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Posted to issues@spark.apache.org by "Mitesh (JIRA)" <ji...@apache.org> on 2019/02/06 06:55:00 UTC
[jira] [Comment Edited] (SPARK-19981) Sort-Merge join inserts
shuffles when joining dataframes with aliased columns
[ https://issues.apache.org/jira/browse/SPARK-19981?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16761535#comment-16761535 ]
Mitesh edited comment on SPARK-19981 at 2/6/19 6:54 AM:
--------------------------------------------------------
Ping any updates here? This still is an issue in 2.3.2.
Also maybe a dupe of SPARK-19468
was (Author: masterddt):
Ping any updates here? This still is an issue in 2.3.2.
> Sort-Merge join inserts shuffles when joining dataframes with aliased columns
> -----------------------------------------------------------------------------
>
> Key: SPARK-19981
> URL: https://issues.apache.org/jira/browse/SPARK-19981
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.2
> Reporter: Allen George
> Priority: Major
>
> Performing a sort-merge join with two dataframes - each of which has the join column aliased - causes Spark to insert an unnecessary shuffle.
> Consider the scala test code below, which should be equivalent to the following SQL.
> {code:SQL}
> SELECT * FROM
> (SELECT number AS aliased from df1) t1
> LEFT JOIN
> (SELECT number AS aliased from df2) t2
> ON t1.aliased = t2.aliased
> {code}
> {code:scala}
> private case class OneItem(number: Long)
> private case class TwoItem(number: Long, value: String)
> test("join with aliases should not trigger shuffle") {
> val df1 = sqlContext.createDataFrame(
> Seq(
> OneItem(0),
> OneItem(2),
> OneItem(4)
> )
> )
> val partitionedDf1 = df1.repartition(10, col("number"))
> partitionedDf1.createOrReplaceTempView("df1")
> partitionedDf1.cache() partitionedDf1.count()
>
> val df2 = sqlContext.createDataFrame(
> Seq(
> TwoItem(0, "zero"),
> TwoItem(2, "two"),
> TwoItem(4, "four")
> )
> )
> val partitionedDf2 = df2.repartition(10, col("number"))
> partitionedDf2.createOrReplaceTempView("df2")
> partitionedDf2.cache() partitionedDf2.count()
>
> val fromDf1 = sqlContext.sql("SELECT number from df1")
> val fromDf2 = sqlContext.sql("SELECT number from df2")
> val aliasedDf1 = fromDf1.select(col(fromDf1.columns.head) as "aliased")
> val aliasedDf2 = fromDf2.select(col(fromDf2.columns.head) as "aliased")
> aliasedDf1.join(aliasedDf2, Seq("aliased"), "left_outer") }
> {code}
> Both the SQL and the Scala code generate a query-plan where an extra exchange is inserted before performing the sort-merge join. This exchange changes the partitioning from {{HashPartitioning("number", 10)}} for each frame being joined into {{HashPartitioning("aliased", 5)}}. I would have expected that since it's a simple column aliasing, and both frames have exactly the same partitioning that the initial frames.
> {noformat}
> *Project [args=[aliased#267L]][outPart=PartitioningCollection(5, hashpartitioning(aliased#267L, 5)%NONNULL,hashpartitioning(aliased#270L, 5)%NONNULL)][outOrder=List(aliased#267L ASC%NONNULL)][output=List(aliased#267:bigint%NONNULL)]
> +- *SortMergeJoin [args=[aliased#267L], [aliased#270L], Inner][outPart=PartitioningCollection(5, hashpartitioning(aliased#267L, 5)%NONNULL,hashpartitioning(aliased#270L, 5)%NONNULL)][outOrder=List(aliased#267L ASC%NONNULL)][output=ArrayBuffer(aliased#267:bigint%NONNULL, aliased#270:bigint%NONNULL)]
> :- *Sort [args=[aliased#267L ASC], false, 0][outPart=HashPartitioning(5, aliased#267:bigint%NONNULL)][outOrder=List(aliased#267L ASC%NONNULL)][output=ArrayBuffer(aliased#267:bigint%NONNULL)]
> : +- Exchange [args=hashpartitioning(aliased#267L, 5)%NONNULL][outPart=HashPartitioning(5, aliased#267:bigint%NONNULL)][outOrder=List()][output=ArrayBuffer(aliased#267:bigint%NONNULL)]
> : +- *Project [args=[number#198L AS aliased#267L]][outPart=HashPartitioning(10, number#198:bigint%NONNULL)][outOrder=List()][output=ArrayBuffer(aliased#267:bigint%NONNULL)]
> : +- InMemoryTableScan [args=[number#198L]][outPart=HashPartitioning(10, number#198:bigint%NONNULL)][outOrder=List()][output=ArrayBuffer(number#198:bigint%NONNULL)]
> : : +- InMemoryRelation [number#198L], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas), false[Statistics(24,false)][output=List(number#198:bigint%NONNULL)]
> : : : +- Exchange [args=hashpartitioning(number#198L, 10)%NONNULL][outPart=HashPartitioning(10, number#198:bigint%NONNULL)][outOrder=List()][output=List(number#198:bigint%NONNULL)]
> : : : +- LocalTableScan [args=[number#198L]][outPart=UnknownPartitioning(0)][outOrder=List()][output=List(number#198:bigint%NONNULL)]
> +- *Sort [args=[aliased#270L ASC], false, 0][outPart=HashPartitioning(5, aliased#270:bigint%NONNULL)][outOrder=List(aliased#270L ASC%NONNULL)][output=ArrayBuffer(aliased#270:bigint%NONNULL)]
> +- Exchange [args=hashpartitioning(aliased#270L, 5)%NONNULL][outPart=HashPartitioning(5, aliased#270:bigint%NONNULL)][outOrder=List()][output=ArrayBuffer(aliased#270:bigint%NONNULL)]
> +- *Project [args=[number#223L AS aliased#270L]][outPart=HashPartitioning(10, number#223:bigint%NONNULL)][outOrder=List()][output=ArrayBuffer(aliased#270:bigint%NONNULL)]
> +- InMemoryTableScan [args=[number#223L]][outPart=HashPartitioning(10, number#223:bigint%NONNULL)][outOrder=List()][output=ArrayBuffer(number#223:bigint%NONNULL)]
> : +- InMemoryRelation [number#223L, value#224], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas), false[Statistics(47,false)][output=List(number#223:bigint%NONNULL, value#224:string%NULL)]
> : : +- Exchange [args=hashpartitioning(number#223L, 10)%NONNULL][outPart=HashPartitioning(10, number#223:bigint%NONNULL)][outOrder=List()][output=List(number#223:bigint%NONNULL, value#224:string%NULL)]
> : : +- LocalTableScan [args=[number#223L, value#224]][outPart=UnknownPartitioning(0)][outOrder=List()][output=List(number#223:bigint%NONNULL, value#224:string%NULL)]
> {noformat}
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