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Posted to issues@spark.apache.org by "Bruce Robbins (JIRA)" <ji...@apache.org> on 2019/06/27 16:19:00 UTC
[jira] [Commented] (SPARK-27466) LEAD function with 'ROWS BETWEEN
UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING' causes exception in Spark
[ https://issues.apache.org/jira/browse/SPARK-27466?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16874292#comment-16874292 ]
Bruce Robbins commented on SPARK-27466:
---------------------------------------
Hi [~hvanhovell] and/or [~yhuai], any comment on my previous comment?
> LEAD function with 'ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING' causes exception in Spark
> -------------------------------------------------------------------------------------------------------
>
> Key: SPARK-27466
> URL: https://issues.apache.org/jira/browse/SPARK-27466
> Project: Spark
> Issue Type: Bug
> Components: Spark Shell
> Affects Versions: 2.2.0
> Environment: Spark version 2.2.0.2.6.4.92-2
> Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_112)
> Reporter: Zoltan
> Priority: Major
>
> *1. Create a table in Hive:*
>
> {code:java}
> CREATE TABLE tab1(
> col1 varchar(1),
> col2 varchar(1)
> )
> PARTITIONED BY (
> col3 varchar(1)
> )
> LOCATION
> 'hdfs://server1/data/tab1'
> {code}
>
> *2. Query the Table in Spark:*
> *2.1: Simple query, no exception thrown:*
> {code:java}
> scala> spark.sql("SELECT * from schema1.tab1").show()
> +-----+---++----
> |col1|col2|col3|
> +-----+---++----
> +-----+---++----
> {code}
> *2.2.: Query causing exception:*
> {code:java}
> scala> spark.sql("*SELECT (LEAD(col1) OVER ( PARTITION BY col3 ORDER BY col1 ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING*)) from schema1.tab1")
> {code}
> {code:java}
> org.apache.spark.sql.AnalysisException: Window Frame ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING must match the required frame ROWS BETWEEN 1 FOLLOWING AND 1 FOLLOWING;
> at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:39)
> at org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:91)
> at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveWindowFrame$$anonfun$apply$30$$anonfun$applyOrElse$11.applyOrElse(Analyzer.scala:2219)
> at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveWindowFrame$$anonfun$apply$30$$anonfun$applyOrElse$11.applyOrElse(Analyzer.scala:2215)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$transformExpressionsDown$1.apply(QueryPlan.scala:258)
> at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$transformExpressionsDown$1.apply(QueryPlan.scala:258)
> at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpression$1(QueryPlan.scala:279)
> at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$1(QueryPlan.scala:289)
> at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$1$1.apply(QueryPlan.scala:293)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.immutable.List.foreach(List.scala:381)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
> at scala.collection.immutable.List.map(List.scala:285)
> at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$1(QueryPlan.scala:293)
> at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$6.apply(QueryPlan.scala:298)
> at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at org.apache.spark.sql.catalyst.plans.QueryPlan.mapExpressions(QueryPlan.scala:298)
> at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsDown(QueryPlan.scala:258)
> at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressions(QueryPlan.scala:249)
> at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveWindowFrame$$anonfun$apply$30.applyOrElse(Analyzer.scala:2215)
> at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveWindowFrame$$anonfun$apply$30.applyOrElse(Analyzer.scala:2214)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256)
> at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveWindowFrame$.apply(Analyzer.scala:2214)
> at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveWindowFrame$.apply(Analyzer.scala:2213)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:85)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:82)
> at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
> at scala.collection.immutable.List.foldLeft(List.scala:84)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:82)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:74)
> at scala.collection.immutable.List.foreach(List.scala:381)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:74)
> at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:69)
> at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:67)
> at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:50)
> at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:67)
> at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:637)
> ... 48 elided
> {code}
>
> *3. The same query in Hive, no exception:*
>
> {code:java}
> Beeline version 1.2.1000.2.6.4.92-2 by Apache Hive
> 0: jdbc:hive2://server1> SELECT (LEAD(col1) OVER ( PARTITION BY col3 ORDER BY col1 ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)) from schema1.tab1;
> INFO : Tez session hasn't been created yet. Opening session
> INFO : Dag name: SELECT (LEAD(col1) OV...schema1.tab1(Stage-1)
> INFO : Status: Running (Executing on YARN cluster with App id application_1554824808741_2080)
> --------------------------------------------------------------------------------
> VERTICES STATUS TOTAL COMPLETED RUNNING PENDING FAILED KILLED
> --------------------------------------------------------------------------------
> Map 1 SUCCEEDED 0 0 0 0 0 0
> Reducer 2 ...... SUCCEEDED 2 2 0 0 0 0
> --------------------------------------------------------------------------------
> VERTICES: 01/02 [==========================>>] 100% ELAPSED TIME: 3.99 s
> --------------------------------------------------------------------------------
> lead_window_0
> No rows selected (13.108 seconds)
> {code}
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