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Posted to issues@spark.apache.org by "Andrew Ray (JIRA)" <ji...@apache.org> on 2017/08/01 19:20:03 UTC

[jira] [Commented] (SPARK-21330) Bad partitioning does not allow to read a JDBC table with extreme values on the partition column

    [ https://issues.apache.org/jira/browse/SPARK-21330?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16109568#comment-16109568 ] 

Andrew Ray commented on SPARK-21330:
------------------------------------

https://github.com/apache/spark/pull/18800

> Bad partitioning does not allow to read a JDBC table with extreme values on the partition column
> ------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-21330
>                 URL: https://issues.apache.org/jira/browse/SPARK-21330
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.1.1
>            Reporter: Stefano Parmesan
>
> When using "extreme" values in the partition column (like having a randomly generated long number) overflow might happen, leading to the following warning message:
> {code}WARN JDBCRelation: The number of partitions is reduced because the specified number of partitions is less than the difference between upper bound and lower bound. Updated number of partitions: -1559072469251914524; Input number of partitions: 20; Lower bound: -7701345953623242445; Upper bound: 9186325650834394647.{code}
> When this happens, no data is read from the table.
> This happens because of the following check in {{org/apache/spark/sql/execution/datasources/jdbc/JDBCRelation.scala}}:
> {code}if ((upperBound - lowerBound) >= partitioning.numPartitions){code}
> Funny thing is that we worry about overflows a few lines later:
> {code}    // Overflow and silliness can happen if you subtract then divide.
>     // Here we get a little roundoff, but that's (hopefully) OK.{code}
> A better check would be:
> {code}if ((upperBound - partitioning.numPartitions) >= lowerBound){code}



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