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Siddhi 5.0 Streaming SQL Guide

Introduction

Siddhi Streaming SQL is designed to process streams of events. It can be used to implement streaming data integration, streaming analytics, rule based and adaptive decision making use cases. It is an evolution of Complex Event Processing (CEP) and Stream Processing systems, hence it can also be used to process stateful computations, detecting of complex event patterns, and sending notifications in real-time.

Siddhi Streaming SQL uses SQL like syntax, and annotations to consume events from diverse event sources with various data formats, process then using stateful and stateless operators and send outputs to multiple endpoints according to their accepted event formats. It also supports exposing rule based and adaptive decision making as service endpoints such that external programs and systems can synchronously get decision support form Siddhi.

The following sections explains how to write processing logic using Siddhi Streaming SQL.

Siddhi Application

The processing logic for your program can be written using the Streaming SQL and put together as a single file with .siddhi extension. This file is called as the Siddhi Application or the SiddhiApp.

SiddhiApps are named by adding @app:name('<name>') annotation on the top of the SiddhiApp file. When the annotation is not added Siddhi assigns a random UUID as the name of the SiddhiApp.

Purpose

SiddhiApp provides an isolated execution environment for your processing logic that allows you to deploy and execute processing logic independent of other SiddhiApp in the system. Therefore it's always recommended to have a processing logic related to single use case in a single SiddhiApp. This will help you to group processing logic and easily manage addition and removal of various use cases.

The following diagram depicts some of the key Siddhi Streaming SQL elements of Siddhi Application and how event flows through the elements.

Below table provides brief description of a few key elements in the Siddhi Streaming SQL Language.

Elements	Description
Stream	A logical series of events ordered in time with a uniquely identifiable name, and a defined set of typed attributes defining its schema.
Event	An event is a single event object associated with a stream. All events of a stream contains a timestamp and an identical set of typed attributes based on the schema of the stream they belong to.
Table	A structured representation of data stored with a defined schema. Stored data can be backed by In-Memory, or external data stores such as RDBMS, MongoDB, etc. The tables can be accessed and manipulated at runtime.
Named Window	A structured representation of data stored with a defined schema and eviction policy. Window data is stored In-Memory and automatically cleared by the named window constrain. Other siddhi elements can only query the values in windows at runtime but they cannot modify them.
Named Aggregation	A structured representation of data that's incrementally aggregated and stored with a defined schema and aggregation granularity such as seconds, minutes, hours, etc. Aggregation data is stored both In-Memory and in external data stores such as RDBMS. Other siddhi elements can only query the values in windows at runtime but they cannot modify them.
Query	A logical construct that processes events in streaming manner by by consuming data from one or more streams, tables, windows and aggregations, and publishes output events into a stream, table or a window.
Source	A construct that consumes data from external sources (such as TCP, Kafka, HTTP, etc) with various event formats such as XML, JSON, binary, etc, convert then to Siddhi events, and passes into streams for processing.
Sink	A construct that consumes events arriving at a stream, maps them to a predefined data format (such as XML, JSON, binary, etc), and publishes them to external endpoints (such as E-mail, TCP, Kafka, HTTP, etc).
Input Handler	A mechanism to programmatically inject events into streams.
Stream/Query Callback	A mechanism to programmatically consume output events from streams or queries.
Partition	A logical container that isolates the processing of queries based on the partition keys derived from the events.
Inner Stream	A positionable stream that connects portioned queries with each other within the partition.

Grammar

SiddhiApp is a collection of Siddhi Streaming SQL elements composed together as a script. Here each Siddhi element must be separated by a semicolon ;.

Hight level syntax of SiddhiApp is as follows.

<siddhi app>  :
        <app annotation> *
        ( <stream definition> | <table definition> | ... ) +
        ( <query> | <partition> ) +
        ;

Example

Siddhi Application with name Temperature-Analytics defined with a stream named TempStream and a query named 5minAvgQuery.

@app:name('Temperature-Analytics')

define stream TempStream (deviceID long, roomNo int, temp double);

@info(name = '5minAvgQuery')
from TempStream#window.time(5 min)
select roomNo, avg(temp) as avgTemp
  group by roomNo
insert into OutputStream;

Stream

A stream is a logical series of events ordered in time. Its schema is defined via the stream definition. A stream definition contains the stream name and a set of attributes with specific types and uniquely identifiable names within the stream. All events associated to the stream will have the same schema (i.e., have the same attributes in the same order).

Purpose

Stream groups common types of events together with a schema. This helps in various ways such as, processing all events together in queries and performing data format transformations together when they are consumed and published via sources and sinks.

Syntax

The syntax for defining a new stream is as follows.

define stream <stream name> (<attribute name> <attribute type>,
                             <attribute name> <attribute type>, ... );

The following parameters are used to configure a stream definition.

Parameter	Description
stream name	The name of the stream created. (It is recommended to define a stream name in PascalCase.)
attribute name	Uniquely identifiable name of the stream attribute. (It is recommended to define attribute names in camelCase.)
attribute type	The type of each attribute defined in the schema. This can be STRING, INT, LONG, DOUBLE, FLOAT, BOOL or OBJECT.

To use and refer stream and attribute names that do not follow [a-zA-Z_][a-zA-Z_0-9]* format enclose them in `. E.g. `$test(0)`.

To make the stream process events in multi-threading and asynchronous way use the @Async annotation as shown in Multi-threading and Asynchronous Processing configuration section.

Example

define stream TempStream (deviceID long, roomNo int, temp double);

The above creates a stream with name TempStream having the following attributes.

• deviceID of type long
• roomNo of type int
• temp of type double

Source

Sources receive events via multiple transports and in various data formats, and direct them into streams for processing.

A source configuration allows to define a mapping in order to convert each incoming event from its native data format to a Siddhi event. When customizations to such mappings are not provided, Siddhi assumes that the arriving event adheres to the predefined format based on the stream definition and the configured message mapping type.

Purpose

Source provides a way to consume events from external systems and convert them to be processed by the associated stream.

Syntax

To configure a stream that consumes events via a source, add the source configuration to a stream definition by adding the @source annotation with the required parameter values.

The source syntax is as follows:

@source(type='<source type>', <static.key>='<value>', <static.key>='<value>',
    @map(type='<map type>', <static.key>='<value>', <static.key>='<value>',
        @attributes( <attribute1>='<attribute mapping>', <attributeN>='<attribute mapping>')
    )
)
define stream <stream name> (<attribute1> <type>, <attributeN> <type>);

This syntax includes the following annotations.

Source

The type parameter of @source annotation defines the source type that receives events. The other parameters of @source annotation depends upon the selected source type, and here some of its parameters can be optional.

For detailed information about the supported parameters see the documentation of the relevant source.

The following is the list of source types supported by Siddhi:

Source type	Description
In-memory	Allow SiddhiApp to consume events from other SiddhiApps running on the same JVM.
HTTP	Expose an HTTP service to consume messages.
Kafka	Subscribe to Kafka topic to consume events.
TCP	Expose a TCP service to consume messages.
Email	Consume emails via POP3 and IMAP protocols.
JMS	Subscribe to JMS topic or queue to consume events.
File	Reads files by tailing or as a whole to extract events out of them.
CDC	Perform change data capture on databases.
Prometheus	Consume data from Prometheus agent.

In-memory is the only source inbuilt in Siddhi, and all other source types are implemented as extensions.

Source Mapper

Each @source configuration can have a mapping denoted by the @map annotation that defines how to convert the incoming event format to Siddhi events.

The type parameter of the @map defines the map type to be used in converting the incoming events. The other parameters of @map annotation depends on the mapper selected, and some of its parameters can be optional.

For detailed information about the parameters see the documentation of the relevant mapper.

Map Attributes

@attributes is an optional annotation used with @map to define custom mapping. When @attributes is not provided, each mapper assumes that the incoming events adheres to its own default message format and attempt to convert the events from that format. By adding the @attributes annotation, users can selectively extract data from the incoming message and assign them to the attributes.

There are two ways to configure @attributes.

1. Define attribute names as keys, and mapping configurations as values:
   @attributes( <attribute1>='<mapping>', <attributeN>='<mapping>')

2. Define the mapping configurations in the same order as the attributes defined in stream definition:
   @attributes( '<mapping for attribute1>', '<mapping for attributeN>')

Supported Source Mapping Types

The following is the list of source mapping types supported by Siddhi:

Source mapping type	Description
PassThrough	Omits data conversion on Siddhi events.
JSON	Converts JSON messages to Siddhi events.
XML	Converts XML messages to Siddhi events.
TEXT	Converts plain text messages to Siddhi events.
Avro	Converts Avro events to Siddhi events.
Binary	Converts Siddhi specific binary events to Siddhi events.
Key Value	Converts key-value HashMaps to Siddhi events.
CSV	Converts CSV like delimiter separated events to Siddhi events.

Tip

When the @map annotation is not provided @map(type='passThrough') is used as default, that passes the consumed Siddhi events directly to the streams without any data conversion.

PassThrough is the only source mapper inbuilt in Siddhi, and all other source mappers are implemented as extensions.

Example 1

Receive JSON messages by exposing an HTTP service, and direct them to InputStream stream for processing. Here the HTTP service will be secured with basic authentication, receives events on all network interfaces on port 8080 and context /foo. The service expects the JSON messages to be on the default data format that's supported by the JSON mapper as follows.

{
  "name":"Paul",
  "age":20,
  "country":"UK"
}

The configuration of the HTTP source and JSON source mapper to achieve the above is as follows.

@source(type='http', receiver.url='http://0.0.0.0:8080/foo',
  @map(type='json'))
define stream InputStream (name string, age int, country string);

Example 2

Receive JSON messages by exposing an HTTP service, and direct them to StockStream stream for processing. Here the incoming JSON, as given below, do not adhere to the default data format that's supported by the JSON mapper.

{
  "portfolio":{
    "stock":{
      "volume":100,
      "company":{
        "symbol":"FB"
      },
      "price":55.6
    }
  }
}

The configuration of the HTTP source and the custom JSON source mapping to achieve the above is as follows.

@source(type='http', receiver.url='http://0.0.0.0:8080/foo',
  @map(type='json', enclosing.element="$.portfolio",
    @attributes(symbol = "stock.company.symbol", price = "stock.price",
                volume = "stock.volume")))
define stream StockStream (symbol string, price float, volume long);

The same can also be configured by omitting the attribute names as below.

@source(type='http', receiver.url='http://0.0.0.0:8080/foo',
  @map(type='json', enclosing.element="$.portfolio",
    @attributes("stock.company.symbol", "stock.price", "stock.volume")))
define stream StockStream (symbol string, price float, volume long);

Sink

Sinks consumes events from streams and publish them via multiple transports to external endpoints in various data formats.

A sink configuration allows users to define a mapping to convert the Siddhi events in to the required output data format (such as JSON, TEXT, XML, etc.) and publish the events to the configured endpoints. When customizations to such mappings are not provided, Siddhi converts events to the predefined event format based on the stream definition and the configured message mapper type before publishing the events.

Purpose

Sink provides a way to publish Siddhi events of a stream to external systems by converting events to their supported format.

Syntax

To configure a stream to publish events via a sink, add the sink configuration to a stream definition by adding the @sink annotation with the required parameter values.

The sink syntax is as follows:

@sink(type='<sink type>', <static.key>='<value>', <dynamic.key>='{{<value>}}',
    @map(type='<map type>', <static.key>='<value>', <dynamic.key>='{{<value>}}',
        @payload('<payload mapping>')
    )
)
define stream <stream name> (<attribute1> <type>, <attributeN> <type>);

Dynamic Properties

The sink and sink mapper properties that are categorized as dynamic have the ability to absorb attribute values dynamically from the Siddhi events of their associated streams. This can be configured by enclosing the relevant attribute names in double curly braces as{{...}}, and using it within the property values.

Some valid dynamic properties values are:

• '{{attribute1}}'
• 'This is {{attribute1}}'
• {{attribute1}} > {{attributeN}}

Here the attribute names in the double curly braces will be replaced with the values from the events before they are published.

This syntax includes the following annotations.

Sink

The type parameter of the @sink annotation defines the sink type that publishes the events. The other parameters of the @sink annotation depends upon the selected sink type, and here some of its parameters can be optional and/or dynamic.

For detailed information about the supported parameters see documentation of the relevant sink.

The following is a list of sink types supported by Siddhi:

Source type	Description
In-memory	Allow SiddhiApp to publish events to other SiddhiApps running on the same JVM.
Log	Logs the events appearing on the streams.
HTTP	Publish events to an HTTP endpoint.
Kafka	Publish events to Kafka topic.
TCP	Publish events to a TCP service.
Email	Send emails via SMTP protocols.
JMS	Publish events to JMS topics or queues.
File	Writes events to files.
Prometheus	Expose data through Prometheus agent.

Distributed Sink

Distributed Sinks publish events from a defined stream to multiple endpoints using load balancing or partitioning strategies.

Any sink can be used as a distributed sink. A distributed sink configuration allows users to define a common mapping to convert and send the Siddhi events for all its destination endpoints.

Purpose

Distributed sink provides a way to publish Siddhi events to multiple endpoints in the configured event format.

Syntax

To configure distributed sink add the sink configuration to a stream definition by adding the @sink annotation and add the configuration parameters that are common of all the destination endpoints inside it, along with the common parameters also add the @distribution annotation specifying the distribution strategy (i.e. roundRobin or partitioned) and @destination annotations providing each endpoint specific configurations.

The distributed sink syntax is as follows:

RoundRobin Distributed Sink

Publishes events to defined destinations in a round robin manner.

@sink(type='<sink type>', <common.static.key>='<value>', <common.dynamic.key>='{{<value>}}',
    @map(type='<map type>', <static.key>='<value>', <dynamic.key>='{{<value>}}',
        @payload('<payload mapping>')
    )
    @distribution(strategy='roundRobin',
        @destination(<destination.specific.key>='<value>'),
        @destination(<destination.specific.key>='<value>')))
)
define stream <stream name> (<attribute1> <type>, <attributeN> <type>);

Partitioned Distributed Sink

Publishes events to defined destinations by partitioning them based on the partitioning key.

@sink(type='<sink type>', <common.static.key>='<value>', <common.dynamic.key>='{{<value>}}',
    @map(type='<map type>', <static.key>='<value>', <dynamic.key>='{{<value>}}',
        @payload('<payload mapping>')
    )
    @distribution(strategy='partitioned', partitionKey='<partition key>',
        @destination(<destination.specific.key>='<value>'),
        @destination(<destination.specific.key>='<value>')))
)
define stream <stream name> (<attribute1> <type>, <attributeN> <type>);

Sink Mapper

Each @sink configuration can have a mapping denoted by the @map annotation that defines how to convert Siddhi events to outgoing messages with the defined format.

