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Posted to reviews@spark.apache.org by dongjoon-hyun <gi...@git.apache.org> on 2016/06/23 06:44:22 UTC

[GitHub] spark pull request #13870: [SPARK-16165][SQL] Fix the update logic for InMem...

GitHub user dongjoon-hyun opened a pull request:

    https://github.com/apache/spark/pull/13870

    [SPARK-16165][SQL] Fix the update logic for InMemoryTableScanExec.readBatches

    ## What changes were proposed in this pull request?
    
    Currently, `readBatches` accumulator of `InMemoryTableScanExec` is updated only when `spark.sql.inMemoryColumnarStorage.partitionPruning` is true. Although this metric is used for only testing purpose, we had better have correct metric without considering SQL options.
    
    ## How was this patch tested?
    
    Pass the Jenkins tests (including a new testcase).

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

    $ git pull https://github.com/dongjoon-hyun/spark SPARK-16165

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

    https://github.com/apache/spark/pull/13870.patch

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

    This closes #13870
    
----
commit b1a80ddf3065c699089ed92102015014bdad5e29
Author: Dongjoon Hyun <do...@apache.org>
Date:   2016-06-23T06:36:19Z

    [SPARK-16165][SQL] Fix the update logic for InMemoryTableScanExec.readBatches accumulator

----


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[GitHub] spark issue #13870: [SPARK-16165][SQL] Fix the update logic for InMemoryTabl...

Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:

    https://github.com/apache/spark/pull/13870
  
    Test PASSed.
    Refer to this link for build results (access rights to CI server needed): 
    https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder/61100/
    Test PASSed.


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[GitHub] spark issue #13870: [SPARK-16165][SQL] Fix the update logic for InMemoryTabl...

Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:

    https://github.com/apache/spark/pull/13870
  
    Merged build finished. Test PASSed.


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[GitHub] spark issue #13870: [SPARK-16165][SQL] Fix the update logic for InMemoryTabl...

Posted by dongjoon-hyun <gi...@git.apache.org>.
Github user dongjoon-hyun commented on the issue:

    https://github.com/apache/spark/pull/13870
  
    No problem! And, thank you for attention! :)


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[GitHub] spark issue #13870: [SPARK-16165][SQL] Fix the update logic for InMemoryTabl...

Posted by liancheng <gi...@git.apache.org>.
Github user liancheng commented on the issue:

    https://github.com/apache/spark/pull/13870
  
    LGTM, merging to master and branch-2.0. Thanks!


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[GitHub] spark issue #13870: [SPARK-16165][SQL] Fix the update logic for InMemoryTabl...

Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:

    https://github.com/apache/spark/pull/13870
  
    **[Test build #61100 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/61100/consoleFull)** for PR 13870 at commit [`b1a80dd`](https://github.com/apache/spark/commit/b1a80ddf3065c699089ed92102015014bdad5e29).
     * This patch passes all tests.
     * This patch merges cleanly.
     * This patch adds no public classes.


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[GitHub] spark issue #13870: [SPARK-16165][SQL] Fix the update logic for InMemoryTabl...

Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:

    https://github.com/apache/spark/pull/13870
  
    **[Test build #61100 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/61100/consoleFull)** for PR 13870 at commit [`b1a80dd`](https://github.com/apache/spark/commit/b1a80ddf3065c699089ed92102015014bdad5e29).


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[GitHub] spark issue #13870: [SPARK-16165][SQL] Fix the update logic for InMemoryTabl...

Posted by davies <gi...@git.apache.org>.
Github user davies commented on the issue:

    https://github.com/apache/spark/pull/13870
  
    @dongjoon-hyun I think that is also used to have better estimation on the dataset, could be used by planner to have better physical plan.


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[GitHub] spark issue #13870: [SPARK-16165][SQL] Fix the update logic for InMemoryTabl...

Posted by dongjoon-hyun <gi...@git.apache.org>.
Github user dongjoon-hyun commented on the issue:

    https://github.com/apache/spark/pull/13870
  
    Hi, @liancheng .
    Could you review this PR, too?
    This was initially introduced in your https://github.com/apache/spark/commit/74049249abb952ad061c0e221c22ff894a9e9c8d#diff-b47dac3d98014877d5879f5cf37ab0d1R256 .


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[GitHub] spark issue #13870: [SPARK-16165][SQL] Fix the update logic for InMemoryTabl...

Posted by davies <gi...@git.apache.org>.
Github user davies commented on the issue:

    https://github.com/apache/spark/pull/13870
  
    @dongjoon-hyun Sorry, I misunderstood it, I thought it was batchStats. The changes look good.


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[GitHub] spark pull request #13870: [SPARK-16165][SQL] Fix the update logic for InMem...

Posted by asfgit <gi...@git.apache.org>.
Github user asfgit closed the pull request at:

    https://github.com/apache/spark/pull/13870


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[GitHub] spark issue #13870: [SPARK-16165][SQL] Fix the update logic for InMemoryTabl...

Posted by dongjoon-hyun <gi...@git.apache.org>.
Github user dongjoon-hyun commented on the issue:

    https://github.com/apache/spark/pull/13870
  
    Oh, thank you for review, @davies .
    
    But, sorry. I'm not sure what you mean. Do you mean there is a reason that `readBatches` should be `0` when the option `spark.sql.inMemoryColumnarStorage.partitionPruning` is false?
    
    According to my observation, `readBatches` was only used in `InMemoryTableScanExec` and the corresponding testsuite. So, I think it's safe to update this correctly. Is there any other places to reference this?


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[GitHub] spark issue #13870: [SPARK-16165][SQL] Fix the update logic for InMemoryTabl...

Posted by dongjoon-hyun <gi...@git.apache.org>.
Github user dongjoon-hyun commented on the issue:

    https://github.com/apache/spark/pull/13870
  
    Thank you for merging, @liancheng and @davies .


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