[jira] [Commented] (FLINK-2147) Approximate calculation of frequencies in data streams

["Felipe Oliveira Gutierrez (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/FLINK-2147?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16825067#comment-16825067 ] 

Felipe Oliveira Gutierrez commented on FLINK-2147:
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Hi [~aljoscha], [~till.rohrmann], and @all from this thread.

I would like to learn the necessary skills to help on this issue. I have implemented the CountMinSketch [1] using a ProcessWindowFunction [2]. However, what I would like to have is a function that I can call after the window like sum(), min(), etc. I have implemented a custom operator to do the Combiner [4], but I believe that this issue is a bit more complex since it has to compute after the window. So, I was planning to base my implementation on a custom JoinFuntion as [~till.rohrmann] did in this example [3].

Where do you think I can start? Do you have any sample code that I can start to build up my own custom window operator and afterward strive to implement the CountMin-Sketch for Flink?

[1] [https://github.com/felipegutierrez/explore-flink/blob/master/src/main/java/org/sense/flink/util/CountMinSketch.java]

[2] [https://github.com/felipegutierrez/explore-flink/blob/master/src/main/java/org/sense/flink/examples/stream/MultiSensorMultiStationsReadingMqtt2.java#L69]

[3] [https://github.com/tillrohrmann/custom-join]

[4] [https://github.com/felipegutierrez/explore-flink/blob/master/src/main/java/org/sense/flink/examples/stream/MqttSensorDataCombinerByKeySkewedDAG.java#L93]

Kind Regards,
Felipe

> Approximate calculation of frequencies in data streams
> ------------------------------------------------------
>
>                 Key: FLINK-2147
>                 URL: https://issues.apache.org/jira/browse/FLINK-2147
>             Project: Flink
>          Issue Type: New Feature
>          Components: API / DataStream
>            Reporter: Gabor Gevay
>            Priority: Major
>              Labels: approximate, statistics
>
> Count-Min sketch is a hashing-based algorithm for approximately keeping track of the frequencies of elements in a data stream. It is described by Cormode et al. in the following paper:
> http://dimacs.rutgers.edu/~graham/pubs/papers/cmsoft.pdf
> Note that this algorithm can be conveniently implemented in a distributed way, as described in section 3.2 of the paper.
> The paper
> http://www.vldb.org/conf/2002/S10P03.pdf
> also describes algorithms for approximately keeping track of frequencies, but here the user can specify a threshold below which she is not interested in the frequency of an element. The error-bounds are also different than the Count-min sketch algorithm.



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