The type parameter of the @map defines the map type to be used in converting the outgoing events. The other parameters of @map annotation depends on the mapper selected, and some of its parameters can be optional and/or dynamic.

For detailed information about the parameters see the documentation of the relevant mapper.

Map Payload

@payload is an optional annotation used with @map to define custom mapping. When the @payload annotation is not provided, each mapper maps the outgoing events to its own default event format. The @payload annotation allow users to configure mappers to produce the output payload of their choice, and by using dynamic properties within the payload they can selectively extract and add data from the published Siddhi events.

There are two ways you to configure @payload annotation.

1. Some mappers such as XML, JSON, and Test only accept one output payload:
   @payload( 'This is a test message from {{user}}.')
2. Some mappers such key-value accept series of mapping values:
   @payload( key1='mapping_1', 'key2'='user : {{user}}')
   Here, the keys of payload mapping can be defined using the dot notation as a.b.c, or using any constant string value as '$abc'.

Supported Sink Mapping Types

The following is a list of sink mapping types supported by Siddhi:

Sink mapping type	Description
PassThrough	Omits data conversion on outgoing Siddhi events.
JSON	Converts Siddhi events to JSON messages.
XML	Converts Siddhi events to XML messages.
TEXT	Converts Siddhi events to plain text messages.
Avro	Converts Siddhi events to Avro Events.
Binary	Converts Siddhi events to Siddhi specific binary events.
Key Value	Converts Siddhi events to key-value HashMaps.
CSV	Converts Siddhi events to CSV like delimiter separated events.

Tip

When the @map annotation is not provided @map(type='passThrough') is used as default, that passes the outgoing Siddhi events directly to the sinks without any data conversion.

PassThrough is the only sink mapper inbuilt in Siddhi, and all other sink mappers are implemented as extensions.

Example 1

Publishes OutputStream events by converting them to JSON messages with the default format, and by sending to an HTTP endpoint http://localhost:8005/endpoint1, using POST method, Accept header, and basic authentication having admin is both username and password.

The configuration of the HTTP sink and JSON sink mapper to achieve the above is as follows.

@sink(type='http', publisher.url='http://localhost:8005/endpoint',
      method='POST', headers='Accept-Date:20/02/2017',
      basic.auth.enabled='true', basic.auth.username='admin',
      basic.auth.password='admin',
      @map(type='json'))
define stream OutputStream (name string, age int, country string);

This will publish a JSON message on the following format:

{
  "event":{
    "name":"Paul",
    "age":20,
    "country":"UK"
  }
}

Example 2

Publishes StockStream events by converting them to user defined JSON messages, and by sending to an HTTP endpoint http://localhost:8005/stocks.

The configuration of the HTTP sink and custom JSON sink mapping to achieve the above is as follows.

@sink(type='http', publisher.url='http://localhost:8005/stocks',
      @map(type='json', validate.json='true', enclosing.element='$.Portfolio',
           @payload("""{"StockData":{ "Symbol":"{{symbol}}", "Price":{{price}} }}""")))
define stream StockStream (symbol string, price float, volume long);

This will publish a single event as the JSON message on the following format:

{
  "Portfolio":{
    "StockData":{
      "Symbol":"GOOG",
      "Price":55.6
    }
  }
}

This can also publish multiple events together as a JSON message on the following format:

{
  "Portfolio":[
    {
      "StockData":{
        "Symbol":"GOOG",
        "Price":55.6
      }
    },
    {
      "StockData":{
        "Symbol":"FB",
        "Price":57.0
      }
    }
  ]  
}

Example 3

Publishes events from the OutputStream stream to multiple the HTTP endpoints using a partitioning strategy. Here the events are sent to either http://localhost:8005/endpoint1 or http://localhost:8006/endpoint2 based on the partitioning key country. It uses default JSON mapping, POST method, and used admin as both the username and the password when publishing to both the endpoints.

The configuration of the distributed HTTP sink and JSON sink mapper to achieve the above is as follows.

@sink(type='http', method='POST', basic.auth.enabled='true',
      basic.auth.username='admin', basic.auth.password='admin',
      @map(type='json'),
      @distribution(strategy='partitioned', partitionKey='country',
        @destination(publisher.url='http://localhost:8005/endpoint1'),
        @destination(publisher.url='http://localhost:8006/endpoint2')))
define stream OutputStream (name string, age int, country string);

This will partition the outgoing events and publish all events with the same country attribute value to the same endpoint. The JSON message published will be on the following format:

{
  "event":{
    "name":"Paul",
    "age":20,
    "country":"UK"
  }
}

Error Handling

Errors in Siddhi can be handled at the Streams and in Sinks.

Error Handling at Stream

When errors are thrown by Siddhi elements subscribed to the stream, the error gets propagated up to the stream that delivered the event to those Siddhi elements. By default the error is logged and dropped at the stream, but this behavior can be altered by by adding @OnError annotation to the corresponding stream definition. @OnError annotation can help users to capture the error and the associated event, and handle them gracefully by sending them to a fault stream.

The @OnError annotation and the required action to be specified as below.

@OnError(action='on error action')
define stream <stream name> (<attribute name> <attribute type>,
                             <attribute name> <attribute type>, ... );

The action parameter of the @OnError annotation defines the action to be executed during failure scenarios. The following actions can be specified to @OnError annotation to handle erroneous scenarios.

• LOG : Logs the event with the error, and drops the event. This is the default action performed even when @OnError annotation is not defined.
• STREAM: Creates a fault stream and redirects the event and the error to it. The created fault stream will have all the attributes defined in the base stream to capture the error causing event, and in addition it also contains _error attribute of type object to containing the error information. The fault stream can be referred by adding ! in front of the base stream name as !<stream name>.

Example

Handle errors in TempStream by redirecting the errors to a fault stream.

The configuration of TempStream stream and @OnError annotation is as follows.

@OnError(action='STREAM')
define stream TempStream (deviceID long, roomNo int, temp double);

Siddhi will infer and automatically defines the fault stream of TempStream as given below.

define stream !TempStream (deviceID long, roomNo int, temp double, _error object);

The SiddhiApp extending the above the use-case by adding failure generation and error handling with the use of queries is as follows.

Note: Details on writing processing logics via queries will be explained in later sections.

-- Define fault stream to handle error occurred at TempStream subscribers
@OnError(action='STREAM')
define stream TempStream (deviceID long, roomNo int, temp double);

-- Error generation through a custom function `createError()`
@info(name = 'error-generation')
from TempStream#custom:createError()
insert into IgnoreStream1;

-- Handling error by simply logging the event and error.
@info(name = 'handle-error')
from !TempStream#log("Error Occurred!")
select deviceID, roomNo, temp, _error
insert into IgnoreStream2;

Error Handling at Sink

There can be cases where external systems becoming unavailable or coursing errors when the events are published to them. By default sinks log and drop the events causing event losses, and this can be handled gracefully by configuring on.error parameter of the @sink annotation.

The on.error parameter of the @sink annotation can be specified as below.

@sink(type='<sink type>', on.error='<on error action>', <key>='<value>', ...)
define stream <stream name> (<attribute name> <attribute type>,
                             <attribute name> <attribute type>, ... );

The following actions can be specified to on.error parameter of @sink annotation to handle erroneous scenarios.

• LOG : Logs the event with the error, and drops the event. This is the default action performed even when on.error parameter is not defined on the @sink annotation.
• WAIT : Publishing threads wait in back-off and re-trying mode, and only send the events when the connection is re-established. During this time the threads will not consume any new messages causing the systems to introduce back pressure on the systems that publishes to it.
• STREAM: Pushes the failed events with the corresponding error to the associated fault stream the sink belongs to.

Example 1

Introduce back pressure on the threads who bring events via TempStream when the system cannot connect to Kafka.

The configuration of TempStream stream and @sink Kafka annotation with on.error property is as follows.

@sink(type='kafka', on.error='WAIT', topic='{{roomNo}}',
      bootstrap.servers='localhost:9092',
      @map(type='xml'))
define stream TempStream (deviceID long, roomNo int, temp double);

Example 2

Send events to the fault stream of TempStream when the system cannot connect to Kafka.

The configuration of TempStream stream with associated fault stream, @sink Kafka annotation with on.error property and a queries to handle the error is as follows.

Note: Details on writing processing logics via queries will be explained in later sections.

@OnError(action='STREAM')
@sink(type='kafka', on.error='STREAM', topic='{{roomNo}}',
      bootstrap.servers='localhost:9092',
      @map(type='xml'))
define stream TempStream (deviceID long, roomNo int, temp double);

-- Handling error by simply logging the event and error.
@info(name = 'handle-error')
from !TempStream#log("Error Occurred!")
select deviceID, roomNo, temp, _error
insert into IgnoreStream;

Query

Query defines the processing logic in Siddhi. It consumes events from one or more streams, named-windows, tables, and/or named-aggregations, process the events in a streaming manner, and generate output events into a stream, named-window, or table.

Purpose

A query provides a way to process the events in the order they arrive and produce output using both stateful and stateless complex event processing and stream processing operations.

Syntax

The high level query syntax for defining processing logics is as follows:

@info(name = '<query name>')
from <input>
<projection>
<output action>

The following parameters are used to configure a stream definition.

Parameter	Description
query name	The name of the query. Since naming the query (i.e the @info(name = '<query name>') annotation) is optional, when the name is not provided Siddhi assign a system generated name for the query.
input	Defines the means of event consumption via streams, named-windows, tables, and/or named-aggregations, and defines the processing logic using filters, windows, stream-functions, joins, patterns and sequences.
projection	Generates output event attributes using select, functions, aggregation-functions, and group by operations, and filters the generated the output using having, limit & offset, order by, and output rate limiting operations before sending them out. Here the projection is optional and when it is omitted all the input events will be sent to the output as it is.
output action	Defines output action (such as insert into, update, delete, etc) that needs to be performed by the generated events on a stream, named-window, or table

Example

A query consumes events from the TempStream stream and output only the roomNo and temp attributes to the RoomTempStream stream, from which another query consumes the events and sends all its attributes to AnotherRoomTempStream stream.

define stream TempStream (deviceID long, roomNo int, temp double);

from TempStream
select roomNo, temp
insert into RoomTempStream;

from RoomTempStream
insert into AnotherRoomTempStream;

Inferred Stream

Here, the RoomTempStream and AnotherRoomTempStream streams are an inferred streams, which means their stream definitions are inferred from the queries and they can be used same as any other defined stream without any restrictions.

Value

Values are typed data, that can be manipulated, transferred and stored. Values can be referred by the attributes defined in definitions such as streams, and tables.

Siddhi supports values of type STRING, INT (Integer), LONG, DOUBLE, FLOAT, BOOL (Boolean) and OBJECT. The syntax of each type and their example use as a constant value is as follows,

Attribute Type	Format	Example
int	<digit>+	123, -75, +95
long	<digit>+L	123000L, -750l, +154L
float	(<digit>+)?('.'<digit>*)? (E(-|+)?<digit>+)?F	123.0f, -75.0e-10F, +95.789f
double	(<digit>+)?('.'<digit>*)? (E(-|+)?<digit>+)?D?	123.0,123.0D, -75.0e-10D,+95.789d
bool	(true|false)	true, false, TRUE, FALSE
string	'(<char>* !('|"|"""|<new line>))' or "(<char>* !("|"""|<new line>))" or """(<char>* !("""))"""	'Any text.', "Text with 'single' quotes.", """ Text with 'single' quotes, "double" quotes, and new lines. """

Time

Time is a special type of LONG value that denotes time using digits and their unit in the format (<digit>+ <unit>)+. At execution, the time gets converted into milliseconds and returns a LONG value.

Unit	Syntax
Year	year | years
Month	month | months
Week	week | weeks
Day	day | days
Hour	hour | hours
Minutes	minute | minutes | min
Seconds	second | seconds | sec
Milliseconds	millisecond | milliseconds

Example

1 hour and 25 minutes can by written as 1 hour and 25 minutes which is equal to the LONG value 5100000.

Select

The select clause in Siddhi query defines the output event attributes of the query. Following are some basic query projection operations supported by select.

Action	Description
Select specific attributes for projection	Only select some of the input attributes as query output attributes. E.g., Select and output only roomNo and temp attributes from the TempStream stream. from TempStream select roomNo, temp insert into RoomTempStream;
Select all attributes for projection	Select all input attributes as query output attributes. This can be done by using asterisk ( * ) or by omitting the select clause itself. E.g., Both following queries select all attributes of TempStream input stream and output all attributes to NewTempStream stream. from TempStream select * insert into NewTempStream; or from TempStream insert into NewTempStream;
Name attribute	Provide a unique name for each output attribute generated by the query. This can help to rename the selected input attributes or assign an attribute name to a projection operation such as function, aggregate-function, mathematical operation, etc, using as keyword. E.g., Query that renames input attribute temp to temperature and function currentTimeMillis() as time. from TempStream select roomNo, temp as temperature, currentTimeMillis() as time insert into RoomTempStream;
Constant values as attributes	Creates output attributes with a constant value. Any constant value of type STRING, INT, LONG, DOUBLE, FLOAT, BOOL, and time as given in the values section can be defined. E.g., Query specifying 'C' as the constant value for the scale attribute. from TempStream select roomNo, temp, 'C' as scale insert into RoomTempStream;
Mathematical and logical expressions in attributes	Defines the mathematical and logical operations that need to be performed to generating output attribute values. These expressions are executed in the precedence order given below. Operator precedence Operator Distribution Example () Scope (cost + tax) * 0.05 IS NULL Null check deviceID is null NOT Logical NOT not (price > 10) * ,/,% Multiplication, division, modulus temp * 9/5 + 32 +,- Addition, subtraction temp * 9/5 - 32 <,<=, >,>= Comparators: less-than, greater-than-equal, greater-than, less-than-equal totalCost >= price * quantity ==,!= Comparisons: equal, not equal totalCost != price * quantity IN Checks if value exist in the table roomNo in ServerRoomsTable AND Logical AND temp < 40 and humidity < 40 OR Logical OR humidity < 40 or humidity >= 60 E.g., Query converts temperature from Celsius to Fahrenheit, and identifies rooms with room number between 10 and 15 as server rooms. from TempStream select roomNo, temp * 9/5 + 32 as temp, 'F' as scale, roomNo > 10 and roomNo < 15 as isServerRoom insert into RoomTempStream;

Function

Function are pre-configured operations that can consumes zero, or more parameters and always produce a single value as result. It can be used anywhere an attribute can be used.

