Hey Jim, This IOException thing is a general issue that we need to fix and your observation is spot-in. There is actually a JIRA for it here I created a few days ago: https://issues.apache.org/jira/browse/SPARK-1579
Aaron is assigned on that one but not actively working on it, so we'd welcome a PR from you on this if you are interested. The first thought we had was to set a volatile flag when the reader sees an exception (indicating there was a failure in the task) and avoid swallowing the IOException in the writer if this happens. But I think there is a race here where the writer sees the error first before the reader knows what is going on. Anyways maybe if you have a simpler solution you could sketch it out in the JIRA and we could talk over there. The current proposal in the JIRA is somewhat complicated... - Patrick On Mon, Apr 28, 2014 at 1:01 PM, Jim Blomo <jim.bl...@gmail.com> wrote: > FYI, it looks like this "stdin writer to Python finished early" error was > caused by a break in the connection to S3, from which the data was being > pulled. A recent commit to > PythonRDD<https://github.com/apache/spark/commit/a967b005c8937a3053e215c952d2172ee3dc300d#commitcomment-6114780>noted > that the current exception catching can potentially mask an exception > for the data source, and that is indeed what I see happening. The > underlying libraries (jets3t and httpclient) do have retry capabilities, > but I don't see a great way of setting them through Spark code. Instead I > added the patch below which kills the worker on the exception. This allows > me to completely load the data source after a few worker retries. > > Unfortunately, java.net.SocketException is the same error that is > sometimes expected from the client when using methods like take(). One > approach around this conflation is to create a new locally scoped exception > class, eg. WriterException, catch java.net.SocketException during output > writing, then re-throw the new exception. The worker thread could then > distinguish between the reasons java.net.SocketException might be thrown. > Perhaps there is a more elegant way to do this in Scala, though? > > Let me know if I should open a ticket or discuss this on the developers > list instead. Best, > > Jim > > diff --git > a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala > b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala > index 0d71fdb..f31158c 100644 > --- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala > +++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala > @@ -95,6 +95,12 @@ private[spark] class PythonRDD[T: ClassTag]( > readerException = e > Try(worker.shutdownOutput()) // kill Python worker process > > + case e: java.net.SocketException => > + // This can happen if a connection to the datasource, eg S3, > resets > + // or is otherwise broken > + readerException = e > + Try(worker.shutdownOutput()) // kill Python worker process > + > case e: IOException => > // This can happen for legitimate reasons if the Python code > stops returning data > // before we are done passing elements through, e.g., for > take(). Just log a message to > > > On Wed, Apr 9, 2014 at 7:04 PM, Jim Blomo <jim.bl...@gmail.com> wrote: > >> This dataset is uncompressed text at ~54GB. stats() returns (count: >> 56757667, mean: 1001.68740583, stdev: 601.775217822, max: 8965, min: >> 343) >> >> On Wed, Apr 9, 2014 at 6:59 PM, Matei Zaharia <matei.zaha...@gmail.com> >> wrote: >> > Okay, thanks. Do you have any info on how large your records and data >> file are? I'd like to reproduce and fix this. >> > >> > Matei >> > >> > On Apr 9, 2014, at 3:52 PM, Jim Blomo <jim.bl...