A simulator that can be run from the command line is also available for this example. The simulator generates a number of XML documents as specified by a command line argument and prints out the totals per sensor. Run "run_autoid.bat" (Windows) or "run_autoid.sh" (Unix) to start the AutoID simulator. Please see the readme file in the same folder for build instructions and command line parameters.

The code snippet below shows the simple statement to compute the total number of tags per sensor. The statement is created by class com.espertech.esper.example.autoid.RFIDTagsPerSensorStmt.

select ID as sensorId, sum(countTags) as numTagsPerSensor
from AutoIdRFIDExample#time(60 seconds)
where Observation[0].Command = 'READ_PALLET_TAGS_ONLY'
group by ID

23.4. Runtime Configuration

This example demonstrates various key runtime configuration options such as adding event types on-the-fly, adding new variables, adding plug-in single-row and aggregation functions and adding variant streams.

The classes for this example live in package com.espertech.esper.example.runtimeconfig.

23.5. JMS Server Shell and Client

23.5.1. Overview

The server shell is a Java Messaging Service (JMS) -based server that listens to messages on a JMS destination, and sends the received events into the runtime. The example also demonstrates a Java Management Extension (JMX) MBean that allows remote dynamic statement management. This server has been designed to run with either Tibco (TM) Enterprise Messaging System (Tibco EMS), or with Apache ActiveMQ, controlled by a properties file.

The server shell has been created as an alternative to the EsperIO Spring JMSTemplate adapter. The server shell is a low-latency processor for byte messages. It employs JMS listeners to process message in multiple threads, this model reduces thread context switching for many JMS providers. The server is configurable and has been tested with two JMS providers. It consists of only 10 classes and is thus easy to understand.

The server shell sample comes with a client (server shell client) that sends events into the JMS-based server, and that also creates a statement on the server remotely through a JMX MBean proxy class.

The server shell classes for this example live in package com.espertech.esper.example.servershell. Configure the server to point to your JMS provider by changing the properties in the file servershell_config.properties in the etc folder. Make sure your JMS provider (ActiveMQ or Tibco EMS) is running, then run "run_servershell.bat" (Windows) or "run_servershell.sh" (Unix) to start the JMS server.

Start the server shell process first before starting the client, since the client also demonstrates remote statement management through JMX by attaching to the server process.

The client classes to the server shell can be found in package com.espertech.esper.example.servershellclient. The client shares the same configuration file as the server shell. Run "run_servershellclient.bat" (Windows) or "run_servershellclient.sh" (Unix) to start the JMS producer client that includes a JMX client as well.

23.5.2. JMS Messages as Events

The server shell starts a configurable number of JMS MessageListener instances that listen to a given JMS destination. The listeners expect a BytesMessage that contain a String payload. The payload consists of an IP address and a double-typed duration value separated by a comma.

Each listener extracts the payload of a message, constructs an event object and sends the event into the shared runtime instance.

At startup time, the server creates a single statement with the runtime that prints out the average duration per IP address for the last 10 seconds of events, and that specifies an output rate of 2 seconds. By running the server and then the client, you can see the output of the averages every 2 seconds.

The server shell client acts as a JMS producer that sends 1000 events with random IP addresses and durations.

23.5.3. JMX for Remote Dynamic Statement Management

The server shell is also a JMX server providing an RMI-based connector. The server shell exposes a JMX MBean that allows remote statement management. The JMX MBean allows to create a statement remotely, attach a listener to the statement and undeploy a statement remotely.

The server shell client, upon startup, obtains a remote instance of the management MBean exposed by the server shell. It creates a statement through the MBean that filters out all durations greater then the value 9.9. After sending 1000 events, the client then undeploys the statement remotely on the server.

23.6. Market Data Feed Monitor

This example processes a raw market data feed. It reports throughput statistics and detects when the data rate of a feed falls off unexpectedly. A rate fall-off may mean that the data is stale and you want to alert when there is a possible problem with the feed.

The classes for this example live in package com.espertech.esper.example.marketdatafeed. Run "run_mktdatafeed.bat" (Windows) or "run_mktdatafeed.sh" (Unix) in the examples/etc folder to start the market data feed simulator.

23.6.1. Input Events

The input stream consists of 1 event stream that contains 2 simulated market data feeds. Each individual event in the stream indicates the feed that supplies the market data, the security symbol and some pricing information:

String symbol;
FeedEnum feed;
double bidPrice;
double askPrice;

23.6.2. Computing Rates per Feed

For throughput statistics and to detect rapid fall-off, the example calculates a ticks per second rate for each market data feed.

You can use a statement that specifies a data window onto the market data event stream that batches together 1 second of events. You specify the feed and a count of events per feed as output values. To make this data available for further processing, you insert output events into the TicksPerSecond event stream:

insert into TicksPerSecond
select feed, count(*) as cnt 
  from MarketDataEvent#time_batch(1 second) 
 group by feed

23.6.3. Detecting a Fall-Off

We define a rapid fall-off by alerting when the number of ticks per second for any second falls below 75% of the average number of ticks per second over the last 10 seconds.

We can compute the average number of ticks per second over the last 10 seconds simply by using the TicksPerSecond events computed by the prior statement and averaging the last 10 seconds. Next, the example compares the current rate with the moving average and filter out any rates that fall below 75% of the average:

select feed, avg(cnt) as avgCnt, cnt as feedCnt 
  from TicksPerSecond#time(10 seconds)
 group by feed 
having cnt < avg(cnt) * 0.75

23.6.4. Event generator

The simulator generates market data events for 2 feeds, feed A and feed B. The first parameter to the simulator is a number of threads. Each thread sends events for each feed in an endless loop. Note that as the Java VM garbage collection kicks in, the example generates rate drop-offs during such pauses.

The second parameter is a rate drop probability parameter specifies the probability in percent that the simulator drops the rate for a randomly chosen feed to 60% of the target rate for that second. Thus rate fall-off alerts can be generated.

The third parameter defines the number of seconds to run the example.

23.7. OHLC Plug-In Data Window

This example contains a fully-functional custom data window based on the extension API that computes OHLC open-high-low-close bars for events that provide a long-typed timestamp and a double-typed value.

OHLC bar is a problem out of the financial domain. The "Open" refers to the first datapoint and the "Close" to the last datapoint in an interval. The "High" refers to the maximum and the "Low" to the minimum value during each interval. The term "bar" is used to describe each interval results of these 4 values.

The example provides an OHLC data window that is hardcoded to 1-minute bars. It considers the timestamp value carried by each event, and not the system time. The cutoff time after which an event is no longer considered for a bar is hardcoded to 5 seconds.

The window assumes that events arrive in timestamp order: Each event's timestamp value is equal to or higher then the timestamp value provided by the prior event.

The window may also be used together with #groupwin to group per criteria, such as symbol. In this case the assumption of timestamp order applies per symbol.

The window gracefully handles no-event and late-event scenarios. Interval boundaries are defined by system time, thus event timestamp and system time must roughly be in-sync, unless using external timer events.

23.8. Transaction 3-Event Challenge

The classes for this example live in package com.espertech.esper.example.transaction. Run "run_txnsim.bat" (Windows) or "run_txnsim.sh" (Unix) to start the transaction simulator. Please see the readme file in the same folder for build instructions and command line parameters.

23.8.1. The Events

The use case involves tracking three components of a transaction. It's important that the example uses at least three components, since some runtimes have different performance or coding for only two events per transaction. Each component comes to the runtime as an event with the following fields:

Transaction ID
Time stamp

In addition, the example has the following extra fields:

In event A:

Customer ID

In event C:

Supplier ID (the ID of the supplier that the order was filled through)

23.8.2. Combined Event

We need to take in events A, B and C and produce a single, combined event with the following fields:

Transaction ID
Customer ID
Time stamp from event A
Time stamp from event B
Time stamp from event C

What we‘re doing here is matching the transaction IDs on each event, to form an aggregate event. If all these events were in a relational database, this could be done as a simple SQL join… except that with 10,000 events per second, you will need some serious database hardware to do it.

23.8.3. Real-Time Summary Data

Further, the example produces the following:

Min,Max,Average total latency from the events (difference in time between A and C) over the past 30 minutes.
Min,Max,Average latency grouped by (a) customer ID and (b) supplier ID. In other words, metrics on the the latency of the orders coming from each customer and going to each supplier.
Min,Max,Average latency between events A/B (time stamp of B minus A) and B/C (time stamp of C minus B).

23.8.4. Find Problems

The example detects a transaction that did not make it through all three events. In other words, a transaction with events A or B, but not C. Note that, in this case, what you care about is event C. The lack of events A or B could indicate a failure in the event transport and should be ignored. Although the lack of an event C could also be a transport failure, it merits looking into.

23.8.5. Event Generator

To make testing easier, standard and to demonstrate how the example works, the example is including an event generator. The generator generates events for a given number of transactions, using the following rules:

One in 5,000 transactions will skip event A
One in 1,000 transactions will skip event B
One in 10,000 transactions will skip event C.
Transaction identifiers are randomly generated
Customer and supplier identifiers are randomly chosen from two lists
The time stamp on each event is based on the system time. Between events A and B as well as B and C, between 0 and 999 is added to the time. So, you have an expected time difference of around 500 milliseconds between each event
Events are randomly shuffled as described below

To make things harder, the example doesn't have transaction events coming in order. This code ensures that they come completely out of order. To do this, the example fills in a bucket with events and, when the bucket is full, it shuffles it. The buckets are sized so that some transactions‘ events will be split between buckets. So, you have a fairly randomized flow of events, representing the worst case from a big, distributed infrastructure.

The generator lets you change the size of the bucket (small, medium, large, larger, largerer). The larger the bucket size, the more events potentially come in between two events in a given transaction and so, the more the performance characteristics like buffers, hashes/indexes and other structures are put to the test as the bucket size increases.

23.9. Self-Service Terminal

The example is about a J2EE-based self-service terminal managing system in an airport that gets a lot of events from connected terminals. The event rate is around 500 events per second. Some events indicate abnormal situations such as 'paper low' or 'terminal out of order'. Other events observe activity as customers use a terminal to check in and print boarding tickets.

23.9.1. Events

Each self-service terminal can publish any of the 6 events below.

Checkin - Indicates a customer started a check-in dialog
Cancelled - Indicates a customer cancelled a check-in dialog
Completed - Indicates a customer completed a check-in dialog
OutOfOrder - Indicates the terminal detected a hardware problem
LowPaper - Indicates the terminal is low on paper
Status - Indicates terminal status, published every 1 minute regardless of activity as a terminal heartbeat

All events provide information about the terminal that published the event, and a timestamp. The terminal information is held in a property named "term" and provides a terminal id. Since all events carry similar information, it models each event as a subtype to a base class BaseTerminalEvent, which will provide the terminal information that all events share. This enables us to treat all terminal events polymorphically, that is you can treat derived event types just like their parent event types. This helps simplify our statements.

All terminals publish Status events every 1 minute. In normal cases, the Status events indicate that a terminal is alive and online. The absence of status events may indicate that a terminal went offline for some reason and that may need to be investigated.

23.9.2. Detecting Customer Check-In Issues

A customer may be in the middle of a check-in when the terminal detects a hardware problem or when the network goes down. In that situation the example alerts a team member to help the customer. When the terminal detects a problem, it issues an OutOfOrder event. A pattern can find situations where the terminal indicates out-of-order and the customer is in the middle of the check-in process:

select * from pattern [ every a=Checkin -> 
      ( OutOfOrder(term.id=a.term.id) and not 
          (Cancelled(term.id=a.term.id) or Completed(term.id=a.term.id)) )]

23.9.3. Absence of Status Events

Since Status events arrive in regular intervals of 60 seconds, you can make use of temporal pattern matching using timer to find events that didn't arrive. You can use the every operator and timer:interval() to repeat an action every 60 seconds. Then you combine this with a not operator to check for absence of Status events. A 65 second interval during which you look for Status events allows 5 seconds to account for a possible delay in transmission or processing:

select 'terminal 1 is offline' from pattern 
  [every timer:interval(60 sec) -> (timer:interval(65 sec) and not Status(term.id = 'T1'))]
output first every 5 minutes

23.9.4. Activity Summary Data

By presenting statistical information about terminal activity to our staff in real-time you enable them to monitor the system and spot problems. The next example statement simply gives us a count per event type every 1 minute. You could further use this data, available through the CountPerType event stream, to join and compare against a recorded usage pattern, or to just summarize activity in real-time.

insert into CountPerType
select type, count(*) as countPerType 
from BaseTerminalEvent#time(10 minutes) 
group by type
output all every 1 minutes

23.9.5. Sample Application for J2EE Application Server

The example code in the distribution package implements a message-driven enterprise java bean (MDB EJB). The example uses an MDB as a convenient place for processing incoming events via a JMS message queue or topic. The example uses 2 JMS queues: One queue to receive events published by terminals, and a second queue to indicate situations detected via statement and listener back to a receiving process.

This example has been packaged for deployment into a JBoss Java application server (see http://www.jboss.org) with default deployment configuration. JBoss is an open-source application server available under LGPL license. Of course the choice of application server does not indicate a requirement or preference for the use of the compiler and/or runtime in a J2EE container. Other quality J2EE application servers are available and perhaps more suitable to run this example or a similar application.

The complete example code can be found in the "examples/terminalsvc" folder of the distribution. The standalone version that does not require a J2EE container is in "examples/terminalsvc-jse".

23.9.5.1. Running the Example

The pre-build EAR file contains the MDB for deployment to a JBoss application server with default deployment options. The JBoss default configuration provides 2 queues that this example utilizes: queue/A and queue/B. The queue/B is used to send events into the MDB, while queue/A is used to indicate back the any data received by listeners to statements.

The application can be deployed by copying the ear file in the "examples/terminalsvc/terminalsvc-ear" folder to your JBoss deployment directory located under the JBoss home directory under "standalone/deployments".

The example contains an event simulator and an event receiver that can be invoked from the command line. See the folder "examples/terminalsvc/etc" folder readme file and start scripts for Windows and Unix, and the documentation set for further information on the simulator.

23.9.5.2. Building the Example

This example requires Maven 2 to build. To build the example, change directory to the folder "examples/terminalsvc" and type "mvn package". The instructions have been tested with JBoss AS 7.1.1 and Maven 3.0.4.

The Maven build packages the EAR file for deployment to a JBoss application server with default deployment options.

23.9.5.3. Running the Event Simulator and Receiver

The example also contains an event simulator that generates meaningful events. The simulator can be run from the directory "examples/terminalsvc/etc" via the command "run_terminalsvc_sender.bat" (Windows) and "run_terminalsvc_sender.sh" (Linux). The event simulator generates a batch of at least 200 events every 1 second. Randomly, with a chance of 1 in 10 for each batch of events, the simulator generates either an OutOfOrder or a LowPaper event for a random terminal. Each batch the simulator generates 100 random terminal ids and generates a Checkin event for each. It then generates either a Cancelled or a Completed event for each. With a chance of 1 in 1000, it generates an OutOfOrder event instead of the Cancelled or Completed event for a terminal.

The event receiver listens to the MDB-outcoming queue for alerts and prints these out to console. The receiver can be run from the directory "examples/terminalsvc/etc" via the command "run_terminalsvc_receiver.bat" (Windows) and "run_terminalsvc_receiver.sh" (Linux). Before running please copy the jboss-client.jar file from your JBoss AS installation bin directory to the "terminalsvc/lib" folder.

The receiver and sender code use "guest" as user and "pass" as password. Add the "guest" user using the Jboss "add-user" script and assign the role "guest". Your JBoss server may need to start with "-c standalone-full.xml" to have the messaging subsystem available.

Add queue configurations to the messaging subsystem configuration as follows:

<jms-queue name="queue_a">
  <entry name="queue_a"/>
  <entry name="java:jboss/exported/jms/queue/queue_a"/>
</jms-queue>
<jms-queue name="queue_b">
  <entry name="queue_b"/>
  <entry name="java:jboss/exported/jms/queue/queue_b"/>
</jms-queue>

Disable persistence in the messaging subsystem for this example so it is not running out of disk space:

<persistence-enabled>false</persistence-enabled>

23.10. Assets Moving Across Zones - An RFID Example

This example out of the RFID domain processes location report events. Each location report event indicates an asset id and the current zone of the asset. The example solves the problem that when a given set of assets is not moving together from zone to zone, then an alert must be fired.

Each asset group is tracked by 2 statements. The two statements to track a single asset group consisting of assets identified by asset ids {1, 2, 3} are as follows:

insert into CountZone_G1
select 1 as groupId, zone, count(*) as cnt
from LocationReport(assetId in 1, 2, 3)#unique(assetId)
group by zone

select Part.zone from pattern [
  every Part=CountZone_G1(cnt in (1,2)) ->
    (timer:interval(10 sec)  and not CountZone_G1(zone=Part.zone, cnt in (0,3)))]

The classes for this example can be found in package com.espertech.esper.example.rfid.

This example provides a Swing-based GUI that can be run from the command line. The GUI allows drag-and-drop of three RFID tags that form one asset group from zone to zone. Each time you move an asset across the screen the example sends an event into the runtime indicating the asset id and current zone. The example detects if within 10 seconds the three assets do not join each other within the same zone, but stay split across zones. Run "run_rfid_swing.bat" (Windows) or "run_rfid_swing.sh" (Unix) to start the example's Swing GUI.

The example also provides a simulator that can be run from the command line. The simulator generates a number of asset groups as specified by a command line argument and starts a number of threads as specified by a command line argument to send location report events into the runtime. Run "run_rfid_sim.bat" (Windows) or "run_rfid_sim.sh" (Unix) to start the RFID location report event simulator. Please see the readme file in the same folder for build instructions and command line parameters.

23.11. StockTicker

The StockTicker example comes from the stock trading domain. The example creates event patterns to filter stock tick events based on price and symbol. When a stock tick event is encountered that falls outside the lower or upper price limit, the example simply displays that stock tick event. The price range itself is dynamically created and changed. This is accomplished by an overlapping context that uses price limit event to determine how to look for price spikes.

The classes StockTick and PriceLimit represent our events. The event patterns are created by the class StockTickerEPLUtil.

Summary:

Good example to learn the API and get started with contexts and patterns.
When price limit events arrive allocates patterns that find the price spike.
Simple, highly-performant filter expressions for event properties in the stock tick event such as symbol and price.

23.12. MatchMaker

In the MatchMaker example every mobile user has an X and Y location, a set of properties (gender, hair color, age range) and a set of preferences (one for each property) to match. The task of the event patterns created by this example is to detect mobile users that are within proximity given a certain range, and for which the properties match preferences.

The event class representing mobile users is MobileUserBean. The MatchMakerEPL class contains the patterns for detecing matches.

Summary:

Uses overlapping context to find matching mobile user events
Uses range matching for X and Y properties of mobile user events

23.13. Named Window Query

This example handles very minimal temperature sensor events which are represented by java.util.Map. It creates a named window and fills it with a large number of events. It then executes a large number of pre-defined statements via on-select as well as performs a large number of fire-and-forget queries against the named window, and reports execution times.

