窗口关联

Window Join

A window join adds the dimension of time into the join criteria themselves. In doing so, the window join joins the elements of two streams that share a common key and are in the same window. The semantic of window join is same to the DataStream window join

For streaming queries, unlike other joins on continuous tables, window join does not emit intermediate results but only emits final results at the end of the window. Moreover, window join purge all intermediate state when no longer needed.

Usually, Window Join is used with Windowing TVF. Besides, Window Join could follow after other operations based on Windowing TVF, such as Window Aggregation, Window TopN and Window Join.

Currently, Window Join requires the join on condition contains window starts equality of input tables and window ends equality of input tables.

Window Join supports INNER/LEFT/RIGHT/FULL OUTER/ANTI/SEMI JOIN.

INNER/LEFT/RIGHT/FULL OUTER

The following shows the syntax of the INNER/LEFT/RIGHT/FULL OUTER Window Join statement.

SELECT ...
FROM L [LEFT|RIGHT|FULL OUTER] JOIN R -- L and R are relations applied windowing TVF
ON L.window_start = R.window_start AND L.window_end = R.window_end AND ...

The syntax of INNER/LEFT/RIGHT/FULL OUTER WINDOW JOIN are very similar with each other, we only give an example for FULL OUTER JOIN here. When performing a window join, all elements with a common key and a common tumbling window are joined together. We only give an example for a Window Join which works on a Tumble Window TVF. By scoping the region of time for the join into fixed five-minute intervals, we chopped our datasets into two distinct windows of time: Windowing TVF, the Windowing TVF has to be with Tumble Windows, Hop Windows or Cumulate Windows instead of Session windows.