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https://issues.apache.org/jira/browse/SPARK-27318?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17162641#comment-17162641
]
Shay Elbaz edited comment on SPARK-27318 at 7/22/20, 10:14 AM:
---------------------------------------------------------------
Was able to reproduce on 2.4.3, without external files/data
Executed via spark-shell, on HDP 2.6.5 cluster:
{code:java}
scala> spark.conf.set("spark.sql.autoBroadcastJoinThreshold", -1)
// create bucketed table:
scala> spark.range(1000).repartition(4, $"id").write.bucketBy(4,
"id").sortBy("id").mode("overwrite").saveAsTable("buckets")
20/07/22 02:26:25 WARN HiveExternalCatalog: Persisting bucketed data source
table `shay_test`.`buckets` into Hive metastore in Spark SQL specific format,
which is NOT compatible with Hive.
// join with pre-partitioned df:
scala> spark.range(100).repartition(4, $"id").join(spark.table("buckets"),
Seq("id")).explain
java.lang.IllegalArgumentException: requirement failed: PartitioningCollection
requires all of its partitionings have the same numPartitions.
at scala.Predef$.require(Predef.scala:224)
at
org.apache.spark.sql.catalyst.plans.physical.PartitioningCollection.<init>(partitioning.scala:291)
at
org.apache.spark.sql.execution.joins.SortMergeJoinExec.outputPartitioning(SortMergeJoinExec.scala:69)
at
org.apache.spark.sql.execution.exchange.EnsureRequirements$$anonfun$org$apache$spark$sql$execution$exchange$EnsureRequirements$$ensureDistributionAndOrdering$1.apply(EnsureRequirements.scala:150)
...
...
...
{code}
was (Author: shay_elbaz):
Was able to reproduce on 2.4.3, without external files/data
Executed via spark-shell, on HDP 2.6.5 cluster:
{code:java}
// create bucketed table:
scala> spark.range(1000).repartition(4, $"id").write.bucketBy(4,
"id").sortBy("id").mode("overwrite").saveAsTable("buckets")
20/07/22 02:26:25 WARN HiveExternalCatalog: Persisting bucketed data source
table `shay_test`.`buckets` into Hive metastore in Spark SQL specific format,
which is NOT compatible with Hive.
// join with pre-partitioned df:
scala> spark.range(100).repartition(4, $"id").join(spark.table("buckets"),
Seq("id")).explain
java.lang.IllegalArgumentException: requirement failed: PartitioningCollection
requires all of its partitionings have the same numPartitions.
at scala.Predef$.require(Predef.scala:224)
at
org.apache.spark.sql.catalyst.plans.physical.PartitioningCollection.<init>(partitioning.scala:291)
at
org.apache.spark.sql.execution.joins.SortMergeJoinExec.outputPartitioning(SortMergeJoinExec.scala:69)
at
org.apache.spark.sql.execution.exchange.EnsureRequirements$$anonfun$org$apache$spark$sql$execution$exchange$EnsureRequirements$$ensureDistributionAndOrdering$1.apply(EnsureRequirements.scala:150)
...
...
...
{code}
> Join operation on bucketing table fails with base adaptive enabled
> ------------------------------------------------------------------
>
> Key: SPARK-27318
> URL: https://issues.apache.org/jira/browse/SPARK-27318
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.4.0
> Reporter: Supritha
> Priority: Major
>
> Join Operation on bucketed table is failing.
> Steps to reproduce the issue.
> {code}
> spark.sql("set spark.sql.adaptive.enabled=true")
> {code}
> 1. Create table bukcet3 and bucket4 Table as below and load the data.