Purpose

Functions encapsulate pre-configured reusable execution logic allowing users to execute the logic anywhere just by calling the function. This also make writing SiddhiApps simple and easy to understand.

Syntax

The syntax of function is as follows,

<function name>( <parameter>* )

Here <function name> uniquely identifies the function. The <parameter> defined input parameters the function can accept. The input parameters can be attributes, constant values, results of other functions, results of mathematical or logical expressions, or time values. The number and type of parameters a function accepts depend on the function itself.

Note

Functions, mathematical expressions, and logical expressions can be used in a nested manner.

Example 1

Function name add accepting two input parameters, is called with an attribute named input and a constant value 75.

add(input, 75)

Example 2

Function name alertAfter accepting two input parameters, is called with a time value of 1 hour and 25 minutes and a mathematical addition operation of startTime + 56.

add(1 hour and 25 minutes, startTime + 56)

Inbuilt functions

Following are some inbuilt Siddhi functions, for more functions refer execution extensions .

Inbuilt function	Description
eventTimestamp	Returns event's timestamp.
currentTimeMillis	Returns current time of SiddhiApp runtime.
default	Returns a default value if the parameter is null.
ifThenElse	Returns parameters based on a conditional parameter.
UUID	Generates a UUID.
cast	Casts parameter type.
convert	Converts parameter type.
coalesce	Returns first not null input parameter.
maximum	Returns the maximum value of all parameters.
minimum	Returns the minimum value of all parameters.
instanceOfBoolean	Checks if the parameter is an instance of Boolean.
instanceOfDouble	Checks if the parameter is an instance of Double.
instanceOfFloat	Checks if the parameter is an instance of Float.
instanceOfInteger	Checks if the parameter is an instance of Integer.
instanceOfLong	Checks if the parameter is an instance of Long.
instanceOfString	Checks if the parameter is an instance of String.
createSet	Creates HashSet with given input parameters.
sizeOfSet	Returns number of items in the HashSet, that's passed as a parameter.

Example

Query that converts the roomNo to string using convert function, finds the maximum temperature reading with maximum function, and adds a unique messageID using the UUID function.

from TempStream
select convert(roomNo, 'string') as roomNo,
       maximum(tempReading1, tempReading2) as temp,
       UUID() as messageID
insert into RoomTempStream;

Filter

Filters provide a way of filtering input stream events based on a specified condition. It accepts any type of condition including a combination of functions and/or attributes that produces a Boolean result. Filters allow events to passthrough if the condition results in true, and drops if it results in a false.

Purpose

Filter helps to select the events that are relevant for the processing and omit the ones that are not.

Syntax

Filter conditions should be defined in square brackets ([]) next to the input stream as shown below.

from <input stream>[<filter condition>]
select <attribute name>, <attribute name>, ...
insert into <output stream>

Example

Query to filter TempStream stream events, having roomNo within the range of 100-210 and temperature greater than 40 degrees, and insert them into HighTempStream stream.

from TempStream[(roomNo >= 100 and roomNo < 210) and temp > 40]
select roomNo, temp
insert into HighTempStream;

Window

Window provides a way to capture a subset of events from an input stream and retain them for a period of time based on a specified criterion. The criterion defines when and how the events should be evicted from the windows. Such as events getting evicted from the window based on the time duration, or number of events and they events are evicted in a sliding (one by one) or tumbling (batch) manner.

Within a query, each input stream can at most have only one window associated with it.

Purpose

Windows help to retain events based on a criterion, such that the values of those events can be aggregated, or checked if an event of interest is within the window or not.

Syntax

Window should be defined by using the #window prefix next to the input stream as shown below.

from <input stream>#window.<window name>(<parameter>, <parameter>, ... )
select <attribute name>, <attribute name>, ...
insert <ouput event type>? into <output stream>

Note

Filter conditions can be applied both before and/or after the window.

Inbuilt windows

Following are some inbuilt Siddhi windows, for more windows refer execution extensions.

Inbuilt function	Description
time	Retains events based on time in a sliding manner.
timeBatch	Retains events based on time in a tumbling/batch manner.
length	Retains events based on number of events in a sliding manner.
lengthBatch	Retains events based on number of events in a tumbling/batch manner.
timeLength	Retains events based on time and number of events in a sliding manner.
session	Retains events for each session based on session key.
batch	Retains events of last arrived event chunk.
sort	Retains top-k or bottom-k events based on a parameter value.
cron	Retains events based on cron time in a tumbling/batch manner.
externalTime	Retains events based on event time value passed as a parameter in a sliding manner.
externalTimeBatch	Retains events based on event time value passed as a parameter in a a tumbling/batch manner.
delay	Retains events and delays the output by the given time period in a sliding manner.

Example 1

Query to find out the maximum temperature out of the last 10 events, using the window of length 10 and max() aggregation function, from the TempStream stream and insert the results into the MaxTempStream stream.

from TempStream#window.length(10)
select max(temp) as maxTemp
insert into MaxTempStream;

Here, the length window operates in a sliding manner where the following 3 event subsets are calculated and outputted when a list of 12 events are received in sequential order.

Subset	Event Range
1	1 - 10
2	2 - 11
3	3 - 12

Example 2

Query to find out the maximum temperature out of the every 10 events, using the window of lengthBatch 10 and max() aggregation function, from the TempStream stream and insert the results into the MaxTempStream stream.

from TempStream#window.lengthBatch(10)
select max(temp) as maxTemp
insert into MaxTempStream;

Here, the window operates in a batch/tumbling manner where the following 3 event subsets are calculated and outputted when a list of 30 events are received in a sequential order.

Subset	Event Range
1	1 - 10
2	11 - 20
3	21 - 30

Example 3

Query to find out the maximum temperature out of the events arrived during last 10 minutes, using the window of time 10 minutes and max() aggregation function, from the TempStream stream and insert the results into the MaxTempStream stream.

from TempStream#window.time(10 min)
select max(temp) as maxTemp
insert into MaxTempStream;

Here, the time window operates in a sliding manner with millisecond accuracy, where it will process events in the following 3 time durations and output aggregated events when a list of events are received in a sequential order.

Subset	Time Range (in ms)
1	1:00:00.001 - 1:10:00.000
2	1:00:01.001 - 1:10:01.000
3	1:00:01.033 - 1:10:01.034

Example 4

Query to find out the maximum temperature out of the events arriving every 10 minutes, using the window of timeBatch 10 and max() aggregation function, from the TempStream stream and insert the results into the MaxTempStream stream.

from TempStream#window.timeBatch(10 min)
select max(temp) as maxTemp
insert into MaxTempStream;

Here, the window operates in a batch/tumbling manner where the window will process evetns in the following 3 time durations and output aggregated events when a list of events are received in a sequential order.

Subset	Time Range (in ms)
1	1:00:00.001 - 1:10:00.000
2	1:10:00.001 - 1:20:00.000
3	1:20:00.001 - 1:30:00.000

Event Type

Query output depends on the current and expired event types it produces based on its internal processing state. By default all queries produce current events upon event arrival to the query. The queries containing windows additionally produce expired events when events expire from the windows.

Purpose

Event type helps to specify when a query should output events to the stream, such as output upon current events, expired events or upon both current and expired events.

Syntax

Event type should be defined in between insert and into keywords for insert queries as follows.

from <input stream>#window.<window name>(<parameter>, <parameter>, ... )
select <attribute name>, <attribute name>, ...
insert <event type> into <output stream>

Event type should be defined next to the for keyword for delete queries as follows.

from <input stream>#window.<window name>(<parameter>, <parameter>, ... )
select <attribute name>, <attribute name>, ...
delete <table> (for <event type>)?
    on <condition>

Event type should be defined next to the for keyword for update queries as follows.

from <input stream>#window.<window name>(<parameter>, <parameter>, ... )
select <attribute name>, <attribute name>, ...
update <table> (for <event type>)?
    set <table>.<attribute name> = (<attribute name>|<expression>)?, <table>.<attribute name> = (<attribute name>|<expression>)?, ...
    on <condition>

Event type should be defined next to the for keyword for update or insert queries as follows.

from <input stream>#window.<window name>(<parameter>, <parameter>, ... )
select <attribute name>, <attribute name>, ...
update or insert into <table> (for <event type>)?
    set <table>.<attribute name> = <expression>, <table>.<attribute name> = <expression>, ...
    on <condition>

Note

Controlling query output based on the event types neither alters query execution nor its accuracy.

The event types can be defined using the following keywords to manipulate query output.

Event types	Description
current events	Outputs events only when incoming events arrive to be processed by the query. This is default behavior when no specific event type is specified.
expired events	Outputs events only when events expires from the window.
all events	Outputs events when incoming events arrive to be processed by the query as well as when events expire from the window.

Example

Query to output only the expired events from a 1 minute time window to the DelayedTempStream stream. This can be used for delaying the events by a minute.

from TempStream#window.time(1 min)
select *
insert expired events into DelayedTempStream

Note

This is just to illustrate how expired events work, it is recommended to use delay window for usecases where we need to delay events by a given time period.

Aggregate Function

Aggregate functions are pre-configured aggregation operations that can consumes zero, or more parameters from multiple events and always produce a single value as result. They can be only used in the query projection (as part of the select clause). When a query comprises a window, the aggregation will be contained to the events in the window, and when it does not have a window, the aggregation is performed from the first event the query has received.

Purpose

Aggregate functions encapsulate pre-configured reusable aggregate logic allowing users to aggregate values of multiple events together. When used with batch/tumbling windows this can also help to reduce the number of output events produced.

Syntax

Aggregate function can be used in query projection (as part of the select clause) alone or as a part of another expression. In all cases, the output produced by the query should be properly mapped to the output stream attribute using the as keyword.

The syntax of aggregate function is as follows,

from <input stream>#window.<window name>(<parameter>, <parameter>, ... )
select <aggregate function>(<parameter>, <parameter>, ... ) as <attribute name>, <attribute2 name>, ...
insert into <output stream>;

Here <aggregate function> uniquely identifies the aggregate function. The <parameter> defined input parameters the aggregate function can accept. The input parameters can be attributes, constant values, results of other functions or aggregate functions, results of mathematical or logical expressions, or time values. The number and type of parameters an aggregate function accepts depend on the function itself.

Inbuilt aggregate functions

Following are some inbuilt aggregation functions, for more functions refer execution extensions.

Inbuilt aggregate function	Description
sum	Calculates the sum from a set of values.
count	Calculates the count from a set of values.
distinctCount	Calculates the distinct count based on a parameter from a set of values.
avg	Calculates the average from a set of values.
max	Finds the maximum value from a set of values.
max	Finds the minimum value from a set of values.

| maxForever | Finds the maximum value from all events throughout its lifetime irrespective of the windows. | | minForever | Finds the minimum value from all events throughout its lifetime irrespective of the windows. | | stdDev | Calculates the standard deviation from a set of values. | | and | Calculates boolean and from a set of values. | | or | Calculates boolean or from a set of values. | | unionSet | Calculates union as a Set from a set of values. |

Example

Query to calculate average, maximum, and minimum values on temp attribute of the TempStream stream in a sliding manner, from the events arrived over the last 10 minutes and to produce outputs avgTemp, maxTemp and minTemp respectively to the AvgTempStream output stream.

from TempStream#window.time(10 min)
select avg(temp) as avgTemp, max(temp) as maxTemp, min(temp) as minTemp
insert into AvgTempStream;

Group By

Group By provides a way of grouping events based on one or more specified attributes to perform aggregate operations.

Purpose

Group By allows users to aggregate values of multiple events based on the given group-by fields.

Syntax

The syntax for the Group By with aggregate function is as follows.

from <input stream>#window.<window name>(...)
select <aggregate function>( <parameter>, <parameter>, ...) as <attribute1 name>, <attribute2 name>, ...
group by <attribute1 name>, <attribute2 name>, ...
insert into <output stream>;

Here the group by attributes should be defined next to the group by keyword separating each attribute by a comma.

Example

Query to calculate the average temp per roomNo and deviceID combination, from the events arrived from TempStream stream, during the last 10 minutes time-window in a sliding manner.

from TempStream#window.time(10 min)
select roomNo, deviceID, avg(temp) as avgTemp
group by roomNo, deviceID
insert into AvgTempStream;

Having

Having provide a way of filtering events based on a specified condition of the query output stream attributes. It accepts any type of condition including a combination of functions and/or attributes that produces a Boolean result. Having, allow events to passthrough if the condition results in true, and drops if it results in a false.

Purpose

Having helps to select the events that are relevant for the output based on the attributes those are produced by the select clause and omit the ones that are not.

Syntax

The syntax for the Having clause is as follows.

from <input stream>#window.<window name>( ... )
select <aggregate function>( <parameter>, <parameter>, ...) as <attribute1 name>, <attribute2 name>, ...
group by <attribute1 name>, <attribute2 name> ...
having <condition>
insert into <output stream>;

Here the having <condition> should be defined next to the having keyword and having can be used with or without group by clause.

Example

Query to calculate the average temp per roomNo for the last 10 minutes, and alerts if the avgTemp exceeds 30 degrees.

from TempStream#window.time(10 min)
select roomNo, avg(temp) as avgTemp
group by roomNo
having avgTemp > 30
insert into AlertStream;

Order By

Order By, orders the query results in ascending and or descending order based on one or more specified attributes. When an attribute is used for order by, by default Siddhi orders the events in ascending order of that attribute's value, and by adding desc keyword, the events can be ordered in descending order. When more than one attribute is defined the attributes defined towards the left will have more precedence in ordering than the ones defined in right.

Purpose

Order By helps to sort the events in the outputs chunks produced by the query. Order By will be more helpful for batch windows, and queries where they output many of event together then for sliding window use cases where the output will be one or few events at a time.

Syntax

The syntax for the Order By clause is as follows:

from <input stream>#window.<window name>( ... )
select <aggregate function>( <parameter>, <parameter>, ...) as <attribute1 name>, <attribute2 name>, ...
group by <attribute1 name>, <attribute2 name> ...
having <condition>
order by <attribute1 name> (asc|desc)?, <attribute2 name> (asc|desc)?, ...
insert into <output stream>;

Here the order by attributes should be defined next to the order by keyword separating each by a comma, and optionally specifying the event ordering using asc (default) or desc keywords.

