Michael:
Thanks for the quick response. I can confirm that once I removed the “order
by” clause the exception below went away. So, I believe this confirms what
you were say and I will be opening a new feature request in JIRA.
However, that exception was replaced by a java.lang.OutOfMemoryError: Java heap
space error. I am guessing this relates to any of the following Issues:
SPARK-2902 Change default options to be more agressive (In memory columnar
compression)
SPARK-3056 Sort-based Aggregation (SparkSQL only support the hash-based
aggregation, which may cause OOM if too many identical keys in the input
tuples.)
SPARK-2926 Add MR-style (merge-sort) SortShuffleReader for sort-based shuffle
The Exception is included below.
Paul Magid
Toyota Motor Sales IS Enterprise Architecture (EA)
Architect I R&D
Ph: 310-468-9091 (X69091)
PCN 1C2970, Mail Drop PN12
Exception
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
14/09/18 10:11:03 INFO TaskSetManager: Finished task 36.0 in stage 0.0 (TID 57)
in 18681 ms on votlbdcd04.tms.toyota.com (5/200)
14/09/18 10:11:09 ERROR Utils: Uncaught exception in thread Result resolver
thread-0
java.lang.OutOfMemoryError: Java heap space
Exception in thread "Result resolver thread-0" 14/09/18 10:11:09 INFO
RemoteActorRefProvider$RemotingTerminator: Shutting down remote daemon.
java.lang.OutOfMemoryError: Java heap space
14/09/18 10:11:09 INFO RemoteActorRefProvider$RemotingTerminator: Remote daemon
shut down; proceeding with flushing remote transports.
14/09/18 10:11:09 INFO Remoting: Remoting shut down
14/09/18 10:11:09 INFO RemoteActorRefProvider$RemotingTerminator: Remoting shut
down.
org.apache.spark.SparkException: Job cancelled because SparkContext was shut
down
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:694)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:693)
at scala.collection.mutable.HashSet.foreach(HashSet.scala:79)
at
org.apache.spark.scheduler.DAGScheduler.cleanUpAfterSchedulerStop(DAGScheduler.scala:693)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessActor.postStop(DAGScheduler.scala:1399)
at
akka.actor.dungeon.FaultHandling$class.akka$actor$dungeon$FaultHandling$$finishTerminate(FaultHandling.scala:201)
at
akka.actor.dungeon.FaultHandling$class.terminate(FaultHandling.scala:163)
at akka.actor.ActorCell.terminate(ActorCell.scala:338)
at akka.actor.ActorCell.invokeAll$1(ActorCell.scala:431)
at akka.actor.ActorCell.systemInvoke(ActorCell.scala:447)
at akka.dispatch.Mailbox.processAllSystemMessages(Mailbox.scala:262)
at akka.dispatch.Mailbox.run(Mailbox.scala:218)
at
akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
at
scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
at
scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
at
scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
scala> 14/09/18 10:11:09 INFO TaskSetManager: Finished task 50.0 in stage 0.0
(TID 71) in 27100 ms on votlbdcd04.tms.toyota.com (6/200)
14/09/18 10:11:10 INFO TaskSetManager: Finished task 22.0 in stage 0.0 (TID 43)
in 27520 ms on votlbdcd04.tms.toyota.com (7/200)
14/09/18 10:11:10 INFO ConnectionManager: Removing ReceivingConnection to
ConnectionManagerId(votlbdcd05.tms.toyota.com,24438)
14/09/18 10:11:10 INFO ConnectionManager: Key not valid ?
sun.nio.ch.SelectionKeyImpl@7a542d34
14/09/18 10:11:10 INFO ConnectionManager: key already cancelled ?
