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Posted to issues@spark.apache.org by "guowei (JIRA)" <ji...@apache.org> on 2015/01/12 09:31:34 UTC
[jira] [Created] (SPARK-5203) union with different decimal type
report error
guowei created SPARK-5203:
-----------------------------
Summary: union with different decimal type report error
Key: SPARK-5203
URL: https://issues.apache.org/jira/browse/SPARK-5203
Project: Spark
Issue Type: Bug
Components: SQL
Reporter: guowei
cases like this
create table test (a decimal(10,1));
select a from test union all select a*2 from test;
15/01/12 16:28:54 ERROR SparkSQLDriver: Failed in [select a from test union all select a*2 from test]
org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Unresolved attributes: *, tree:
'Project [*]
'Subquery _u1
'Union
Project [a#1]
MetastoreRelation default, test, None
Project [CAST((CAST(a#2, DecimalType()) * CAST(CAST(2, DecimalType(10,0)), DecimalType())), DecimalType(21,1)) AS _c0#0]
MetastoreRelation default, test, None
at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$$anonfun$1.applyOrElse(Analyzer.scala:85)
at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$$anonfun$1.applyOrElse(Analyzer.scala:83)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:144)
at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:135)
at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$.apply(Analyzer.scala:83)
at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$.apply(Analyzer.scala:81)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1$$anonfun$apply$2.apply(RuleExecutor.scala:61)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1$$anonfun$apply$2.apply(RuleExecutor.scala:59)
at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:51)
at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:60)
at scala.collection.mutable.WrappedArray.foldLeft(WrappedArray.scala:34)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1.apply(RuleExecutor.scala:59)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1.apply(RuleExecutor.scala:51)
at scala.collection.immutable.List.foreach(List.scala:318)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.apply(RuleExecutor.scala:51)
at org.apache.spark.sql.SQLContext$QueryExecution.analyzed$lzycompute(SQLContext.scala:410)
at org.apache.spark.sql.SQLContext$QueryExecution.analyzed(SQLContext.scala:410)
at org.apache.spark.sql.SQLContext$QueryExecution.withCachedData$lzycompute(SQLContext.scala:411)
at org.apache.spark.sql.SQLContext$QueryExecution.withCachedData(SQLContext.scala:411)
at org.apache.spark.sql.SQLContext$QueryExecution.optimizedPlan$lzycompute(SQLContext.scala:412)
at org.apache.spark.sql.SQLContext$QueryExecution.optimizedPlan(SQLContext.scala:412)
at org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan$lzycompute(SQLContext.scala:417)
at org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan(SQLContext.scala:415)
at org.apache.spark.sql.SQLContext$QueryExecution.executedPlan$lzycompute(SQLContext.scala:421)
at org.apache.spark.sql.SQLContext$QueryExecution.executedPlan(SQLContext.scala:421)
at org.apache.spark.sql.hive.HiveContext$QueryExecution.stringResult(HiveContext.scala:369)
at org.apache.spark.sql.hive.thriftserver.AbstractSparkSQLDriver.run(AbstractSparkSQLDriver.scala:58)
at org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver.processCmd(SparkSQLCLIDriver.scala:275)
at org.apache.hadoop.hive.cli.CliDriver.processLine(CliDriver.java:423)
at org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver$.main(SparkSQLCLIDriver.scala:211)
at org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver.main(SparkSQLCLIDriver.scala)
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