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https://issues.apache.org/jira/browse/SPARK-25156?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16585350#comment-16585350
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Dongjoon Hyun commented on SPARK-25156:
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Hi, [~leeyh0216]. Yes. It looks like SPARK-23207 and SPARK-23243.
> Same query returns different result
> -----------------------------------
>
> Key: SPARK-25156
> URL: https://issues.apache.org/jira/browse/SPARK-25156
> Project: Spark
> Issue Type: Question
> Components: Spark Core
> Affects Versions: 2.1.1
> Environment: * Spark Version: 2.1.1
> * Java Version: Java 7
> * Scala Version: 2.11.8
> Reporter: Yonghwan Lee
> Priority: Major
> Labels: Question
>
> I performed two joins and two left outer join on five tables.
> There are several different results when you run the same query multiple
> times.
> Table A
>
> ||Column a||Column b||Column c||Column d||
> |Long(nullable: false)|Integer(nullable: false)|String(nullable:
> true)|String(nullable: false)|
> Table B
> ||Column a||Column b||
> |Long(nullable: false)|String(nullable: false)|
> Table C
> ||Column a||Column b||
> |Integer(nullable: false)|String(nullable: false)|
> Table D
> ||Column a||Column b||Column c||
> |Long(nullable: true)|Long(nullable: false)|Integer(nullable: false)|
> Table E
> ||Column a||Column b||Column c||
> |Long(nullable: false)|Integer(nullable: false)|String|
> Query(Spark SQL)
> {code:java}
> select A.c, B.b, C.b, D.c, E.c
> inner join B on A.a = B.a
> inner join C on A.b = C.a
> left outer join D on A.d <=> cast(D.a as string)
> left outer join E on D.b = E.a and D.c = E.b{code}
>
> I performed above query 10 times, it returns 7 times correct result(count:
> 830001460) and 3 times incorrect result(count: 830001299)
>
> + I execute
> {code:java}
> sql("set spark.sql.shuffle.partitions=801"){code}
> before execute query.
> A, B Table has lot of rows but C Table has small dataset, so when i saw
> physical plan, A<-> B join performed with SortMergeJoin and (A,B) <-> C join
> performed with Broadcast hash join.
>
> And now, i removed set spark.sql.shuffle.partitions statement, it works fine.
> Is this spark sql's bug?
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