This topic describes the window functions, time attributes, and window types that Flink SQL supports.
Flink SQL supports aggregation over infinite windows. Windows do not need to be explicitly defined in SQL statements. Flink SQL also supports aggregation over a specific window. For example, to count the number of users who clicked a URL in the last minute, you can define a window to collect data about user clicks for the previous minute. Then, you can compute the data in the window to obtain the result.
Flink SQL supports window aggregates and over aggregates. This topic describes window aggregates. Window aggregates support the windows that are defined based on the following two time attributes: event time and processing time. For each time attribute, Flink SQL supports three window types: tumbling window, sliding window, and session window.
- The event time attribute specifies the time when each data entry is created. It is provided in the schema.
- The processing time attribute specifies the local system time at which the system processes an event.
The event time attribute of the rowtime column no longer takes effect after a window
operation is complete. You can use a helper function such as
SESSION_ROWTIME to obtain
max(rowtime) of the rowtime column in a window. You can use the obtained value as the rowtime
of the time window. The value is
window_end - 1 and is of the TIMESTAMP data type. The TIMESTAMP value has the rowtime attribute.
For example, if the time span of a window is
00:14:59.999 is returned.
CREATE TEMPORARY TABLE user_clicks( username varchar, click_url varchar, eventtime varchar, ts AS TO_TIMESTAMP(eventtime), WATERMARK FOR ts AS ts - INTERVAL '2' SECOND -- Define a watermark for the rowtime. ) with ( 'connector'='sls', ... ); CREATE TEMPORARY TABLE tumble_output( window_start TIMESTAMP, window_end TIMESTAMP, username VARCHAR, clicks BIGINT ) with ( 'connector'='datahub' -- Log Service allows you to export only VARCHAR-type DDL statements. Therefore, DataHub is used for storage. ... ); CREATE TEMPORARY VIEW one_minute_window_output AS SELECT TUMBLE_ROWTIME(ts, INTERVAL '1' MINUTE) as rowtime, -- Use TUMBLE_ROWTIME as the aggregation time of the level-two window. username, COUNT(click_url) as cnt FROM user_clicks GROUP BY TUMBLE(ts, INTERVAL '1' MINUTE),username; BEGIN statement set; INSERT INTO tumble_output SELECT TUMBLE_START(rowtime, INTERVAL '1' HOUR), TUMBLE_END(rowtime, INTERVAL '1' HOUR), username, SUM(cnt) FROM one_minute_window_output GROUP BY TUMBLE(rowtime, INTERVAL '1' HOUR), username; END;