Hi,
I saw the issue you opened. :-) I'll try and figure out how to get all the Scaladocs on there.
Regarding the other questions. A WindowedStream is basically not a Stream in itself but a stepping stone towards specifying a windowed operation that results in a new Stream. So the pattern always has to be like this:
in
.keyBy(...)
.window(...)
.reduce() // or fold() or apply()
Only with the final specification of the operation do you get a new DataStream of elements. You can do a map() before the window operation but not in between specifying a window and an operation that works on windows.
This differs from the Apache Beam (formerly Google Dataflow) model where the window is a property of stream elements. There you can do:
in
.map()
.window()
.map()
.reduce()
And the reduce operation will work on the windows that where assigned in the window operation.
I hope this helps somewhat but please let me know if I should go into details.
Cheers,
Aljoscha
An observation and a couple of question from a novice.
The observation: The Flink web site makes available ScalaDocs for org.apache.flink.api.scala but not for org.apache.flink.streaming.api.scala.
Now for the questions:
Why can't you use map to transform a data stream, say convert all the elements to integers (e.g. .map { x => 1 }), then create a tumbling processing time window (e.g. .windowAll(TumblingProcessingTimeWindows.of(Time.seconds(2))))?
Then the inverse: Why do AllWindiowedStream and WindowStream not have a map method?
Locked