sun.nio.ch.SelectionKeyImpl@7a542d34
java.nio.channels.CancelledKeyException
at
org.apache.spark.network.ConnectionManager.run(ConnectionManager.scala:386)
at
org.apache.spark.network.ConnectionManager$$anon$4.run(ConnectionManager.scala:139)
14/09/18 10:11:10 INFO ConnectionManager: Removing SendingConnection to
ConnectionManagerId(votlbdcd05.tms.toyota.com,24438)
14/09/18 10:11:10 INFO ConnectionManager: Removing SendingConnection to
ConnectionManagerId(votlbdcd05.tms.toyota.com,24438)
14/09/18 10:11:10 INFO ConnectionManager: Handling connection error on
connection to ConnectionManagerId(votlbdcd05.tms.toyota.com,24438)
14/09/18 10:11:10 INFO ConnectionManager: Removing SendingConnection to
ConnectionManagerId(votlbdcd05.tms.toyota.com,24438)
14/09/18 10:11:10 INFO ConnectionManager: Removing SendingConnection to
ConnectionManagerId(votlbdcd05.tms.toyota.com,24438)
14/09/18 10:11:10 INFO ConnectionManager: Removing SendingConnection to
ConnectionManagerId(votlbdcd06.tms.toyota.com,19998)
14/09/18 10:11:10 INFO ConnectionManager: Removing ReceivingConnection to
ConnectionManagerId(votlbdcd06.tms.toyota.com,19998)
14/09/18 10:11:10 ERROR ConnectionManager: Corresponding SendingConnection to
ConnectionManagerId(votlbdcd06.tms.toyota.com,19998) not found
14/09/18 10:11:10 INFO ConnectionManager: Removing SendingConnection to
ConnectionManagerId(votlbdcd04.tms.toyota.com,25043)
14/09/18 10:11:10 INFO ConnectionManager: Removing ReceivingConnection to
ConnectionManagerId(votlbdcd04.tms.toyota.com,25043)
14/09/18 10:11:10 ERROR ConnectionManager: Corresponding SendingConnection to
ConnectionManagerId(votlbdcd04.tms.toyota.com,25043) not found
14/09/18 10:11:10 INFO ConnectionManager: Removing SendingConnection to
ConnectionManagerId(votlbdcd07.tms.toyota.com,28529)
14/09/18 10:11:10 INFO ConnectionManager: Removing ReceivingConnection to
ConnectionManagerId(votlbdcd07.tms.toyota.com,28529)
14/09/18 10:11:10 ERROR ConnectionManager: Corresponding SendingConnection to
ConnectionManagerId(votlbdcd07.tms.toyota.com,28529) not found
14/09/18 10:11:10 INFO ConnectionManager: Removing SendingConnection to
ConnectionManagerId(votlbdcd02.tms.toyota.com,16101)
14/09/18 10:11:10 INFO ConnectionManager: Removing ReceivingConnection to
ConnectionManagerId(votlbdcd02.tms.toyota.com,16101)
14/09/18 10:11:10 ERROR ConnectionManager: Corresponding SendingConnection to
ConnectionManagerId(votlbdcd02.tms.toyota.com,16101) not found
14/09/18 10:11:10 INFO ConnectionManager: Removing SendingConnection to
ConnectionManagerId(votlbdcd08.tms.toyota.com,49294)
14/09/18 10:11:10 INFO ConnectionManager: Removing ReceivingConnection to
ConnectionManagerId(votlbdcd08.tms.toyota.com,49294)
14/09/18 10:11:10 ERROR ConnectionManager: Corresponding SendingConnection to
ConnectionManagerId(votlbdcd08.tms.toyota.com,49294) not found
From: Michael Armbrust [mailto:mich...@databricks.com]
Sent: Thursday, September 18, 2014 9:47 AM
To: Paul Magid
Cc: user@spark.apache.org; Brian Kursar (TMS)
Subject: Re: Spark SQL Exception
Its failing to sort because the columns are of Binary type (though maybe we
should support this as well). Is this parquet data that was generated by
impala that you would expect to be a String? If so turn on
spark.sql.parquet.binaryAsString<http://spark.apache.org/docs/latest/sql-programming-guide.html#configuration>.
