Tumbling Window Example

This example demonstrates how to summarize data for a specific number of events and how to do that summarization for batches of events.

To demonstrate this, assume that a factory manager wants to track the maximum production in every 10 production runs. For this purpose, a stream worker can be created as follows:

Stream Worker Code

@App:name('MaximumProductionApp') 
@App:qlVersion("2")

-- Define an input stream to capture details about each production run.
CREATE STREAM ProductionStream (name string, amount long);

-- Define an output stream to publish the maximum production for the last ten runs.
CREATE SINK DetectedMaximumProductionStream WITH (type='log', prefix='Maximum production in last 10 runs') (name string, maximumValue long);

-- Query to process events
INSERT INTO DetectedMaximumProductionStream
SELECT name, MAX(amount) AS maximumValue
FROM ProductionStream WINDOW TUMBLING_LENGTH(10)
GROUP BY name;

Input

Events from the ProductionStream include the product name and amount of each production run.

Output

A sink annotation is connected to the output stream to log the output events. You can view the logged events by clicking on the Log Viewer on the stream worker editor tab.

Processing

INSERT INTO defines where the processed results are sent, which in this case, is DetectedMaximumProductionStream.

The maximum is derived by applying the max() function is applied to the amount attribute to derive the maximum value of the ProductionStream input stream.

To specify that the processing done as defined via the SELECT statement applies to a tumbling length window, the FROM clause includes the tumbling (batch) window.

WINDOW TUMBLING_LENGTH indicates that the window added is a length window that considers events in batches when determining subsets. The number of events in each batch is 10.

The GROUP BY clause groups results by the product name.