@gmail.com> wrote: >> > >> >> Hi Matei, thanks for working with me to find these issues. >> >> >> >> To summarize, the issues I've seen are: >> >> 0.9.0: >> >> - https://issues.apache.org/jira/browse/SPARK-1323 >> >> >> >> SNAPSHOT 2014-03-18: >> >> - When persist() used and batchSize=1, java.lang.OutOfMemoryError: >> >> Java heap space. To me this indicates a memory leak since Spark >> >> should simply be counting records of size < 3MB >> >> - Without persist(), "stdin writer to Python finished early" hangs the >> >> application, unknown root cause >> >> >> >> I've recently rebuilt another SNAPSHOT, git commit 16b8308 with >> >> debugging turned on. This gives me the stacktrace on the new "stdin" >> >> problem: >> >> >> >> 14/04/09 22:22:45 DEBUG PythonRDD: stdin writer to Python finished >> early >> >> java.net.SocketException: Connection reset >> >> at java.net.SocketInputStream.read(SocketInputStream.java:196) >> >> at java.net.SocketInputStream.read(SocketInputStream.java:122) >> >> at sun.security.ssl.InputRecord.readFully(InputRecord.java:442) >> >> at >> sun.security.ssl.InputRecord.readV3Record(InputRecord.java:554) >> >> at sun.security.ssl.InputRecord.read(InputRecord.java:509) >> >> at >> sun.security.ssl.SSLSocketImpl.readRecord(SSLSocketImpl.java:927) >> >> at >> sun.security.ssl.SSLSocketImpl.readDataRecord(SSLSocketImpl.java:884) >> >> at sun.security.ssl.AppInputStream.read(AppInputStream.java:102) >> >> at >> java.io.BufferedInputStream.read1(BufferedInputStream.java:273) >> >> at >> java.io.BufferedInputStream.read(BufferedInputStream.java:334) >> >> at >> org.apache.commons.httpclient.WireLogInputStream.read(WireLogInputStream.java:69) >> >> at >> org.apache.commons.httpclient.ContentLengthInputStream.read(ContentLengthInputStream.java:170) >> >> at java.io.FilterInputStream.read(FilterInputStream.java:133) >> >> at >> org.apache.commons.httpclient.AutoCloseInputStream.read(AutoCloseInputStream.java:108) >> >> at >> org.jets3t.service.io.InterruptableInputStream.read(InterruptableInputStream.java:76) >> >> at >> org.jets3t.service.impl.rest.httpclient.HttpMethodReleaseInputStream.read(HttpMethodReleaseInputStream.java:136) >> >> at >> org.apache.hadoop.fs.s3native.NativeS3FileSystem$NativeS3FsInputStream.read(NativeS3FileSystem.java:98) >> >> at >> java.io.BufferedInputStream.read1(BufferedInputStream.java:273) >> >> at >> java.io.BufferedInputStream.read(BufferedInputStream.java:334) >> >> at java.io.DataInputStream.read(DataInputStream.java:100) >> >> at >> org.apache.hadoop.util.LineReader.readLine(LineReader.java:134) >> >> at >> org.apache.hadoop.mapred.LineRecordReader.next(LineRecordReader.java:133) >> >> at >> org.apache.hadoop.mapred.LineRecordReader.next(LineRecordReader.java:38) >> >> at >> org.apache.spark.rdd.HadoopRDD$$anon$1.getNext(HadoopRDD.scala:192) >> >> at >> org.apache.spark.rdd.HadoopRDD$$anon$1.getNext(HadoopRDD.scala:175) >> >> at >> org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:71) >> >> at >> org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:27) >> >> at >> scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) >> >> at >> scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:350) >> >> at scala.collection.Iterator$class.foreach(Iterator.scala:727) >> >> at >> scala.collection.AbstractIterator.foreach(Iterator.scala:1157) >> >> at >> org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:242) >> >> at >> org.apache.spark.api.python.PythonRDD$$anon$2.run(PythonRDD.scala:85) >> >> >> >> >> >> On Thu, Apr 3, 2014 at 8:37 PM, Matei Zaharia <matei.zaha...@gmail.com> >> wrote: >> >>> Cool, thanks for the update. Have you tried running a branch with >> this fix (e.g. branch-0.9, or the 0.9.1 release candidate?) Also, what >> memory leak issue are you referring to, is it separate from this? (Couldn't >> find it earlier in the thread.) >> >>> >> >>> To turn on debug logging, copy conf/log4j.properties.template to >> conf/log4j.properties and change the line log4j.rootCategory=INFO, console >> to log4j.rootCategory=DEBUG, console. Then make sure this file is present >> in "conf" on all workers. >> >>> >> >>> BTW I've managed to run PySpark with this fix on some reasonably >> large S3 data (multiple GB) and it was fine. It might happen only if >> records are large, or something like that. How much heap are you giving to >> your executors, and does it show that much in the web UI? >> >>> >> >>> Matei >> >>> >> >>> On Mar 29, 2014, at 10:44 PM, Jim Blomo <jim.bl...