Amazon Kinesis Data Analytics
Developer Guide

Windowed Queries

SQL queries in your application code execute continuously over in-application streams. And, an in-application stream represents unbounded data that is flowing continuously through your application. Therefore, to get result sets from this continuously updating input, you often bound queries using a window defined in terms of time or rows. These are also called windowed SQL.

For a time-based windowed query, you specify the window size in terms of time (for example, a one-minute window). This requires a timestamp column in your in-application stream that is monotonically increasing (timestamp for a new row is greater than or equal to previous row). Amazon Kinesis Data Analytics provides such a timestamp column called ROWTIME for each in-application stream. You can use this column when specifying time-based queries. For your application, you might choose some other timestamp option. For more information, see Timestamps and the ROWTIME Column.

For a row-based windowed query, you specify window size in terms of the number of rows.

You can specify a query to process records in a tumbling window, sliding window, or stagger window manner, depending on your application needs. Kinesis Data Analytics supports the following window types:

  • Stagger Windows: A query that aggregates data using keyed time-based windows that open as data arrives. The keys allow for multiple overlapping windows. This is the recommended way to aggregate data using time-based windows, because Stagger Windows reduce late or out-of-order data compared to Tumbling windows.

  • Tumbling Windows: A query that aggregates data using distinct time-based windows that open and close at regular intervals.

  • Sliding Windows: A query that aggregates data continuously, using a fixed time or rowcount interval.