[ 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} -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org