Example

Query to calculate the average temp per roomNo and deviceID combination on every 10 minutes batches, and order the generated output events in ascending order by avgTemp and then by descending order of roomNo (if the more than one event have the same avgTemp value).

from TempStream#window.timeBatch(10 min)
select roomNo, deviceID, avg(temp) as avgTemp
group by roomNo, deviceID
order by avgTemp, roomNo desc
insert into AvgTempStream;

Limit & Offset

These provide a way to select the number of events (via limit) from the desired index (by specifying an offset) from the output event chunks produced by the query.

Purpose

Limit & Offset helps to output only the selected set of events from large event batches. This will be more useful with Order By clause where one can order the output for topK, bottomK, or even to paginate through the dataset by obtaining a set of events from the middle.

Syntax

The syntax for the Limit & Offset clauses is as follows:

from <input stream>#window.<window name>( ... )
select <aggregate function>( <parameter>, <parameter>, ...) as <attribute1 name>, <attribute2 name>, ...
group by <attribute1 name>, <attribute2 name> ...
having <condition>
order by <attribute1 name> (asc | desc)?, <attribute2 name> (<ascend/descend>)?, ...
limit <positive integer>?
offset <positive integer>?
insert into <output stream>;

Here both limit and offset are optional, when limit is omitted the query will output all the events, and when offset is omitted 0 is taken as the default offset value.

Example 1

Query to calculate the average temp per roomNo and deviceID combination for every 10 minutes batches, from the events arriving at the TempStream stream, and emit only two events having the highest avgTemp value.

from TempStream#window.timeBatch(10 min)
select roomNo, deviceID, avg(temp) as avgTemp
group by roomNo, deviceID
order by avgTemp desc
limit 2
insert into HighestAvgTempStream;

Example 2 Query to calculate the average temp per roomNo and deviceID combination for every 10 minutes batches, for events that arriving at the TempStream stream, and emits only the third, forth and fifth events when sorted in descending order based on their avgTemp value.

from TempStream#window.timeBatch(10 min)
select roomNo, deviceID, avg(temp) as avgTemp
group by roomNo, deviceID
order by avgTemp desc
limit 3
offset 2
insert into HighestAvgTempStream;

Join (Stream)

Joins allow you to get a combined result from two streams in real-time based on a specified condition.

Purpose

Streams are stateless. Therefore, in order to join two streams, they need to be connected to a window so that there is a pool of events that can be used for joining. Joins also accept conditions to join the appropriate events from each stream.

During the joining process each incoming event of each stream is matched against all the events in the other stream's window based on the given condition, and the output events are generated for all the matching event pairs.

Note

Join can also be performed with stored data, aggregation or externally named windows.

Syntax

The syntax for a join is as follows:

from <input stream>#window.<window name>(<parameter>, ... ) {unidirectional} {as <reference>}
         join <input stream>#window.<window name>(<parameter>,  ... ) {unidirectional} {as <reference>}
    on <join condition>
select <attribute name>, <attribute name>, ...
insert into <output stream>

Here, the <join condition> allows you to match the attributes from both the streams.

Unidirectional join operation

By default, events arriving at either stream can trigger the joining process. However, if you want to control the join execution, you can add the unidirectional keyword next to a stream in the join definition as depicted in the syntax in order to enable that stream to trigger the join operation. Here, events arriving at other stream only update the window of that stream, and this stream does not trigger the join operation.

Note

The unidirectional keyword cannot be applied to both the input streams because the default behaviour already allows both streams to trigger the join operation.

Example

Assuming that the temperature of regulators are updated every minute. Following is a Siddhi App that controls the temperature regulators if they are not already on for all the rooms with a room temperature greater than 30 degrees.

define stream TempStream(deviceID long, roomNo int, temp double);
define stream RegulatorStream(deviceID long, roomNo int, isOn bool);

from TempStream[temp > 30.0]#window.time(1 min) as T
  join RegulatorStream[isOn == false]#window.length(1) as R
  on T.roomNo == R.roomNo
select T.roomNo, R.deviceID, 'start' as action
insert into RegulatorActionStream;

Supported join types

Following are the supported operations of a join clause.

• Inner join (join)

  This is the default behaviour of a join operation. join is used as the keyword to join both the streams. The output is generated only if there is a matching event in both the streams.

• Left outer join

  The left outer join operation allows you to join two streams to be merged based on a condition. left outer join is used as the keyword to join both the streams.

  Here, it returns all the events of left stream even if there are no matching events in the right stream by having null values for the attributes of the right stream.

  Example

  The following query generates output events for all events from the StockStream stream regardless of whether a matching symbol exists in the TwitterStream stream or not.

  from StockStream#window.time(1 min) as S
    left outer join TwitterStream#window.length(1) as T
    on S.symbol== T.symbol
  select S.symbol as symbol, T.tweet, S.price
  insert into outputStream ;    

• Right outer join

  This is similar to a left outer join. Right outer join is used as the keyword to join both the streams. It returns all the events of the right stream even if there are no matching events in the left stream.

• Full outer join

  The full outer join combines the results of left outer join and right outer join. full outer join is used as the keyword to join both the streams. Here, output event are generated for each incoming event even if there are no matching events in the other stream.

  Example

  The following query generates output events for all the incoming events of each stream regardless of whether there is a match for the symbol attribute in the other stream or not.

  from StockStream#window.time(1 min) as S
    full outer join TwitterStream#window.length(1) as T
    on S.symbol== T.symbol
  select S.symbol as symbol, T.tweet, S.price
  insert into outputStream ;    

Pattern

This is a state machine implementation that allows you to detect patterns in the events that arrive over time. This can correlate events within a single stream or between multiple streams.

Purpose

Patterns allow you to identify trends in events over a time period.

Syntax

The following is the syntax for a pattern query:

from (every)? <event reference>=<input stream>[<filter condition>] ->
    (every)? <event reference>=<input stream [<filter condition>] ->
    ...
    (within <time gap>)?     
select <event reference>.<attribute name>, <event reference>.<attribute name>, ...
insert into <output stream>

Items	Description
->	This is used to indicate an event that should be following another event. The subsequent event does not necessarily have to occur immediately after the preceding event. The condition to be met by the preceding event should be added before the sign, and the condition to be met by the subsequent event should be added after the sign.
<event reference>	This allows you to add a reference to the the matching event so that it can be accessed later for further processing.
(within <time gap>)?	The within clause is optional. It defines the time duration within which all the matching events should occur.
every	every is an optional keyword. This defines whether the event matching should be triggered for every event arrival in the specified stream with the matching condition. When this keyword is not used, the matching is carried out only once.

Siddhi also supports pattern matching with counting events and matching events in a logical order such as (and, or, and not). These are described in detail further below in this guide.

Example

This query sends an alert if the temperature of a room increases by 5 degrees within 10 min.

from every( e1=TempStream ) -> e2=TempStream[ e1.roomNo == roomNo and (e1.temp + 5) <= temp ]
    within 10 min
select e1.roomNo, e1.temp as initialTemp, e2.temp as finalTemp
insert into AlertStream;

Here, the matching process begins for each event in the TempStream stream (because every is used with e1=TempStream), and if another event arrives within 10 minutes with a value for the temp attribute that is greater than or equal to e1.temp + 5 of the event e1, an output is generated via the AlertStream.

Counting Pattern

Counting patterns allow you to match multiple events that may have been received for the same matching condition. The number of events matched per condition can be limited via condition postfixes.

Syntax

Each matching condition can contain a collection of events with the minimum and maximum number of events to be matched as shown in the syntax below.

from (every)? <event reference>=<input stream>[<filter condition>] (<<min count>:<max count>>)? ->  
    ...
    (within <time gap>)?     
select <event reference>([event index])?.<attribute name>, ...
insert into <output stream>

Postfix	Description	Example
<n1:n2>	This matches n1 to n2 events (including n1 and not more than n2).	1:4 matches 1 to 4 events.
<n:>	This matches n or more events (including n).	<2:> matches 2 or more events.
<:n>	This matches up to n events (excluding n).	<:5> matches up to 5 events.
<n>	This matches exactly n events.	<5> matches exactly 5 events.

Specific occurrences of the event in a collection can be retrieved by using an event index with its reference. Square brackets can be used to indicate the event index where 1 can be used as the index of the first event and last can be used as the index for the last available event in the event collection. If you provide an index greater then the last event index, the system returns null. The following are some valid examples.

• e1[3] refers to the 3^rd event.
• e1[last] refers to the last event.
• e1[last - 1] refers to the event before the last event.

Example

The following Siddhi App calculates the temperature difference between two regulator events.

define stream TempStream (deviceID long, roomNo int, temp double);
define stream RegulatorStream (deviceID long, roomNo int, tempSet double, isOn bool);

from every( e1=RegulatorStream) -> e2=TempStream[e1.roomNo==roomNo]<1:> -> e3=RegulatorStream[e1.roomNo==roomNo]
select e1.roomNo, e2[0].temp - e2[last].temp as tempDiff
insert into TempDiffStream;

Logical Patterns

Logical patterns match events that arrive in temporal order and correlate them with logical relationships such as and, or and not.

Syntax

from (every)? (not)? <event reference>=<input stream>[<filter condition>]
          ((and|or) <event reference>=<input stream>[<filter condition>])? (within <time gap>)? ->  
    ...
select <event reference>([event index])?.<attribute name>, ...
insert into <output stream>

Keywords such as and, or, or not can be used to illustrate the logical relationship.

Key Word	Description
and	This allows both conditions of and to be matched by two events in any order.
or	The state succeeds if either condition of or is satisfied. Here the event reference of the other condition is null.
not <condition1> and <condition2>	When not is included with and, it identifies the events that match arriving before any event that match .
not <condition> for <time period>	When not is included with for, it allows you to identify a situation where no event that matches <condition1> arrives during the specified <time period>. e.g.,from not TemperatureStream[temp > 60] for 5 sec.

Here the not pattern can be followed by either an and clause or the effective period of not can be concluded after a given <time period>. Further in Siddhi more than two streams cannot be matched with logical conditions using and, or, or not clauses at this point.

Detecting Non-occurring Events

Siddhi allows you to detect non-occurring events via multiple combinations of the key words specified above as shown in the table below.

In the patterns listed, P* can be either a regular event pattern, an absent event pattern or a logical pattern.

Pattern	Detected Scenario
not A for <time period>	The non-occurrence of event A within <time period> after system start up. e.g., Generating an alert if a taxi has not reached its destination within 30 minutes, to indicate that the passenger might be in danger.
not A for <time period> and B	After system start up, event A does not occur within time period, but event B occurs at some point in time. e.g., Generating an alert if a taxi has not reached its destination within 30 minutes, and the passenger marked that he/she is in danger at some point in time.
not A for <time period 1> and not B for <time period 2>	After system start up, event A doess not occur within time period 1, and event B also does not occur within <time period 2>. e.g., Generating an alert if the driver of a taxi has not reached the destination within 30 minutes, and the passenger has not marked himself/herself to be in danger within that same time period.
not A for <time period> or B	After system start up, either event A does not occur within <time period>, or event B occurs at some point in time. e.g., Generating an alert if the taxi has not reached its destination within 30 minutes, or if the passenger has marked that he/she is in danger at some point in time.
not A for <time period 1> or not B for <time period 2>	After system start up, either event A does not occur within <time period 1>, or event B occurs within <time period 2>. e.g., Generating an alert to indicate that the driver is not on an expected route if the taxi has not reached destination A within 20 minutes, or reached destination B within 30 minutes.
A → not B for <time period>	Event B does not occur within <time period> after the occurrence of event A. e.g., Generating an alert if the taxi has reached its destination, but this was not followed by a payment record.
P* → not A for <time period> and B	After the occurrence of P*, event A does not occur within <time period>, and event B occurs at some point in time.
P* → not A for <time period 1> and not B for <time period 2>	After the occurrence of P*, event A does not occur within <time period 1>, and event B does not occur within <time period 2>.
P* → not A for <time period> or B	After the occurrence of P*, either event A does not occur within <time period>, or event B occurs at some point in time.
P* → not A for <time period 1> or not B for <time period 2>	After the occurrence of P*, either event A does not occur within <time period 1>, or event B does not occur within <time period 2>.
not A for <time period> → B	Event A does occur within <time period> after the system start up, but event B occurs after that <time period> has elapsed.
not A for <time period> and B → P*	Event A does not occur within <time period>, and event B occurs at some point in time. Then P* occurs after the <time period> has elapsed, and after B has occurred.
not A for <time period 1> and not B for <time period 2> → P*	After system start up, event A does not occur within <time period 1>, and event B does not occur within <time period 2>. However, P* occurs after both A and B.
not A for <time period> or B → P*	After system start up, event A does not occur within <time period> or event B occurs at some point in time. The P* occurs after <time period> has elapsed, or after B has occurred.
not A for <time period 1> or not B for <time period 2> → P*	After system start up, either event A does not occur within <time period 1>, or event B does not occur within <time period 2>. Then P* occurs after both <time period 1> and <time period 2> have elapsed.
not A and B	Event A does not occur before event B.
A and not B	Event B does not occur before event A.

Example

Following Siddhi App, sends the stop control action to the regulator when the key is removed from the hotel room.

define stream RegulatorStateChangeStream(deviceID long, roomNo int, tempSet double, action string);
define stream RoomKeyStream(deviceID long, roomNo int, action string);


from every( e1=RegulatorStateChangeStream[ action == 'on' ] ) ->
      e2=RoomKeyStream[ e1.roomNo == roomNo and action == 'removed' ] or e3=RegulatorStateChangeStream[ e1.roomNo == roomNo and action == 'off']
select e1.roomNo, ifThenElse( e2 is null, 'none', 'stop' ) as action
having action != 'none'
insert into RegulatorActionStream;

This Siddhi Application generates an alert if we have switch off the regulator before the temperature reaches 12 degrees.

define stream RegulatorStateChangeStream(deviceID long, roomNo int, tempSet double, action string);
define stream TempStream (deviceID long, roomNo int, temp double);

from e1=RegulatorStateChangeStream[action == 'start'] -> not TempStream[e1.roomNo == roomNo and temp < 12] and e2=RegulatorStateChangeStream[action == 'off']
select e1.roomNo as roomNo
insert into AlertStream;

This Siddhi Application generates an alert if the temperature does not reduce to 12 degrees within 5 minutes of switching on the regulator.

define stream RegulatorStateChangeStream(deviceID long, roomNo int, tempSet double, action string);
define stream TempStream (deviceID long, roomNo int, temp double);

from e1=RegulatorStateChangeStream[action == 'start'] -> not TempStream[e1.roomNo == roomNo and temp < 12] for '5 min'
select e1.roomNo as roomNo
insert into AlertStream;

Sequence

Sequence is a state machine implementation that allows you to detect the sequence of event occurrences over time. Here all matching events need to arrive consecutively to match the sequence condition, and there cannot be any non-matching events arriving within a matching sequence of events. This can correlate events within a single stream or between multiple streams.

