I have the exact same error running on a bare metal cluster with CentOS6 and Python 2.6.6. Any other thoughts on the problem here? I only get the error on operations that require communication, like reduceByKey or groupBy.
On Sun, Mar 2, 2014 at 1:29 PM, Nicholas Chammas <nicholas.cham...@gmail.com > wrote: > Alright, so this issue is related to the upgrade to Python 2.7, which > relates it to the other Python 2.7 issue I reported in this > thread<http://apache-spark-user-list.1001560.n3.nabble.com/Python-2-7-numpy-break-sortByKey-td2214.html> > . > > I modified my code not to rely on Python 2.7, spun up a new cluster and > did *not* upgrade its version of Python from 2.6.8. The code ran fine. > > I'd open a JIRA issue about this, but I cannot provide a simple repro that > anyone can walk through. > > Nick > > > On Fri, Feb 28, 2014 at 11:44 PM, Nicholas Chammas < > nicholas.cham...@gmail.com> wrote: > >> 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. >>> >> >> > -- Uri Laserson, PhD Data Scientist, Cloudera Twitter/GitHub: @laserson +1 617 910 0447 laser...@cloudera.com
