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)*