The following functions shipped with Siddhi, consume zero, one or more parameters from streaming events and produce a desired value as output. These functions are executed per event. For more information on Siddhi functions, please refer to Siddhi Query Guide - Functions. More functions are made available as Siddhi Extensions.
Filters are applied to input data received in streams to filter information based on given conditions. For more information, see Siddhi Query Guide - Filters.
e.g., Filtering cash withdrawals from an ATM machine where the withdrawal amount is greater tha $100, and the withdrawal data is between 01/12/2017-15/12/2017.
This window feature differs from the Defined Window concept elaborated in Siddhi Application Overview due to this being specific to a single query only. If a window is to be shared among queries, the Defined Window must be used
For more information about windows, see Siddhi Query Guide - Window.
Aggregation functions allow executing aggregations such as sum, avg, min, etc. on a set of events grouped by a window. If a window is not defined, the aggregation(s) would be calculated by considering all the events arriving at a stream.
For more information on aggregate function, please refer to Siddhi Query Guide - Aggregate Functions.
With the group by functionality, events could be grouped based on a certain attribute, when performing aggregations.
For more information on group by, please refer to Siddhi Query Guide - Group By.
Having allows to filter events after processing the select statement.
For more information on having clause, please refer to Siddhi Query Guide - Having.
Join is an important feature of Siddhi, which allows combining pair of streams, pair of windows, stream with window, stream/ window with a table and stream/window with an aggregation
For more information on join queries, please refer to Siddhi Query Guide - Join.
Output Rate Limiting
Output rate limiting allows queries to output events periodically based on a specified condition. This helps to limit continuously sending events as output.
For more information on output rate limiting, please refer to Siddhi Query Guide - Output Rate Limiting
Partitioning in Siddhi allows to logically seperate events arriving at a stream, and to process them separately, in parallel.
Partitioning can be done based on an attribute value as above, or based on a condition. For more information on partitioning, please refer to Siddhi Query Guide - Partitioning
Triggers could be used to get events generated by the system itself, based on some time duration.
Trigger could be defined as a time interval, a cron job or to generate an event when Siddhi is started. For more information on triggers, please refer to Siddhi Query Guide - Trigger
Scripts allow to define function operations in a different programming language. An example is as follows.
For more information on scripts, please refer to Siddhi Query Guide - Script