Hi Spark developers,
I have the following hqls that spark will throw exceptions of this kind:
14/07/10 15:07:55 INFO TaskSetManager: Loss was due to
org.apache.spark.TaskKilledException [duplicate 17]
org.apache.spark.SparkException: Job aborted due to stage failure: Task
0.0:736 failed 4 times, most recent failure: Exception failure in TID 167
on host etl2-node05:
org.apache.spark.sql.catalyst.errors.package$TreeNodeException: No function
to evaluate expression. type: UnresolvedAttribute, tree: 'm.id
org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute.eval(unresolved.scala:59)
org.apache.spark.sql.catalyst.expressions.Equals.eval(predicates.scala:151)
org.apache.spark.sql.execution.Filter$$anonfun$2$$anonfun$apply$1.apply(basicOperators.scala:52)
org.apache.spark.sql.execution.Filter$$anonfun$2$$anonfun$apply$1.apply(basicOperators.scala:52)
scala.collection.Iterator$$anon$14.hasNext(Iterator.scala:390)
scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
scala.collection.Iterator$class.foreach(Iterator.scala:727)
scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
scala.collection.AbstractIterator.to(Iterator.scala:1157)
scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
org.apache.spark.rdd.RDD$$anonfun$15.apply(RDD.scala:717)
org.apache.spark.rdd.RDD$$anonfun$15.apply(RDD.scala:717)
org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1080)
org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1080)
org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:111)
org.apache.spark.scheduler.Task.run(Task.scala:51)
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:187)
java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:886)
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:908)
java.lang.Thread.run(Thread.java:662)
The hql looks like this (I trimmed the hql down to the essentials to
demonstrate the potential bugs, the actual join is more complex and
irrelevant to the bug):
val hiveContext = new org.apache.spark.sql.hive.HiveContext(sc)
import hiveContext._
hql("USE test")
hql("select id from m").registerAsTable("m")
hql("select s.id from m join s on (s.id=m.id)").collect().foreach(println)
Apparently, spark is unable to understand the m.id in the "(s.id=m.id)". If
I change it to:
hql("select m_id from m").registerAsTable("m")
hql("select s.id from m join s on (s.id=m_id)").collect().foreach(println)
It will work. Am I doing something wrong or it is a bug in spark sql?
Best Regards,
Jerry
