Github user mbalassi commented on a diff in the pull request:
https://github.com/apache/flink/pull/395#discussion_r24737233
--- Diff: docs/streaming_guide.md ---
@@ -383,9 +389,40 @@
dataStream.groupBy(groupingField).window(Count.of(100)).every(â¦).max(field);
This will create separate windows for different keys and apply the trigger
and eviction policies on a per group basis.
+#### Applying multiple transformations on a window
+Using the `WindowedDataStream` abstraction we can apply several
transformations one after another on the discretised streams without having to
re-discretise it:
+
+~~~java
+dataStream.window(Count.of(1000)).groupBy(firstKey).mapWindow(â¦).groupBy(secondKey).reduceWindow(â¦).flatten()
+~~~
+
+The above call would create global windows of 1000 elements group it by
the first key and then apply a mapWindow transformation. The resulting windowed
stream will then be grouped by the second key and further reduced. The results
of the reduce transformation are then flattened.
--- End diff --
Nice example.
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