Even a count() on the result of the flatMap() fails with the same error.
Somehow the formatting on the error output got messed in my previous email,
so here's a relevant snippet of the output again.
14/03/01 04:39:01 INFO scheduler.DAGScheduler: Failed to run count at
<stdin>:1
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/root/spark/python/pyspark/rdd.py", line 542, in count
return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
File "/root/spark/python/pyspark/rdd.py", line 533, in sum
return self.mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
File "/root/spark/python/pyspark/rdd.py", line 499, in reduce
vals = self.mapPartitions(func).collect()
File "/root/spark/python/pyspark/rdd.py", line 463, in collect
bytesInJava = self._jrdd.collect().iterator()
File "/root/spark/python/lib/py4j-0.8.1-src.zip/py4j/java_gateway.py",
line 537, in __call__
File "/root/spark/python/lib/py4j-0.8.1-src.zip/py4j/protocol.py", line
300, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o396.collect.
: org.apache.spark.SparkException: Job aborted: Task 29.0:0 failed 4 times
(most recent failure: Exception failure: java.net.SocketException:
Connection reset)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1028)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1026)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.DAGScheduler.org
$apache$spark$scheduler$DAGScheduler$$abortStage(DAGScheduler.scala:1026)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:619)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:619)
at scala.Option.foreach(Option.scala:236)
at
org.apache.spark.scheduler.DAGScheduler.processEvent(DAGScheduler.scala:619)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$start$1$$anon$2$$anonfun$receive$1.applyOrElse(DAGScheduler.scala:207)
at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
at akka.actor.ActorCell.invoke(ActorCell.scala:456)
at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
at akka.dispatch.Mailbox.run(Mailbox.scala:219)
at
akka.dispatch.ForkJoinExecutorConfigurator$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)
Any pointers to where I should look, or things to try?
Nick
On Fri, Feb 28, 2014 at 6:33 PM, nicholas.chammas <
nicholas.cham...@gmail.com> wrote:
> I've done a whole bunch of things to this RDD, and now when I try to
> sortByKey(), this is what I get:
>
> >>> flattened_po.flatMap(lambda x:
> >>> map_to_database_types(x)).sortByKey()14/02/28
> 23:18:41 INFO spark.SparkContext: Starting job: sortByKey at <stdin>:114/02/28
> 23:18:41 INFO scheduler.DAGScheduler: Got job 22 (sortByKey at <stdin>:1)
> with 1 output partitions (allowLocal=false)14/02/28 23:18:41 INFO
> scheduler.DAGScheduler: Final stage: Stage 23 (sortByKey at <stdin>:1)14/02/28
> 23:18:41 INFO scheduler.DAGScheduler: Parents of final stage: List()14/02/28
> 23:18:41 INFO scheduler.DAGScheduler: Missing parents: List()14/02/28
> 23:18:41 INFO scheduler.DAGScheduler: Submitting Stage 23 (PythonRDD[41] at
> sortByKey at <stdin>:1), which has no missing parents14/02/28 23:18:41
> INFO scheduler.DAGScheduler: Submitting 1 missing tasks from Stage 23
> (PythonRDD[41] at sortByKey at <stdin>:1)14/02/28 23:18:41 INFO
> scheduler.TaskSchedulerImpl: Adding task set 23.0 with 1 tasks14/02/28
> 23:18:41 INFO scheduler.TaskSetManager: Starting task 23.0:0 as TID 32 on
> executor 0: ip-<blah>.ec2.internal (PROCESS_LOCAL)14/02/28 23:18:41 INFO
> scheduler.TaskSetManager: Serialized task 23.0:0 as 4985 bytes in 1 ms14/02/28
> 23:18:41 WARN scheduler.TaskSetManager: Lost TID 32 (task 23.0:0)14/02/28
> 23:18:41 WARN scheduler.TaskSetManager: Loss was due to
> java.net.SocketExceptionjava.net.SocketException: Connection reset at
> java.net.SocketInputStream.read(SocketInputStream.java:196) at
> java.net.SocketInputStream.read(SocketInputStream.java:122) at
> java.io.BufferedInputStream.fill(BufferedInputStream.java:235) at
> java.io.BufferedInputStream.read(BufferedInputStream.java:254) at
> java.io.DataInputStream.readInt(DataInputStream.java:387) at
> org.apache.spark.api.python.PythonRDD$$anon$1.read(PythonRDD.scala:110) at
> org.apache.spark.api.python.PythonRDD$$anon$1.<init>(PythonRDD.scala:153) at
> org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:96) at
> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:241) at
> org.apache.spark.rdd.RDD.iterator(RDD.scala:232) at
> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:109) at
> org.apache.spark.scheduler.Task.run(Task.scala:53) at
> org.apache.spark.executor.Executor$TaskRunner$$anonfun$run$1.apply$mcV$sp(Executor.scala:213)
> at
> org.apache.spark.deploy.SparkHadoopUtil.runAsUser(SparkHadoopUtil.scala:49) 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:744)14/02/28 23:18:41 INFO
