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From "ASF GitHub Bot (JIRA)" <>
Subject [jira] [Commented] (BEAM-2728) Extension for sketch-based statistics
Date Fri, 04 Aug 2017 13:28:02 GMT


ASF GitHub Bot commented on BEAM-2728:

GitHub user ArnaudFnr opened a pull request:

    [BEAM-2728] Extension for sketch-based statistics

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    Extension to compute approximate statistics with the use of probabilistic data structure,
or sketches.
    For now, 4 sketches are supported :
    - HyperLogLog (ApproximateDistinct) for stream's cardinality
    - Count-min Sketch (SketchFrequencies) for computing frequency by element
    - Stream Summary (KMostFrequent) for computing a top k of most frequent elements
    - T-Digest (TDigestQuantiles) for computing quantiles in the stream
    The sketches are implemented as Beam Combiners, allowing a user to build the sketch dynamically
in the Pipeline and then make some dynamic queries and/or store it in a database.

You can merge this pull request into a Git repository by running:

    $ git pull sketching

Alternatively you can review and apply these changes as the patch at:

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

    This closes #3686
commit 6bf51fdf6afe6f389415792f335dd08adf9fa85a
Author: Arnaud Fournier <>
Date:   2017-07-20T14:57:38Z

    [BEAM-2728] Extension for sketch-based statistics


> Extension for sketch-based statistics
> -------------------------------------
>                 Key: BEAM-2728
>                 URL:
>             Project: Beam
>          Issue Type: New Feature
>          Components: sdk-java-extensions
>            Reporter: Arnaud Fournier
>            Assignee: Arnaud Fournier
>            Priority: Minor
> Goal : Provide an extension library to compute approximate statistics on streams.
> Interest : Probabilistic data structures can create an approximation (sketch) of the
current state of a stream without storing every element but rather processing each observation
quickly to summarize its current state and find useful statistical insights.
> Implementation is here :
> More info :

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