23.14. Sample Virtual Data Window

Virtual data windows are an extension API used to integrate external stores and expose the data therein as a named window.

See the virtualdw folder for example code, compile and run scripts.

23.15. Sample Cycle Detection

The example is also discussed in the section on extension APIs specifically the aggregation multi-function development. The example uses the jgrapht library for a cycle-detection problem detecting cycles in transactions between accounts.

See the examples/cycledetect folder for example code, compile and run scripts.

23.16. Quality of Service

This example develops some code for measuring quality-of-service levels such as for a service-level agreement (SLA). A SLA is a contract between 2 parties that defines service constraints such as maximum latency for service operations or error rates.

The example measures and monitors operation latency and error counts per customer and operation. When one of our operations oversteps these constraints, you want to be alerted right away. Additionally, you would like to have some monitoring in place that checks the health of our service and provides some information on how the operations are used.

Some of the constraints you need to check are:

That the latency (time to finish) of some of the operations is always less then X seconds.
That the latency average is always less then Y seconds over Z operation invocations.

The com.espertech.esper.example.qos_sla.events.OperationMeasurement event class with its latency and status properties is the main event used for the SLA analysis. The other event LatencyLimit serves to set latency limits on the fly.

The com.espertech.esper.example.qos_sla.monitor.AverageLatencyMonitor creates a statement that computes latency statistics per customer and operation for the last 100 events. The DynaLatencySpikeMonitor uses an event pattern to listen to spikes in latency with dynamically set limits. The ErrorRateMonitor uses the timer 'at' operator in an event pattern that wakes up periodically and polls the error rate within the last 10 minutes. The ServiceHealthMonitor simply alerts when 3 errors occur, and the SpikeAndErrorMonitor alerts when a fixed latency is overstepped or an error status is reported.

Summary:

This example combines event patterns with statements for event stream analysis.
Shows the use of the timer 'at' operator and followed-by operator -> in event patterns.
Outlines basic statements.
Shows how to pull data out of statements rather then subscribing to events a statement publishes.

23.17. Trivia Geeks Club

This example was developed for the DEBS 2011 conference and demonstrates how scoring rules for a trivia game can be implemented in EPL.

The module that implements all scoring rules is located in the etc folder in file trivia.epl. The EPL is all required to run the solution without any custom functions required.

The trivia geeks club rules (the requirements) are provided in the etc folder in file trivia_scoring_requirements.htm.

The implementation provided tests the questions, answers and scoring according to the data provided in trivia_test_questions_small.htm and trivia_test_questions_large.htm.

Chapter 24. Performance

24.1. Big O Notation

24.1.1. Big-O Complexity of Matching Events to Statements and Context Partitions
24.1.2. Big-O Complexity of Matching Time to Statements and Context Partitions
24.1.3. Big-O Complexity of Joins, Subqueries, On-Select, On-Merge, On-Update, On-Delete
24.1.4. Big-O Complexity of Enumeration Methods
24.1.5. Big-O Complexity of Aggregation Methods

24.2. Performance Tips

24.2.1. Understand How to Tune Your Java Virtual Machine
24.2.2. Input and Output Bottlenecks
24.2.3. Threading
24.2.4. Select the Underlying Event Rather Than Individual Fields
24.2.5. Prefer Stream-Level Filtering Over Where-Clause Filtering
24.2.6. Reduce the Use of Arithmetic in Expressions
24.2.7. Remove Unneccessary Constructs
24.2.8. End Pattern Sub-Expressions
24.2.9. Consider Using EventPropertyGetter for Fast Access to Event Properties
24.2.10. Consider Casting the Underlying Event
24.2.11. Turn Off Logging and Audit
24.2.12. Tune or Disable Delivery Order Guarantees
24.2.13. Use a Subscriber Object to Receive Events
24.2.14. Consider Data Flows
24.2.15. High-Arrival-Rate Streams and Single Statements
24.2.16. Subqueries Versus Joins and Where-Clause and Data Windows
24.2.17. Patterns and Pattern Sub-Expression Instances
24.2.18. Pattern Sub-Expression Instance Versus Data Window Use
24.2.19. The Keep-All Data Window
24.2.20. Statement Design for Reduced Memory Consumption - Diagnosing OutOfMemoryError
24.2.21. Performance, JVM, OS and Hardware
24.2.22. Consider Using Hints
24.2.23. Optimizing Stream Filter Expressions
24.2.24. Statement and Runtime Metric Reporting
24.2.25. Expression Evaluation Order and Early Exit
24.2.26. Large Number of Threads
24.2.27. Filter Evaluation Tuning
24.2.28. Context Partition Related Information
24.2.29. Prefer Constant Variables Over Non-Constant Variables
24.2.30. Prefer POJO Events or alternatively Object-Array Events
24.2.31. Notes on Query Planning
24.2.32. Query Planning Expression Analysis Hints
24.2.33. Query Planning Index Hints
24.2.34. Measuring Throughput
24.2.35. Do Not Create the Same or Similar Statement X Times
24.2.36. Comparing Single-Threaded and Multi-Threaded Performance
24.2.37. Incremental Versus Recomputed Aggregation for Named Window Events
24.2.38. When Does Memory Get Released
24.2.39. Measure Throughput of Non-Matches as Well as Watches
24.2.40. Options for When an Event Type has a Large Number of Event Properties i.e. Large Events

24.3. Using the Performance Kit

24.3.1. How to Use the Performance Kit

24.1. Big O Notation

The big O notation is used to classify algorithms according to how their running time grow as the input size grows. This chapter discusses big O complexity of algorithms implemented by the EPL runtime.

For hash lookups the O-notation value is approximate. For logarithmic running-time function we use O(log N) but mean O(log_t N) with an unspecified value for t.

Note

Big-O provided here is general guidance. This section may not be entirely complete and may not list all exceptions.

24.1.1. Big-O Complexity of Matching Events to Statements and Context Partitions

The runtime determines, for each event, which EPL statements must process the event. For EPL statements that are context partitioned with multiple partitions, such as for overlapping, keyed, hash or category contexts (see Chapter 4, Context and Context Partitions), the runtime determines, for each event, which partitions of each statement must process the event.

This operation takes place when:

When an application calls the sendEvent method of EPEventService.
For example, the application invokes eventService.sendEventBean(new StockTickEvent(...), "StockTick") and the runtime determines which EPL statement and partition must process the StockTick event.
When the runtime evaluates a statement that uses insert into.
For example, the application creates a statement insert into StockTickOverPrice100 select * as price from StockTick(price>100). After processing a StockTick event with a price greater 100, the runtime allocates a new StockTickOverPrice100 event and determines which EPL statement and partition must process the new StockTickOverPrice100 event.

The parameter to the operation is the individual event e.g. the StockTick or the StockTickOverPrice100 event.

The data structure is the filter indexes, a nestable tree of indexes organized by event type, see Section 2.18.2, “Filter Indexes”.

The algorithm takes the individual event and walks the filter indexes to determine which statements and context partitions must process the event.

The input is the presence and nature of filter expressions of EPL statements and the number of partitions, including where-clauses (see Section 5.5, “Specifying Search Conditions: The Where Clause” for where-clause rewrites).

The big-o complexity depends on the input. It always includes a hash lookup by event type that is constant time O(1).

24.1.1.1. Example 1

Assume the input is N statements as follows.

select * from Event(property=value)

The complexity is constant time O(1) as the same property appears in all filter expressions and with the equals-operator (=) and therefore hash lookup.

As for example in this EPL:

select * from StockTick(symbol='A');
select * from StockTick(symbol='B');

The runtime obtains the symbol value of the stock tick event once and performs a single hash lookup.

24.1.1.2. Example 2

Assume the input is N statements as follows wherein each property name is a different event property name:

select * from Event(property_n=value)

The complexity is linear time O(N) as a different property appears in all filter expressions.

As for example in this EPL:

select * from StockTick(symbol='A');
select * from StockTick(feed='001');

24.1.1.3. Example 3

Assume the input is N statements as follows.

select * from Event(property_1=constant and property_2>value)

The complexity is O(log N). The same property names appears in all filter expressions. The relational greater-than operator (>) is a btree lookup.

As for example in this EPL:

select * from StockTick(symbol='A', price>100);
select * from StockTick(symbol='A', price>200);

The runtime obtains the symbol value of the stock tick event once and performs a single hash lookup. It obtains the price value of the stock tick event once and performs a single btree lookup.

24.1.2. Big-O Complexity of Matching Time to Statements and Context Partitions

The runtime determines, when time advances, which EPL statements must process the new runtime time. For EPL statements that are context partitioned with multiple partitions, such as for overlapping, keyed, hash or category contexts (see Chapter 4, Context and Context Partitions), the runtime determines which partitions of each statement must process the new runtime time.

This operation takes place when:

When an application calls the advanceTime or advanceTimeSpan method of EPEventService (when using external timer).
For example, the application invokes eventService.advanceTime(DateTime.parse("2002-05-30T09:01:02.003")) and the runtime determines which EPL statement and partition must process the new runtime time.
When the runtime uses the internal timer (aka. system time) and the current system time becomes current runtime time.

The parameter to the operation is the new runtime time.

The data structure is the schedule maintained internally by the runtime which is a data structure sorted by time.

The algorithm takes the new runtime time and performs a lookup.

The input is the presence and nature of time-related expressions of EPL statements and the number of partitions.

The big-o complexity is O(log N).

24.1.3. Big-O Complexity of Joins, Subqueries, On-Select, On-Merge, On-Update, On-Delete

The runtime performs a join, a subquery, an on-select, an on-merge, an on-update, an on-delete or a fire-and-forget query. The runtime determines the subset of events (or rows of a table) by performing an index lookup. It performs additional actions on the subset.

The query planner is responsible for determining the indexes to use. The query planner uses the where-clause (if any) and the on-clause (if any) to plan index use. Use query plan logging to obtain information about the query plans.

This operation takes place when:

It is a join and the runtime performs a lookup into a stream's events (or named window or table) to resolve the subset of events for that stream (or named window or table) to determine final join results.
It is a subquery and the runtime performs a lookup into subqueries's events (or aggregation rows) to resolve the subset of subquery result events and to process these.
It is an on-action statement such as on-select, on-merge, on-update and on-delete and the runtime performs a lookup into a named window events or table rows to resolve the subset and process these.
It is a fire-and-forget select, update or delete query.

The parameters to the operation are the events of the from-clause and on-trigger. For fire-and-forget queries the parameters originate from the filter expressions and where-clause.

The data structure is the event index, see Section 2.18.3, “Event Indexes”.

The algorithm takes event data and performs an index lookup according to the chosen index organization, to determine and process the subset of events.

The input is the indexed events (or table rows).

The big-o complexity depends on the type of lookup operation.

For lookups that are hash only the complexity is constant time O(1).
For lookups that are btree or that combine hash and btree the complexity is O(log N).
Otherwise the complexity is O(N). Without indexes a scan has to inspect every event which means it will scale with the number of events.

24.1.3.1. Example 1

This example is a subquery. When an RFIDEvent arrives the runtime finds Zone events for the same zone id:

select * from RFIDEvent as rfid where exists (select * from Zone#unique(zoneId) as zone where rfid.zoneId = zone.zoneId)

The where-clause uses equals ('=') and the query planner plans a hash index lookup. The operation is constant time O(1).

24.1.3.2. Example 2

This example declares a TickWindow named window holding StockTick events. It uses on-select to select all stock ticks with a price greater the price provided by PriceQuery:

create window TickWindow#time(10) from StockTick; 
on PriceQuery as priceQuery select * from TickWindow as ticks where ticks.price > priceQuery.price;

The where-clause uses the relational greater operator ('>') and the query planner plans a btree index lookup. The operation is O(log N) with N related to the number of TickWindow events.

24.1.3.3. Example 3

This example is an unidirectional join. When an RFIDEvent arrives it outputs a row for each Zone event unique by zone id:

select * from RFIDEvent unidirectional, Zone#unique(zoneId)

There is no where-clause and the query planner cannot use an index. The operation is linear time O(N) with N related to the number of Zone events.

24.1.4. Big-O Complexity of Enumeration Methods

The runtime evaluates enumeration methods by applying an operation to each element in a collection.

This operation takes place for each enumeration method.

The parameters to the operation are the parameters passed to the enumeration method.

The algorithm iterates the collection and performs the operation.

The input are the collection of events or scalar values.

The big-o complexity depends on the enumeration method.

For take and takeLast the complexity is constant time O(1) for a small number of elements, when the size parameter is small, and linear time O(N) in the worst case, when the size parameter is large.
For countOf the complexity is constant time O(1) and can be linear time O(N) for data structures that don't provide a size.
For all other enumeration methods the complexity is linear time O(N).

24.1.5. Big-O Complexity of Aggregation Methods

The runtime evaluates aggregation methods by applying an operation to aggregation state.

This operation takes place for each aggregation method.

The parameters to the operation are the parameters passed to the aggregation method.

The algorithm evaluates parameters and queries aggregation state.

The input is the number of aggregated events held by the aggregation state.

The big-o complexity for sorted aggregations is:

For eventsBetween the complexity is O(log N) (for small ranges) and can be linear time O(N) in the worst case (for large ranges) .
For all other aggregation methods on sorted aggregations the complexity is O(log N).

The big-o complexity for aggregation methods on window aggregations is constant O(1).

24.2. Performance Tips

24.2.1. Understand How to Tune Your Java Virtual Machine

The compiler and runtime run on a JVM and you need to be familiar with JVM tuning. Key parameters to consider include minimum and maximum heap memory and nursery heap sizes. Statements with time-based or length-based data windows can consume large amounts of memory as their size or length can be large.

For time-based data windows, one needs to be aware that the memory consumed depends on the actual event stream input throughput. Event pattern instances also consume memory, especially when using the "every" keyword in patterns to repeat pattern sub-expressions - which again will depend on the actual event stream input throughput.

24.2.2. Input and Output Bottlenecks

Your application receives output events from statements through the UpdateListener interface or via the strongly-typed subscriber POJO object. Such output events are delivered by the application or timer thread(s) that sends an input event into the runtime instance.

The processing of output events that your listener or subscriber performs temporarily blocks the thread until the processing completes, and may thus reduce throughput. It can therefore be beneficial for your application to process output events asynchronously and not block the runtime while an output event is being processed by your listener, especially if your listener code performs blocking IO operations.

For example, your application may want to send output events to a JMS destination or write output event data to a relational database. For optimal throughput, consider performing such blocking operations in a separate thread.

Additionally, when reading input events from a store or network in a performance test, you may find that the runtime processes events faster then you are able to feed events into the runtime. In such case you may want to consider an in-memory driver for use in performance testing. Also consider decoupling your read operation from the event processing operation (sendEvent method) by having multiple readers or by pre-fetching your data from the store.

24.2.3. Threading

We recommend using multiple threads to send events into the runtime. There is a test class below. Our test class does not use a blocking queue and thread pool so as to avoid a point of contention.

A sample code for testing performance with multiple threads is provided:

public class SampleClassThreading {

    public static void main(String[] args) throws InterruptedException {

        int numEvents = 1000000;
        int numThreads = 3;

        Configuration config = new Configuration();
        config.getRuntime().getThreading().setListenerDispatchPreserveOrder(false);
        config.getRuntime().getThreading().setInternalTimerEnabled(false);   // remove thread that handles time advancing
        config.getCommon().addEventType(MyEvent.class);

        String epl = "create context MyContext coalesce by consistent_hash_crc32(id) " +
                     "from MyEvent granularity 64 preallocate;\n" +
                     "@name('result') context MyContext select count(*) from MyEvent group by id;\n";
        EPCompiled compiled;
        try {
            compiled = EPCompilerProvider.getCompiler().compile(epl, new CompilerArguments(config);
        }
        catch (EPCompileException ex) {
            throw new RuntimeException(ex.getMessage(), ex);
        }
                
        EPRuntime runtime = EPRuntimeProvider.getDefaultRuntime(config);
        EPDeployment deployment;
        try {
            deployment = runtime.getDeploymentService().deploy(compiled);
        }
        catch (EPDeployException ex) {
            throw new RuntimeException(ex.getMessage(), ex);
        }
        EPStatement stmt = runtime.getDeploymentService().getStatement(deployment.getDeploymentId(), "result");
        stmt.setSubscriber(new MySubscriber());

        Thread[] threads = new Thread[numThreads];
        CountDownLatch latch = new CountDownLatch(numThreads);

        int eventsPerThreads = numEvents / numThreads;
        for (int i = 0; i < numThreads; i++) {
            threads[i] = new Thread(
              new MyRunnable(latch, eventsPerThreads, runtime.getEventService()));
        }
        long startTime = System.currentTimeMillis();
        for (int i = 0; i < numThreads; i++) {
            threads[i].start();
        }

        latch.await(10, TimeUnit.MINUTES);
        if (latch.getCount() > 0) {
            throw new RuntimeException("Failed to complete in 10 minute");
        }
        long delta = System.currentTimeMillis() - startTime;
        System.out.println("Took " + delta + " millis");
    }

    public static class MySubscriber {
        public void update(Object[] args) {
        }
    }

    public static class MyRunnable implements Runnable {
        private final CountDownLatch latch;
        private final int numEvents;
        private final EPEventService eventService;

        public MyRunnable(CountDownLatch latch, int numEvents, EPEventService eventService) {
            this.latch = latch;
            this.numEvents = numEvents;
            this.eventService = eventService;
        }

        public void run() {
            Random r = new Random();
            for (int i = 0; i < numEvents; i++) {
                eventService.sendEventBean(new MyEvent(r.nextInt(512)), "MyEvent");
            }
            latch.countDown();
        }
    }

    public static class MyEvent {
        private final int id;

        public MyEvent(int id) {
            this.id = id;
        }

        public int getId() {
            return id;
        }
    }
}

We recommend using Java threads as above, or a blocking queue and thread pool with sendEventType or alternatively we recommend configuring inbound threading if your application does not already employ threading. The runtime provides the configuration option to use runtime-level queues and threadpools for inbound, outbound and internal executions. See Section 16.8.1, “Advanced Threading” for more information.

We recommend the outbound threading if your listeners are blocking. For outbound threading also see the section below on tuning and disabling listener delivery guarantees.

If enabling advanced threading options keep in mind that the runtime will maintain a queue and thread pool. There is additional overhead associated with entering work units into the queue, maintaining the queue and the hand-off between threads. The Java blocking queues are not necessarily fast on all JVM. It is not necessarily true that your application will perform better with any of the advanced threading options.

We found scalability better on Linux systems and running Java with -server and pinning threads to exclusive CPUs and after making sure CPUs are available on your system.

We recommend looking at LMAX Disruptor, an inter-thread messaging library, for setting up processing stages. Disruptor, however, is reportedly less suitable for setting up a worker pool.