Otherwise you could file a JIRA asking us to add support for sorting binary
data (or both :) ).
Michael
On Thu, Sep 18, 2014 at 9:31 AM, Paul Magid
<paul_ma...@toyota.com<mailto:paul_ma...@toyota.com>> wrote:
All:
I am putting Spark SQL 1.1 through its paces (in a POC) and have been
pleasantly surprised with what can be done with such a young technology. I
have run into an exception (listed below) that I suspect relates to the number
of columns in the table I am querying. There are 336 columns in the table.
I have included the Scala / Spark SQL I am running. This Spark SQL code runs
just fine when run against “narrower” tables. Also, we have purpose built
this POC cluster with lots of memory and we have set up Impala and Spark SQL
with roughly the same amounts of memory. There are 7 worker nodes with 20GB
memory for Impala and Spark SQL each. We are using Impala as a comparative
benchmark and sanity check. The equivalent SQL runs just fine in Impala (see
below). I am a bit of a noob and any help (even with the code below) is
greatly appreciated. Also, is there a document that lists current Spark SQL
limitations/issues?
Paul Magid
Toyota Motor Sales IS Enterprise Architecture (EA)
Architect I R&D
Ph: 310-468-9091<tel:310-468-9091> (X69091)
PCN 1C2970, Mail Drop PN12
Successful Result In Impala
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+----------------+
| marital_status |
+----------------+
| M |
| S |
| U |
| null |
+----------------+
Returned 4 row(s) in 0.91s
Code
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
//Timer code
def time[R](block: => R): R = {
val t0 = System.nanoTime()
val result = block // call-by-name
val t1 = System.nanoTime()
println("Elapsed time: " + (t1 - t0).toFloat/1000000000 + "s")
result
}
//Declare and import SQLContext
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
import sqlContext._
//Load Parquet file into a table
val parquetFile_db2 =
sqlContext.parquetFile("hdfs://x.x.x.x:8020/user/hive/warehouse/c360poc.db/customer_demographic_pq/")
parquetFile_db2.registerAsTable("customer_demographic_pq")
//Run SQL code with timer
val records= time {sql("select marital_status from customer_demographic_pq
group by marital_status order by marital_status ").collect().foreach(println)}
Exception
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
14/09/18 08:50:39 INFO SparkContext: Job finished: RangePartitioner at
Exchange.scala:79, took 21.885859255 s
org.apache.spark.sql.catalyst.errors.package$TreeNodeException: sort, tree:
Sort [marital_status#9 ASC], true
Exchange (RangePartitioning [marital_status#9 ASC], 200)
Aggregate false, [marital_status#9], [marital_status#9]
Exchange (HashPartitioning [marital_status#9], 200)
Aggregate true, [marital_status#9], [marital_status#9]
ParquetTableScan [marital_status#9], (ParquetRelation
hdfs://x.x.x.x:8020/user/hive/warehouse/c360poc.db/customer_demographic_pq/,
Some(Configuration: core-default.xml, core-site.xml, mapred-default.xml,
mapred-site.xml, yarn-default.xml, yarn-site.xml, hdfs-default.xml,
hdfs-site.xml),
org.apache.spark.sql.SQLContext@4d79d3de<mailto:org.apache.spark.sql.SQLContext@4d79d3de>,
[]), []
at
org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:47)
at org.apache.spark.sql.execution.Sort.execute(basicOperators.scala:191)
at
org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:85)
at org.apache.spark.sql.SchemaRDD.collect(SchemaRDD.scala:438)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply$mcV$sp(<console>:19)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:19)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:19)
at $iwC$$iwC$$iwC$$iwC.time(<console>:12)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:19)
at $iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:24)
at $iwC$$iwC$$iwC$$iwC.<init>(<console>:26)
at $iwC$$iwC$$iwC.<init>(<console>:28)
at $iwC$$iwC.<init>(<console>:30)
at $iwC.<init>(<console>:32)
at <init>(<console>:34)