> {code}
> sql("create table bucket3(id3 int,country3 String, sports3 String) row format
> delimited fields terminated by ','").show()
> sql("create table bucket4(id4 int,country4 String) row format delimited
> fields terminated by ','").show()
> sql("load data local inpath '/opt/abhidata/bucket2.txt' into table
> bucket3").show()
> sql("load data local inpath '/opt/abhidata/bucket3.txt' into table
> bucket4").show()
> {code}
> 2. Create bucketing table as below
> {code}
> spark.sqlContext.table("bucket3").write.bucketBy(3,
> "id3").saveAsTable("bucketed_table_3");
> spark.sqlContext.table("bucket4").write.bucketBy(4,
> "id4").saveAsTable("bucketed_table_4");
> {code}
> 3. Execute the join query on the bucketed table
> {code}
> sql("select * from bucketed_table_3 join bucketed_table_4 on
> bucketed_table_3.id3 = bucketed_table_4.id4").show()
> {code}
>
> {code:java}
> java.lang.IllegalArgumentException: requirement failed:
> PartitioningCollection requires all of its partitionings have the same
> numPartitions. at scala.Predef$.require(Predef.scala:224) at
> org.apache.spark.sql.catalyst.plans.physical.PartitioningCollection.<init>(partitioning.scala:291)
> at
> org.apache.spark.sql.execution.joins.SortMergeJoinExec.outputPartitioning(SortMergeJoinExec.scala:69)
> at
> org.apache.spark.sql.execution.exchange.EnsureRequirements$$anonfun$org$apache$spark$sql$execution$exchange$EnsureRequirements$$ensureDistributionAndOrdering$1.apply(EnsureRequirements.scala:150)
> at
> org.apache.spark.sql.execution.exchange.EnsureRequirements$$anonfun$org$apache$spark$sql$execution$exchange$EnsureRequirements$$ensureDistributionAndOrdering$1.apply(EnsureRequirements.scala:149)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.immutable.List.foreach(List.scala:392) at
> scala.collection.TraversableLike$class.map(TraversableLike.scala:234) at
> scala.collection.immutable.List.map(List.scala:296) at
> org.apache.spark.sql.execution.exchange.EnsureRequirements.org$apache$spark$sql$execution$exchange$EnsureRequirements$$ensureDistributionAndOrdering(EnsureRequirements.scala:149)
> at
> org.apache.spark.sql.execution.exchange.EnsureRequirements$$anonfun$apply$1.applyOrElse(EnsureRequirements.scala:304)
> at
> org.apache.spark.sql.execution.exchange.EnsureRequirements$$anonfun$apply$1.applyOrElse(EnsureRequirements.scala:296)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$2.apply(TreeNode.scala:282)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$2.apply(TreeNode.scala:282)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:281)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:275)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:275)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:326)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:324)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:275)
> at
> org.apache.spark.sql.execution.exchange.EnsureRequirements.apply(EnsureRequirements.scala:296)
> at
> org.apache.spark.sql.execution.exchange.EnsureRequirements.apply(EnsureRequirements.scala:38)
> at
> org.apache.spark.sql.execution.QueryExecution$$anonfun$prepareForExecution$1.apply(QueryExecution.scala:87)
> at
> org.apache.spark.sql.execution.QueryExecution$$anonfun$prepareForExecution$1.apply(QueryExecution.scala:87)
> at
> scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
> at scala.collection.immutable.List.foldLeft(List.scala:84) at
> org.apache.spark.sql.execution.QueryExecution.prepareForExecution(QueryExecution.scala:87)
> at
> org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:77)
> at
> org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:77)
> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3360) at
> org.apache.spark.sql.Dataset.head(Dataset.scala:2545) at
> org.apache.spark.sql.Dataset.take(Dataset.scala:2759) at
> org.apache.spark.sql.Dataset.getRows(Dataset.scala:255) at
> org.apache.spark.sql.Dataset.showString(Dataset.scala:292) at
> org.apache.spark.sql.Dataset.show(Dataset.scala:746) at
> org.apache.spark.sql.Dataset.show(Dataset.scala:705) at
> org.apache.spark.sql.Dataset.show(Dataset.scala:714) ... 49 elided
> {code}
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