24.2.3.1. Thead Pool Pattern

The sample code below may help you get started setting up a thread pool of workers with back pressure and consideration for IO threads and clean shutdown.

The sample code starts by setting up a thread factory:

private static class RuntimeThreadFactory implements ThreadFactory {
  private AtomicInteger id = new AtomicInteger(0);

  public Thread newThread(Runnable r) {
    Thread t = new Thread(r, "Event Runtime Thread #" + id.incrementAndGet());
    t.setDaemon(true);
    t.setPriority(Thread.NORM_PRIORITY);
    return t;
  }
}

The sample uses a fixed-size array blocking queue. To handle the situation where the queue is full and accepts no more messages, it uses a rejection handler that counts the number of rejections and retries:

private class RuntimeRejectionHandler implements RejectedExecutionHandler {
  private volatile long spinCount = 0;
  
  public long getSpinCount() {
    return spinCount;
  }

  public void rejectedExecution(Runnable r, ThreadPoolExecutor executor) {
    ++spinCount;

    try {
      boolean isAccepted = false;
      while (!isAccepted) {
        isAccepted = executorQueue.offer(r, 120, TimeUnit.MICROSECONDS);
      }
    }
    catch (InterruptedException e) {
      log.warn("could not queue work entry");
    }
  }
}

The Runnable that submits an event for processing could look like this:

class Holder implements Runnable {
  public void run() {
    // do any stuff needed to "prepare" event which doesn't involve IO
    runtime.getEventService().sendEventBean(lm, "LMEventType");
  }
}

Initialize the queue and worker pool as follows:

  private final static int CAPACITY = 10000;
  private final static int THREAD_COUNT = 4;

  private static EPRuntime runtime;
  private ThreadFactory threadFactory = new RuntimeThreadFactory();
  private RuntimeRejectionHandler rejectionHandler = new RuntimeRejectionHandler();
  private BlockingQueue<Runnable> executorQueue;
  private ThreadPoolExecutor executor;

  public void start() {
    executorQueue = new ArrayBlockingQueue<Runnable>(CAPACITY);
    executor = new ThreadPoolExecutor(THREAD_COUNT, THREAD_COUNT, 0, TimeUnit.SECONDS,
    executorQueue, threadFactory, rejectionHandler);
    executor.allowCoreThreadTimeOut(false);
    while (executor.getPoolSize() < executor.getCorePoolSize()) {
      executor.prestartCoreThread();
    }
  }

To shut down cleanly, and before destroying the runtime, the sample code is:

  executor.shutdown();
  while (!executor.isTerminated()) {
    Thread.sleep(100);
  }

The next sample code goes into the IO or input thread(s) such as NIO mapped file, file channel, socket channel, or zmq / nanomsg etc., and submits a work unit to the queue:

  while (programAlive) {
    // deserialize event to POJO, Map, Array, etc.,
    // pass along an event type name when needed
    executor.execute(new Holder(myeventobject));
  }

You could periodically dump the spinCount variable to get an idea of queue depth. You can tune the size of the Executor's pool, and the size of the TimeUnit's of sleep used inside the rejectedExecution method, until you get 1) stable performance at highest level (determined by optimal number of threads in pool, 2) avoid wasting CPU in IO thread(s) (determined by optimal sleeping time between each attempt to re-queue rejected events to the thread pool).

24.2.4. Select the Underlying Event Rather Than Individual Fields

By selecting the underlying event in the select-clause you can reduce load on the runtime, since the runtime does not need to generate a new output event for each input event.

For example, the following statement returns the underlying event to update listeners:

// Better performance
select * from RFIDEvent

In comparison, the next statement selects individual properties. This statement requires the runtime to generate an output event that contains exactly the required properties:

// Less good performance
select assetId, zone, xlocation, ylocation from RFIDEvent

24.2.5. Prefer Stream-Level Filtering Over Where-Clause Filtering

The runtime stream-level filtering is very well optimized, while filtering via the where-clause post any data windows is not optimized.

The same is true for named windows. If your application is only interested in a subset of named window data and such filters are not correlated to arriving events, place the filters into parenthesis after the named window name.

24.2.5.1. Examples Without Named Windows

Consider the example below, which performs stream-level filtering:

// Better performance : stream-level filtering
select * from MarketData(ticker = 'GOOG')

The example below is the equivalent (same semantics) statement and performs post-data-window filtering without a data window. The compiler does not optimize statements that filter in the where-clause for the reason that data windows are generally present.

// Less good performance : post-data-window filtering
select * from Market where ticker = 'GOOG'

Thus this optimization technique applies to statements without any data window.

When a data window is used, the semantics change. Let's look at an example to better understand the difference: In the next statement only GOOG market events enter the length window:

select avg(price) from MarketData(ticker = 'GOOG')#length(100)

The above statement computes the average price of GOOG market data events for the last 100 GOOG market data events.

Compare the filter position to a filter in the where clause. The following statement is NOT equivalent as all events enter the data window (not just GOOG events):

select avg(price) from Market#length(100) where ticker = 'GOOG'

The statement above computes the average price of all market data events for the last 100 market data events, and outputs results only for GOOG.

24.2.5.2. Examples Using Named Windows

The next two example statements put the account number filter criteria directly into parenthesis following the named window name:

// Better performance : stream-level filtering
select * from WithdrawalNamedWindow(accountNumber = '123')

// Better performance : example with subquery
select *, (select * from LoginSucceededWindow(accountNumber = '123'))
from WithdrawalNamedWindow(accountNumber = '123')

24.2.5.3. Common Computations in Where-Clauses

If you have a number of statements performing a given computation on incoming events, consider moving the computation from the where-clause to a plug-in user-defined function that is listed as part of stream-level filter criteria. The compiler optimizes evaluation of user-defined functions in filters such that an incoming event can undergo the computation just once even in the presence of N statements.

// Prefer stream-level filtering with a user-defined function
select * from MarketData(vstCompare(*))

// Less preferable when there are N similar statements: 
// Move the computation in the where-clause to the "vstCompare" function.
select * from MarketData where (VST * RT) – (VST / RT) > 1

24.2.6. Reduce the Use of Arithmetic in Expressions

The compiler and runtime do not yet pre-evaluate arithmetic expressions that produce constant results, however since the compiler generates byte code the JVM byte code optimization takes place and may pre-evaluate certain expressions.

Therefore, a filter expression as below is optimized:

// Better performance : no arithmetic
select * from MarketData(price>40)

While the compiler cannot currently optimize this expression:

// Less good performance : with arithmetic
select * from MarketData(price+10>50)

24.2.7. Remove Unneccessary Constructs

If your statement uses order by to order output events, consider removing order by unless your application does indeed require the events it receives to be ordered.

If your statement specifies group by but does not use aggregation functions, consider removing group by.

If your statement specifies group by but the filter criteria only allows one group, consider removing group by:

// Prefer:
select * from MarketData(symbol = 'GE') having sum(price) > 1000

// Don't use this since the filter specifies a single symbol:
select * from MarketData(symbol = 'GE') group by symbol having sum(price) > 1000

If your statement specifies the grouped data window #groupwin but the window being grouped retains the same set of events regardless of grouping, remove #groupwin, for example:

// Prefer:
create window MarketDataWindow#keepall as MarketDataEventType

// Don't use this, since keeping all events 
// or keeping all events per symbol is the same thing:
create window MarketDataWindow#groupwin(symbol)#keepall as MarketDataEventType

// Don't use this, since keeping the last 1-minute of events 
// or keeping 1-minute of events per symbol is the same thing:
create window MarketDataWindow#groupwin(symbol)#time(1 min) as MarketDataEventType

It is not necessary to specify a data window for each stream.

// Prefer:
select * from MarketDataWindow

// Don't have a data window if just listening to events, prefer the above
select * from MarketDataWindow#lastevent

If your statement specifies unique data window but the filter criteria only allows one unique criteria, consider removing the unique data window:

// Prefer:
select * from MarketDataWindow(symbol = 'GE')#lastevent

// Don't have a unique-key data window if your filter specifies a single value
select * from MarketDataWindow(symbol = 'GE')#unique(symbol)

24.2.8. End Pattern Sub-Expressions

In patterns, the every keyword in conjunction with followed by (->) starts a new sub-expression per match.

For example, the following pattern starts a sub-expression looking for a B event for every A event that arrives.

every A -> B

Determine under what conditions a subexpression should end so the runtime can stop looking for a B event. Here are a few generic examples:

every A -> (B and not C)
every A -> B where timer:within(1 sec)

24.2.9. Consider Using EventPropertyGetter for Fast Access to Event Properties

The EventPropertyGetter interface is useful for obtaining an event property value without property name table lookup given an EventBean instance that is of the same event type that the property getter was obtained from.

When compiling a statement, the EPStatement instance lets us know the EventType via the getEventType() method. From the EventType you can obtain EventPropertyGetter instances for named event properties.

To demonstrate, consider the following simple statement:

select symbol, avg(price) from Market group by symbol

After compiling and deploying the module, obtain the EventType and pass the type to the listener:

EPStatement stmt = runtime.getDeploymentService().getStatement(deploymentId, statementName);
MyGetterUpdateListener listener = new MyGetterUpdateListener(stmt.getEventType());

The listener can use the type to obtain fast getters for property values of events for the same type:

public class MyGetterUpdateListener implements StatementAwareUpdateListener {
    private final EventPropertyGetter symbolGetter;
    private final EventPropertyGetter avgPriceGetter;

    public MyGetterUpdateListener(EventType eventType) {
        symbolGetter = eventType.getGetter("symbol");
        avgPriceGetter = eventType.getGetter("avg(price)");
    }

Last, the update method can invoke the getters to obtain event property values:

    public void update(EventBean[] eventBeans, EventBean[] oldBeans, EPStatement epStatement, EPRuntime runtime) {
        String symbol = (String) symbolGetter.get(eventBeans[0]);
        long volume = (Long) volumeGetter.get(eventBeans[0]);
        // some more logic here
    }

24.2.10. Consider Casting the Underlying Event

When an application requires the value of most or all event properties, it can often be best to simply select the underlying event via wildcard and cast the received events.

Let's look at the sample statement:

select * from MarketData(symbol regexp 'E[a-z]')

An update listener to the statement may want to cast the received events to the expected underlying event class:

    public void update(EventBean[] eventBeans, EventBean[] eventBeans) {
        MarketData md = (MarketData) eventBeans[0].getUnderlying();
        // some more logic here
    }

24.2.11. Turn Off Logging and Audit

Even if you don't have a log4j configuration file in place, the runtime will make sure to minimize execution path logging overhead. For prior versions, and to reduce logging overhead overall, we recommend the "WARN" log level or the "INFO" log level.

Please see the log4j configuration file in "etc/infoonly_log4j.xml" for example log4j settings.

EPL provides the @Audit annotation for statements. For performance testing and production deployment, we recommend removing @Audit.

24.2.12. Tune or Disable Delivery Order Guarantees

If your application is not a multithreaded application, or your application is not sensitive to the order of delivery of result events to your application listeners, then consider disabling the delivery order guarantees the runtime makes towards ordered delivery of results to listeners:

Configuration config = new Configuration();
config.getRuntime().getThreading().setListenerDispatchPreserveOrder(false);

If your application is not a multithreaded application, or your application uses the insert into clause to make results of one statement available for further consuming statements but does not require ordered delivery of results from producing statements to consuming statements, you may disable delivery order guarantees between statements:

Configuration config = new Configuration();
config.getRuntime().getThreading().setInsertIntoDispatchPreserveOrder(false);

If your application declares only stateless statements then the settings described herein are not relevant.

Additional configuration options are available and described in the configuration section that specify timeout values and spin or thread context switching.

the runtime logging will log the following informational message when guaranteed delivery order to listeners is enabled and spin lock times exceed the default or configured timeout : Spin wait timeout exceeded in listener dispatch. The respective message for delivery from insert into statements to consuming statements is Spin wait timeout exceeded in insert-into dispatch.

If your application sees messages that spin lock times are exceeded, your application has several options: First, disabling preserve order is an option. Second, ensure your listener does not perform (long-running) blocking operations before returning, for example by performing output event processing in a separate thread. Third, change the timeout value to a larger number to block longer without logging the message.

24.2.13. Use a Subscriber Object to Receive Events

The subscriber object is a technique to receive result data that has performance advantages over the UpdateListener interface. Please refer to Section 16.5.2, “Setting a Subscriber Object”.

24.2.14. Consider Data Flows

Data flows offer a high-performance means to execute EPL select statements and use other built-in data flow operators. The data flow Emitter operator allows sending underlying event objects directly into a data flow. Thereby the runtime does not need to wrap each underlying event into a EventBean instance and the runtime does not need to match events to statements. Instead, the underling event directly applies to only that data flow instance that your application submits the event to, and no other statements or data flows see the same event.

Data flows are described in Chapter 21, EPL Reference: Data Flow.

24.2.15. High-Arrival-Rate Streams and Single Statements

A context partition is associated with certain context partition state that consists of current aggregation values, partial pattern matches, data windows depending on whether your statement uses such constructs. When an runtime receives events it updates context partition state under locking such that context partition state remains consistent under concurrent multi-threaded access.

For high-volume streams, the locking required to protected context partition state may slow down or introduce blocking for very high arrival rates of events that apply to the very same context partition and its state.

Your first choice should be to utilize a context that allows for multiple context partitions, such as the hash segmented context. The hash segmented context usually performs better compared to the keyed segmented context since in the keyed segmented context the runtime must check whether a partition exists or must be created for a given key.

Your second choice is to split the statement into multiple statements that each perform part of the intended function or that each look for a certain subset of the high-arrival-rate stream. There is very little cost in terms of memory or CPU resources per statement, the runtime can handle larger number of statements usually as efficiently as single statements.

For example, consider the following statement:

// less effective in a highly threaded environment 
select venue, ccyPair, side, sum(qty)
from CumulativePrice
where side='O'
group by venue, ccyPair, side

The runtime protects state of each context partition by a separate lock for each context partition, as discussed in the API section. In highly threaded applications threads may block on a specific context partition. You would therefore want to use multiple context partitions.

Consider creating either a hash segmented context or a keyed segmented context. In the hash segmented context incoming data is simply assigned to one of the buckets using a small computation. In the keyed segmented context the runtime must check keys to see if a partition already exists or whether a new partition must be allocated. We'll discuss both below. For both types of context, since locking is on the level of context partition, the locks taken by the runtime are very fine grained allowing for highly concurrent processing.

This sample EPL declares a hash segmented context. In a hash segmented context the runtime can pre-allocate context partitions and therefore does not need to check whether a partition exists already. In a hash segmented context the runtime simply assigns events to context partitions based on result of a hash function and modulo operation.

create context MyContext coalesce by consistent_hash_crc32(venue) from CumulativePrice(side='O') granularity 16 preallocate

This sample EPL declares a keyed segmented context. The keyed segmented context instructs the runtime to employ a context partition per venue, ccyPair, side key combination. The runtime must check for each event whether a partition exists for that combination of venue, ccyPair and side:

create context MyContext partition by venue, ccyPair, side from CumulativePrice(side='O')

After declaring the context using create context, make sure all your statements, including those statements that create named windows and tables, specify that context. This is done by prefixing each statement with context context_name .....

The new statement that refers to the context as created above is below. Note the context MyContext which tells the runtime that this statement executes context partitioned. This must be provided otherwise the statement does not execute context partitioned.

context MyContext select venue, ccyPair, side, sum(qty) from CumulativePrice

For testing purposes or if your application controls concurrency, you may disable context partition locking, see Section 17.6.10.4, “Disable Locking”.

24.2.16. Subqueries Versus Joins and Where-Clause and Data Windows

When joining streams the runtime builds a product of the joined data windows based on the where clause. It analyzes the where clause at time of statement compilation and builds the appropriate indexes and query strategy. Avoid using expressions in the join where clause that require evaluation, such as user-defined functions or arithmatic expressions.

When joining streams and not providing a where clause, consider using the #unique data window or #lastevent data window to join only the last event or the last event per unique key(s) of each stream.

The sample statement below can produce up to 5,000 rows when both data windows are filled and an event arrives for either stream:

// Avoid joins between streams with data windows without where-clause
select * from StreamA#length(100), StreamB#length(50)

Consider using a subquery, consider using separate statements with insert-into and consider providing a where clause to limit the product of rows.

Below examples show different approaches, that are not semantically equivalent, assuming that an MyEvent is defined with the properties symbol and value:

// Replace the following statement as it may not perform well
select a.symbol, avg(a.value), avg(b.value) 
from MyEvent#length(100) a, MyEvent#length(50) b

// Join with where-clause
select a.symbol, avg(a.value), avg(b.value) 
from MyEvent#length(100) a, MyEvent#length(50) b 
where a.symbol = b.symbol

// Unidirectional join with where-clause
select a.symbol, avg(b.value) 
from MyEvent unidirectional, MyEvent#length(50) b 
where a.symbol = b.symbol

// Subquery
select 
  (select avg(value) from MyEvent#length(100)) as avgA, 
  (select avg(value) from MyEvent#length(50)) as avgB,
  a.symbol
from MyEvent

// Since streams cost almost nothing, use insert-into to populate and a unidirectional join 
insert into StreamAvgA select symbol, avg(value) as avgA from MyEvent#length(100)
insert into StreamAvgB select symbol, avg(value) as avgB from MyEvent#length(50)
select a.symbol, avgA, avgB from StreamAvgA unidirectional, StreamAvgB#unique(symbol) b
where a.symbol = b.symbol

A join is multidirectionally evaluated: When an event of any of the streams participating in the join arrive, the join gets evaluated, unless using the unidirectional keyword. Consider using a subquery instead when evaluation only needs to take place when a certain event arrives:

// Rewrite this join since you don't need to join when a LoginSucceededWindow arrives
// Also rewrite because the account number always is the value 123.
select * from LoginSucceededWindow as l, WithdrawalWindow as w
where w.accountNumber = '123' and w.accountNumber = l.accountNumber

// Rewritten as a subquery, 
select *, (select * from LoginSucceededWindow where accountNumber=’123’) 
from WithdrawalWindow(accountNumber=’123’) as w

24.2.17. Patterns and Pattern Sub-Expression Instances

The every and repeat operators in patterns control the number of sub-expressions that are active. Each sub-expression can consume memory as it may retain, depending on the use of tags in the pattern, the matching events. A large number of active sub-expressions can reduce performance or lead to out-of-memory errors.

During the design of the pattern statement consider the use of timer:within to reduce the amount of time a sub-expression lives, or consider the not operator to end a sub-expression.

The examples herein assume an AEvent and a BEvent event type that have an id property that may correlate between arriving events of the two event types.

In the following sample pattern the runtime starts, for each arriving AEvent, a new pattern sub-expression looking for a matching BEvent. Since the AEvent is tagged with a the runtime retains each AEvent until a match is found for delivery to listeners or subscribers:

every a=AEvent -> b=BEvent(b.id = a.id)

One way to end a sub-expression is to attach a time how long it may be active.

