[jira] [Commented] (SPARK-16753) Spark SQL doesn't handle skewed dataset joins properly

["Thomas Sebastian (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-16753?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15406348#comment-15406348 ] 

Thomas Sebastian commented on SPARK-16753:
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[~jurriaanpruis]    try a repartition before your join,so that the partition size tried to join would become even and improve the performance. But pls make sure you have enough cores to process these partitions.

> Spark SQL doesn't handle skewed dataset joins properly
> ------------------------------------------------------
>
>                 Key: SPARK-16753
>                 URL: https://issues.apache.org/jira/browse/SPARK-16753
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 1.6.1, 1.6.2, 2.0.0, 2.0.1
>            Reporter: Jurriaan Pruis
>         Attachments: screenshot-1.png
>
>
> I'm having issues with joining a 1 billion row dataframe with skewed data with multiple dataframes with sizes ranging from 100,000 to 10 million rows. This means some of the joins (about half of them) can be done using broadcast, but not all.
> Because the data in the large dataframe is skewed we get out of memory errors in the executors or errors like: `org.apache.spark.shuffle.FetchFailedException: Too large frame`.
> We tried a lot of things, like broadcast joining the skewed rows separately and unioning them with the dataset containing the sort merge joined data. Which works perfectly when doing one or two joins, but when doing 10 joins like this the query planner gets confused (see [SPARK-15326]).
> As most of the rows are skewed on the NULL value we use a hack where we put unique values in those NULL columns so the data is properly distributed over all partitions. This works fine for NULL values, but since this table is growing rapidly and we have skewed data for non-NULL values as well this isn't a full solution to the problem.
> Right now this specific spark task runs well 30% of the time and it's getting worse and worse because of the increasing amount of data.
> How to approach these kinds of joins using Spark? It seems weird that I can't find proper solutions for this problem/other people having the same kind of issues when Spark profiles itself as a large-scale data processing engine. Doing joins on big datasets should be a thing Spark should have no problem with out of the box.



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