Thanks Cheng. For the time being , As a work around, I had applied the schema to Queryresult1, and then registered the result as temp table. Although that works, but I was not sure of performance impact, as that might block some optimisation in some scenarios.
This flow (on spark 1.1 ) works: registerTempTable(cachedSchema) Queryresult1 = Query1 using cachedSchema [ works ] *queryResult1withSchema = hiveContext.applySchema( Queryresult1, Queryresult1.schema )* registerTempTable(*queryResult1withSchema*) Queryresult2 = Query2 using *queryResult1withSchema* [ *works* ] On Fri, Sep 26, 2014 at 5:13 PM, Cheng Lian <lian.cs....@gmail.com> wrote: > H Twinkle, > > The failure is caused by case sensitivity. The temp table actually stores > the original un-analyzed logical plan, thus field names remain capital (F1, > F2, etc.). I believe this issue has already been fixed by PR #2382 > <https://github.com/apache/spark/pull/2382>. As a workaround, you can use > lowercase letters in field names instead. > > Cheng > > On 9/25/14 1:18 PM, twinkle sachdeva wrote: > > Hi, > > I am using Hive Context to fire the sql queries inside spark. I have > created a schemaRDD( Let's call it cachedSchema ) inside my code. > If i fire a sql query ( Query 1 ) on top of it, then it works. > > But if I refer to Query1's result inside another sql, that fails. Note > that I have already registered Query1's result as temp table. > > registerTempTable(cachedSchema) > Queryresult1 = Query1 using cachedSchema [ works ] > registerTempTable(Queryresult1) > > Queryresult2 = Query2 using Queryresult1 [ FAILS ] > > Is it expected?? Any known work around? > > Following is the exception I am receiving : > > > *org.apache.spark.sql.catalyst.errors.package$TreeNodeException: > Unresolved attributes: 'f1,'f2,'f3,'f4, tree:* > > *Project ['f1,'f2,'f3,'f4]* > > * Filter ('count > 3)* > > * LowerCaseSchema * > > * Subquery x* > > * Project ['F1,'F2,'F3,'F4,'F6,'Count]* > > * LowerCaseSchema * > > * Subquery src* > > * SparkLogicalPlan (ExistingRdd > [F1#0,F2#1,F3#2,F4#3,F5#4,F6#5,Count#6], MappedRDD[4] at map at > SQLBlock.scala:64)* > > > * at > org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$anonfun$apply$1.applyOrElse(Analyzer.scala:72)* > > * at > org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$anonfun$apply$1.applyOrElse(Analyzer.scala:70)* > > * at > org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:165)* > > * at > org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:156)* > > * at > org.apache.spark.sql.catalyst.analysis.Analyzer$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$QueryExecution.executedPlan(SQLContext.scala:406)* > > * at > org.apache.spark.sql.hive.HiveContext$QueryExecution.toRdd$lzycompute(HiveContext.scala:360)* > > * at > org.apache.spark.sql.hive.HiveContext$QueryExecution.toRdd(HiveContext.scala:360)* > > * at org.apache.spark.sql.SchemaRDD.getDependencies(SchemaRDD.scala:120)* > > * at org.apache.spark.rdd.RDD$anonfun$dependencies$2.apply(RDD.scala:191)* > > * at org.apache.spark.rdd.RDD$anonfun$dependencies$2.apply(RDD.scala:189)* > > >