The next statement ends sub-expressions looking for a matching BEvent 10 seconds after arrival of the AEvent event that started the sub-expression:

every a=AEvent -> (b=BEvent(b.id = a.id) where timer:within(10 sec))

A second way to end a sub-expression is to use the not operator. You can use the not operator together with the and operator to end a sub-expression when a certain event arrives.

The next statement ends sub-expressions looking for a matching BEvent when, in the order of arrival, the next BEvent that arrives after the AEvent event that started the sub-expression does not match the id of the AEvent:

every a=AEvent -> (b=BEvent(b.id = a.id) and not BEvent(b.id != a.id))

The every-distinct operator can be used to keep one sub-expression alive per one or more keys. The next pattern demonstrates an alternative to every-distinct. It ends sub-expressions looking for a matching BEvent when an AEvent arrives that matches the id of the AEvent that started the sub-expression:

every a=AEvent -> (b=BEvent(b.id = a.id) and not AEvent(b.id = a.id))

24.2.18. Pattern Sub-Expression Instance Versus Data Window Use

For some use cases you can either specify one or more data windows as the solution, or you can specify a pattern that also solves your use case.

For patterns, you should understand that the runtime employs a dynamic state machine. For data windows, the runtime employs a delta network and collections. Generally you may find patterns that require a large number of sub-expression instances to consume more memory and more CPU then data windows.

For example, consider the following statement that filters out duplicate transaction ids that occur within 20 seconds of each other:

select * from TxnEvent#firstunique(transactionId)#time(20 sec)

You could also address this solution using a pattern:

select * from pattern [every-distinct(a.transactionId) a=TxnEvent where timer:within(20 sec)]

If you have a fairly large number of different transaction ids to track, you may find the pattern to perform less well then the data window solution as the pattern asks the runtime to manage a pattern sub-expression per transaction id. The data window solution asks the runtime to manage expiry, which can give better performance in many cases.

24.2.19. The Keep-All Data Window

The #keepall data window is a data window that retains all arriving events. The data window can be useful during the development phase and to implement a custom expiry policy using on-delete and named windows. Care should be taken to timely remove from the keep-all data window however. Use on-select or fire-and-forget queries to count the number of rows currently held by a named window with keep-all expiry policy.

24.2.20. Statement Design for Reduced Memory Consumption - Diagnosing OutOfMemoryError

This section describes common sources of out-of-memory problems.

If using the keep-all data window please consider the information above. If using pattern statements please consider pattern sub-expression instantiation and lifetime as discussed prior to this section.

When using the group-by clause or #groupwin grouped data windows please consider the hints as described below. Make sure your grouping criteria are fields that don't have an unlimited number of possible values or specify hints otherwise.

The #unique unique data window can also be a source for error. If your uniqueness criteria include a field which is never unique the memory use of the data window can grow, unless your application deletes events.

When using the every-distinct pattern construct parameterized by distinct value expressions that generate an unlimited number of distinct values, consider specifying a time period as part of the parameters to indicate to the runtime how long a distinct value should be considered.

In a match-recognize pattern consider limiting the number of optional events if optional events are part of the data reported in the measures clause. Also when using the partition clause, if your partitioning criteria include a field which is never unique the memory use of the match-recognize runtime can grow.

A further source of memory use is when your application deploys modules but fails to undeploy modules when they are no longer needed.

In your application design you may also want to be conscious when the application listener or subscriber objects retain output data.

A runtime, uniquely identified by a runtime URI is a relatively heavyweight object. Optimally your application allocates less than one-thousand (1000) runtime instances per JVM. A statement instance is associated to one runtime instance, is uniquely identified by a statement name and is a medium weight object. We have seen applications allocate 100,000 statements easily. A statement's context partition instance is associated to one statement, is uniquely identified by a context partition id and is a light weight object. We have seen applications allocate 5000 context partitions for 100 statements easily, i.e. 5,000,000 context partitions. An aggregation row, data window row, pattern etc. is associated to a statement context partition and is a very lightweight object itself.

The prev, prevwindow and prevtail functions access a data window directly. The runtime does not need to maintain a separate data structure and grouping is based on the use of the #groupwin grouped data window. Compare this to the use of event aggregation functions such as first, window and last which group according to the group by clause. If your statement utilizes both together consider reformulating to use prev instead.

24.2.21. Performance, JVM, OS and Hardware

Performance will also depend on your JVM (Sun HotSpot, BEA JRockit, IBM J9), your operating system and your hardware. A JVM performance index such as specJBB at spec.org can be used. For memory intensive statement, you may want to consider 64bit architecture that can address more than 2GB or 3GB of memory, although a 64bit JVM usually comes with a slow performance penalty due to more complex pointer address management.

The choice of JVM, OS and hardware depends on a number of factors and therefore a definite suggestion is hard to make. The choice depends on the number of statements, and number of threads. A larger number of threads would benefit of more CPU and cores. If you have very low latency requirements, you should consider getting more GHz per core, and possibly soft real-time JVM to enforce GC determinism at the JVM level, or even consider dedicated hardware such as Azul. If your statements utilize large data windows, more RAM and heap space will be utilized hence you should clearly plan and account for that and possibly consider 64bit architectures or consider EsperHA.

The number and type of statements is a factor that cannot be generically accounted for. The benchmark kit can help test out some requirements and establish baselines, and for more complex use cases a simulation or proof of concept would certainly works best. EsperTech' experts can be available to help write interfaces in a consulting relationship.

24.2.22. Consider Using Hints

The @Hint annotation provides a single keyword or a comma-separated list of keywords that provide instructions to the compiler and runtime towards statement execution that affect runtime performance and memory-use of statements. Also see Section 5.2.7.9, “@Hint”.

The query planning in general is described in Section 24.2.31, “Notes on Query Planning”.

The hint for influencing query planning expression analysis is described at Section 24.2.32, “Query Planning Expression Analysis Hints”.

The hint for influencing query planning index choice is described at Section 24.2.33, “Query Planning Index Hints”.

Further hints, also related to query planning, for use with joins, outer joins, unidirectional joins, relational and non-relational joins are described in Section 5.12.6, “Hints Related to Joins”.

The hint for use with group by to specify how state for groups is reclaimed is described in Section 5.6.2.1, “Hints Pertaining to Group-By” and Section 14.3.15, “Grouped Data Window (groupwin or std:groupwin)”.

The hint for use with group by to specify aggregation state reclaim for unbound streams and timestamp groups is described in Section 5.6.2.1, “Hints Pertaining to Group-By”.

The hint for use with match_recognize to specify iterate-only is described in Section 8.4.7, “Eliminating Duplicate Matches”.

To tune subquery performance when your subquery selects from a named window, consider the hints discussed in Section 5.11.8, “Hints Related to Subqueries”.

The @NoLock hint to remove context partition locking (also read caution note) is described at Section 16.8, “Runtime Threading and Concurrency”.

The hint to control expansion of filter expressions, further described at Section 17.5.8.1, “Filter Service Max Filter Width”.

24.2.23. Optimizing Stream Filter Expressions

Assume your statement invokes a static method in the stream filter as the below statement shows as an example:

select * from MyEvent(MyHelperLibrary.filter(field1, field2, field3, field4*field5))

As a result of starting above statement, the runtime must evaluate each MyEvent event invoking the MyHelperLibrary.filter method and passing certain event properties. The same applies to pattern filters that specify functions to evaluate.

If possible, consider moving some of the checking performed by the function back into the filter or consider splitting the function into a two parts separated by and conjunction. In general for all expressions, the runtime evaluates expressions left of the and first and can skip evaluation of the further expressions in the conjunction in the case when the first expression returns false. In addition the compiler can determine filter index fields and the runtime can build a filter index for fields provided in stream or pattern filters.

For example, the below statement could be faster to evaluate:

select * from MyEvent(field1="value" and 
  MyHelperLibrary.filter(field1, field2, field3, field4*field5))

24.2.24. Statement and Runtime Metric Reporting

You can use statement and runtime metric reporting as described in Section 16.12, “Runtime and Statement Metrics Reporting” to monitor performance or identify slow statements.

24.2.25. Expression Evaluation Order and Early Exit

The term "early exit" or "short-circuit evaluation" refers to when the runtime can evaluate an expression without a complete evaluation of all sub-expressions.

Consider an expression such as follows:

where expr1 and expr2 and expr3

If expr1 is false the runtime does not need to evaluate expr2 and expr3. Therefore when using the AND logical operator consider reordering expressions placing the most-selective expression first and less selective expressions thereafter.

The same is true for the OR logical operator: If expr1 is true the runtime does not need to evaluate expr2 and expr3. Therefore when using the OR logical operator consider reordering expressions placing the least-selective expression first and more selective expressions thereafter.

The order of expressions (here: expr1, expr2 and expr3) does not make a difference for the join and subquery query planner.

Note that the runtime does not guarantee short-circuit evaluation in all cases. The runtime may rewrite the where-clause or filter conditions into another order of evaluation so that it can perform index lookups.

24.2.26. Large Number of Threads

When using a large number of threads with the runtime, such as more then 100 threads, you can provide a setting in the configuration that instructs the runtime to reduce the use of thread-local variables. Please see Section 17.6.10, “Runtime Settings Related to Execution of Statements” for more information.

24.2.27. Filter Evaluation Tuning

We offer a switch for tuning evaluation of incoming events against filters. Please see Section 17.6.10, “Runtime Settings Related to Execution of Statements” for more information.

24.2.28. Context Partition Related Information

As the runtime locks on the level of context partition, high concurrency under threading can be achieved by using context partitions.

Generally context partitions require more memory then the more fine-grained grouping that can be achieved by group by or #groupwin.

24.2.29. Prefer Constant Variables Over Non-Constant Variables

The create-variable syntax as well as the APIs can identify a variable as a constant value. When a variable's value is not intended to change it is best to declare the variable as constant.

For example, consider the following two statements that each declares a variable. The first statement declares a constant variable and the second statement declares a non-constant variable:

// declare a constant variable
create constant variable string CONST_DEPARTMENT = 'PURCHASING'

// declare a non-constant variable
create variable string VAR_DEPARTMENT = 'SALES'

When your application compiles a statement that has filters for events according to variable values, the compiler internally inspects such expressions and performs filter optimizations for constant variables that are more effective in evaluation.

For example, consider the following two statements that each look for events related to persons that belong to a given department:

// perfer the constant
select * from PersonEvent(department=CONST_DEPARTMENT)

// less efficient
select * from PersonEvent(department=VAR_DEPARTMENT)

The runtime can more efficiently evaluate the expression using a variable declared as constant. The same observation can be made for subquery and join query planning.

24.2.30. Prefer POJO Events or alternatively Object-Array Events

POJO (plain-old java objects, JavaBean convention) events generally offer the best performance and also the most flexibility.

However, object-array events also offer excellent read access performance for access to event property values as compared to Map-type events. In addition, object-array events use much less memory then Map-type events. They also offer the excellent write access performance.

A comparison of different event representations is in Section 3.5, “Comparing Event Representations”.

We recommend that your application sends POJO or object-array events into the runtime, instead of Map-type events. See Appendix F, Event Representation: Object-Array (Object[]) Events for more information.

Also, we advise considering that your application sets the compiler configuration of the default event representation to object array, as described in Section 17.4.9.1, “Default Event Representation”. Alternatively you can use the @EventRepresentation(objectarray) annotation with individual statements.

24.2.31. Notes on Query Planning

Query planning takes place for subqueries, joins (any type), named window and table on-actions (on-select, on-merge, on-insert, on-update, on-select) and fire-and-forget queries. Query planning affects query execution speed. Enable query plan logging to output query plan information.

For query planning, the compiler draws information from:

The where-clauses, if any are specified. Where-clauses correlate streams, patterns, named windows, tables etc. with more streams, patterns, tables and named windows and are thus the main source of information for query planning.
The data window(s) declared on streams and named windows. The #unique and the #firstunique data window instruct the compiler to retain the last event per unique criteria.
For named windows and tables, the explicit indexes created via create unique index or create index.
For named windows (and not tables), the previously created implicit indexes. The compiler can plan to create implicit indexes automatically if explicit indexes do not match correlation requirements.
Any hints specified for the statement in question and including hints specified during the creation of named windows with create window.

The compiler prefers unique indexes over non-unique indexes.

The compiler prefers hash-based lookups (equals) and combination hash-btree lookups (equals and relational-operator or range) over btree lookups (relational-operator or range) over in-keyword (single and multi-index) lookup plans. This behavior can be controlled by hints that are discussed next.

24.2.32. Query Planning Expression Analysis Hints

The expression analysis hints impact query planning for any statement and fire-and-forget query that performs a join or subquery. They also impact named window and table on-action statements.

This hint instructs the compiler which expressions, operators or streams should be excluded and therefore not considered for query planning. The hint applies to the where-clause and, for outer joins, to the on-clause when present.

The hint takes a single expression as its sole parameter, which is placed in parenthesis. The expression must return a boolean value.

When the provided expression returns true for a given combination, that combination will not be considered for the query plan. A combination consists of a from-stream (name or number), a to-stream (name or number), an operator (i.e. equals, relational, in-keyword) and a set of expressions.

Table 24.1. Built-In Properties of the Expression Analysis Hint

Name	Type	Description
exprs	string-array (`String[]`)	Expression texts with minified whitespace.
from_streamname	string	The stream name of the stream providing lookup values as provided by the `as` keyword.
from_streamnum	int	The integer ordinal number of the stream providing lookup values as listed in the from-clause.
opname	string	The operator name. Valid values are `equals`, `relop` (relational operators and ranges) and `inkw` (`in`-keyword).
to_streamname	string	The stream name of the stream providing indexable values as provided by the `as` keyword.
to_streamnum	int	The integer ordinal number of the stream providing indexable values as listed in the from-clause.

Consider two event types A and B. Event type A has a property aprop and event type B has a property bprop. Let's assume A and B are related by aprop and bprop.

An inner join of all A and B events might look like this:

select * from A#keepall as a, B#keepall as b where aprop = bprop

In the default query plan, when an A event comes in, the runtime obtains the value of aprop and performs an index lookup against bprop values to obtain matching B events. Vice versa, when a B event comes in, the runtime obtains the value of bprop and performs an index lookup against aprop values to obtain matching A events.

The compiler evaluates the hint expression for each combination. The table below outlines the two rows provided to the hint expression:

Table 24.2. Built-In Properties of the Expression Analysis Hint

exprs	from_streamname	from_streamnum	opname	to_streamname	to_streamnum
`["aprop", "bprop"]`	`a`	`0`	`equals`	`b`	`1`
`["bprop", "aprop"]`	`b`	`1`	`equals`	`a`	`0`

The following statement with hint causes the analyzer to exclude all combinations since the expression passed in always returns true, in effect causing the query planner to always execute the statement as a full table scan.

@hint('exclude_plan(true)')
select * from A#keepall as a, B#keepall as b where aprop = bprop

This hint instructs the compiler to ignore all equals-operators for query planning:

@hint('exclude_plan(opname="equals")') select ....

The next hint instructs the compiler to ignore the equals-operator for the direction of lookup from A to B:

@hint('exclude_plan(opname="equals" and from_streamname="a")') select ....

Conversely, this hint instructs the compiler to ignore the equals-operator for the direction of lookup from B to A:

@hint('exclude_plan(opname="equals" and from_streamname="b")') select ....

Use the exprs array of expression texts to exclude specific expressions:

@hint('exclude_plan(exprs[0]="aprop")') select ....

For subqueries the stream number zero is the subquery from-clause itself and 1 to N are the enclosing statement's from-clause streams. For named window and table on-action statements the stream number zero is the named window or table and stream number 1 refers to the triggering pattern or event.

To specify multiple expressions, please specify multiple hints. The compiler excludes a specific combination when any of the hint expressions returns true.

To inspect values passed to the hint expression, please enable query plan logging. To inspect expression evaluation, please use @Audit.

24.2.33. Query Planning Index Hints

Currently index hints are only supported for the following types of statements:

Named window and table on-action statements (on-select, on-merge, on-insert, on-update, on-select).
Statements that have subselects against named windows that have index sharing enabled (the default is disabled).
Statements that have subselects against tables.
Fire-and-forget queries.

For the above statements, you may dictate to the compiler which explicit index (created via create index syntax) to use.

Specify the name of the explicit index in parentheses following @Hint and the index literal.

The following example instructs the compiler to use the UserProfileIndex if possible:

@Hint('index(UserProfileIndex)')

Add the literal bust to instruct the compiler to use the index, or if the compiler cannot use the index fail query planning with an exception and therefore fail statement compilation.

The following example instructs the compiler to use the UserProfileIndex if possible or fail with an exception if the index cannot be used:

@Hint('index(UserProfileIndex, bust)')

Multiple indexes can be listed separated by comma (,).

The next example instructs the compiler to consider the UserProfileIndex and the SessionIndex or fail with an exception if either index cannot be used:

@Hint('index(UserProfileIndex, SessionIndex, bust)')

The literal explicit can be added to instruct the compiler to use only explicitly created indexes.

The final example instructs the compiler to consider any explicitly create index or fail with an exception if any of the explicitly created indexes cannot be used:

@Hint('index(explicit, bust)')

24.2.34. Measuring Throughput

We recommend using System.nanoTime() to measure elapsed time when processing a batch of, for example, 1000 events.

Note that System.nanoTime() provides nanosecond precision, but not necessarily nanosecond resolution.

Therefore don't try to measure the time spent by the runtime processing a single event: The resolution of System.nanoTime() is not sufficient. Also, there are reports that System.nanoTime() can be actually go "backwards" and may not always behave as expected under threading. Please check your JVM platform documentation.

In the default configuration, the best way to measure performance is to take nano time, send a large number of events, for example 10.000 events, and take nano time again reporting on the difference between the two numbers.

If your configuration has inbound threading or other threading options set, you should either monitor the queue depth to determine performance, or disable threading options when measuring performance, or have your application use multiple threads to send events instead.

24.2.35. Do Not Create the Same or Similar Statement X Times

It is vastly more efficient to create a statement once and attach multiple listeners, then to create the same statement X times.

It is vastly more efficient to use context declarations to factor out commonalities between statements then creating X similar statements.

EPL, the compiler and runtime are optimized for low-latency and high-throughput execution. In order to accomplish that the compiler analyzes and query-plans. Certain information within each statement can effectively shared in the runtime (indexes) so that the runtime can remove duplication of processing and thus the runtime can achieve low-latency and high-throughput. The tradeoff is that the compiler must, for each statement, perform some upfront analysis.

Since your goal will be to make all test code as realistic, real-world and production-like as possible, we recommend against production code or test code deploying the same exact statement multiple times. Instead consider creating the same statement once and attaching multiple listeners. The compiler and runtime do not try to detect duplicate statements, since that can easily be done by your application.

