Siddhi Application Overview

A Siddhi application (.siddhi) file is the deployment artifact containing the Stream Processing logic for WSO2 Stream Processor. 

The format of a Siddhi application is as follows:

@App:name("ReceiveAndCount")
@App:description('Receive events via HTTP transport and view the output on the console')

/* 
	Sample Siddhi App block comment
*/

-- Sample Siddhi App line comment

@Source(type = 'http',
        receiver.url='http://localhost:8006/productionStream',
        basic.auth.enabled='false',
        @map(type='json'))
define stream SweetProductionStream (name string, amount double);

@sink(type='log')
define stream TotalCountStream (totalCount long);

-- Count the incoming events
@info(name='query1')
from SweetProductionStream
select count() as totalCount
insert into TotalCountStream;

Basic information about Siddhi applications

Following are some important things to note about Siddhi applications:

• The file name of each Siddhi application must be equal to the name specified via the @App:name() annotation.
  e.g., In the sample Siddhi application given above, the application name is ReceiveAndCount. Therefore, the Siddhi file name must be ReceiveAndCount.Siddhi. 

• It is optional to provide a description via the @App:description() annotation.

• The definitions of the required streams, windows, tables, triggers and aggregations need to be included before the Siddhi queries.
  e.g., In the above sample Siddhi file, the streams  (lines 14 and 17) are defined before the queries (lines 21-23).
• Siddhi can infer the definition of the streams. It is not required to define all the streams. However, if annotations need to be added to a stream, that stream must be defined.
• In the above sample, lines 4-6 nd 8 demonstrate how to nclude comments within Siddhi applications.

 For more information about Siddhi applications, see  Siddhi Application at Siddhi Streaming SQL Guide.

Common components of a Siddhi application

This section explains the common types of definitions and queries that are included in  Siddhi application:

Queries

Queries define the logical processing and selections that must be executed for streaming events. They consume from the pre-defined streams/ windows/ tables/ aggregations, process them in a streaming manner, and insert the output to another stream, window or table. For more information about Siddhi queries, see Queries at Siddhi Streaming SQL Guide .

Streams

Streams are one of the core elements of a stream processing application. A stream is a logical series of events ordered in time with a uniquely identifiable name and set of defined attributes with specific data types defining its schema. In Siddhi, streams are defined by giving it a name and the set of attributes it contains. Lines 14 and 17 of the above sample are examples of defined streams. For more information on Siddhi streams, see Streams at Siddhi Streaming SQL Guide.

Tables

A table is a collection of events that can be used to store streaming data. The capability to store events in a table allows you to query for stored events later or process them again with a different stream. The generic table concept holds here as well, however, Siddhi tables also support numerous table specific data manipulations such as defining primary keys, indexing, etc. For more information on Siddhi tables, see Storage Integration and Tables at Siddhi Streaming SQL Guide .

Windows

Windows allow you to retain a collection of streaming events based on a time duration (time window), or a given number of events (length window). It allows you to process events that fall into the defined window or expire from it. For more information on Siddhi windows, see Windows at Siddhi Streaming SQL Guide.

Aggregations

Aggregation allows you to aggregate streaming events for different time granularities. The time granularities supported are seconds, minutes, hours, days, months and years. Aggregations such as sum, min, avg can be calculated for the desired duration(s) via Siddhi aggregation. For more information on Siddhi aggregations, see Aggregations at Siddhi Streaming SQL Guide.