Purpose

This allows you to detect a specified event sequence over a specified time period.

Syntax

The syntax for a sequence query is as follows:

from (every)? <event reference>=<input stream>[<filter condition>],
    <event reference>=<input stream [<filter condition>],
    ...
    (within <time gap>)?     
select <event reference>.<attribute name>, <event reference>.<attribute name>, ...
insert into <output stream>

Items	Description
,	This represents the immediate next event i.e., when an event that matches the first condition arrives, the event that arrives immediately after it should match the second condition.
<event reference>	This allows you to add a reference to the the matching event so that it can be accessed later for further processing.
(within <time gap>)?	The within clause is optional. It defines the time duration within which all the matching events should occur.
every	every is an optional keyword. This defines whether the matching event should be triggered for every event that arrives at the specified stream with the matching condition. When this keyword is not used, the matching is carried out only once.

Example

This query generates an alert if the increase in the temperature between two consecutive temperature events exceeds one degree.

from every e1=TempStream, e2=TempStream[e1.temp + 1 < temp]
select e1.temp as initialTemp, e2.temp as finalTemp
insert into AlertStream;

Counting Sequence

Counting sequences allow you to match multiple events for the same matching condition. The number of events matched per condition can be limited via condition postfixes such as Counting Patterns, or by using the *, +, and ? operators.

The matching events can also be retrieved using event indexes, similar to how it is done in Counting Patterns.

Syntax

Each matching condition in a sequence can contain a collection of events as shown below.

from (every)? <event reference>=<input stream>[<filter condition>](+|*|?)?,
    <event reference>=<input stream [<filter condition>](+|*|?)?,
    ...
    (within <time gap>)?     
select <event reference>.<attribute name>, <event reference>.<attribute name>, ...
insert into <output stream>

Postfix symbol	Required/Optional	Description
+	Optional	This matches one or more events to the given condition.
*	Optional	This matches zero or more events to the given condition.
?	Optional	This matches zero or one events to the given condition.

Example

This Siddhi application identifies temperature peeks.

define stream TempStream(deviceID long, roomNo int, temp double);

from every e1=TempStream, e2=TempStream[e1.temp <= temp]+, e3=TempStream[e2[last].temp > temp]
select e1.temp as initialTemp, e2[last].temp as peakTemp
insert into PeekTempStream;

Logical Sequence

Logical sequences identify logical relationships using and, or and not on consecutively arriving events.

Syntax The syntax for a logical sequence is as follows:

from (every)? (not)? <event reference>=<input stream>[<filter condition>]
          ((and|or) <event reference>=<input stream>[<filter condition>])? (within <time gap>)?,
    ...
select <event reference>([event index])?.<attribute name>, ...
insert into <output stream>

Keywords such as and, or, or not can be used to illustrate the logical relationship, similar to how it is done in Logical Patterns.

Example

This Siddhi application notifies the state when a regulator event is immediately followed by both temperature and humidity events.

define stream TempStream(deviceID long, temp double);
define stream HumidStream(deviceID long, humid double);
define stream RegulatorStream(deviceID long, isOn bool);

from every e1=RegulatorStream, e2=TempStream and e3=HumidStream
select e2.temp, e3.humid
insert into StateNotificationStream;

Output rate limiting

Output rate limiting allows queries to output events periodically based on a specified condition.

Purpose

This allows you to limit the output to avoid overloading the subsequent executions, and to remove unnecessary information.

Syntax

The syntax of an output rate limiting configuration is as follows:

from <input stream> ...
select <attribute name>, <attribute name>, ...
output <rate limiting configuration>
insert into <output stream>

Siddhi supports three types of output rate limiting configurations as explained in the following table:

Rate limiting configuration	Syntax	Description
Based on time	<output event> every <time interval>	This outputs <output event> every <time interval> time interval.
Based on number of events	<output event> every <event interval> events	This outputs <output event> for every <event interval> number of events.
Snapshot based output	snapshot every <time interval>	This outputs all events in the window (or the last event if no window is defined in the query) for every given <time interval> time interval.

Here the <output event> specifies the event(s) that should be returned as the output of the query. The possible values are as follows: * first : Only the first event processed by the query during the specified time interval/sliding window is emitted. * last : Only the last event processed by the query during the specified time interval/sliding window is emitted. * all : All the events processed by the query during the specified time interval/sliding window are emitted. When no <output event> is defined, all is used by default.

Examples

• Returning events based on the number of events

  Here, events are emitted every time the specified number of events arrive. You can also specify whether to emit only the first event/last event, or all the events out of the events that arrived.

  In this example, the last temperature per sensor is emitted for every 10 events.

  from TempStreamselect
  select temp, deviceID
  group by deviceID
  output last every 10 events
  insert into LowRateTempStream;    

• Returning events based on time

  Here events are emitted for every predefined time interval. You can also specify whether to emit only the first event, last event, or all events out of the events that arrived during the specified time interval.

  In this example, emits all temperature events every 10 seconds

  from TempStreamoutput
  output every 10 sec
  insert into LowRateTempStream;    

• Returning a periodic snapshot of events

  This method works best with windows. When an input stream is connected to a window, snapshot rate limiting emits all the current events that have arrived and do not have corresponding expired events for every predefined time interval. If the input stream is not connected to a window, only the last current event for each predefined time interval is emitted.

  This query emits a snapshot of the events in a time window of 5 seconds every 1 second.

  from TempStream#window.time(5 sec)
  output snapshot every 1 sec
  insert into SnapshotTempStream;    

Partition

Partitions divide streams and queries into isolated groups in order to process them in parallel and in isolation. A partition can contain one or more queries and there can be multiple instances where the same queries and streams are replicated for each partition. Each partition is tagged with a partition key. Those partitions only process the events that match the corresponding partition key.

Purpose

Partitions allow you to process the events groups in isolation so that event processing can be performed using the same set of queries for each group.

Partition key generation

A partition key can be generated in the following two methods:

• Partition by value

  This is created by generating unique values using input stream attributes.

  Syntax

  partition with ( <expression> of <stream name>, 
                   <expression> of <stream name>, ... )
  begin
      <query>
      <query>
      ...
  end; 

  Example

  This query calculates the maximum temperature recorded within the last 10 events per deviceID.

  partition with ( deviceID of TempStream )
  begin
      from TempStream#window.length(10)
      select roomNo, deviceID, max(temp) as maxTemp
      insert into DeviceTempStream;
  end;
  

• Partition by range

  This is created by mapping each partition key to a range condition of the input streams numerical attribute.

  Syntax

  partition with ( <condition> as <partition key> or 
                   <condition> as <partition key> or ... of <stream name>,
                   ... )
  begin
      <query>
      <query>
      ...
  end;
  

  Example

  This query calculates the average temperature for the last 10 minutes per office area.

  partition with ( roomNo >= 1030 as 'serverRoom' or
                   roomNo < 1030 and roomNo >= 330 as 'officeRoom' or
                   roomNo < 330 as 'lobby' of TempStream)
  begin
      from TempStream#window.time(10 min)
      select roomNo, deviceID, avg(temp) as avgTemp
      insert into AreaTempStream
  end;
  

Inner Stream

Queries inside a partition block can use inner streams to communicate with each other while preserving partition isolation. Inner streams are denoted by a "#" placed before the stream name, and these streams cannot be accessed outside a partition block.

Purpose

Inner streams allow you to connect queries within the partition block so that the output of a query can be used as an input only by another query within the same partition. Therefore, you do not need to repartition the streams if they are communicating within the partition.

Example

This partition calculates the average temperature of every 10 events for each sensor, and sends an output to the DeviceTempIncreasingStream stream if the consecutive average temperature values increase by more than 5 degrees.

partition with ( deviceID of TempStream )
begin
    from TempStream#window.lengthBatch(10)
    select roomNo, deviceID, avg(temp) as avgTemp
    insert into #AvgTempStream

    from every (e1=#AvgTempStream),e2=#AvgTempStream[e1.avgTemp + 5 < avgTemp]
    select e1.deviceID, e1.avgTemp as initialAvgTemp, e2.avgTemp as finalAvgTemp
    insert into DeviceTempIncreasingStream
end;

Purge Partition

Based on the partition key used for the partition, multiple instances of streams and queries will be generated. When an extremely large number of unique partition keys are used there is a possibility of very high instances of streams and queries getting generated and eventually system going out of memory. In order to overcome this, users can define a purge interval to clean partitions that will not be used anymore.

Purpose

@purge allows you to clean the partition instances that will not be used anymore.

Syntax

The syntax of partition purge configuration is as follows:

@purge(enable='true', interval='<purge interval>', idle.period='<idle period of partition instance>')
partition with ( <partition key> of <input stream> )
begin
    from <input stream> ...
    select <attribute name>, <attribute name>, ...
    insert into <output stream>
end;

Partition purge configuration	Description
Purge interval	The periodic time interval to purge the purgeable partition instances.
Idle period of partition instance	The period, a particular partition instance (for a given partition key) needs to be idle before it becomes purgeable.

Examples

Mark partition instances eligible for purging, if there are no events from a particular deviceID for 15 seconds, and periodically purge those partition instances every 1 second.

@purge(enable='true', interval='1 sec', idle.period='15 sec')
partition with ( deviceID of TempStream )
begin
    from TempStream#window.lengthBatch(10)
    select roomNo, deviceID, avg(temp) as avgTemp
    insert into #AvgTempStream

    from every (e1=#AvgTempStream),e2=#AvgTempStream[e1.avgTemp + 5 < avgTemp]
    select e1.deviceID, e1.avgTemp as initialAvgTemp, e2.avgTemp as finalAvgTemp
    insert into DeviceTempIncreasingStream
end;

Table

A table is a stored version of an stream or a table of events. Its schema is defined via the table definition that is similar to a stream definition. These events are by default stored in-memory, but Siddhi also provides store extensions to work with data/events stored in various data stores through the table abstraction.

Purpose

Tables allow Siddhi to work with stored events. By defining a schema for tables Siddhi enables them to be processed by queries using their defined attributes with the streaming data. You can also interactively query the state of the stored events in the table.

Syntax

The syntax for a new table definition is as follows:

define table <table name> (<attribute name> <attribute type>, <attribute name> <attribute type>, ... );

The following parameters are configured in a table definition:

Parameter	Description
table name	The name of the table defined. (PascalCase is used for table name as a convention.)
attribute name	The schema of the table is defined by its attributes with uniquely identifiable attribute names (camelCase is used for attribute names as a convention.)
attribute type	The type of each attribute defined in the schema. This can be STRING, INT, LONG, DOUBLE, FLOAT, BOOL or OBJECT.

Example

The following defines a table named RoomTypeTable with roomNo and type attributes of data types int and string respectively.

define table RoomTypeTable ( roomNo int, type string );

Primary Keys

Tables can be configured with primary keys to avoid the duplication of data.

Primary keys are configured by including the @PrimaryKey( 'key1', 'key2' ) annotation to the table definition. Each event table configuration can have only one @PrimaryKey annotation. The number of attributes supported differ based on the table implementations. When more than one attribute is used for the primary key, the uniqueness of the events stored in the table is determined based on the combination of values for those attributes.

Examples

This query creates an event table with the symbol attribute as the primary key. Therefore each entry in this table must have a unique value for symbol attribute.

@PrimaryKey('symbol')
define table StockTable (symbol string, price float, volume long);

Indexes

Indexes allow tables to be searched/modified much faster.

Indexes are configured by including the @Index( 'key1', 'key2' ) annotation to the table definition. Each event table configuration can have 0-1 @Index annotations. Support for the @Index annotation and the number of attributes supported differ based on the table implementations. When more then one attribute is used for index, each one of them is used to index the table for fast access of the data. Indexes can be configured together with primary keys.

Examples

This query creates an indexed event table named RoomTypeTable with the roomNo attribute as the index key.

@Index('roomNo')
define table RoomTypeTable (roomNo int, type string);

Store

Store is a table that refers to data/events stored in data stores outside of Siddhi such as RDBMS, Cassandra, etc. Store is defined via the @store annotation, and the store schema is defined via a table definition associated with it.

Purpose

Store allows Siddhi to search, retrieve and manipulate data stored in external data stores through Siddhi queries.

Syntax

The syntax for a defining store and it's associated table definition is as follows:

@store(type='store_type', static.option.key1='static_option_value1', static.option.keyN='static_option_valueN')
define table TableName (attribute1 Type1, attributeN TypeN);

Example

The following defines a RDBMS data store pointing to a MySQL database with name hotel hosted in loacalhost:3306 having a table RoomTypeTable with columns roomNo of INTEGER and type of VARCHAR(255) mapped to Siddhi data types int and string respectively.

@Store(type="rdbms", jdbc.url="jdbc:mysql://localhost:3306/hotel", username="siddhi", password="123",
       jdbc.driver.name="com.mysql.jdbc.Driver")
define table RoomTypeTable ( roomNo int, type string );

Supported Store Types

The following is a list of currently supported store types:

• RDBMS (MySQL, Oracle, SQL Server, PostgreSQL, DB2, H2)
• MongoDB

Operators on Table (and Store)

The following operators can be performed on tables (and stores).

Insert

This allows events to be inserted into tables. This is similar to inserting events into streams.

Warning

If the table is defined with primary keys, and if you insert duplicate data, primary key constrain violations can occur. In such cases use the update or insert into operation.

Syntax

from <input stream>
select <attribute name>, <attribute name>, ...
insert into <table>

Similar to streams, you need to use the current events, expired events or the all events keyword between insert and into keywords in order to insert only the specific event types. For more information, see Event Type

Example

This query inserts all the events from the TempStream stream to the TempTable table.

from TempStream
select *
insert into TempTable;

Join (Table)

This allows a stream to retrieve information from a table in a streaming manner.

Note

Joins can also be performed with two streams, aggregation or against externally named windows.

Syntax

from <input stream> join <table>
    on <condition>
select (<input stream>|<table>).<attribute name>, (<input stream>|<table>).<attribute name>, ...
insert into <output stream>

Note

A table can only be joint with a stream. Two tables cannot be joint because there must be at least one active entity to trigger the join operation.

