Hello there Krzystzof!
Thanks a lot for the answer. Sorry for the late reply. I can see the logic
behind custom window processing in Flink.
Once, an incoming tuple arrives, you add a timer to it, which is going to
tick after "RatingExpiration" time units, as shown in your code. This, is
made *for each tuple*.
I have the following questions :
1 -- In my case, I do not have timestamps a-priori so I must append a
timestamp to the tuples as they arrive at the sources. Here [1] shows how to
assign timestamps to my data. Is this the correct way to do it? Also, what
type of watermarks is it better to assign? And what notion of time is it
more reasonable to use {EventTime, ProcessingTime, IngestionTime}?
2 -- The range of the sliding window equals to "RatingExpiration" if I am
correct. But, where is the slide of the sliding window defined? I guess the
slide has to do with the query, meaning each *s* time units evaluate the
query with the data residing in the range *r* last time units.
3 -- If I get to assign correctly the timestamps from above then it is
trivial, based also on your skeleton code, to simulate *time-based* sliding
windows. What about the case of *count-based* sliding windows???
Thanks a lot in advance.
Best,
Max
[1] --
https://ci.apache.org/projects/flink/flink-docs-release-1.4/dev/event_timestamps_watermarks.html
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