at .<init>(<console>:38)
at .<clinit>(<console>)
at .<init>(<console>:7)
at .<clinit>(<console>)
at $print(<console>)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
at java.lang.reflect.Method.invoke(Unknown Source)
at
org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:789)
at
org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1062)
at
org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:615)
at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:646)
at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:610)
at
org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:814)
at
org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:859)
at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:771)
at org.apache.spark.repl.SparkILoop.processLine$1(SparkILoop.scala:616)
at org.apache.spark.repl.SparkILoop.innerLoop$1(SparkILoop.scala:624)
at org.apache.spark.repl.SparkILoop.loop(SparkILoop.scala:629)
at
org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply$mcZ$sp(SparkILoop.scala:954)
at
org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply(SparkILoop.scala:902)
at
org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply(SparkILoop.scala:902)
at
scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135)
at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:902)
at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:997)
at org.apache.spark.repl.Main$.main(Main.scala:31)
at org.apache.spark.repl.Main.main(Main.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
at java.lang.reflect.Method.invoke(Unknown Source)
at org.apache.spark.deploy.SparkSubmit$.launch(SparkSubmit.scala:328)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:75)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: org.apache.spark.sql.catalyst.errors.package$TreeNodeException:
execute, tree:
Exchange (RangePartitioning [marital_status#9 ASC], 200)
Aggregate false, [marital_status#9], [marital_status#9]
Exchange (HashPartitioning [marital_status#9], 200)
Aggregate true, [marital_status#9], [marital_status#9]
ParquetTableScan [marital_status#9], (ParquetRelation
hdfs://x.x.x.x:8020/user/hive/warehouse/c360poc.db/customer_demographic_pq/,
Some(Configuration: core-default.xml, core-site.xml, mapred-default.xml,
mapred-site.xml, yarn-default.xml, yarn-site.xml, hdfs-default.xml,
hdfs-site.xml),
org.apache.spark.sql.SQLContext@4d79d3de<mailto:org.apache.spark.sql.SQLContext@4d79d3de>,
[]), []
at
org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:47)
at org.apache.spark.sql.execution.Exchange.execute(Exchange.scala:44)
at
org.apache.spark.sql.execution.Sort$$anonfun$execute$3.apply(basicOperators.scala:192)
at
org.apache.spark.sql.execution.Sort$$anonfun$execute$3.apply(basicOperators.scala:193)
at
org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:46)
... 49 more
Caused by: scala.MatchError: BinaryType (of class
org.apache.spark.sql.catalyst.types.BinaryType$)
at
org.apache.spark.sql.catalyst.expressions.RowOrdering.compare(Row.scala:256)
at
org.apache.spark.sql.catalyst.expressions.RowOrdering.compare(Row.scala:238)
at scala.math.Ordering$$anon$5.compare(Ordering.scala:122)
at java.util.TimSort.countRunAndMakeAscending(Unknown Source)
at java.util.TimSort.sort(Unknown Source)
at java.util.TimSort.sort(Unknown Source)
at java.util.Arrays.sort(Unknown Source)
at scala.collection.SeqLike$class.sorted(SeqLike.scala:615)
at scala.collection.AbstractSeq.sorted(Seq.scala:40)
at scala.collection.SeqLike$class.sortBy(SeqLike.scala:594)
at scala.collection.AbstractSeq.sortBy(Seq.scala:40)
at
org.apache.spark.RangePartitioner$.determineBounds(Partitioner.scala:279)
at org.apache.spark.RangePartitioner.<init>(Partitioner.scala:152)
at
org.apache.spark.sql.execution.Exchange$$anonfun$execute$1.apply(Exchange.scala:79)
at
org.apache.spark.sql.execution.Exchange$$anonfun$execute$1.apply(Exchange.scala:45)
at
org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:46)
... 53 more