Let's assume your test statement computes an aggregation over a 1-minute time window, for example select symbol, count(*) from StockTick#time(1 min) group by symbol. If your code creates the same statement 100 times the code instructs the runtime to track 100 logically independent time windows and to track aggregations for each group 100 times. Obviously, this is not a good use of EPL and the design of your statements and code may not be optimal.

Consider the world of relational databases. Your code could attach to a relational database, create the same table with a different name 100 times, and populate each of the 100 different tables with the same row data. A relational database administrator would probably recommend against creating 100 identical tables holding the same row data. Compare a statement to a relational database table in respect to how many there should be. In a good design there are limited number of statements. The runtime is not specifically designed for very large number of statements. Similarly a relational database schema design that has 100,000 tables would be something one would seriously question. It depends on the statement itself in respect to how many statements fit into memory and there is no general guideline.

EPL allows you the freedom to design your EPL in a way that reuses state and processing. For example, your EPL design could utilize a named window instead of allocating 100 independent time window. Since named windows are shared, the runtime only needs to track one time window instead of 100. And your EPL design could use an EPL table to maintain aggregations once and in a central place, so that tracking counts per symbol is done once instead of 100 times.

Context declarations can be an efficient way to take commonalities between statements (things that are similar between multiple statements) and factor them out into a context declaration. Instead of creating X similar statements, declare a context and attach one statement to the context, thus having X context partitions. This eliminates compiling and/or deploying X same statements. Using context the compiler only needs to analyze the context declaration and the statement. Your application can send start and stop events to control which context partitions exist and what events each context partition analyzes. Use the context partition administrative API to browse or terminate context partitions.

For example, assume you need to create 100000 similar statements that all filter GeoEvent events:

create schema GeoEvent(id string, value int, marker string)

@name('statment-1) select * from GeoEvent(id = '0001', value between 10 and 20, marker in ('a', 'b'))

@name('statment-N) select * from GeoEvent(id = '0002', value between 20 and 30, marker in ('c', 'd'))

If your application compiles and deploys 100k statements as above, the compiler must analyze and query plan each statement separately, and the runtime must enter each set of filter criteria into a shared filter index tree. Remember that the runtime can process incoming events very fast, with low latency and high throughput, even for 100k statements. However compiling and deploying 100k individual statements does take CPU time.

In this example, the statements have similar filters: id = an_id, value between start_range and end_range and marker in (markers). You could say that statements are similar and look like:

select * from GeoEvent(id=an_id, value between start_range and end_range, marker in (markers))

The an_id, start_range, end_range and markers are essential parameters to an instance of the filtering statement. Instances of statements are context partitions. Declare a context to refactor and change our design so the common filters are in one place. This apprach just requires two statements: the context declaration and the statement with the filters. You may declare two event types: one to allocate new context partitions and one to terminate context partitions.

Start by creating an event type that controls which instances of the filtering statement (the context partitions) are active:

create schema InitEvent(id string, startRange int, endRange int, markers string[])

Next, create an event type that controls when a context partition terminates:

create schema TermEvent(id string)

The context declaration tells the runtime that when an InitEvent arrives you want have a new instance that is parameterized by the InitEvent properties:

create context GeoEventFilterContext
  initiated by InitEvent as initevent
  terminated by by TermEvent(id=initevent.id)

Define the statement that filters:

context GeoEventFilterContext select * from GeoEvent(id = context.initevent.id, 
  value between context.initevent.startRange and context.initevent.endRange, 
  marker in (context.initevent.markers))

Your application can now send InitEvent instances, for example (notation from the online EPL tool):

InitEvent={id='0001', startRange=10, endRange=20, markers={'a', 'b'}}
InitEvent={id='0002', startRange=20, endRange=30, markers={'c', 'd'}}

When the runtime receives an InitEvent instance, it can simply take the id, startRange, endRange and markers values and instantiate the EPL filter statement (aka. allocate a new context partition) and start looking for matching GeoEvent events.

To stop looking for a given id, send a TermEvent, like so:

TermEvent={id='0001'}

24.2.36. Comparing Single-Threaded and Multi-Threaded Performance

The Java Virtual Machine optimizes locks such that the time to obtain a read lock, for example, differs widely between single-threaded and multi-threaded applications. We compared code that obtains an unfair ReentrantReadWriteLock read lock 100 million times, without any writer. We measured 3 seconds for a single-threaded application and 15 seconds for an application with 2 threads. It can therefore not be expected that scaling from single-threaded to 2 threads will always double performance. There is a base cost for multiple threads to coordinate.

Tip

For logging of lock activity please enable runtime lock activity logging as described in Section 17.6.2.3, “Lock Activity Logging”.

24.2.37. Incremental Versus Recomputed Aggregation for Named Window Events

Whether aggregations of named window rows are computed incrementally or are recomputed from scratch depends on the type of statement.

When the runtime computes aggregation values incrementally, meaning it continuously updates the aggregation value as events enter and leave a named window, it means that the runtime internally subscribes to named window updates and applies these updates as they occur. For some applications this is the desired behavior.

For some applications re-computing aggregation values from scratch when a certain condition occurs, for example when a triggering event arrives or time passes, is beneficial. Re-computing an aggregation can be less expensive if the number of rows to consider is small and/or when the triggering event or time condition triggers infrequently.

The next paragraph assumes that a named window has been created to hold some historical financial data per symbol and minute:

create window HistoricalWindow#keepall as (symbol string, int minute, double price)

insert into HistoricalWindow select symbol, minute, price from HistoricalTick

For statements that simply select from a named window (excludes on-select) the runtime computes aggregation values incrementally, continuously updating the aggregation, as events enter and leave the named window.

For example, the below statement updates the total price incrementally as events enter and leave the named window. If events in the named window already exist at the time the statement gets created, the total price gets pre-computed once when the statement gets created and incrementally updated when events enter and leave the named window:

select sum(price) from HistoricalWindow(symbol='GE')

The same is true for uncorrelated subqueries. For statements that sub-select from a named window, the runtime computes aggregation values incrementally, continuously updating the aggregation, as events enter and leave the named window. This is only true for uncorrelated subqueries that don't have a where-clause.

// Output GE symbol total price, incrementally computed
// Outputs every 15 minutes on the hour.
select (sum(price) from HistoricalWindow(symbol='GE')) 
from pattern [every timer:at(0, 15, 30, 45), *, *, *, *, 0)]

If instead your application uses on-select or a correlated subquery, the runtime recomputes aggregation values from scratch every time the triggering event fires.

For example, the below statement does not incrementally compute the total price (use a plain select or subselect as above instead). Instead the runtime computes the total price from scratch based on the where-clause and matching rows:

// Output GE symbol total price (recomputed from scratch) every 15 minutes on the hour
on pattern [every timer:at(0, 15, 30, 45), *, *, *, *, 0)]
select sum(price) from HistoricalWindow where symbol='GE'

Unidirectional joins against named windows also do not incrementally compute aggregation values.

Joins and outer joins, that are not unidirectional, compute aggregation values incrementally.

24.2.38. When Does Memory Get Released

Java Virtual Machines (JVMs) release memory only when a garbage collection occurs. Depending on your JVM settings a garbage collection can occur frequently or infrequently and may consider all or only parts of heap memory.

The runtime is optimized towards latency and throughput. The runtime does not force garbage collection or interfere with garbage collection. For performance-sensitive code areas, the runtime utilizes thread-local buffers such as arrays or ringbuffers that can retain small amounts of recently processed state. The runtime does not try to clean such buffers after every event for performance reasons. It does clean such buffers when destroying the runtime and undeploying. It is therefore normal to see a small non-increasing amount of memory to be retained after processing events that the garbage collector may not free immediately.

24.2.39. Measure Throughput of Non-Matches as Well as Watches

When an event comes in and the event does not match any statement, the runtime can discard that event since the event is a non-match. When measuring throughput, we suggest including non-matching events. The fact that the runtime can discard non-matching events extremely fast is an important aspect of processing.

Many use cases look for a needle-in-a-haystack situation or rarely occurring pattern. For example, a use case looking for security breaches may analyze 10 million events and find only a single situation consisting, for example, of 5 correlated events of the 10 million input events. We'd recommend your benchmark to closely mimic or to play back production data and watch the expected ratio of input and output events. Reducing the number of output events generally increases performance.

For example, assume you have 10 statements:

select * from pattern[A -> B(id = 1)];
select * from pattern[A -> B(id = 2)];
.....
select * from pattern[A -> B(id = 10)];

The above patterns each match once when an A event comes in followed by a B event with a given id between 1 and 10.

We recommend to measure throughput by sending in B events that have a value of minus one (-1) for id, for example, to determine how fast such events are discarded.

24.2.40. Options for When an Event Type has a Large Number of Event Properties i.e. Large Events

We would consider an event type that has more than 1000 event properties to be an event type with a large number of properties. Here are some of the available options for handling large events (not in order of preference, this is just a list):

One can design the EPL so that only those event properties that matter for the specific use case are retained and the Esper runtime can thus garbage collect the original event and retain only the few event properties that the specific use case needs. For example using insert into ReducedEvent select a,b from LargeEvent and select ... from ReducedEvent....
One can design the EPL so that there is no need for the runtime to hold on to the large event objects themselves. This documentation and the FAQ page describe in detail when the runtime does and does not retain events.
Use EsperHA since EsperHA has the ability to remove state from memory and swap it back into memory when needed.
The JVM in general needs more memory for objects like strings or arrays. Making sure that event properties have the smallest memory footprint can help, i.e. a primitive integer uses less memory than a string.
Instead of keeping all event properties on the event, reduce the event properties to only those properties that matter. The application code could lazy-fetch the remaining properties from an external source when they are needed.
Event properties could be stored binary-encoded which also uses less memory (use an external library or write your own binary encoding; Esper does not provide a binary encoding). The application code could for instance provide "getter"-methods that query the encoded data.

24.3. Using the Performance Kit

24.3.1. How to Use the Performance Kit

The benchmark application is basically an event server build with the runtime that listens to remote clients over TCP. Remote clients send MarketData(ticker, price, volume) streams to the event server. The event server is started with 1000 statements of one single kind (unless otherwise written), with one statement per ticker symbol, unless the statement kind does not depend on the symbol. The statement prototype is provided along the results with a '$' instead of the actual ticker symbol value. The event server is entirely multithreaded and can leverage the full power of 32bit or 64bit underlying hardware multi-processor multi-core architecture.

The kit also prints out when starting up the event size and the theoretical maximal throughput you can get on a 100 Mbit/s and 1 Gbit/s network. Keep in mind a 100 Mbit/s network will be overloaded at about 400 000 event/s when using our kit despite the small size of events.

Results are posted on our Wiki page at Performance Wiki. Reported results do not represent best ever obtained results. Reported results may help you better compare Esper to other solutions (for latency, throughput and CPU utilization) and also assess your target hardware and JVMs.

The event server, client and statement prototypes are provided in the source repository esper/trunk/examples/benchmark/. Refer to http://www.espertech.com/esper for source access.

If you use the kit you should:

Choose the statement you want to benchmark, add it to etc/statements.properties under your own KEY and use the -mode KEY when you start the event server.
Prepare your runServer.sh/runServer.cmd and runClient.sh/runclient.cmd scripts. You'll need to drop required jar libraries in lib/ , make sure the classpath is configured in those script to include build and etc . The required libraries are Esper (any compatible version, we have tested started with Esper 1.7.0) and its dependencies. Note that ./etc and ./build have to be in the classpath. At that stage you should also start to set min and max JVM heap. A good start is 1GB as in -Xms1g -Xmx1g
Write the statement you want to benchmark given that client will send a stream MarketData(String ticker, int volume, double price), add it to etc/statements.properties under your own KEY and use the -mode KEY when you start the event server. Use '$' in the statement to create a prototype. For every symbol, a statement will get registered with all '$' replaced by the actual symbol value (f.e. 'GOOG')
Ensure client and server are using the same -Desper.benchmark.symbol=1000 value. This sets the number of symbol to use (thus may set the number of statement if you are using a statement prototype, and governs how MarketData event are represented over the network. Basically all events will have the same size over the network to ensure predictability and will be ranging between S0AA and S999A if you use 1000 as a value here (prefix with S and padded with A up to a fixed length string. Volume and price attributes will be randomized.
By default the benchmark registers a subscriber to the statement(s). Use -Desper.benchmark.ul to use an UpdateListener instead. Note that the subscriber contains suitable update(..) methods for the default proposed statement in the etc/statements.properties file but might not be suitable if you change statements due to the strong binding with statement results. Refer to Table 16.2, “Choices For Receiving Statement Results”.
Establish a performance baseline in simulation mode (without clients). Use the -rate 1x5000 option to simulate one client (one thread) sending 5000 evt/s. You can ramp up both the number of client simulated thread and their emission rate to maximize CPU utilization. The right number should mimic the client emission rate you will use in the client/server benchmark and should thus be consistent with what your client machine and network will be able to send. On small hardware, having a lot of thread with slow rate will not help getting high throughput in this simulation mode.

Do performance runs with client/server mode. Remove the -rate NxM option from the runServer script or Ant task. Start the server with -help to display the possible server options (listen port, statistics, fan out options etc). On the remote machine, start one or more client. Use -help to display the possible client options (remote port, host, emission rate). The client will output the actual number of event it is sending to the server. If the server gets overloaded (or if you turned on -queue options on the server) the client will likely not be able to reach its target rate.

Usually you will get better performance by using server side -queue -1 option so as to have each client connection handled by a single thread pipeline. If you change to 0 or more, there will be intermediate structures to pass the event stream in an asynchronous fashion. This will increase context switching, although if you are using many clients, or are using the -sleep xxx (xxx in milliseconds) to simulate a listener delay you may get better performance.

The most important server side option is -stat xxx (xxx in seconds) to print out throughput and latency statistics aggregated over the last xxx seconds (and reset every time). It will produce both internal latency (in nanosecond) and also end to end latency (in millisecond, including network time). If you are measuring end to end latency you should make sure your server and client machine(s) are having the same time with f.e. ntpd with a good enough precision. The stat format is like:

---Stats - runtime (unit: ns)
  Avg: 2528 #4101107
        0 <    5000:  97.01%  97.01% #3978672
     5000 <   10000:   2.60%  99.62% #106669
    10000 <   15000:   0.35%  99.97% #14337
    15000 <   20000:   0.02%  99.99% #971
    20000 <   25000:   0.00%  99.99% #177
    25000 <   50000:   0.00% 100.00% #89
    50000 <  100000:   0.00% 100.00% #41
   100000 <  500000:   0.00% 100.00% #120
   500000 < 1000000:   0.00% 100.00% #2
  1000000 < 2500000:   0.00% 100.00% #7
  2500000 < 5000000:   0.00% 100.00% #5
  5000000 <    more:   0.00% 100.00% #18
---Stats - endToEnd (unit: ms)
  Avg: -2704829444341073400 #4101609
        0 <       1:  75.01%  75.01% #3076609
        1 <       5:   0.00%  75.01% #0
        5 <      10:   0.00%  75.01% #0
       10 <      50:   0.00%  75.01% #0
       50 <     100:   0.00%  75.01% #0
      100 <     250:   0.00%  75.01% #0
      250 <     500:   0.00%  75.01% #0
      500 <    1000:   0.00%  75.01% #0
     1000 <    more:  24.99% 100.00% #1025000
Throughput 412503 (active 0 pending 0 cnx 4)

This one reads as:

"Throughput is 412 503 event/s with 4 client connected. No -queue options 
was used thus no event is pending at the time the statistics are printed. 
latency average is at 2528 ns (that is 2.5 us) for 4 101 107 events 
(which means we have 10 seconds stats here). Less than 10us latency 
was achieved for 106 669 events that is 99.62%. Latency between 5us 
and 10us was achieved for those 2.60% of all the events in the interval."

"End to end latency was ... in this case likely due to client clock difference
we ended up with unusable end to end statistics."

Consider the second output paragraph on end-to-end latency:

---Stats - endToEnd (unit: ms)
  Avg: 15 #863396
        0 <       1:   0.75%   0.75% #6434
        1 <       5:   0.99%   1.74% #8552
        5 <      10:   2.12%   3.85% #18269
       10 <      50:  91.27%  95.13% #788062
       50 <     100:   0.10%  95.32% #827
      100 <     250:   4.36%  99.58% #37634
      250 <     500:   0.42% 100.00% #3618
      500 <    1000:   0.00% 100.00% #0
     1000 <    more:   0.00% 100.00% #0

This would read:

"End to end latency average is at 15 milliseconds for the 863 396 events 
considered for this statistic report. 95.13% ie 788 062 events were handled 
(end to end) below 50ms, and 91.27% were handled between 10ms and 50ms."

Chapter 25. References

25.1. Reference List

25.1. Reference List

Luckham, David. 2002. The Power of Events. Addison-Wesley.
The Stanford Rapide (TM) Project. http://pavg.stanford.edu/rapide.
Arasu, Arvind, et.al.. 2004. Linear Road: A Stream Data Management Benchmark, Stanford University http://www.cs.brown.edu/research/aurora/Linear_Road_Benchmark_Homepage.html.

Appendix A. Output Reference and Samples

This section specifies the output of a subset of statements, for two purposes: First, to help application developers understand streaming runtime output in response to incoming events and in response to time passing. Second, to document and standardize output for statements in a testable and trackable fashion.

The section focuses on a subset of features, namely the time window, aggregation, grouping, and output rate limiting. The section does not currently provide examples for many of the other language features, thus there is no example for other data windows (the time window is used here), joins, sub-selects or named windows etc.

Rather then just describe syntax and output, this section provides detailed examples for each of the types of statements presented. The input for each type of statement is always the same set of events, and the same timing. Each event has three properties: symbol, volume and price. The property types are string, long and double, respectively.

The chapters are organized by the type of statement: The presence or absence of aggregation functions, as well as the presence or absence of a group by clause change statement output as described in Section 2.15, “Basic Aggregated Statement Types”.

You will notice that some statements utilize the order by clause for sorting output. The reason is that when multiple output rows are produced at once, the output can be easier to read if it is sorted.

With output rate limiting, the runtime invokes your listener even if there are no results to indicate when the output condition has been reached. Such is indicated as (empty result) in the output result columns.

The output columns show both insert and remove stream events. Insert stream events are delivered as an array of EventBean instances to listeners in the newData parameter, while remove stream events are delivered to the oldData parameter of listeners. Delivery to observers follows similar rules.

A.1. Introduction and Sample Data

For the purpose of illustration and documentation, the example data set demonstrates input and remove streams based on a time window of a 5.5 second interval. The statement utilizing the time window could look as follows:

select symbol, volume, price from MarketData#time(5.5 sec)

We have picked a time window to demonstrate the output for events entering and leaving a data window with an expiration policy. The time window provides a simple expiration policy based on time: if an event resides in the time window more then 5.5 seconds, the runtime expires the event from the time window.

The input events and their timing are below. The table should be read, starting from top, as "The time starts at 0.2 seconds. Event E1 arrives at 0.2 seconds with properties [S1, 100, 25]. At 0.8 second event E2 arrives with properties [S2, 5000, 9.0]" and so on.

