You can't use columns (timestamp) that aren't in the GROUP BY clause. Spark 1.2+ give you a better error message for this case.
On Fri, Feb 6, 2015 at 3:12 PM, Mohnish Kodnani <mohnish.kodn...@gmail.com> wrote: > Hi, > i am trying to issue a sql query against a parquet file and am getting > errors and would like some help to figure out what is going on. > > The sql : > select timestamp, count(rid), qi.clientname from records where timestamp > > 0 group by qi.clientname > > I am getting the following error: > *org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding > attribute, tree: timestamp#0L* > at > org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:47) > at > org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:43) > at > org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:42) > at > org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:165) > at > org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:156) > at > org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:42) > at > org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection$$anonfun$$init$$2.apply(Projection.scala:52) > at > org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection$$anonfun$$init$$2.apply(Projection.scala:52) > at > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) > at > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) > at scala.collection.immutable.List.foreach(List.scala:318) > at scala.collection.TraversableLike$class.map(TraversableLike.scala:244) > at scala.collection.AbstractTraversable.map(Traversable.scala:105) > at > org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.<init>(Projection.scala:52) > at > org.apache.spark.sql.execution.Aggregate$$anonfun$execute$1$$anonfun$7$$anon$1.<init>(Aggregate.scala:176) > at > org.apache.spark.sql.execution.Aggregate$$anonfun$execute$1$$anonfun$7.apply(Aggregate.scala:172) > at > org.apache.spark.sql.execution.Aggregate$$anonfun$execute$1$$anonfun$7.apply(Aggregate.scala:151) > at org.apache.spark.rdd.RDD$$anonfun$13.apply(RDD.scala:596) > at org.apache.spark.rdd.RDD$$anonfun$13.apply(RDD.scala:596) > at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) > at org.apache.spark.sql.SchemaRDD.compute(SchemaRDD.scala:115) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:62) > at org.apache.spark.scheduler.Task.run(Task.scala:54) > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:178) > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) > at java.lang.Thread.run(Thread.java:745) > *Caused by: java.lang.RuntimeException: Couldn't find timestamp#0L in > [aggResult:SUM(PartialCount#14L)#17L,clientName#11]* > at scala.sys.package$.error(package.scala:27) > at > org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:46) > at > org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:43) > at > org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:46) > >