Example

This Siddhi App performs a join to retrieve the room type from RoomTypeTable table based on the room number, so that it can filter the events related to server-rooms.

define table RoomTypeTable (roomNo int, type string);
define stream TempStream (deviceID long, roomNo int, temp double);

from TempStream join RoomTypeTable
    on RoomTypeTable.roomNo == TempStream.roomNo
select deviceID, RoomTypeTable.type as roomType, type, temp
    having roomType == 'server-room'
insert into ServerRoomTempStream;

Supported join types

Table join supports following join operations.

• Inner join (join)

  This is the default behaviour of a join operation. join is used as the keyword to join the stream with the table. The output is generated only if there is a matching event in both the stream and the table.

• Left outer join

  The left outer join operation allows you to join a stream on left side with a table on the right side based on a condition. Here, it returns all the events of left stream even if there are no matching events in the right table by having null values for the attributes of the right table.

• Right outer join

  This is similar to a left outer join. right outer join is used as the keyword to join a stream on right side with a table on the left side based on a condition. It returns all the events of the right stream even if there are no matching events in the left table.

Delete

To delete selected events that are stored in a table.

Syntax

from <input stream>
select <attribute name>, <attribute name>, ...
delete <table> (for <event type>)?
    on <condition>

The condition element specifies the basis on which events are selected to be deleted. When specifying the condition, table attributes should be referred to with the table name.

To execute delete for specific event types, use the current events, expired events or the all events keyword with for as shown in the syntax. For more information, see Event Type

Note

Table attributes must be always referred to with the table name as follows: <table name>.<attibute name>

Example

In this example, the script deletes a record in the RoomTypeTable table if it has a value for the roomNo attribute that matches the value for the roomNumber attribute of an event in the DeleteStream stream.

define table RoomTypeTable (roomNo int, type string);

define stream DeleteStream (roomNumber int);

from DeleteStream
delete RoomTypeTable
    on RoomTypeTable.roomNo == roomNumber;

Update

This operator updates selected event attributes stored in a table based on a condition.

Syntax

from <input stream>
select <attribute name>, <attribute name>, ...
update <table> (for <event type>)?
    set <table>.<attribute name> = (<attribute name>|<expression>)?, <table>.<attribute name> = (<attribute name>|<expression>)?, ...
    on <condition>

The condition element specifies the basis on which events are selected to be updated. When specifying the condition, table attributes must be referred to with the table name.

You can use the set keyword to update selected attributes from the table. Here, for each assignment, the attribute specified in the left must be the table attribute, and the one specified in the right can be a stream/table attribute a mathematical operation, or other. When the set clause is not provided, all the attributes in the table are updated.

To execute an update for specific event types use the current events, expired events or the all events keyword with for as shown in the syntax. For more information, see Event Type.

Note

Table attributes must be always referred to with the table name as shown below: <table name>.<attibute name>.

Example

This Siddhi application updates the room occupancy in the RoomOccupancyTable table for each room number based on new arrivals and exits from the UpdateStream stream.

define table RoomOccupancyTable (roomNo int, people int);
define stream UpdateStream (roomNumber int, arrival int, exit int);

from UpdateStream
select *
update RoomOccupancyTable
    set RoomOccupancyTable.people = RoomOccupancyTable.people + arrival - exit
    on RoomOccupancyTable.roomNo == roomNumber;

Update or Insert

This allows you update if the event attributes already exist in the table based on a condition, or else insert the entry as a new attribute.

Syntax

from <input stream>
select <attribute name>, <attribute name>, ...
update or insert into <table> (for <event type>)?
    set <table>.<attribute name> = <expression>, <table>.<attribute name> = <expression>, ...
    on <condition>

The condition element specifies the basis on which events are selected for update. When specifying the condition, table attributes should be referred to with the table name. If a record that matches the condition does not already exist in the table, the arriving event is inserted into the table.

The set clause is only used when an update is performed during the insert/update operation. When set clause is used, the attribute to the left is always a table attribute, and the attribute to the right can be a stream/table attribute, mathematical operation or other. The attribute to the left (i.e., the attribute in the event table) is updated with the value of the attribute to the right if the given condition is met. When the set clause is not provided, all the attributes in the table are updated.

Note

When the attribute to the right is a table attribute, the operations supported differ based on the database type.

To execute update upon specific event types use the current events, expired events or the all events keyword with for as shown in the syntax. To understand more see Event Type.

Note

Table attributes should be always referred to with the table name as <table name>.<attibute name>.

Example

The following query update for events in the UpdateTable event table that have room numbers that match the same in the UpdateStream stream. When such events are found in the event table, they are updated. When a room number available in the stream is not found in the event table, it is inserted from the stream.

define table RoomAssigneeTable (roomNo int, type string, assignee string);
define stream RoomAssigneeStream (roomNumber int, type string, assignee string);

from RoomAssigneeStream
select roomNumber as roomNo, type, assignee
update or insert into RoomAssigneeTable
    set RoomAssigneeTable.assignee = assignee
    on RoomAssigneeTable.roomNo == roomNo;

In

This allows the stream to check whether the expected value exists in the table as a part of a conditional operation.

Syntax

from <input stream>[<condition> in <table>]
select <attribute name>, <attribute name>, ...
insert into <output stream>

The condition element specifies the basis on which events are selected to be compared. When constructing the condition, the table attribute must be always referred to with the table name as shown below: <table>.<attibute name>.

Example

This Siddhi application filters only room numbers that are listed in the ServerRoomTable table.

define table ServerRoomTable (roomNo int);
define stream TempStream (deviceID long, roomNo int, temp double);

from TempStream[ServerRoomTable.roomNo == roomNo in ServerRoomTable]
insert into ServerRoomTempStream;

Named Aggregation

Named aggregation allows you to obtain aggregates in an incremental manner for a specified set of time periods.

This not only allows you to calculate aggregations with varied time granularity, but also allows you to access them in an interactive manner for reports, dashboards, and for further processing. Its schema is defined via the aggregation definition.

Purpose

Named aggregation allows you to retrieve the aggregate values for different time durations. That is, it allows you to obtain aggregates such as sum, count, avg, min, max, count and distinctCount of stream attributes for durations such as sec, min, hour, etc.

This is of considerable importance in many Analytics scenarios because aggregate values are often needed for several time periods. Furthermore, this ensures that the aggregations are not lost due to unexpected system failures because aggregates can be stored in different persistence stores.

Syntax

@store(type="<store type>", ...)
@purge(enable="<true or false>",interval=<purging interval>,@retentionPeriod(<granularity> = <retention period>, ...) )
define aggregation <aggregator name>
from <input stream>
select <attribute name>, <aggregate function>(<attribute name>) as <attribute name>, ...
    group by <attribute name>
    aggregate by <timestamp attribute> every <time periods> ;

The above syntax includes the following:

Item	Description
@store	This annotation is used to refer to the data store where the calculated aggregate results are stored. This annotation is optional. When no annotation is provided, the data is stored in the in-memory store.
@purge	This annotation is used to configure purging in aggregation granularities. If this annotation is not provided, the default purging mentioned above is applied. If you want to disable automatic data purging, you can use this annotation as follows: '@purge(enable=false) /You should disable data purging if the aggregation query in included in the Siddhi application for read-only purposes.
@retentionPeriod	This annotation is used to specify the length of time the data needs to be retained when carrying out data purging. If this annotation is not provided, the default retention period is applied.
<aggregator name>	This specifies a unique name for the aggregation so that it can be referred when accessing aggregate results.
<input stream>	The stream that feeds the aggregation. Note! this stream should be already defined.
group by <attribute name>	The group by clause is optional. If it is included in a Siddhi application, aggregate values are calculated per each group by attribute. If it is not used, all the events are aggregated together.
by <timestamp attribute>	This clause is optional. This defines the attribute that should be used as the timestamp. If this clause is not used, the event time is used by default. The timestamp could be given as either a string or a long value. If it is a long value, the unix timestamp in milliseconds is expected (e.g. 1496289950000). If it is a string value, the supported formats are <yyyy>-<MM>-<dd> <HH>:<mm>:<ss> (if time is in GMT) and <yyyy>-<MM>-<dd> <HH>:<mm>:<ss> <Z> (if time is not in GMT), here the ISO 8601 UTC offset must be provided for <Z> . (e.g., +05:30, -11:00).
<time periods>	Time periods can be specified as a range where the minimum and the maximum value are separated by three dots, or as comma-separated values. e.g., A range can be specified as sec...year where aggregation is done per second, minute, hour, day, month and year. Comma-separated values can be specified as min, hour. Skipping time durations (e.g., min, day where the hour duration is skipped) when specifying comma-separated values is supported only from v4.1.1 onwards

Aggregation's granularity data holders are automatically purged every 15 minutes. When carrying out data purging, the retention period you have specified for each granularity in the named aggregation query is taken into account. The retention period defined for a granularity needs to be greater than or equal to its minimum retention period as specified in the table below. If no valid retention period is defined for a granularity, the default retention period (as specified in the table below) is applied.

Granularity	Default retention	Minimum retention
second	120 seconds	120 seconds
minute	24 hours	120 minutes
hour	30 days	25 hours
day	1 year	32 days
month	All	13 month
year	All	none

Note

Aggregation is carried out at calendar start times for each granularity with the GMT timezone

Note

The same aggregation can be defined in multiple Siddhi apps for joining, however, only one siddhi app should carry out the processing (i.e. the aggregation input stream should only feed events to one aggregation definition).

Example

This Siddhi Application defines an aggregation named TradeAggregation to calculate the average and sum for the price attribute of events arriving at the TradeStream stream. These aggregates are calculated per every time granularity in the second-year range.

define stream TradeStream (symbol string, price double, volume long, timestamp long);

@purge(enable='true', interval='10 sec',@retentionPeriod(sec='120 sec',min='24 hours',hours='30 days',days='1 year',months='all',years='all'))
define aggregation TradeAggregation
  from TradeStream
  select symbol, avg(price) as avgPrice, sum(price) as total
    group by symbol
    aggregate by timestamp every sec ... year;

Distributed Aggregation

Distributed Aggregation allows you to partially process aggregations in different shards. This allows Siddhi app in one shard to be responsible only for processing a part of the aggregation. However for this, all aggregations must be based on a common physical database(@store).

Syntax

@store(type="<store type>", ...)
@PartitionById
define aggregation <aggregator name>
from <input stream>
select <attribute name>, <aggregate function>(<attribute name>) as <attribute name>, ...
    group by <attribute name>
    aggregate by <timestamp attribute> every <time periods> ;

Following table includes the annotation to be used to enable distributed aggregation,

Item	Description
@PartitionById	If the annotation is given, then the distributed aggregation is enabled. Further this can be disabled by using enable element, @PartitionById(enable='false').

Further, following system properties are also available,

System Property	Description	Possible Values	Optional	Default Value
shardId	The id of the shard one of the distributed aggregation is running in. This should be unique to a single shard	Any string	No
partitionById	This allows user to enable/disable distributed aggregation for all aggregations running in one siddhi manager .(Available from v4.3.3)	true/false	Yesio	false

Note

ShardIds should not be changed after the first configuration in order to keep data consistency.

Join (Aggregation)

This allows a stream to retrieve calculated aggregate values from the aggregation.

Note

A join can also be performed with two streams, with a table and a stream, or with a stream against externally named windows.

Syntax

A join with aggregation is similer to the join with table, but with additional within and per clauses.

from <input stream> join <aggrigation>
  on <join condition>
  within <time range>
  per <time granularity>
select <attribute name>, <attribute name>, ...
insert into <output stream>;

Apart from constructs of table join this includes the following. Please note that the 'on' condition is optional :

Item	Description
within <time range>	This allows you to specify the time interval for which the aggregate values need to be retrieved. This can be specified by providing the start and end time separated by a comma as string or long values, or by using the wildcard string specifying the data range. For details refer examples.
per <time granularity>	This specifies the time granularity by which the aggregate values must be grouped and returned. e.g., If you specify days, the retrieved aggregate values are grouped for each day within the selected time interval.

within and per clauses also accept attribute values from the stream.
The timestamp of the aggregations can be accessed through the AGG_TIMESTAMP attribute.

Example

Following aggregation definition will be used for the examples.

define stream TradeStream (symbol string, price double, volume long, timestamp long);

define aggregation TradeAggregation
  from TradeStream
  select AGG_TIMESTAMP, symbol, avg(price) as avgPrice, sum(price) as total
    group by symbol
    aggregate by timestamp every sec ... year;

This query retrieves daily aggregations within the time range "2014-02-15 00:00:00 +05:30", "2014-03-16 00:00:00 +05:30" (Please note that +05:30 can be omitted if timezone is GMT)

define stream StockStream (symbol string, value int);

from StockStream as S join TradeAggregation as T
  on S.symbol == T.symbol
  within "2014-02-15 00:00:00 +05:30", "2014-03-16 00:00:00 +05:30"
  per "days"
select S.symbol, T.total, T.avgPrice
insert into AggregateStockStream;

This query retrieves hourly aggregations within the day 2014-02-15.

define stream StockStream (symbol string, value int);

from StockStream as S join TradeAggregation as T
  on S.symbol == T.symbol
  within "2014-02-15 **:**:** +05:30"
  per "hours"
select S.symbol, T.total, T.avgPrice
insert into AggregateStockStream;

This query retrieves all aggregations per perValue stream attribute within the time period between timestamps 1496200000000 and 1596434876000.

define stream StockStream (symbol string, value int, perValue string);

from StockStream as S join TradeAggregation as T
  on S.symbol == T.symbol
  within 1496200000000L, 1596434876000L
  per S.perValue
select S.symbol, T.total, T.avgPrice
insert into AggregateStockStream;

Supported join types

Aggregation join supports following join operations.

• Inner join (join)

  This is the default behaviour of a join operation. join is used as the keyword to join the stream with the aggregation. The output is generated only if there is a matching event in the stream and the aggregation.

• Left outer join

  The left outer join operation allows you to join a stream on left side with a aggregation on the right side based on a condition. Here, it returns all the events of left stream even if there are no matching events in the right aggregation by having null values for the attributes of the right aggregation.

• Right outer join

  This is similar to a left outer join. right outer join is used as the keyword to join a stream on right side with a aggregation on the left side based on a condition. It returns all the events of the right stream even if there are no matching events in the left aggregation.

Named Window

A named window is a window that can be shared across multiple queries. Events can be inserted to a named window from one or more queries and it can produce output events based on the named window type.