                       Input                                 
-----------------------------------------------  
 Time Symbol  Volume   Price
  0.2                          
          S1     100    25.0   Event E1 arrives
  0.8                          
          S2    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
  1.5                          
          S1     150    24.0   Event E3 arrives
          S3   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
          S1     155    26.0   Event E5 arrives
  2.2                          
  2.5                          
  3.0                          
  3.2                          
  3.5                          
          S3   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
  4.3                          
          S1     150    22.0   Event E7 arrives
  4.9                          
          S3   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
  5.7                          Event E1 leaves the time window
  5.9                          
          S3   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2

The event data set assumes a time window of 5.5 seconds. Thus at time 5.7 seconds the first arriving event (E1) leaves the time window.

The data set as above shows times between 0.2 seconds and 7.2 seconds. Only a couple of time points have been picked for the table to keep the set of time points constant between statements, and thus make the test data and output easier to understand.

A.2. Output for Un-Aggregated and Un-Grouped Statements

This chapter provides sample output for statements that do not have aggregation functions and do not have a group by clause.

A.2.1. No Output Rate Limiting

Without an output clause, the runtime dispatches to listeners as soon as events arrive, or as soon as time passes such that events leave data windows.

select irstream symbol, volume, price from MarketData#time(5.5 sec)

With an output clause, the runtime dispatches to listeners when the output condition occurs. Here, the output condition is a 1-second time interval. The runtime thus outputs every 1 second, starting from the first event, even if there are no new events or no expiring events to output.

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
                                                 [IBM, 100, 25.0]                       
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
                                                 [MSFT, 5000, 9.0]                      
  1.0                          
  1.2                          
  1.5                          
         IBM     150    24.0   Event E3 arrives
                                                 [IBM, 150, 24.0]                       
         YAH   10000     1.0   Event E4 arrives
                                                 [YAH, 10000, 1.0]                      
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
                                                 [IBM, 155, 26.0]                       
  2.2                          
  2.5                          
  3.0                          
  3.2                          
  3.5                          
         YAH   11000     2.0   Event E6 arrives
                                                 [YAH, 11000, 2.0]                      
  4.0                          
  4.2                          
  4.3                          
         IBM     150    22.0   Event E7 arrives
                                                 [IBM, 150, 22.0]                       
  4.9                          
         YAH   11500     3.0   Event E8 arrives
                                                 [YAH, 11500, 3.0]                      
  5.0                          
  5.2                          
  5.7                          Event E1 leaves the time window
                                                                    [IBM, 100, 25.0]    
  5.9                          
         YAH   10500     1.0   Event E9 arrives
                                                 [YAH, 10500, 1.0]                      
  6.0                          
  6.2                          
  6.3                          Event E2 leaves the time window
                                                                    [MSFT, 5000, 9.0]   
  7.0                          Event E3 and E4 leave the time window
                                                                    [IBM, 150, 24.0]    
                                                                    [YAH, 10000, 1.0]   
  7.2

A.2.2. Output Rate Limiting - Default

The default (no keyword) and the ALL keyword result in the same output.

select irstream symbol, volume, price from MarketData#time(5.5 sec) 
output every 1 seconds

Using the LAST keyword in the output clause, the runtime dispatches to listeners only the last event of each insert and remove stream.

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
                                                 [IBM, 100, 25.0]                       
                                                 [MSFT, 5000, 9.0]                      
  1.5                          
         IBM     150    24.0   Event E3 arrives
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
                                                 [IBM, 150, 24.0]                       
                                                 [YAH, 10000, 1.0]                      
                                                 [IBM, 155, 26.0]                       
  2.5                          
  3.0                          
  3.2                          
                                                 (empty result)     (empty result)      
  3.5                          
         YAH   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
                                                 [YAH, 11000, 2.0]                      
  4.3                          
         IBM     150    22.0   Event E7 arrives
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
                                                 [IBM, 150, 22.0]                       
                                                 [YAH, 11500, 3.0]                      
  5.7                          Event E1 leaves the time window
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
                                                 [YAH, 10500, 1.0]  [IBM, 100, 25.0]    
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2                          
                                                                    [MSFT, 5000, 9.0]   
                                                                    [IBM, 150, 24.0]    
                                                                    [YAH, 10000, 1.0]

A.2.3. Output Rate Limiting - Last

select irstream symbol, volume, price from MarketData#time(5.5 sec) 
output last every 1 seconds

Using the FIRST keyword in the output clause, the runtime dispatches to listeners only the first event of each insert or remove stream, and does not output further events until the output condition is reached.

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
                                                 [MSFT, 5000, 9.0]                      
  1.5                          
         IBM     150    24.0   Event E3 arrives
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
                                                 [IBM, 155, 26.0]                       
  2.5                          
  3.0                          
  3.2                          
                                                 (empty result)     (empty result)      
  3.5                          
         YAH   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
                                                 [YAH, 11000, 2.0]                      
  4.3                          
         IBM     150    22.0   Event E7 arrives
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
                                                 [YAH, 11500, 3.0]                      
  5.7                          Event E1 leaves the time window
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
                                                 [YAH, 10500, 1.0]  [IBM, 100, 25.0]    
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2                          
                                                                    [YAH, 10000, 1.0]

A.2.4. Output Rate Limiting - First

select irstream symbol, volume, price from MarketData#time(5.5 sec)
output first every 1 seconds

Using the SNAPSHOT keyword in the output clause, the runtime posts data window contents when the output condition is reached.

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
                                                 [IBM, 100, 25.0]                       
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
  1.5                          
         IBM     150    24.0   Event E3 arrives
                                                 [IBM, 150, 24.0]                       
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
  2.5                          
  3.0                          
  3.2                          
  3.5                          
         YAH   11000     2.0   Event E6 arrives
                                                 [YAH, 11000, 2.0]                      
  4.0                          
  4.2                          
  4.3                          
         IBM     150    22.0   Event E7 arrives
                                                 [IBM, 150, 22.0]                       
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
  5.7                          Event E1 leaves the time window
                                                                    [IBM, 100, 25.0]    
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
  6.3                          Event E2 leaves the time window
                                                                    [MSFT, 5000, 9.0]   
  7.0                          Event E3 and E4 leave the time window
  7.2

A.2.5. Output Rate Limiting - Snapshot

select irstream symbol, volume, price from MarketData#time(5.5 sec)
output snapshot every 1 seconds

This chapter provides sample output for statements that have aggregation functions, and that do not have a group by clause, and in which all event properties are under aggregation.

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
                                                 [IBM, 100, 25.0]                       
                                                 [MSFT, 5000, 9.0]                      
  1.5                          
         IBM     150    24.0   Event E3 arrives
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
                                                 [IBM, 100, 25.0]                       
                                                 [MSFT, 5000, 9.0]                      
                                                 [IBM, 150, 24.0]                       
                                                 [YAH, 10000, 1.0]                      
                                                 [IBM, 155, 26.0]                       
  2.5                          
  3.0                          
  3.2                          
                                                 [IBM, 100, 25.0]                       
                                                 [MSFT, 5000, 9.0]                      
                                                 [IBM, 150, 24.0]                       
                                                 [YAH, 10000, 1.0]                      
                                                 [IBM, 155, 26.0]                       
  3.5                          
         YAH   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
                                                 [IBM, 100, 25.0]                       
                                                 [MSFT, 5000, 9.0]                      
                                                 [IBM, 150, 24.0]                       
                                                 [YAH, 10000, 1.0]                      
                                                 [IBM, 155, 26.0]                       
                                                 [YAH, 11000, 2.0]                      
  4.3                          
         IBM     150    22.0   Event E7 arrives
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
                                                 [IBM, 100, 25.0]                       
                                                 [MSFT, 5000, 9.0]                      
                                                 [IBM, 150, 24.0]                       
                                                 [YAH, 10000, 1.0]                      
                                                 [IBM, 155, 26.0]                       
                                                 [YAH, 11000, 2.0]                      
                                                 [IBM, 150, 22.0]                       
                                                 [YAH, 11500, 3.0]                      
  5.7                          Event E1 leaves the time window
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
                                                 [MSFT, 5000, 9.0]                      
                                                 [IBM, 150, 24.0]                       
                                                 [YAH, 10000, 1.0]                      
                                                 [IBM, 155, 26.0]                       
                                                 [YAH, 11000, 2.0]                      
                                                 [IBM, 150, 22.0]                       
                                                 [YAH, 11500, 3.0]                      
                                                 [YAH, 10500, 1.0]                      
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2                          
                                                 [IBM, 155, 26.0]                       
                                                 [YAH, 11000, 2.0]                      
                                                 [IBM, 150, 22.0]                       
                                                 [YAH, 11500, 3.0]                      
                                                 [YAH, 10500, 1.0]

A.3. Output for Fully-Aggregated and Un-Grouped Statements

A.3.1. No Output Rate Limiting

select irstream sum(price) from MarketData#time(5.5 sec)

Output occurs when the output condition is reached after each 1-second time interval. For each event arriving, the new aggregation value is output as part of the insert stream. As part of the remove stream, the prior aggregation value is output. This is useful for getting a delta-change for each event or group. If there is a having clause, the filter expression applies to each row.

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
                                                 [25.0]             [null]              
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
                                                 [34.0]             [25.0]              
  1.0                          
  1.2                          
  1.5                          
         IBM     150    24.0   Event E3 arrives
                                                 [58.0]             [34.0]              
         YAH   10000     1.0   Event E4 arrives
                                                 [59.0]             [58.0]              
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
                                                 [85.0]             [59.0]              
  2.2                          
  2.5                          
  3.0                          
  3.2                          
  3.5                          
         YAH   11000     2.0   Event E6 arrives
                                                 [87.0]             [85.0]              
  4.0                          
  4.2                          
  4.3                          
         IBM     150    22.0   Event E7 arrives
                                                 [109.0]            [87.0]              
  4.9                          
         YAH   11500     3.0   Event E8 arrives
                                                 [112.0]            [109.0]             
  5.0                          
  5.2                          
  5.7                          Event E1 leaves the time window
                                                 [87.0]             [112.0]             
  5.9                          
         YAH   10500     1.0   Event E9 arrives
                                                 [88.0]             [87.0]              
  6.0                          
  6.2                          
  6.3                          Event E2 leaves the time window
                                                 [79.0]             [88.0]              
  7.0                          Event E3 and E4 leave the time window
                                                 [54.0]             [79.0]              
  7.2

A.3.2. Output Rate Limiting - Default

Here also the default (no keyword) and the ALL keyword result in the same output.

select irstream sum(price) from MarketData#time(5.5 sec)
output every 1 seconds

With the LAST keyword, the insert stream carries one event that holds the last aggregation value, and the remove stream carries the prior aggregation value.

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
                                                 [25.0]             [null]              
                                                 [34.0]             [25.0]              
  1.5                          
         IBM     150    24.0   Event E3 arrives
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
                                                 [58.0]             [34.0]              
                                                 [59.0]             [58.0]              
                                                 [85.0]             [59.0]              
  2.5                          
  3.0                          
  3.2                          
                                                 [85.0]             [85.0]              
  3.5                          
         YAH   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
                                                 [87.0]             [85.0]              
  4.3                          
         IBM     150    22.0   Event E7 arrives
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
                                                 [109.0]            [87.0]              
                                                 [112.0]            [109.0]             
  5.7                          Event E1 leaves the time window
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
                                                 [87.0]             [112.0]             
                                                 [88.0]             [87.0]              
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2                          
                                                 [79.0]             [88.0]              
                                                 [54.0]             [79.0]

A.3.3. Output Rate Limiting - Last

select irstream sum(price) from MarketData#time(5.5 sec) 
output last every 1 seconds

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
                                                 [34.0]             [null]              
  1.5                          
         IBM     150    24.0   Event E3 arrives
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
                                                 [85.0]             [34.0]              
  2.5                          
  3.0                          
  3.2                          
                                                 [85.0]             [85.0]              
  3.5                          
         YAH   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
                                                 [87.0]             [85.0]              
  4.3                          
         IBM     150    22.0   Event E7 arrives
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
                                                 [112.0]            [87.0]              
  5.7                          Event E1 leaves the time window
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
                                                 [88.0]             [112.0]             
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2                          
                                                 [54.0]             [88.0]

A.3.4. Output Rate Limiting - First

select irstream sum(price) from MarketData#time(5.5 sec)
output first every 1 seconds

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
                                                 [25.0]             [null]              
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
  1.5                          
         IBM     150    24.0   Event E3 arrives
                                                 [58.0]             [34.0]              
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
  2.5                          
  3.0                          
  3.2                          
  3.5                          
         YAH   11000     2.0   Event E6 arrives
                                                 [87.0]             [85.0]              
  4.0                          
  4.2                          
  4.3                          
         IBM     150    22.0   Event E7 arrives
                                                 [109.0]            [87.0]              
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
  5.7                          Event E1 leaves the time window
                                                 [87.0]             [112.0]             
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
  6.3                          Event E2 leaves the time window
                                                 [79.0]             [88.0]              
  7.0                          Event E3 and E4 leave the time window
  7.2

A.3.5. Output Rate Limiting - Snapshot

select irstream sum(price) from MarketData#time(5.5 sec)
output snapshot every 1 seconds

This chapter provides sample output for statements that have aggregation functions, and that do not have a group by clause, and in which there are event properties that are not under aggregation.

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
                                                 [34.0]                                 
  1.5                          
         IBM     150    24.0   Event E3 arrives
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
                                                 [85.0]                                 
  2.5                          
  3.0                          
  3.2                          
                                                 [85.0]                                 
  3.5                          
         YAH   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
                                                 [87.0]                                 
  4.3                          
         IBM     150    22.0   Event E7 arrives
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
                                                 [112.0]                                
  5.7                          Event E1 leaves the time window
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
                                                 [88.0]                                 
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2                          
                                                 [54.0]

A.4. Output for Aggregated and Un-Grouped Statements

A.4.1. No Output Rate Limiting

select irstream symbol, sum(price) from MarketData#time(5.5 sec)

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
                                                 [IBM, 25.0]                            
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
                                                 [MSFT, 34.0]                           
  1.0                          
  1.2                          
  1.5                          
         IBM     150    24.0   Event E3 arrives
                                                 [IBM, 58.0]                            
         YAH   10000     1.0   Event E4 arrives
                                                 [YAH, 59.0]                            
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
                                                 [IBM, 85.0]                            
  2.2                          
  2.5                          
  3.0                          
  3.2                          
  3.5                          
         YAH   11000     2.0   Event E6 arrives
                                                 [YAH, 87.0]                            
  4.0                          
  4.2                          
  4.3                          
         IBM     150    22.0   Event E7 arrives
                                                 [IBM, 109.0]                           
  4.9                          
         YAH   11500     3.0   Event E8 arrives
                                                 [YAH, 112.0]                           
  5.0                          
  5.2                          
  5.7                          Event E1 leaves the time window
                                                                    [IBM, 87.0]         
  5.9                          
         YAH   10500     1.0   Event E9 arrives
                                                 [YAH, 88.0]                            
  6.0                          
  6.2                          
  6.3                          Event E2 leaves the time window
                                                                    [MSFT, 79.0]        
  7.0                          Event E3 and E4 leave the time window
                                                                    [IBM, 54.0]         
                                                                    [YAH, 54.0]         
  7.2

A.4.2. Output Rate Limiting - Default

select irstream symbol, sum(price) from MarketData#time(5.5 sec)
output every 1 seconds

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
                                                 [IBM, 25.0]                            
                                                 [MSFT, 34.0]                           
  1.5                          
         IBM     150    24.0   Event E3 arrives
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
                                                 [IBM, 58.0]                            
                                                 [YAH, 59.0]                            
                                                 [IBM, 85.0]                            
  2.5                          
  3.0                          
  3.2                          
                                                 (empty result)     (empty result)      
  3.5                          
         YAH   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
                                                 [YAH, 87.0]                            
  4.3                          
         IBM     150    22.0   Event E7 arrives
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
                                                 [IBM, 109.0]                           
                                                 [YAH, 112.0]                           
  5.7                          Event E1 leaves the time window
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
                                                 [YAH, 88.0]        [IBM, 87.0]         
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2                          
                                                                    [MSFT, 79.0]        
                                                                    [IBM, 54.0]         
                                                                    [YAH, 54.0]

A.4.3. Output Rate Limiting - Last

select irstream symbol, sum(price) from MarketData#time(5.5 sec) 
output last every 1 seconds

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
                                                 [MSFT, 34.0]                           
  1.5                          
         IBM     150    24.0   Event E3 arrives
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
                                                 [IBM, 85.0]                            
  2.5                          
  3.0                          
  3.2                          
                                                 (empty result)     (empty result)      
  3.5                          
         YAH   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
                                                 [YAH, 87.0]                            
  4.3                          
         IBM     150    22.0   Event E7 arrives
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
                                                 [YAH, 112.0]                           
  5.7                          Event E1 leaves the time window
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
                                                 [YAH, 88.0]        [IBM, 87.0]         
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2                          
                                                                    [YAH, 54.0]

A.4.4. Output Rate Limiting - First

select irstream symbol, sum(price) from MarketData#time(5.5 sec)
output first every 1 seconds

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
                                                 [IBM, 25.0]                            
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
  1.5                          
         IBM     150    24.0   Event E3 arrives
                                                 [IBM, 58.0]                            
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
  2.5                          
  3.0                          
  3.2                          
  3.5                          
         YAH   11000     2.0   Event E6 arrives
                                                 [YAH, 87.0]                            
  4.0                          
  4.2                          
  4.3                          
         IBM     150    22.0   Event E7 arrives
                                                 [IBM, 109.0]                           
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
  5.7                          Event E1 leaves the time window
                                                                    [IBM, 87.0]         
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
  6.3                          Event E2 leaves the time window
                                                                    [MSFT, 79.0]        
  7.0                          Event E3 and E4 leave the time window
  7.2

A.4.5. Output Rate Limiting - Snapshot

select irstream symbol, sum(price) from MarketData#time(5.5 sec)
output snapshot every 1 seconds

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
                                                 [IBM, 34.0]                            
                                                 [MSFT, 34.0]                           
  1.5                          
         IBM     150    24.0   Event E3 arrives
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
                                                 [IBM, 85.0]                            
                                                 [MSFT, 85.0]                           
                                                 [IBM, 85.0]                            
                                                 [YAH, 85.0]                            
                                                 [IBM, 85.0]                            
  2.5                          
  3.0                          
  3.2                          
                                                 [IBM, 85.0]                            
                                                 [MSFT, 85.0]                           
                                                 [IBM, 85.0]                            
                                                 [YAH, 85.0]                            
                                                 [IBM, 85.0]                            
  3.5                          
         YAH   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
                                                 [IBM, 87.0]                            
                                                 [MSFT, 87.0]                           
                                                 [IBM, 87.0]                            
                                                 [YAH, 87.0]                            
                                                 [IBM, 87.0]                            
                                                 [YAH, 87.0]                            
  4.3                          
         IBM     150    22.0   Event E7 arrives
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
                                                 [IBM, 112.0]                           
                                                 [MSFT, 112.0]                          
                                                 [IBM, 112.0]                           
                                                 [YAH, 112.0]                           
                                                 [IBM, 112.0]                           
                                                 [YAH, 112.0]                           
                                                 [IBM, 112.0]                           
                                                 [YAH, 112.0]                           
  5.7                          Event E1 leaves the time window
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
                                                 [MSFT, 88.0]                           
                                                 [IBM, 88.0]                            
                                                 [YAH, 88.0]                            
                                                 [IBM, 88.0]                            
                                                 [YAH, 88.0]                            
                                                 [IBM, 88.0]                            
                                                 [YAH, 88.0]                            
                                                 [YAH, 88.0]                            
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2                          
                                                 [IBM, 54.0]                            
                                                 [YAH, 54.0]                            
                                                 [IBM, 54.0]                            
                                                 [YAH, 54.0]                            
                                                 [YAH, 54.0]

A.5. Output for Fully-Aggregated and Grouped Statements

A.5.1. No Output Rate Limiting

select irstream symbol, sum(price) from MarketData#time(5.5 sec) 
group by symbol
order by symbol

The default (no keyword) and the ALL keyword do not result in the same output. The default generates an output row per input event, while the ALL keyword generates a row for all groups.

