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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2017/09/17 01:17:00 UTC
[jira] [Assigned] (SPARK-22042) ReorderJoinPredicates can break
when child's partitioning is not decided
[ https://issues.apache.org/jira/browse/SPARK-22042?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-22042:
------------------------------------
Assignee: (was: Apache Spark)
> ReorderJoinPredicates can break when child's partitioning is not decided
> ------------------------------------------------------------------------
>
> Key: SPARK-22042
> URL: https://issues.apache.org/jira/browse/SPARK-22042
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.1.0, 2.2.0
> Reporter: Tejas Patil
> Priority: Minor
>
> When `ReorderJoinPredicates` tries to get the `outputPartitioning` of its children, the children may not be properly constructed as the child-subtree has to still go through other planner rules.
> In this particular case, the child is `SortMergeJoinExec`. Since the required `Exchange` operators are not in place (because `EnsureRequirements` runs _after_ `ReorderJoinPredicates`), the join's children would not have partitioning defined. This breaks while creation the `PartitioningCollection` here : https://github.com/apache/spark/blob/94439997d57875838a8283c543f9b44705d3a503/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoinExec.scala#L69
> Small repro:
> {noformat}
> context.sql("SET spark.sql.autoBroadcastJoinThreshold=0")
> val df = (0 until 50).map(i => (i % 5, i % 13, i.toString)).toDF("i", "j", "k")
> df.write.format("parquet").saveAsTable("table1")
> df.write.format("parquet").saveAsTable("table2")
> df.write.format("parquet").bucketBy(8, "j", "k").saveAsTable("bucketed_table")
> sql("""
> SELECT *
> FROM (
> SELECT a.i, a.j, a.k
> FROM bucketed_table a
> JOIN table1 b
> ON a.i = b.i
> ) c
> JOIN table2
> ON c.i = table2.i
> """).explain
> {noformat}
> This fails with :
> {noformat}
> java.lang.IllegalArgumentException: requirement failed: PartitioningCollection requires all of its partitionings have the same numPartitions.
> at scala.Predef$.require(Predef.scala:224)
> at org.apache.spark.sql.catalyst.plans.physical.PartitioningCollection.<init>(partitioning.scala:324)
> at org.apache.spark.sql.execution.joins.SortMergeJoinExec.outputPartitioning(SortMergeJoinExec.scala:69)
> at org.apache.spark.sql.execution.ProjectExec.outputPartitioning(basicPhysicalOperators.scala:82)
> at org.apache.spark.sql.execution.joins.ReorderJoinPredicates$$anonfun$apply$1.applyOrElse(ReorderJoinPredicates.scala:91)
> at org.apache.spark.sql.execution.joins.ReorderJoinPredicates$$anonfun$apply$1.applyOrElse(ReorderJoinPredicates.scala:76)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289)
> at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:288)
> at org.apache.spark.sql.execution.joins.ReorderJoinPredicates.apply(ReorderJoinPredicates.scala:76)
> at org.apache.spark.sql.execution.joins.ReorderJoinPredicates.apply(ReorderJoinPredicates.scala:34)
> at org.apache.spark.sql.execution.QueryExecution$$anonfun$prepareForExecution$1.apply(QueryExecution.scala:100)
> at org.apache.spark.sql.execution.QueryExecution$$anonfun$prepareForExecution$1.apply(QueryExecution.scala:100)
> at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
> at scala.collection.immutable.List.foldLeft(List.scala:84)
> at org.apache.spark.sql.execution.QueryExecution.prepareForExecution(QueryExecution.scala:100)
> at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:90)
> at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:90)
> at org.apache.spark.sql.execution.QueryExecution$$anonfun$simpleString$1.apply(QueryExecution.scala:201)
> at org.apache.spark.sql.execution.QueryExecution$$anonfun$simpleString$1.apply(QueryExecution.scala:201)
> at org.apache.spark.sql.execution.QueryExecution.stringOrError(QueryExecution.scala:114)
> at org.apache.spark.sql.execution.QueryExecution.simpleString(QueryExecution.scala:201)
> at org.apache.spark.sql.execution.command.ExplainCommand.run(commands.scala:147)
> at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:78)
> at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:75)
> at org.apache.spark.sql.execution.command.ExecutedCommandExec.executeCollect(commands.scala:91)
> at org.apache.spark.sql.Dataset.explain(Dataset.scala:464)
> at org.apache.spark.sql.Dataset.explain(Dataset.scala:477)
> ... 60 elided
> {noformat}
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