Syntax

The syntax for a named window is as follows:

define window <window name> (<attribute name> <attribute type>, <attribute name> <attribute type>, ... ) <window type>(<parameter>, <parameter>, …) <event type>;

The following parameters are configured in a table definition:

Parameter	Description
window name	The name of the window defined. (PascalCase is used for window names as a convention.)
attribute name	The schema of the window is defined by its attributes with uniquely identifiable attribute names (camelCase is used for attribute names as a convention.)
attribute type	The type of each attribute defined in the schema. This can be STRING, INT, LONG, DOUBLE, FLOAT, BOOL or OBJECT.
<window type>(<parameter>, ...)	The window type associated with the window and its parameters.
output <event type>	This is optional. Keywords such as current events, expired events and all events (the default) can be used to specify when the window output should be exposed. For more information, see Event Type.

Examples

• Returning all output when events arrive and when events expire from the window.

  In this query, the event type is not specified. Therefore, it returns both current and expired events as the output.

  define window SensorWindow (name string, value float, roomNo int, deviceID string) timeBatch(1 second);

• Returning an output only when events expire from the window.

  In this query, the event type of the window is expired events. Therefore, it only returns the events that have expired from the window as the output.

  define window SensorWindow (name string, value float, roomNo int, deviceID string) timeBatch(1 second) output expired events;

Operators on Named Windows

The following operators can be performed on named windows.

Insert

This allows events to be inserted into windows. This is similar to inserting events into streams.

Syntax

from <input stream>
select <attribute name>, <attribute name>, ...
insert into <window>

To insert only events of a specific event type, add the current events, expired events or the all events keyword between insert and into keywords (similar to how it is done for streams).

For more information, see Event Type.

Example

This query inserts all events from the TempStream stream to the OneMinTempWindow window.

define stream TempStream(tempId string, temp double);
define window OneMinTempWindow(tempId string, temp double) time(1 min);

from TempStream
select *
insert into OneMinTempWindow;

Join (Window)

To allow a stream to retrieve information from a window based on a condition.

Note

A join can also be performed with two streams, aggregation or with tables tables.

Syntax

from <input stream> join <window>
    on <condition>
select (<input stream>|<window>).<attribute name>, (<input stream>|<window>).<attribute name>, ...
insert into <output stream>

Example

This Siddhi Application performs a join count the number of temperature events having more then 40 degrees within the last 2 minutes.

define window TwoMinTempWindow (roomNo int, temp double) time(2 min);
define stream CheckStream (requestId string);

from CheckStream as C join TwoMinTempWindow as T
    on T.temp > 40
select requestId, count(T.temp) as count
insert into HighTempCountStream;

Supported join types

Window join supports following operations of a join clause.

• Inner join (join)

  This is the default behaviour of a join operation. join is used as the keyword to join two windows or a stream with a window. The output is generated only if there is a matching event in both stream/window.

• Left outer join

  The left outer join operation allows you to join two windows or a stream with a window to be merged based on a condition. Here, it returns all the events of left stream/window even if there are no matching events in the right stream/window by having null values for the attributes of the right stream/window.

• Right outer join

  This is similar to a left outer join. Right outer join is used as the keyword to join two windows or a stream with a window. It returns all the events of the right stream/window even if there are no matching events in the left stream/window.

• Full outer join

  The full outer join combines the results of left outer join and right outer join. full outer join is used as the keyword to join two windows or a stream with a window. Here, output event are generated for each incoming event even if there are no matching events in the other stream/window.

From

A window can be an input to a query, similar to streams.

Note !!! When window is used as an input to a query, another window cannot be applied on top of this.

Syntax

from <window>
select <attribute name>, <attribute name>, ...
insert into <output stream>

Example This Siddhi Application calculates the maximum temperature within the last 5 minutes.

define window FiveMinTempWindow (roomNo int, temp double) time(5 min);


from FiveMinTempWindow
select max(temp) as maxValue, roomNo
insert into MaxSensorReadingStream;

Trigger

Triggers allow events to be periodically generated. Trigger definition can be used to define a trigger. A trigger also works like a stream with a predefined schema.

Purpose

For some use cases the system should be able to periodically generate events based on a specified time interval to perform some periodic executions.

A trigger can be performed for a 'start' operation, for a given <time interval>, or for a given '<cron expression>'.

Syntax

The syntax for a trigger definition is as follows.

define trigger <trigger name> at ('start'| every <time interval>| '<cron expression>');

Similar to streams, triggers can be used as inputs. They adhere to the following stream definition and produce the triggered_time attribute of the long type.

define stream <trigger name> (triggered_time long);

The following types of triggeres are currently supported:

Trigger type	Description
'start'	An event is triggered when Siddhi is started.
every <time interval>	An event is triggered periodically at the given time interval.
'<cron expression>'	An event is triggered periodically based on the given cron expression. For configuration details, see quartz-scheduler.

Examples

• Triggering events regularly at specific time intervals

  The following query triggers events every 5 minutes.

     define trigger FiveMinTriggerStream at every 5 min;

• Triggering events at a specific time on specified days

  The following query triggers an event at 10.15 AM on every weekdays.

     define trigger FiveMinTriggerStream at '0 15 10 ? * MON-FRI';

Script

Scripts allow you to write functions in other programming languages and execute them within Siddhi queries. Functions defined via scripts can be accessed in queries similar to any other inbuilt function. Function definitions can be used to define these scripts.

Function parameters are passed into the function logic as Object[] and with the name data .

Purpose

Scripts allow you to define a function operation that is not provided in Siddhi core or its extension. It is not required to write an extension to define the function logic.

Syntax

The syntax for a Script definition is as follows.

define function <function name>[<language name>] return <return type> {
    <operation of the function>
};

The following parameters are configured when defining a script.

Parameter	Description
function name	The name of the function (camelCase is used for the function name) as a convention.
language name	The name of the programming language used to define the script, such as javascript, r and scala.
return type	The attribute type of the function’s return. This can be int, long, float, double, string, bool or object. Here the function implementer should be responsible for returning the output attribute on the defined return type for proper functionality.
operation of the function	Here, the execution logic of the function is added. This logic should be written in the language specified under the language name, and it should return the output in the data type specified via the return type parameter.

Examples

This query performs concatenation using JavaScript, and returns the output as a string.

define function concatFn[javascript] return string {
    var str1 = data[0];
    var str2 = data[1];
    var str3 = data[2];
    var responce = str1 + str2 + str3;
    return responce;
};

define stream TempStream(deviceID long, roomNo int, temp double);

from TempStream
select concatFn(roomNo,'-',deviceID) as id, temp
insert into DeviceTempStream;

Store Query

Siddhi store queries are a set of on-demand queries that can be used to perform operations on Siddhi tables, windows, and aggregators.

Purpose

Store queries allow you to execute the following operations on Siddhi tables, windows, and aggregators without the intervention of streams.

Queries supported for tables:

• SELECT
• INSERT
• DELETE
• UPDATE
• UPDATE OR INSERT

Queries supported for windows and aggregators:

• SELECT

This is be done by submitting the store query to the Siddhi application runtime using its query() method.

In order to execute store queries, the Siddhi application of the Siddhi application runtime you are using, should have a store defined, which contains the table that needs to be queried.

Example

If you need to query the table named RoomTypeTable the it should have been defined in the Siddhi application.

In order to execute a store query on RoomTypeTable, you need to submit the store query using query() method of SiddhiAppRuntime instance as below.

siddhiAppRuntime.query(<store query>);

(Table/Window) Select

The SELECT store query retrieves records from the specified table or window, based on the given condition.

Syntax

from <table/window>
<on condition>?
select <attribute name>, <attribute name>, ...
<group by>?
<having>?
<order by>?
<limit>?

Example

This query retrieves room numbers and types of the rooms starting from room no 10.

from roomTypeTable
on roomNo >= 10;
select roomNo, type

(Aggregation) Select

The SELECT store query retrieves records from the specified aggregation, based on the given condition, time range, and granularity.

Syntax

from <aggregation>
<on condition>?
within <time range>
per <time granularity>
select <attribute name>, <attribute name>, ...
<group by>?
<having>?
<order by>?
<limit>?

Example

Following aggregation definition will be used for the examples.

define stream TradeStream (symbol string, price double, volume long, timestamp long);

define aggregation TradeAggregation
  from TradeStream
  select symbol, avg(price) as avgPrice, sum(price) as total
    group by symbol
    aggregate by timestamp every sec ... year;

This query retrieves daily aggregations within the time range "2014-02-15 00:00:00 +05:30", "2014-03-16 00:00:00 +05:30" (Please note that +05:30 can be omitted if timezone is GMT)

from TradeAggregation
  within "2014-02-15 00:00:00 +05:30", "2014-03-16 00:00:00 +05:30"
  per "days"
select symbol, total, avgPrice ;

This query retrieves hourly aggregations of "FB" symbol within the day 2014-02-15.

from TradeAggregation
  on symbol == "FB"
  within "2014-02-15 **:**:** +05:30"
  per "hours"
select symbol, total, avgPrice;

Insert

This allows you to insert a new record to the table with the attribute values you define in the select section.

Syntax

select <attribute name>, <attribute name>, ...
insert into <table>;

Example

This store query inserts a new record to the table RoomOccupancyTable, with the specified attribute values.

select 10 as roomNo, 2 as people
insert into RoomOccupancyTable

Delete

The DELETE store query deletes selected records from a specified table.

Syntax

<select>?  
delete <table>  
on <conditional expresssion>

The condition element specifies the basis on which records are selected to be deleted.

Note

Table attributes must always be referred to with the table name as shown below:
<table name>.<attibute name>.

Example

In this example, query deletes a record in the table named RoomTypeTable if it has value for the roomNo attribute that matches the value for the roomNumber attribute of the selection which has 10 as the actual value.

select 10 as roomNumber
delete RoomTypeTable
on RoomTypeTable.roomNo == roomNumber;

delete RoomTypeTable
on RoomTypeTable.roomNo == 10;

Update

The UPDATE store query updates selected attributes stored in a specific table, based on a given condition.

Syntax

select <attribute name>, <attribute name>, ...?
update <table>
    set <table>.<attribute name> = (<attribute name>|<expression>)?, <table>.<attribute name> = (<attribute name>|<expression>)?, ...
    on <condition>

The condition element specifies the basis on which records are selected to be updated. When specifying the condition, table attributes must be referred to with the table name.

You can use the set keyword to update selected attributes from the table. Here, for each assignment, the attribute specified in the left must be the table attribute, and the one specified in the right can be a stream/table attribute a mathematical operation, or other. When the set clause is not provided, all the attributes in the table are updated.

Note

Table attributes must always be referred to with the table name as shown below:
<table name>.<attibute name>.

Example

The following query updates the room occupancy by increasing the value of people by 1, in the RoomOccupancyTable table for each room number greater than 10.

select 10 as roomNumber, 1 as arrival
update RoomTypeTable
    set RoomTypeTable.people = RoomTypeTable.people + arrival
    on RoomTypeTable.roomNo == roomNumber;

update RoomTypeTable
    set RoomTypeTable.people = RoomTypeTable.people + 1
    on RoomTypeTable.roomNo == 10;

Update or Insert

This allows you to update selected attributes if a record that meets the given conditions already exists in the specified table. If a matching record does not exist, the entry is inserted as a new record.

Syntax

select <attribute name>, <attribute name>, ...
update or insert into <table>
    set <table>.<attribute name> = <expression>, <table>.<attribute name> = <expression>, ...
    on <condition>

The condition element specifies the basis on which records are selected for update. When specifying the condition, table attributes should be referred to with the table name. If a record that matches the condition does not already exist in the table, the arriving event is inserted into the table.

The set clause is only used when an update is performed during the insert/update operation. When set clause is used, the attribute to the left is always a table attribute, and the attribute to the right can be a stream/table attribute, mathematical operation or other. The attribute to the left (i.e., the attribute in the event table) is updated with the value of the attribute to the right if the given condition is met. When the set clause is not provided, all the attributes in the table are updated.

Note

Table attributes must always be referred to with the table name as shown below:
<table name>.<attibute name>.

Example

The following query tries to update the records in the RoomAssigneeTable table that have room numbers that match the same in the selection. If such records are not found, it inserts a new record based on the values provided in the selection.

select 10 as roomNo, "single" as type, "abc" as assignee
update or insert into RoomAssigneeTable
    set RoomAssigneeTable.assignee = assignee
    on RoomAssigneeTable.roomNo == roomNo;

Extensions

Siddhi supports an extension architecture to enhance its functionality by incorporating other libraries in a seamless manner.

Purpose

Extensions are supported because, Siddhi core cannot have all the functionality that's needed for all the use cases, mostly use cases require different type of functionality, and for some cases there can be gaps and you need to write the functionality by yourself.

All extensions have a namespace. This is used to identify the relevant extensions together, and to let you specifically call the extension.

Syntax

Extensions follow the following syntax;

<namespace>:<function name>(<parameter>, <parameter>, ... )

The following parameters are configured when referring a script function.

Parameter	Description
namespace	Allows Siddhi to identify the extension without conflict
function name	The name of the function referred.
parameter	The function input parameter for function execution.

Extension Types

Siddhi supports following extension types:

• Function

  For each event, it consumes zero or more parameters as input parameters and returns a single attribute. This can be used to manipulate existing event attributes to generate new attributes like any Function operation.

  This is implemented by extending io.siddhi.core.executor.function.FunctionExecutor.

  Example :

  math:sin(x)

  Here, the sin function of math extension returns the sin value for the x parameter.

• Aggregate Function

  For each event, it consumes zero or more parameters as input parameters and returns a single attribute with aggregated results. This can be used in conjunction with a window in order to find the aggregated results based on the given window like any Aggregate Function operation.

  This is implemented by extending io.siddhi.core.query.selector.attribute.aggregator.AttributeAggregatorExecutor.

  Example :

  custom:std(x)

  Here, the std aggregate function of custom extension returns the standard deviation of the x value based on its assigned window query.

• Window

  This allows events to be collected, generated, dropped and expired anytime without altering the event format based on the given input parameters, similar to any other Window operator.

  This is implemented by extending io.siddhi.core.query.processor.stream.window.WindowProcessor.

  Example :

  custom:unique(key)

  Here, the unique window of the custom extension retains one event for each unique key parameter.

• Stream Function

  This allows events to be generated or dropped only during event arrival and altered by adding one or more attributes to it.

  This is implemented by extending io.siddhi.core.query.processor.stream.function.StreamFunctionProcessor.

