Did https://issues.apache.org/jira/browse/SPARK-3807 fix the issue seen by you? If yes, then please note that it shall be part of 1.1.1 and 1.2
Chirag From: Chen Song <chen.song...@gmail.com<mailto:chen.song...@gmail.com>> Date: Wednesday, 15 October 2014 4:03 AM To: "user@spark.apache.org<mailto:user@spark.apache.org>" <user@spark.apache.org<mailto:user@spark.apache.org>> Subject: Re: Does SparkSQL work with custom defined SerDe? Looks like it may be related to https://issues.apache.org/jira/browse/SPARK-3807. I will build from branch 1.1 to see if the issue is resolved. Chen On Tue, Oct 14, 2014 at 10:33 AM, Chen Song <chen.song...@gmail.com<mailto:chen.song...@gmail.com>> wrote: Sorry for bringing this out again, as I have no clue what could have caused this. I turned on DEBUG logging and did see the jar containing the SerDe class was scanned. More interestingly, I saw the same exception (org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Unresolved attributes) when running simple select on valid column names and malformed column names. This lead me to suspect that syntactical breaks somewhere. select [valid_column] from table limit 5; select [malformed_typo_column] from table limit 5; On Mon, Oct 13, 2014 at 6:04 PM, Chen Song <chen.song...@gmail.com<mailto:chen.song...@gmail.com>> wrote: In Hive, the table was created with custom SerDe, in the following way. row format serde "abc.ProtobufSerDe" with serdeproperties ("serialization.class"="abc.protobuf.generated.LogA$log_a") When I start spark-sql shell, I always got the following exception, even for a simple query. select user from log_a limit 25; I can desc the table without any problem. When I explain the query, I got the same exception. 14/10/13 22:01:13 INFO impl.AMRMClientImpl: Waiting for application to be successfully unregistered. Exception in thread "Driver" java.lang.reflect.InvocationTargetException at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:606) at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:162) Caused by: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Unresolved attributes: 'user, tree: Project ['user] Filter (dh#4 = 2014-10-13 05) LowerCaseSchema MetastoreRelation test, log_a, None at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$$anonfun$apply$1.applyOrElse(Analyzer.scala:72) at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$$anonfun$apply$1.applyOrElse(Analyzer.scala:70) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:165) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:183) at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) at scala.collection.Iterator$class.foreach(Iterator.scala:727) at scala.collection.AbstractIterator.foreach(Iterator.scala:1157) at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47) at scala.collection.TraversableOnce$class.to<http://class.to>(TraversableOnce.scala:273) at scala.collection.AbstractIterator.to<http://scala.collection.AbstractIterator.to>(Iterator.scala:1157) at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265) at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157) at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252) at scala.collection.AbstractIterator.toArray(Iterator.scala:1157) at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildrenDown(TreeNode.scala:212) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:168) at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:156) at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$.apply(Analyzer.scala:70) at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$.apply(Analyzer.scala:68) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$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$lzycompute(SQLContext.scala:397) at org.apache.spark.sql.SQLContext$QueryExecution.analyzed(SQLContext.scala:397) at org.apache.spark.sql.hive.HiveContext$QueryExecution.optimizedPlan$lzycompute(HiveContext.scala:358) at org.apache.spark.sql.hive.HiveContext$QueryExecution.optimizedPlan(HiveContext.scala:357) at org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan$lzycompute(SQLContext.scala:402) at org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan(SQLContext.scala:400) at org.apache.spark.sql.SQLContext$QueryExecution.executedPlan$lzycompute(SQLContext.scala:406) at org.apache.spark.sql.SQLContext$QueryExecution.executedPlan(SQLContext.scala:406) at org.apache.spark.sql.SchemaRDD.collect(SchemaRDD.scala:438) at com.appnexus.data.spark.sql.Test$.main(Test.scala:23) at com.appnexus.data.spark.sql.Test.main(Test.scala) ... 5 more -- Chen Song -- Chen Song -- Chen Song