> scheduler.TaskSetManager: Starting task 23.0:0 as TID 33 on executor 0:
> ip-<blah>.ec2.internal (PROCESS_LOCAL)14/02/28 23:18:41 INFO
> scheduler.TaskSetManager: Serialized task 23.0:0 as 4985 bytes in 1 ms14/02/28
> 23:18:41 WARN scheduler.TaskSetManager: Lost TID 33 (task 23.0:0)14/02/28
> 23:18:41 INFO scheduler.TaskSetManager: Loss was due to
> java.net.SocketException: Connection reset [duplicate 1]14/02/28 23:18:41
> INFO scheduler.TaskSetManager: Starting task 23.0:0 as TID 34 on executor
> 0: ip-<blah>.ec2.internal (PROCESS_LOCAL)14/02/28 23:18:41 INFO
> scheduler.TaskSetManager: Serialized task 23.0:0 as 4985 bytes in 1 ms14/02/28
> 23:18:41 WARN scheduler.TaskSetManager: Lost TID 34 (task 23.0:0)14/02/28
> 23:18:41 INFO scheduler.TaskSetManager: Loss was due to
> java.net.SocketException: Connection reset [duplicate 2]14/02/28 23:18:41
> INFO scheduler.TaskSetManager: Starting task 23.0:0 as TID 35 on executor
> 0: ip-<blah>.ec2.internal (PROCESS_LOCAL)14/02/28 23:18:41 INFO
> scheduler.TaskSetManager: Serialized task 23.0:0 as 4985 bytes in 1 ms14/02/28
> 23:18:41 WARN scheduler.TaskSetManager: Lost TID 35 (task 23.0:0)14/02/28
> 23:18:41 INFO scheduler.TaskSetManager: Loss was due to
> java.net.SocketException: Connection reset [duplicate 3]14/02/28 23:18:41
> ERROR scheduler.TaskSetManager: Task 23.0:0 failed 4 times; aborting
> job14/02/28
> 23:18:41 INFO scheduler.TaskSchedulerImpl: Remove TaskSet 23.0 from pool
> 14/02/28
> 23:18:41 INFO scheduler.DAGScheduler: Failed to run sortByKey at
> <stdin>:1Traceback
> (most recent call last): File "<stdin>", line 1, in <module> File
> "/root/spark/python/pyspark/rdd.py", line 361, in sortByKey rddSize =
> self.count() File "/root/spark/python/pyspark/rdd.py", line 542, in count
> return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum() File
> "/root/spark/python/pyspark/rdd.py", line 533, in sum return
> self.mapPartitions(lambda x: [sum(x)]).reduce(operator.add) File
> "/root/spark/python/pyspark/rdd.py", line 499, in reduce vals =
> self.mapPartitions(func).collect() File
> "/root/spark/python/pyspark/rdd.py", line 463, in collect bytesInJava
> = self._jrdd.collect().iterator() File
> "/root/spark/python/lib/py4j-0.8.1-src.zip/py4j/java_gateway.py", line 537,
> in __call__ File
> "/root/spark/python/lib/py4j-0.8.1-src.zip/py4j/protocol.py", line 300, in
> get_return_valuepy4j.protocol.Py4JJavaError: An error occurred while
> calling o332.collect.: org.apache.spark.SparkException: Job aborted: Task
> 23.0:0 failed 4 times (most recent failure: Exception failure:
> java.net.SocketException: Connection reset) at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1028)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1026)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at
> scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) at
> org.apache.spark.scheduler.DAGScheduler.org
> $apache$spark$scheduler$DAGScheduler$$abortStage(DAGScheduler.scala:1026) at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:619)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:619)
> at
> scala.Option.foreach(Option.scala:236) at
> org.apache.spark.scheduler.DAGScheduler.processEvent(DAGScheduler.scala:619)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$start$1$$anon$2$$anonfun$receive$1.applyOrElse(DAGScheduler.scala:207)
> at
> akka.actor.ActorCell.receiveMessage(ActorCell.scala:498) at
> akka.actor.ActorCell.invoke(ActorCell.scala:456) at
> akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237) at
> akka.dispatch.Mailbox.run(Mailbox.scala:219) at
> akka.dispatch.ForkJoinExecutorConfigurator$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)
> >>>
>
>
> The lambda passed to flatMap() returns a list of tuples; take() works fine
> just on the flatMap().
>
> Where would I start to troubleshoot this error?
>
> The error output includes mention of reset connections, so I naively
> confirmed that the master node can reach its 1 slave. Dunno if those are
> related things.
>
> If it matters any, I upgraded the cluster to Python 2.7 using the
> instructions here <https://spark-project.atlassian.net/browse/SPARK-922>.
> Also, I am running Spark 0.9.0, though I notice that in the error output is
> mention of 0.8.1 files.
>
> Nick
>
> ------------------------------
> View this message in context: java.net.SocketException on reduceByKey()
> in
> pyspark<http://apache-spark-user-list.1001560.n3.nabble.com/java-net-SocketException-on-reduceByKey-in-pyspark-tp2184.html>
> Sent from the Apache Spark User List mailing list
> archive<http://apache-spark-user-list.1001560.n3.nabble.com/>at Nabble.com.
>
