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From Chen Song <chen.song...@gmail.com>
Subject Re: Does SparkSQL work with custom defined SerDe?
Date Tue, 14 Oct 2014 14:33:46 GMT
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.82@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$$anon$11.next(Iterator.scala:328)
>
>         at scala.collection.Iterator$class.foreach(Iterator.scala:727)
>
>         at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
>
>         at
> scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
>
>         at
> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
>
>         at
> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
>
>         at scala.collection.TraversableOnce$class.to
> (TraversableOnce.scala:273)
>
>         at scala.collection.AbstractIterator.to(Iterator.scala:1157)
>
>         at
> scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
>
>         at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
>
>         at
> scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
>
>         at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
>
>         at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformChildrenDown(TreeNode.scala:212)
>
>         at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:168)
>
>         at
> org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:156)
>
>         at
> org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$.apply(Analyzer.scala:70)
>
>         at
> org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$.apply(Analyzer.scala:68)
>
>         at
> org.apache.spark.sql.catalyst.rules.RuleExecutor$$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

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