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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2015/06/30 01:27:04 UTC
[jira] [Assigned] (SPARK-8588) Could not use concat with UDF in
where clause
[ https://issues.apache.org/jira/browse/SPARK-8588?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-8588:
-----------------------------------
Assignee: Apache Spark (was: Wenchen Fan)
> Could not use concat with UDF in where clause
> ---------------------------------------------
>
> Key: SPARK-8588
> URL: https://issues.apache.org/jira/browse/SPARK-8588
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.4.0
> Environment: Centos 7, java 1.7.0_67, scala 2.10.5, run in a spark standalone cluster(or local).
> Reporter: StanZhai
> Assignee: Apache Spark
> Priority: Critical
>
> After upgraded the cluster from spark 1.3.1 to 1.4.0(rc4), I encountered the following exception when use concat with UDF in where clause:
> {code}
> org.apache.spark.sql.catalyst.analysis.UnresolvedException: Invalid call to dataType on unresolved object, tree: 'concat(HiveSimpleUdf#org.apache.hadoop.hive.ql.udf.UDFYear(date#1776),年)
> at org.apache.spark.sql.catalyst.analysis.UnresolvedFunction.dataType(unresolved.scala:82)
> at org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion$InConversion$$anonfun$apply$5$$anonfun$applyOrElse$15.apply(HiveTypeCoercion.scala:299)
> at org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion$InConversion$$anonfun$apply$5$$anonfun$applyOrElse$15.apply(HiveTypeCoercion.scala:299)
> at scala.collection.LinearSeqOptimized$class.exists(LinearSeqOptimized.scala:80)
> at scala.collection.immutable.List.exists(List.scala:84)
> at org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion$InConversion$$anonfun$apply$5.applyOrElse(HiveTypeCoercion.scala:299)
> at org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion$InConversion$$anonfun$apply$5.applyOrElse(HiveTypeCoercion.scala:298)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:222)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:222)
> at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:51)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:221)
> at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$transformExpressionDown$1(QueryPlan.scala:75)
> at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$1.apply(QueryPlan.scala:85)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
> at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
> at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
> at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
> at scala.collection.AbstractIterator.to(Iterator.scala:1157)
> at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
> at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
> at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
> at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsDown(QueryPlan.scala:94)
> at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressions(QueryPlan.scala:64)
> at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$transformAllExpressions$1.applyOrElse(QueryPlan.scala:136)
> at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$transformAllExpressions$1.applyOrElse(QueryPlan.scala:135)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:222)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:222)
> at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:51)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:221)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:242)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
> at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
> at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
> at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
> at scala.collection.AbstractIterator.to(Iterator.scala:1157)
> at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
> at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
> at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildrenDown(TreeNode.scala:272)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:227)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:242)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
> at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
> at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
> at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
> at scala.collection.AbstractIterator.to(Iterator.scala:1157)
> at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
> at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
> at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildrenDown(TreeNode.scala:272)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:227)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:212)
> at org.apache.spark.sql.catalyst.plans.QueryPlan.transformAllExpressions(QueryPlan.scala:135)
> at org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion$InConversion$.apply(HiveTypeCoercion.scala:298)
> at org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion$InConversion$.apply(HiveTypeCoercion.scala:297)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:61)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:59)
> at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:111)
> at scala.collection.immutable.List.foldLeft(List.scala:84)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:59)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:51)
> at scala.collection.immutable.List.foreach(List.scala:318)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:51)
> at org.apache.spark.sql.SQLContext$QueryExecution.analyzed$lzycompute(SQLContext.scala:922)
> at org.apache.spark.sql.SQLContext$QueryExecution.analyzed(SQLContext.scala:922)
> at org.apache.spark.sql.SQLContext$QueryExecution.assertAnalyzed(SQLContext.scala:920)
> at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:131)
> at org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:51)
> at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:744)
> at test.service.SparkHiveService.query(SparkHiveService.scala:79)
> ...
> at java.lang.Thread.run(Thread.java:745)
> {code}
> The SQL is:
> {quote}
> select * from test where concat(year(date), '年') in ( '2015年', '2014年' ) limit 10 {quote}
> This SQL can be run in spark 1.3.1 but error in spark 1.4. I've tried run some similar sql in spark 1.4.0, found the following sql could be run correctly:
> select * from test where concat(year(date), '年') = '2015年' limit 10
> select * from test where concat(sex, 'T') in ( 'MT' ) limit 10
> In short, when I use 'concat', UDF and 'in' together in sql, I will get the exception: Invalid call to dataType on unresolved object.
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