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
                                                 [IBM, 25.0]        [IBM, null]         
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
                                                 [MSFT, 9.0]        [MSFT, null]        
  1.0                          
  1.2                          
  1.5                          
         IBM     150    24.0   Event E3 arrives
                                                 [IBM, 49.0]        [IBM, 25.0]         
         YAH   10000     1.0   Event E4 arrives
                                                 [YAH, 1.0]         [YAH, null]         
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
                                                 [IBM, 75.0]        [IBM, 49.0]         
  2.2                          
  2.5                          
  3.0                          
  3.2                          
  3.5                          
         YAH   11000     2.0   Event E6 arrives
                                                 [YAH, 3.0]         [YAH, 1.0]          
  4.0                          
  4.2                          
  4.3                          
         IBM     150    22.0   Event E7 arrives
                                                 [IBM, 97.0]        [IBM, 75.0]         
  4.9                          
         YAH   11500     3.0   Event E8 arrives
                                                 [YAH, 6.0]         [YAH, 3.0]          
  5.0                          
  5.2                          
  5.7                          Event E1 leaves the time window
                                                 [IBM, 72.0]        [IBM, 97.0]         
  5.9                          
         YAH   10500     1.0   Event E9 arrives
                                                 [YAH, 7.0]         [YAH, 6.0]          
  6.0                          
  6.2                          
  6.3                          Event E2 leaves the time window
                                                 [MSFT, null]       [MSFT, 9.0]         
  7.0                          Event E3 and E4 leave the time window
                                                 [IBM, 48.0]        [IBM, 72.0]         
                                                 [YAH, 6.0]         [YAH, 7.0]          
  7.2

A.5.2. Output Rate Limiting - Default

select irstream symbol, sum(price) from MarketData#time(5.5 sec) 
group by symbol 
output every 1 seconds

			                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
                                                 [IBM, 25.0]        [IBM, null]         
                                                 [MSFT, 9.0]        [MSFT, null]        
  1.5                          
         IBM     150    24.0   Event E3 arrives
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
                                                 [IBM, 49.0]        [IBM, 25.0]         
                                                 [YAH, 1.0]         [YAH, null]         
                                                 [IBM, 75.0]        [IBM, 49.0]         
  2.5                          
  3.0                          
  3.2                          
                                                 (empty result)     (empty result)      
  3.5                          
         YAH   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
                                                 [YAH, 3.0]         [YAH, 1.0]          
  4.3                          
         IBM     150    22.0   Event E7 arrives
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
                                                 [IBM, 97.0]        [IBM, 75.0]         
                                                 [YAH, 6.0]         [YAH, 3.0]          
  5.7                          Event E1 leaves the time window
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
                                                 [IBM, 72.0]        [IBM, 97.0]         
                                                 [YAH, 7.0]         [YAH, 6.0]          
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2                          
                                                 [MSFT, null]       [MSFT, 9.0]         
                                                 [YAH, 6.0]         [YAH, 7.0]          
                                                 [IBM, 48.0]        [IBM, 72.0]

A.5.3. Output Rate Limiting - All

select irstream symbol, sum(price) from MarketData#time(5.5 sec) 
group by symbol 
output all every 1 seconds 
order by symbol

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
                                                 [IBM, 25.0]        [IBM, null]         
                                                 [MSFT, 9.0]        [MSFT, null]        
  1.5                          
         IBM     150    24.0   Event E3 arrives
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
                                                 [IBM, 75.0]        [IBM, 25.0]         
                                                 [MSFT, 9.0]        [MSFT, 9.0]         
                                                 [YAH, 1.0]         [YAH, null]         
  2.5                          
  3.0                          
  3.2                          
                                                 [IBM, 75.0]        [IBM, 75.0]         
                                                 [MSFT, 9.0]        [MSFT, 9.0]         
                                                 [YAH, 1.0]         [YAH, 1.0]          
  3.5                          
         YAH   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
                                                 [IBM, 75.0]        [IBM, 75.0]         
                                                 [MSFT, 9.0]        [MSFT, 9.0]         
                                                 [YAH, 3.0]         [YAH, 1.0]          
  4.3                          
         IBM     150    22.0   Event E7 arrives
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
                                                 [IBM, 97.0]        [IBM, 75.0]         
                                                 [MSFT, 9.0]        [MSFT, 9.0]         
                                                 [YAH, 6.0]         [YAH, 3.0]          
  5.7                          Event E1 leaves the time window
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
                                                 [IBM, 72.0]        [IBM, 97.0]         
                                                 [MSFT, 9.0]        [MSFT, 9.0]         
                                                 [YAH, 7.0]         [YAH, 6.0]          
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2                          
                                                 [IBM, 48.0]        [IBM, 72.0]         
                                                 [MSFT, null]       [MSFT, 9.0]         
                                                 [YAH, 6.0]         [YAH, 7.0]

A.5.4. Output Rate Limiting - Last

select irstream symbol, sum(price) from MarketData#time(5.5 sec)
group by symbol 
output last every 1 seconds 
order by symbol

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
                                                 [IBM, 25.0]        [IBM, null]         
                                                 [MSFT, 9.0]        [MSFT, null]        
  1.5                          
         IBM     150    24.0   Event E3 arrives
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
                                                 [IBM, 75.0]        [IBM, 25.0]         
                                                 [YAH, 1.0]         [YAH, null]         
  2.5                          
  3.0                          
  3.2                          
                                                 (empty result)     (empty result)      
  3.5                          
         YAH   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
                                                 [YAH, 3.0]         [YAH, 1.0]          
  4.3                          
         IBM     150    22.0   Event E7 arrives
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
                                                 [IBM, 97.0]        [IBM, 75.0]         
                                                 [YAH, 6.0]         [YAH, 3.0]          
  5.7                          Event E1 leaves the time window
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
                                                 [IBM, 72.0]        [IBM, 97.0]         
                                                 [YAH, 7.0]         [YAH, 6.0]          
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2                          
                                                 [IBM, 48.0]        [IBM, 72.0]         
                                                 [MSFT, null]       [MSFT, 9.0]         
                                                 [YAH, 6.0]         [YAH, 7.0]

A.5.5. Output Rate Limiting - First

select irstream symbol, sum(price) from MarketData#time(5.5 sec)
group by symbol
output first every 1 seconds

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
                                                 [IBM, 25.0]        [IBM, null]         
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
                                                 [MSFT, 9.0]        [MSFT, null]        
  1.0                          
  1.2                          
  1.5                          
         IBM     150    24.0   Event E3 arrives
                                                 [IBM, 49.0]        [IBM, 25.0]         
         YAH   10000     1.0   Event E4 arrives
                                                 [YAH, 1.0]         [YAH, null]         
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
  2.5                          
  3.0                          
  3.2                          
  3.5                          
         YAH   11000     2.0   Event E6 arrives
                                                 [YAH, 3.0]         [YAH, 1.0]          
  4.0                          
  4.2                          
  4.3                          
         IBM     150    22.0   Event E7 arrives
                                                 [IBM, 97.0]        [IBM, 75.0]         
  4.9                          
         YAH   11500     3.0   Event E8 arrives
                                                 [YAH, 6.0]         [YAH, 3.0]          
  5.0                          
  5.2                          
  5.7                          Event E1 leaves the time window
                                                 [IBM, 72.0]        [IBM, 97.0]         
  5.9                          
         YAH   10500     1.0   Event E9 arrives
                                                 [YAH, 7.0]         [YAH, 6.0]          
  6.0                          
  6.2                          
  6.3                          Event E2 leaves the time window
                                                 [MSFT, null]       [MSFT, 9.0]         
  7.0                          Event E3 and E4 leave the time window
                                                 [IBM, 48.0]        [IBM, 72.0]         
                                                 [YAH, 6.0]         [YAH, 7.0]          
  7.2

A.5.6. Output Rate Limiting - Snapshot

select irstream symbol, sum(price) from MarketData#time(5.5 sec) 
group by symbol 
output snapshot every 1 seconds 
order by symbol

This chapter provides sample output for statements that have aggregation functions, and that have a group by clause, and in which some event properties are not under aggregation.

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
                                                 [IBM, 25.0]                            
                                                 [MSFT, 9.0]                            
  1.5                          
         IBM     150    24.0   Event E3 arrives
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
                                                 [IBM, 75.0]                            
                                                 [MSFT, 9.0]                            
                                                 [YAH, 1.0]                             
  2.5                          
  3.0                          
  3.2                          
                                                 [IBM, 75.0]                            
                                                 [MSFT, 9.0]                            
                                                 [YAH, 1.0]                             
  3.5                          
         YAH   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
                                                 [IBM, 75.0]                            
                                                 [MSFT, 9.0]                            
                                                 [YAH, 3.0]                             
  4.3                          
         IBM     150    22.0   Event E7 arrives
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
                                                 [IBM, 97.0]                            
                                                 [MSFT, 9.0]                            
                                                 [YAH, 6.0]                             
  5.7                          Event E1 leaves the time window
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
                                                 [IBM, 72.0]                            
                                                 [MSFT, 9.0]                            
                                                 [YAH, 7.0]                             
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2                          
                                                 [IBM, 48.0]                            
                                                 [YAH, 6.0]

A.6. Output for Aggregated and Grouped Statements

A.6.1. No Output Rate Limiting

select irstream symbol, volume, sum(price) from MarketData#time(5.5 sec) group by symbol

The default (no keyword) and the ALL keyword do not result in the same output. The default generates an output row per input event, while the ALL keyword generates a row for all groups based on the last new event for each group.

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
                                                 [IBM, 100, 25.0]                       
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
                                                 [MSFT, 5000, 9.0]                      
  1.0                          
  1.2                          
  1.5                          
         IBM     150    24.0   Event E3 arrives
                                                 [IBM, 150, 49.0]                       
         YAH   10000     1.0   Event E4 arrives
                                                 [YAH, 10000, 1.0]                      
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
                                                 [IBM, 155, 75.0]                       
  2.2                          
  2.5                          
  3.0                          
  3.2                          
  3.5                          
         YAH   11000     2.0   Event E6 arrives
                                                 [YAH, 11000, 3.0]                      
  4.0                          
  4.2                          
  4.3                          
         IBM     150    22.0   Event E7 arrives
                                                 [IBM, 150, 97.0]                       
  4.9                          
         YAH   11500     3.0   Event E8 arrives
                                                 [YAH, 11500, 6.0]                      
  5.0                          
  5.2                          
  5.7                          Event E1 leaves the time window
                                                                    [IBM, 100, 72.0]    
  5.9                          
         YAH   10500     1.0   Event E9 arrives
                                                 [YAH, 10500, 7.0]                      
  6.0                          
  6.2                          
  6.3                          Event E2 leaves the time window
                                                                    [MSFT, 5000, null]  
  7.0                          Event E3 and E4 leave the time window
                                                                    [IBM, 150, 48.0]    
                                                                    [YAH, 10000, 6.0]   
  7.2

A.6.2. Output Rate Limiting - Default

select irstream symbol, volume, sum(price) from MarketData#time(5.5 sec) 
group by symbol 
output every 1 seconds

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
                                                 [IBM, 100, 25.0]                       
                                                 [MSFT, 5000, 9.0]                      
  1.5                          
         IBM     150    24.0   Event E3 arrives
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
                                                 [IBM, 150, 49.0]                       
                                                 [YAH, 10000, 1.0]                      
                                                 [IBM, 155, 75.0]                       
  2.5                          
  3.0                          
  3.2                          
                                                 (empty result)     (empty result)      
  3.5                          
         YAH   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
                                                 [YAH, 11000, 3.0]                      
  4.3                          
         IBM     150    22.0   Event E7 arrives
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
                                                 [IBM, 150, 97.0]                       
                                                 [YAH, 11500, 6.0]                      
  5.7                          Event E1 leaves the time window
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
                                                 [YAH, 10500, 7.0]  [IBM, 100, 72.0]    
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2                          
                                                                    [MSFT, 5000, null]  
                                                                    [IBM, 150, 48.0]    
                                                                    [YAH, 10000, 6.0]

A.6.3. Output Rate Limiting - All

select irstream symbol, volume, sum(price) from MarketData#time(5.5 sec) 
group by symbol 
output all every 1 seconds 
order by symbol

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
                                                 [IBM, 100, 25.0]                       
                                                 [MSFT, 5000, 9.0]                      
  1.5                          
         IBM     150    24.0   Event E3 arrives
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
                                                 [IBM, 150, 49.0]                       
                                                 [IBM, 155, 75.0]                       
                                                 [MSFT, 5000, 9.0]                      
                                                 [YAH, 10000, 1.0]                      
  2.5                          
  3.0                          
  3.2                          
                                                 [IBM, 155, 75.0]                       
                                                 [MSFT, 5000, 9.0]                      
                                                 [YAH, 10000, 1.0]                      
  3.5                          
         YAH   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
                                                 [IBM, 155, 75.0]                       
                                                 [MSFT, 5000, 9.0]                      
                                                 [YAH, 11000, 3.0]                      
  4.3                          
         IBM     150    22.0   Event E7 arrives
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
                                                 [IBM, 150, 97.0]                       
                                                 [MSFT, 5000, 9.0]                      
                                                 [YAH, 11500, 6.0]                      
  5.7                          Event E1 leaves the time window
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
                                                 [IBM, 150, 72.0]   [IBM, 100, 72.0]    
                                                 [MSFT, 5000, 9.0]                      
                                                 [YAH, 10500, 7.0]                      
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2                          
                                                 [IBM, 150, 48.0]   [IBM, 150, 48.0]    
                                                 [MSFT, 5000, null] [MSFT, 5000, null]  
                                                 [YAH, 10500, 6.0]  [YAH, 10000, 6.0]

A.6.4. Output Rate Limiting - Last

select irstream symbol, volume, sum(price) from MarketData#time(5.5 sec)
group by symbol 
output last every 1 seconds 
order by symbol

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
                                                 [IBM, 100, 25.0]                       
                                                 [MSFT, 5000, 9.0]                      
  1.5                          
         IBM     150    24.0   Event E3 arrives
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
                                                 [IBM, 155, 75.0]                       
                                                 [YAH, 10000, 1.0]                      
  2.5                          
  3.0                          
  3.2                          
                                                 (empty result)     (empty result)      
  3.5                          
         YAH   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
                                                 [YAH, 11000, 3.0]                      
  4.3                          
         IBM     150    22.0   Event E7 arrives
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
                                                 [IBM, 150, 97.0]                       
                                                 [YAH, 11500, 6.0]                      
  5.7                          Event E1 leaves the time window
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
                                                 [YAH, 10500, 7.0]  [IBM, 100, 72.0]    
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2                          
                                                                    [IBM, 150, 48.0]    
                                                                    [MSFT, 5000, null]  
                                                                    [YAH, 10000, 6.0]

A.6.5. Output Rate Limiting - First

select irstream symbol, volume, sum(price) from MarketData#time(5.5 sec) 
group by symbol 
output first every 1 seconds

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
                                                 [IBM, 100, 25.0]                       
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
                                                 [MSFT, 5000, 9.0]                      
  1.0                          
  1.2                          
  1.5                          
         IBM     150    24.0   Event E3 arrives
                                                 [IBM, 150, 49.0]                       
         YAH   10000     1.0   Event E4 arrives
                                                 [YAH, 10000, 1.0]                      
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
  2.5                          
  3.0                          
  3.2                          
  3.5                          
         YAH   11000     2.0   Event E6 arrives
                                                 [YAH, 11000, 3.0]                      
  4.0                          
  4.2                          
  4.3                          
         IBM     150    22.0   Event E7 arrives
                                                 [IBM, 150, 97.0]                       
  4.9                          
         YAH   11500     3.0   Event E8 arrives
                                                 [YAH, 11500, 6.0]                      
  5.0                          
  5.2                          
  5.7                          Event E1 leaves the time window
                                                 [IBM, 100, 72.0]                       
  5.9                          
         YAH   10500     1.0   Event E9 arrives
                                                 [YAH, 10500, 7.0]                      
  6.0                          
  6.2                          
  6.3                          Event E2 leaves the time window
                                                 [MSFT, 5000, null]                     
  7.0                          Event E3 and E4 leave the time window
                                                 [IBM, 150, 48.0]                       
                                                 [YAH, 10000, 6.0]                      
  7.2

A.6.6. Output Rate Limiting - Snapshot

select irstream symbol, volume, sum(price) from MarketData#time(5.5 sec) 
group by symbol 
output snapshot every 1 seconds

This chapter provides sample output for statements that have aggregation functions, and that have a group by clause, and in which all event properties are under aggregation or appear in the group by clause, and the group by clause has a rollup, cube or grouping sets keyword(s) instructing the runtime to perform multi-level aggregation.

