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. >> > >
