This documentation is for WSO2 Stream Processor 4.2.0. View documentation for the latest release.

All docs This doc

Versions Compared

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.

...

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

Common elements of a Siddhi application

...

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.


The elements mentioned above work together in a Siddhi application to form an event flow. To understand how the elements os a Siddhi application are interconnected, you can view the design view of a Siddhi application. For more information, see Stream Processor Studio Overview.

...