[jira] [Commented] (FLINK-1297) Add support for tracking statistics of intermediate results

["ASF GitHub Bot (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/FLINK-1297?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14653257#comment-14653257 ] 

ASF GitHub Bot commented on FLINK-1297:
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Github user tammymendt commented on the pull request:

    https://github.com/apache/flink/pull/605#issuecomment-127519735
  
    Hey! So I've been using and testing this code throughout my master thesis. Collecting count distinct makes jobs about 10% slower whereas collecting heavy hitters can make a job be 20 to 50% slower (depending on the algorithm and the distribution of the data). However this overhead is lower than that of using a histogram accumulator (not to mention the histogram might not fit in memory). I think it can be a nice addition to the code, specially since it does not affect any core components. 
    
    The version that I pushed now uses a bunch of conditionals to check which statistic is being collected. I know @fhueske did not really like this. I implemented another version which avoids the conditionals by using a different class for every type of statistic. I preferred to push this version though, since it has been more thoroughly tested.


> Add support for tracking statistics of intermediate results
> -----------------------------------------------------------
>
>                 Key: FLINK-1297
>                 URL: https://issues.apache.org/jira/browse/FLINK-1297
>             Project: Flink
>          Issue Type: Improvement
>          Components: Distributed Runtime
>            Reporter: Alexander Alexandrov
>            Assignee: Alexander Alexandrov
>             Fix For: 0.9
>
>   Original Estimate: 1,008h
>  Remaining Estimate: 1,008h
>
> One of the major problems related to the optimizer at the moment is the lack of proper statistics.
> With the introduction of staged execution, it is possible to instrument the runtime code with a statistics facility that collects the required information for optimizing the next execution stage.
> I would therefore like to contribute code that can be used to gather basic statistics for the (intermediate) result of dataflows (e.g. min, max, count, count distinct) and make them available to the job manager.
> Before I start, I would like to hear some feedback form the other users.
> In particular, to handle skew (e.g. on grouping) it might be good to have some sort of detailed sketch about the key distribution of an intermediate result. I am not sure whether a simple histogram is the most effective way to go. Maybe somebody would propose another lightweight sketch that provides better accuracy.



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