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Sliding Window Example

This example demonstrates how to summarize data for a short term based on time and how to do a summarization in a sliding manner.

Imagine a factory manager who wants to be able to check the production for the last hour at any given time. Every event represents a production run. For this purpose, a stream worker can be created as follows:

Stream Worker Code


-- 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 production for the last hour.
CREATE SINK PastHourProductionStream WITH (type='log', prefix='Production totals over the past hour:') (name string, pastHourTotal long);

-- Query to process events
INSERT INTO PastHourProductionStream
SELECT name, SUM(amount) AS pastHourTotal
FROM ProductionStream WINDOW SLIDING_TIME(1 hour)
GROUP BY name;


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


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.


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

The total is derived by applying the sum() function to the amount attribute of the ProductionStream input stream.

To specify that the processing done as defined via the SELECT statement applies to a time window, the FROM clause includes the time window.

WINDOW SLIDING_TIME indicates that the window added is a time window. The time considered is one hour. The window is a sliding window that considers the last hour at any given time.

For example, when the stream processor calculates the total production during the time 13.00-14.00, next it calculates the total production during the time 13.01-14.01 after the 13.01 minute as elapsed.

The GROUP BY clause groups results by the product name.