  Example :

  custom:pol2cart(theta,rho)

  Here, the pol2cart function of the custom extension returns all the events by calculating the cartesian coordinates x & y and adding them as new attributes to the events.

• Stream Processor

  This allows events to be collected, generated, dropped and expired anytime by altering the event format by adding one or more attributes to it based on the given input parameters.

  Implemented by extending io.siddhi.core.query.processor.stream.StreamProcessor.

  Example :

  custom:perMinResults(<parameter>, <parameter>, ...)

  Here, the perMinResults function of the custom extension returns all events by adding one or more attributes to the events based on the conversion logic. Altered events are output every minute regardless of event arrivals.

• Sink

  Sinks provide a way to publish Siddhi events to external systems in the preferred data format. Sinks publish events from the streams via multiple transports to external endpoints in various data formats.

  Implemented by extending io.siddhi.core.stream.output.sink.Sink.

  Example :

  @sink(type='sink_type', static_option_key1='static_option_value1')

  To configure a stream to publish events via a sink, add the sink configuration to a stream definition by adding the @sink annotation with the required parameter values. The sink syntax is as above

• Source

  Source allows Siddhi to consume events from external systems, and map the events to adhere to the associated stream. Sources receive events via multiple transports and in various data formats, and direct them into streams for processing.

  Implemented by extending io.siddhi.core.stream.input.source.Source.

  Example :

  @source(type='source_type', static.option.key1='static_option_value1')

  To configure a stream that consumes events via a source, add the source configuration to a stream definition by adding the @source annotation with the required parameter values. The source syntax is as above

• Store

  You can use Store extension type to work with data/events stored in various data stores through the table abstraction. You can find more information about these extension types under the heading 'Extension types' in this document.

  Implemented by extending io.siddhi.core.table.record.AbstractRecordTable.

• Script

  Scripts allow you to define a function operation that is not provided in Siddhi core or its extension. It is not required to write an extension to define the function logic. Scripts allow you to write functions in other programming languages and execute them within Siddhi queries. Functions defined via scripts can be accessed in queries similar to any other inbuilt function.

  Implemented by extending io.siddhi.core.function.Script.

• Source Mapper

  Each @source configuration has a mapping denoted by the @map annotation that converts the incoming messages format to Siddhi events.The type parameter of the @map defines the map type to be used to map the data. The other parameters to be configured depends on the mapper selected. Some of these parameters are optional.

  Implemented by extending io.siddhi.core.stream.output.sink.SourceMapper.

  Example :

  @map(type='map_type', static_option_key1='static_option_value1')

• Sink Mapper

  Each @sink configuration has a mapping denoted by the @map annotation that converts the outgoing Siddhi events to configured messages format.The type parameter of the @map defines the map type to be used to map the data. The other parameters to be configured depends on the mapper selected. Some of these parameters are optional.

  Implemented by extending io.siddhi.core.stream.output.sink.SinkMapper.

  Example :

  @map(type='map_type', static_option_key1='static_option_value1')

Example

A window extension created with namespace foo and function name unique can be referred as follows:

from StockExchangeStream[price >= 20]#window.foo:unique(symbol)
select symbol, price
insert into StockQuote

Available Extensions

Siddhi currently has several pre written extensions that are available here

We value your contribution on improving Siddhi and its extensions further.

Writing Custom Extensions

Custom extensions can be written in order to cater use case specific logic that are not available in Siddhi out of the box or as an existing extension.

There are five types of Siddhi extensions that you can write to cater your specific use cases. These extension types and the related maven archetypes are given below. You can use these archetypes to generate Maven projects for each extension type.

• Follow the procedure for the required archetype, based on your project:

Note

When using the generated archetype please make sure you complete the @Extension annotation with proper values. This annotation will be used to identify and document the extension, hence your extension will not work without @Extension annotation.

siddhi-execution

Siddhi-execution provides following extension types:

• Function
• Aggregate Function
• Stream Function
• Stream Processor
• Window

You can use one or more from above mentioned extension types and implement according to your requirement.

For more information about these extension types, see Extension Types.

To install and implement the siddhi-io extension archetype, follow the procedure below:

1. Issue the following command from your CLI.

           mvn archetype:generate
               -DarchetypeGroupId=io.siddhi.extension.archetype
               -DarchetypeArtifactId=siddhi-archetype-execution
               -DgroupId=io.siddhi.extension.execution
               -Dversion=1.0.0-SNAPSHOT

2. Enter the mandatory properties prompted, please see the description for all properties below.

   Properties	Description	Mandatory	Default Value
   _nameOfFunction	Name of the custom function to be created	Y	-
   _nameSpaceOfFunction	Namespace of the function, used to grouped similar custom functions	Y	-
   groupIdPostfix	Namespace of the function is added as postfix to the groupId as a convention	N
   artifactId	Artifact Id of the project	N	siddhi-execution-
   classNameOfAggregateFunction	Class name of the Aggregate Function	N	$
   classNameOfFunction	Class name of the Function	N	$
   classNameOfStreamFunction	Class name of the Stream Function	N	$
   classNameOfStreamProcessor	Class name of the Stream Processor	N	$
   classNameOfWindow	Class name of the Window	N	$

3. To confirm that all property values are correct, type Y in the console. If not, press N.

siddhi-io

Siddhi-io provides following extension types:

• Sink
• Source

You can use one or more from above mentioned extension types and implement according to your requirement. siddhi-io is generally used to work with IO operations as follows: * The Source extension type gets inputs to your Siddhi application. * The Sink extension publishes outputs from your Siddhi application.

For more information about these extension types, see Extension Types.

To implement the siddhi-io extension archetype, follow the procedure below:

1. Issue the following command from your CLI.

          mvn archetype:generate
              -DarchetypeGroupId=io.siddhi.extension.archetype
              -DarchetypeArtifactId=siddhi-archetype-io
              -DgroupId=io.siddhi.extension.io
              -Dversion=1.0.0-SNAPSHOT

2. Enter the mandatory properties prompted, please see the description for all properties below.

   Properties	Description	Mandatory	Default Value
   _IOType	Type of IO for which Siddhi-io extension is written	Y	-
   groupIdPostfix	Type of the IO is added as postfix to the groupId as a convention	N
   artifactId	Artifact Id of the project	N	siddhi-io-
   classNameOfSink	Class name of the Sink	N
   classNameOfSource	Class name of the Source	N

3. To confirm that all property values are correct, type Y in the console. If not, press N.

siddhi-map

Siddhi-map provides following extension types,

• Sink Mapper
• Source Mapper

You can use one or more from above mentioned extension types and implement according to your requirement as follows.

• The Source Mapper maps events to a predefined data format (such as XML, JSON, binary, etc), and publishes them to external endpoints (such as E-mail, TCP, Kafka, HTTP, etc).
• The Sink Mapper also maps events to a predefined data format, but it does it at the time of publishing events from a Siddhi application.

For more information about these extension types, see Extension Types.

To implement the siddhi-map extension archetype, follow the procedure below:

1. Issue the following command from your CLI.

           mvn archetype:generate
               -DarchetypeGroupId=io.siddhi.extension.archetype
               -DarchetypeArtifactId=siddhi-archetype-map
               -DgroupId=io.siddhi.extension.map
               -Dversion=1.0.0-SNAPSHOT

2. Enter the mandatory properties prompted, please see the description for all properties below.

   Properties	Description	Mandatory	Default Value
   _mapType	Type of Mapper for which Siddhi-map extension is written	Y	-
   groupIdPostfix	Type of the Map is added as postfix to the groupId as a convention	N
   artifactId	Artifact Id of the project	N	siddhi-map-
   classNameOfSinkMapper	Class name of the Sink Mapper	N
   classNameOfSourceMapper	Class name of the Source Mapper	N

3. To confirm that all property values are correct, type Y in the console. If not, press N.

siddhi-script

Siddhi-script provides the Script extension type.

The script extension type allows you to write functions in other programming languages and execute them within Siddhi queries. Functions defined via scripts can be accessed in queries similar to any other inbuilt function.

For more information about these extension types, see Extension Types.

To implement the siddhi-script extension archetype, follow the procedure below:

1. Issue the following command from your CLI.

          mvn archetype:generate
              -DarchetypeGroupId=io.siddhi.extension.archetype
              -DarchetypeArtifactId=siddhi-archetype-script
              -DgroupId=io.siddhi.extension.script
              -Dversion=1.0.0-SNAPSHOT

2. Enter the mandatory properties prompted, please see the description for all properties below.

   Properties	Description	Mandatory	Default Value
   _nameOfScript	Name of Custom Script for which Siddhi-script extension is written	Y	-
   groupIdPostfix	Name of the Script is added as postfix to the groupId as a convention	N
   artifactId	Artifact Id of the project	N	siddhi-script-
   classNameOfScript	Class name of the Script	N	Eval

3. To confirm that all property values are correct, type Y in the console. If not, press N.

siddhi-store

Siddhi-store provides the Store extension type.

The Store extension type allows you to work with data/events stored in various data stores through the table abstraction.

For more information about these extension types, see Extension Types.

To implement the siddhi-store extension archetype, follow the procedure below:

1. Issue the following command from your CLI.

          mvn archetype:generate
             -DarchetypeGroupId=io.siddhi.extension.archetype
             -DarchetypeArtifactId=siddhi-archetype-store
             -DgroupId=io.siddhi.extension.store
             -Dversion=1.0.0-SNAPSHOT

2. Enter the mandatory properties prompted, please see the description for all properties below.

   Properties	Description	Mandatory	Default Value
   _storeType	Type of Store for which Siddhi-store extension is written	Y	-
   groupIdPostfix	Type of the Store is added as postfix to the groupId as a convention	N
   artifactId	Artifact Id of the project	N	siddhi-store-
   className	Class name of the Store	N

3. To confirm that all property values are correct, type Y in the console. If not, press N.

Configuring and Monitoring Siddhi Applications

Multi-threading and Asynchronous Processing

When @Async annotation is added to the Streams it enable the Streams to introduce asynchronous and multi-threading behaviour.

@Async(buffer.size='256', workers='2', batch.size.max='5')
define stream <stream name> (<attribute name> <attribute type>, <attribute name> <attribute type>, ... );

The following elements are configured with this annotation.

Annotation	Description	Default Value
buffer.size	The size of the event buffer that will be used to handover the execution to other threads.	-
workers	Number of worker threads that will be be used to process the buffered events.	1
batch.size.max	The maximum number of events that will be processed together by a worker thread at a given time.	buffer.size

Statistics

Use @app:statistics app level annotation to evaluate the performance of an application, you can enable the statistics of a Siddhi application to be published. This is done via the @app:statistics annotation that can be added to a Siddhi application as shown in the following example.

@app:statistics(reporter = 'console')

The following elements are configured with this annotation.

Annotation	Description	Default Value
reporter	The interface in which statistics for the Siddhi application are published. Possible values are as follows: console jmx	console
interval	The time interval (in seconds) at which the statistics for the Siddhi application are reported.	60
include	If this parameter is added, only the types of metrics you specify are included in the reporting. The required metric types can be specified as a comma-separated list. It is also possible to use wild cards	All (.)

The metrics are reported in the following format. io.siddhi.SiddhiApps.<SiddhiAppName>.Siddhi.<Component Type>.<Component Name>. <Metrics name>

The following table lists the types of metrics supported for different Siddhi application component types.

Component Type	Metrics Type
Stream	Throughput The size of the buffer if parallel processing is enabled via the @async annotation.
Trigger	Throughput (Trigger and Stream)
Source	Throughput
Sink	Throughput
Mapper	Latency Input/output throughput
Table	Memory Throughput (For all operations) Throughput (For all operations)
Query	Memory Latency
Window	Throughput (For all operations) Latency (For all operation)
Partition	Throughput (For all operations) Latency (For all operation)

e.g., the following is a Siddhi application that includes the @app annotation to report performance statistics.

@App:name('TestMetrics')
@App:Statistics(reporter = 'console')

define stream TestStream (message string);

@info(name='logQuery')
from TestSream#log("Message:")
insert into TempSream;

Statistics are reported for this Siddhi application as shown in the extract below.

Click to view the extract

11/26/17 8:01:20 PM ============================================================ -- Gauges ---------------------------------------------------------------------- io.siddhi.SiddhiApps.TestMetrics.Siddhi.Queries.logQuery.memory value = 5760 io.siddhi.SiddhiApps.TestMetrics.Siddhi.Streams.TestStream.size value = 0 -- Meters ---------------------------------------------------------------------- io.siddhi.SiddhiApps.TestMetrics.Siddhi.Sources.TestStream.http.throughput count = 0 mean rate = 0.00 events/second 1-minute rate = 0.00 events/second 5-minute rate = 0.00 events/second 15-minute rate = 0.00 events/second io.siddhi.SiddhiApps.TestMetrics.Siddhi.Streams.TempSream.throughput count = 2 mean rate = 0.04 events/second 1-minute rate = 0.03 events/second 5-minute rate = 0.01 events/second 15-minute rate = 0.00 events/second io.siddhi.SiddhiApps.TestMetrics.Siddhi.Streams.TestStream.throughput count = 2 mean rate = 0.04 events/second 1-minute rate = 0.03 events/second 5-minute rate = 0.01 events/second 15-minute rate = 0.00 events/second -- Timers ---------------------------------------------------------------------- io.siddhi.SiddhiApps.TestMetrics.Siddhi.Queries.logQuery.latency count = 2 mean rate = 0.11 calls/second 1-minute rate = 0.34 calls/second 5-minute rate = 0.39 calls/second 15-minute rate = 0.40 calls/second min = 0.61 milliseconds max = 1.08 milliseconds mean = 0.84 milliseconds stddev = 0.23 milliseconds median = 0.61 milliseconds 75% <= 1.08 milliseconds 95% <= 1.08 milliseconds 98% <= 1.08 milliseconds 99% <= 1.08 milliseconds 99.9% <= 1.08 milliseconds

Event Playback

When @app:playback annotation is added to the app, the timestamp of the event (specified via an attribute) is treated as the current time. This results in events being processed faster. The following elements are configured with this annotation.

Annotation	Description
idle.time	If no events are received during a time interval specified (in milliseconds) via this element, the Siddhi system time is incremented by a number of seconds specified via the increment element.
increment	The number of seconds by which the Siddhi system time must be incremented if no events are received during the time interval specified via the idle.time element.

e.g., In the following example, the Siddhi system time is incremented by two seconds if no events arrive for a time interval of 100 milliseconds.

@app:playback(idle.time = '100 millisecond', increment = '2 sec')

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