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
                                                 [IBM, 100, 25.0]                       
                                                 [MSFT, 5000, 9.0]                      
  1.5                          
         IBM     150    24.0   Event E3 arrives
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
                                                 [IBM, 100, 75.0]                       
                                                 [MSFT, 5000, 9.0]                      
                                                 [IBM, 150, 75.0]                       
                                                 [YAH, 10000, 1.0]                      
                                                 [IBM, 155, 75.0]                       
  2.5                          
  3.0                          
  3.2                          
                                                 [IBM, 100, 75.0]                       
                                                 [MSFT, 5000, 9.0]                      
                                                 [IBM, 150, 75.0]                       
                                                 [YAH, 10000, 1.0]                      
                                                 [IBM, 155, 75.0]                       
  3.5                          
         YAH   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
                                                 [IBM, 100, 75.0]                       
                                                 [MSFT, 5000, 9.0]                      
                                                 [IBM, 150, 75.0]                       
                                                 [YAH, 10000, 3.0]                      
                                                 [IBM, 155, 75.0]                       
                                                 [YAH, 11000, 3.0]                      
  4.3                          
         IBM     150    22.0   Event E7 arrives
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
                                                 [IBM, 100, 97.0]                       
                                                 [MSFT, 5000, 9.0]                      
                                                 [IBM, 150, 97.0]                       
                                                 [YAH, 10000, 6.0]                      
                                                 [IBM, 155, 97.0]                       
                                                 [YAH, 11000, 6.0]                      
                                                 [IBM, 150, 97.0]                       
                                                 [YAH, 11500, 6.0]                      
  5.7                          Event E1 leaves the time window
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
                                                 [MSFT, 5000, 9.0]                      
                                                 [IBM, 150, 72.0]                       
                                                 [YAH, 10000, 7.0]                      
                                                 [IBM, 155, 72.0]                       
                                                 [YAH, 11000, 7.0]                      
                                                 [IBM, 150, 72.0]                       
                                                 [YAH, 11500, 7.0]                      
                                                 [YAH, 10500, 7.0]                      
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2                          
                                                 [IBM, 155, 48.0]                       
                                                 [YAH, 11000, 6.0]                      
                                                 [IBM, 150, 48.0]                       
                                                 [YAH, 11500, 6.0]                      
                                                 [YAH, 10500, 6.0]

A.7. Output for Fully-Aggregated, Grouped Statements With Rollup

A.7.1. No Output Rate Limiting

select irstream symbol, volume, sum(price)
from MarketData#time(5.5 sec)
group by rollup(symbol)

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
                                                 [IBM, 25.0]        [IBM, null]         
                                                 [null, 25.0]       [null, null]        
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
                                                 [MSFT, 9.0]        [MSFT, null]        
                                                 [null, 34.0]       [null, 25.0]        
  1.0                          
  1.2                          
  1.5                          
         IBM     150    24.0   Event E3 arrives
                                                 [IBM, 49.0]        [IBM, 25.0]         
                                                 [null, 58.0]       [null, 34.0]        
         YAH   10000     1.0   Event E4 arrives
                                                 [YAH, 1.0]         [YAH, null]         
                                                 [null, 59.0]       [null, 58.0]        
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
                                                 [IBM, 75.0]        [IBM, 49.0]         
                                                 [null, 85.0]       [null, 59.0]        
  2.2                          
  2.5                          
  3.0                          
  3.2                          
  3.5                          
         YAH   11000     2.0   Event E6 arrives
                                                 [YAH, 3.0]         [YAH, 1.0]          
                                                 [null, 87.0]       [null, 85.0]        
  4.0                          
  4.2                          
  4.3                          
         IBM     150    22.0   Event E7 arrives
                                                 [IBM, 97.0]        [IBM, 75.0]         
                                                 [null, 109.0]      [null, 87.0]        
  4.9                          
         YAH   11500     3.0   Event E8 arrives
                                                 [YAH, 6.0]         [YAH, 3.0]          
                                                 [null, 112.0]      [null, 109.0]       
  5.0                          
  5.2                          
  5.7                          Event E1 leaves the time window
                                                 [IBM, 72.0]        [IBM, 97.0]         
                                                 [null, 87.0]       [null, 112.0]       
  5.9                          
         YAH   10500     1.0   Event E9 arrives
                                                 [YAH, 7.0]         [YAH, 6.0]          
                                                 [null, 88.0]       [null, 87.0]        
  6.0                          
  6.2                          
  6.3                          Event E2 leaves the time window
                                                 [MSFT, null]       [MSFT, 9.0]         
                                                 [null, 79.0]       [null, 88.0]        
  7.0                          Event E3 and E4 leave the time window
                                                 [IBM, 48.0]        [IBM, 72.0]         
                                                 [YAH, 6.0]         [YAH, 7.0]          
                                                 [null, 54.0]       [null, 79.0]        
  7.2

A.7.2. Output Rate Limiting - Default

select irstream symbol, volume, sum(price)
from MarketData#time(5.5 sec) 
group by rollup(symbol)
output every 1 seconds

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
                                                 [IBM, 25.0]        [IBM, null]         
                                                 [null, 25.0]       [null, null]        
                                                 [MSFT, 9.0]        [MSFT, null]        
                                                 [null, 34.0]       [null, 25.0]        
  1.5                          
         IBM     150    24.0   Event E3 arrives
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
                                                 [IBM, 49.0]        [IBM, 25.0]         
                                                 [null, 58.0]       [null, 34.0]        
                                                 [YAH, 1.0]         [YAH, null]         
                                                 [null, 59.0]       [null, 58.0]        
                                                 [IBM, 75.0]        [IBM, 49.0]         
                                                 [null, 85.0]       [null, 59.0]        
  2.5                          
  3.0                          
  3.2                          
                                                 (empty result)     (empty result)      
  3.5                          
         YAH   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
                                                 [YAH, 3.0]         [YAH, 1.0]          
                                                 [null, 87.0]       [null, 85.0]        
  4.3                          
         IBM     150    22.0   Event E7 arrives
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
                                                 [IBM, 97.0]        [IBM, 75.0]         
                                                 [null, 109.0]      [null, 87.0]        
                                                 [YAH, 6.0]         [YAH, 3.0]          
                                                 [null, 112.0]      [null, 109.0]       
  5.7                          Event E1 leaves the time window
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
                                                 [IBM, 72.0]        [IBM, 97.0]         
                                                 [null, 87.0]       [null, 112.0]       
                                                 [YAH, 7.0]         [YAH, 6.0]          
                                                 [null, 88.0]       [null, 87.0]        
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2                          
                                                 [MSFT, null]       [MSFT, 9.0]         
                                                 [null, 79.0]       [null, 88.0]        
                                                 [IBM, 48.0]        [IBM, 72.0]         
                                                 [YAH, 6.0]         [YAH, 7.0]          
                                                 [null, 54.0]       [null, 79.0]

A.7.3. Output Rate Limiting - All

select irstream symbol, volume, sum(price)
from MarketData#time(5.5 sec) 
group by rollup(symbol)
output all every 1 seconds

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
                                                 [IBM, 25.0]        [IBM, null]         
                                                 [MSFT, 9.0]        [MSFT, null]        
                                                 [null, 34.0]       [null, null]        
  1.5                          
         IBM     150    24.0   Event E3 arrives
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
                                                 [IBM, 75.0]        [IBM, 25.0]         
                                                 [MSFT, 9.0]        [MSFT, 9.0]         
                                                 [YAH, 1.0]         [YAH, null]         
                                                 [null, 85.0]       [null, 34.0]        
  2.5                          
  3.0                          
  3.2                          
                                                 [IBM, 75.0]        [IBM, 75.0]         
                                                 [MSFT, 9.0]        [MSFT, 9.0]         
                                                 [YAH, 1.0]         [YAH, 1.0]          
                                                 [null, 85.0]       [null, 85.0]        
  3.5                          
         YAH   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
                                                 [IBM, 75.0]        [IBM, 75.0]         
                                                 [MSFT, 9.0]        [MSFT, 9.0]         
                                                 [YAH, 3.0]         [YAH, 1.0]          
                                                 [null, 87.0]       [null, 85.0]        
  4.3                          
         IBM     150    22.0   Event E7 arrives
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
                                                 [IBM, 97.0]        [IBM, 75.0]         
                                                 [MSFT, 9.0]        [MSFT, 9.0]         
                                                 [YAH, 6.0]         [YAH, 3.0]          
                                                 [null, 112.0]      [null, 87.0]        
  5.7                          Event E1 leaves the time window
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
                                                 [IBM, 72.0]        [IBM, 97.0]         
                                                 [MSFT, 9.0]        [MSFT, 9.0]         
                                                 [YAH, 7.0]         [YAH, 6.0]          
                                                 [null, 88.0]       [null, 112.0]       
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2                          
                                                 [IBM, 48.0]        [IBM, 72.0]         
                                                 [MSFT, null]       [MSFT, 9.0]         
                                                 [YAH, 6.0]         [YAH, 7.0]          
                                                 [null, 54.0]       [null, 88.0]

A.7.4. Output Rate Limiting - Last

select irstream symbol, volume, sum(price)
from MarketData#time(5.5 sec)
group by rollup(symbol)
output last every 1 seconds

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
                                                 [IBM, 25.0]        [IBM, null]         
                                                 [MSFT, 9.0]        [MSFT, null]        
                                                 [null, 34.0]       [null, null]        
  1.5                          
         IBM     150    24.0   Event E3 arrives
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
                                                 [IBM, 75.0]        [IBM, 25.0]         
                                                 [YAH, 1.0]         [YAH, null]         
                                                 [null, 85.0]       [null, 34.0]        
  2.5                          
  3.0                          
  3.2                          
                                                 (empty result)     (empty result)      
  3.5                          
         YAH   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
                                                 [YAH, 3.0]         [YAH, 1.0]          
                                                 [null, 87.0]       [null, 85.0]        
  4.3                          
         IBM     150    22.0   Event E7 arrives
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
                                                 [IBM, 97.0]        [IBM, 75.0]         
                                                 [YAH, 6.0]         [YAH, 3.0]          
                                                 [null, 112.0]      [null, 87.0]        
  5.7                          Event E1 leaves the time window
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
                                                 [IBM, 72.0]        [IBM, 97.0]         
                                                 [YAH, 7.0]         [YAH, 6.0]          
                                                 [null, 88.0]       [null, 112.0]       
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2                          
                                                 [MSFT, null]       [MSFT, 9.0]         
                                                 [IBM, 48.0]        [IBM, 72.0]         
                                                 [YAH, 6.0]         [YAH, 7.0]          
                                                 [null, 54.0]       [null, 88.0]

A.7.5. Output Rate Limiting - First

select irstream symbol, volume, sum(price)
from MarketData#time(5.5 sec) 
group by rollup(symbol) 
output first every 1 seconds

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
                                                 [IBM, 25.0]        [IBM, null]         
                                                 [null, 25.0]       [null, null]        
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
                                                 [MSFT, 9.0]        [MSFT, null]        
  1.0                          
  1.2                          
  1.5                          
         IBM     150    24.0   Event E3 arrives
                                                 [IBM, 49.0]        [IBM, 25.0]         
                                                 [null, 58.0]       [null, 34.0]        
         YAH   10000     1.0   Event E4 arrives
                                                 [YAH, 1.0]         [YAH, null]         
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
  2.5                          
  3.0                          
  3.2                          
  3.5                          
         YAH   11000     2.0   Event E6 arrives
                                                 [YAH, 3.0]         [YAH, 1.0]          
                                                 [null, 87.0]       [null, 85.0]        
  4.0                          
  4.2                          
  4.3                          
         IBM     150    22.0   Event E7 arrives
                                                 [IBM, 97.0]        [IBM, 75.0]         
  4.9                          
         YAH   11500     3.0   Event E8 arrives
                                                 [YAH, 6.0]         [YAH, 3.0]          
                                                 [null, 112.0]      [null, 109.0]       
  5.0                          
  5.2                          
  5.7                          Event E1 leaves the time window
                                                 [IBM, 72.0]        [IBM, 97.0]         
  5.9                          
         YAH   10500     1.0   Event E9 arrives
                                                 [YAH, 7.0]         [YAH, 6.0]          
                                                 [null, 88.0]       [null, 87.0]        
  6.0                          
  6.2                          
  6.3                          Event E2 leaves the time window
                                                 [MSFT, null]       [MSFT, 9.0]         
  7.0                          Event E3 and E4 leave the time window
                                                 [IBM, 48.0]        [IBM, 72.0]         
                                                 [YAH, 6.0]         [YAH, 7.0]          
                                                 [null, 54.0]       [null, 79.0]

A.7.6. Output Rate Limiting - Snapshot

select irstream symbol, volume, sum(price)
from MarketData#time(5.5 sec) 
group by rollup(symbol)
output snapshot every 1 seconds

Output rate limiting provides output events to your application in regular intervals. Between intervals, the runtime may use a buffer to hold data until the output condition is reached, as described below. If your application has high-volume streams, you may need to be mindful of the memory needs for buffers especially if the output condition triggers infrequently.

                       Input                                  Output
                                                 Insert Stream    Remove Stream
-----------------------------------------------  ----------------------------------
 Time Symbol  Volume   Price
  0.2                          
         IBM     100    25.0   Event E1 arrives
  0.8                          
        MSFT    5000     9.0   Event E2 arrives
  1.0                          
  1.2                          
                                                 [IBM, 25.0]                            
                                                 [MSFT, 9.0]                            
                                                 [null, 34.0]                           
  1.5                          
         IBM     150    24.0   Event E3 arrives
         YAH   10000     1.0   Event E4 arrives
  2.0                          
  2.1                          
         IBM     155    26.0   Event E5 arrives
  2.2                          
                                                 [IBM, 75.0]                            
                                                 [MSFT, 9.0]                            
                                                 [YAH, 1.0]                             
                                                 [null, 85.0]                           
  2.5                          
  3.0                          
  3.2                          
                                                 [IBM, 75.0]                            
                                                 [MSFT, 9.0]                            
                                                 [YAH, 1.0]                             
                                                 [null, 85.0]                           
  3.5                          
         YAH   11000     2.0   Event E6 arrives
  4.0                          
  4.2                          
                                                 [IBM, 75.0]                            
                                                 [MSFT, 9.0]                            
                                                 [YAH, 3.0]                             
                                                 [null, 87.0]                           
  4.3                          
         IBM     150    22.0   Event E7 arrives
  4.9                          
         YAH   11500     3.0   Event E8 arrives
  5.0                          
  5.2                          
                                                 [IBM, 97.0]                            
                                                 [MSFT, 9.0]                            
                                                 [YAH, 6.0]                             
                                                 [null, 112.0]                          
  5.7                          Event E1 leaves the time window
  5.9                          
         YAH   10500     1.0   Event E9 arrives
  6.0                          
  6.2                          
                                                 [MSFT, 9.0]                            
                                                 [IBM, 72.0]                            
                                                 [YAH, 7.0]                             
                                                 [null, 88.0]                           
  6.3                          Event E2 leaves the time window
  7.0                          Event E3 and E4 leave the time window
  7.2                          
                                                 [IBM, 48.0]                            
                                                 [YAH, 6.0]                             
                                                 [null, 54.0]

Appendix B. Runtime Considerations for Output Rate Limiting

The output clause with the snapshot keyword does not require a buffer for any type of statement.

The output clause with the first keyword does not require a buffer for any type of statement.

We use the term change set to describe all insert and remove stream events that occur since the last triggering of the output condition.

You can override the default behavior for some types of statements by specifying a hint.

Please see Section 2.15, “Basic Aggregated Statement Types” for information on the types of statements discussed below.

B.1. For Un-Aggregated and Un-Grouped Statements

B.1.1. `Output Last`

For statements that define output last the runtime retains only the first remove stream event and the last insert stream event, both matching the having-clause, if present, to compute insert and remove stream output when the output condition triggers.

B.1.2. `Output All`

With an order-by clause or when your EPL specifies the @Hint('disable_outputlimit_opt') hint:

Upon arrival of any row the runtime applies the having-clause and retains only matching events, or retains all events if there is no having-clause.
Upon triggering of the output condition the runtime computes the insert and remove stream output events according to the select-clause for output.

The runtime retains the change set and computes output from the change set at the time the output condition triggers, after which it discards the change set.

B.2. For Fully Aggregated and Un-Grouped Statements

B.2.1. `Output Last`

With an order-by clause or when your EPL specifies the @Hint('disable_outputlimit_opt') hint:

Upon arrival of the first row since the last triggering of the output condition the runtime computes the remove stream output event according to the select-clause for later output (when applicable).
Upon triggering of the output condition the runtime computes the insert stream output event according to the select-clause for output.

The runtime retains the change set and computes output from the change set at the time the output condition triggers, after which it discards the change set.

B.2.2. `Output All`

With an order-by clause or when your EPL specifies the @Hint('disable_outputlimit_opt') hint:

Upon arrival of rows the runtime applies the having-clause and computes the insert and remove stream output event according to the select-clause for later output (when applicable).
Upon triggering of the output condition the runtime outputs the insert and remove stream output events.

The runtime retains the change set and computes output from the change set at the time the output condition triggers, after which it discards the change set.

B.3. For Aggregated and Un-Grouped Statements

B.3.1. `Output Last`

With an order-by clause or when your EPL specifies the @Hint('disable_outputlimit_opt') hint:

Upon arrival of the first row since the last triggering of the output condition the runtime computes the insert and remove stream output event according to the having-clause (if present) and the select-clause for later output (when applicable), retaining only the last computed insert and remove stream output event.
Upon triggering of the output condition the runtime outputs the pre-computed last insert stream and remove stream output event.

The runtime retains the change set and computes output from the change set at the time the output condition triggers, after which it discards the change set.

B.3.2. `Output All`

With an order-by clause or when your EPL specifies the @Hint('disable_outputlimit_opt') hint:

Upon arrival of rows the runtime computes the insert and remove stream output events according to the having-clause (if present) and the select-clause for later output, retaining only the computed insert and remove stream output events.
Upon triggering of the output condition the runtime outputs the retained output events.

The runtime retains the change set and computes output from the change set at the time the output condition triggers, after which it discards the change set.

B.4. For Fully Aggregated and Grouped Statements (Includes Rollup)

B.4.1. `Output Last`

With an order-by clause or when your EPL specifies the @Hint('disable_outputlimit_opt') hint:

Upon arrival of the first row for a given group since the last triggering of the output condition the runtime computes the remove stream output event for that group according to the select-clause for later output (when applicable), and also retains a single insert stream event per group.
Upon triggering of the output condition the runtime uses the retained insert stream events per group to compute output events according to the select-clause.

The runtime retains the change set and computes output from the change set at the time the output condition triggers, after which it discards the change set.

B.4.2. `Output All`

With an order-by clause or when your EPL specifies the @Hint('disable_outputlimit_opt') hint:

The runtime retains, for each group, a row to represent the group.
Upon arrival of rows the runtime computes the remove stream output events according to the having-clause (if present) and the select-clause for later output.
Upon triggering of the output condition the runtime computes the insert stream output events according to the having-clause (if present) and the select-clause for output, for each group.

The runtime retains the change set and computes output from the change set at the time the output condition triggers, after which it discards the change set.

B.5. For Aggregated and Grouped Statements

B.5.1. `Output Last`

With an order-by clause or when your EPL specifies the @Hint('disable_outputlimit_opt') hint:

Upon arrival of the first row for a given group since the last triggering of the output condition the runtime computes the insert and remove stream output event for that group according to the select-clause for later output (when applicable), and retains a last insert and remove stream event per group.
Upon triggering of the output condition the runtime outputs the retained insert and remove stream output events per group.

The runtime retains the change set and computes output from the change set at the time the output condition triggers, after which it discards the change set.

B.5.2. `Output All`