@gmail.com> wrote: >> >>> >> >>>> I think the problem I ran into in 0.9 is covered in >> >>>> https://issues.apache.org/jira/browse/SPARK-1323 >> >>>> >> >>>> When I kill the python process, the stacktrace I gets indicates that >> >>>> this happens at initialization. It looks like the initial write to >> >>>> the Python process does not go through, and then the iterator hangs >> >>>> waiting for output. I haven't had luck turning on debugging for the >> >>>> executor process. Still trying to learn the lgo4j properties that >> >>>> need to be set. >> >>>> >> >>>> No luck yet on tracking down the memory leak. >> >>>> >> >>>> 14/03/30 05:15:04 ERROR executor.Executor: Exception in task ID 11 >> >>>> org.apache.spark.SparkException: Python worker exited unexpectedly >> (crashed) >> >>>> at >> org.apache.spark.api.python.PythonRDD$$anon$1.read(PythonRDD.scala:168) >> >>>> at >> org.apache.spark.api.python.PythonRDD$$anon$1.<init>(PythonRDD.scala:174) >> >>>> at >> org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:113) >> >>>> at >> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:231) >> >>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:222) >> >>>> at >> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:111) >> >>>> at org.apache.spark.scheduler.Task.run(Task.scala:52) >> >>>> at >> org.apache.spark.executor.Executor$TaskRunner$$anonfun$run$1.apply$mcV$sp(Executor.scala:212) >> >>>> at >> org.apache.spark.deploy.SparkHadoopUtil$$anon$1.run(SparkHadoopUtil.scala:43) >> >>>> at >> org.apache.spark.deploy.SparkHadoopUtil$$anon$1.run(SparkHadoopUtil.scala:42) >> >>>> at java.security.AccessController.doPrivileged(Native Method) >> >>>> at javax.security.auth.Subject.doAs(Subject.java:415) >> >>>> at >> org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1121) >> >>>> at >> org.apache.spark.deploy.SparkHadoopUtil.runAsUser(SparkHadoopUtil.scala:42) >> >>>> at >> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:177) >> >>>> 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:724) >> >>>> >> >>>> >> >>>> On Sat, Mar 29, 2014 at 3:17 PM, Jim Blomo <jim.bl...@gmail.com> >> wrote: >> >>>>> I've only tried 0.9, in which I ran into the `stdin writer to Python >> >>>>> finished early` so frequently I wasn't able to load even a 1GB file. >> >>>>> Let me know if I can provide any other info! >> >>>>> >> >>>>> On Thu, Mar 27, 2014 at 8:48 PM, Matei Zaharia < >> matei.zaha...@gmail.com> wrote: >> >>>>>> I see, did this also fail with previous versions of Spark (0.9 or >> 0.8)? We'll try to look into these, seems like a serious error. >> >>>>>> >> >>>>>> Matei >> >>>>>> >> >>>>>> On Mar 27, 2014, at 7:27 PM, Jim Blomo <jim.bl...@gmail.com> >> wrote: >> >>>>>> >> >>>>>>> Thanks, Matei. I am running "Spark 1.0.0-SNAPSHOT built for >> Hadoop >> >>>>>>> 1.0.4" from GitHub on 2014-03-18. >> >>>>>>> >> >>>>>>> I tried batchSizes of 512, 10, and 1 and each got me further but >> none >> >>>>>>> have succeeded. >> >>>>>>> >> >>>>>>> I can get this to work -- with manual interventions -- if I omit >> >>>>>>> `parsed.persist(StorageLevel.MEMORY_AND_DISK)` and set >> batchSize=1. 5 >> >>>>>>> of the 175 executors hung, and I had to kill the python process >> to get >> >>>>>>> things going again. The only indication of this in the logs was >> `INFO >> >>>>>>> python.PythonRDD: stdin writer to Python finished early`. >> >>>>>>> >> >>>>>>> With batchSize=1 and persist, a new memory error came up in >> several >> >>>>>>> tasks, before the app was failed: >> >>>>>>> >> >>>>>>> 14/03/28 01:51:15 ERROR executor.Executor: Uncaught exception in >> >>>>>>> thread Thread[stdin writer for python,5,main] >> >>>>>>> java.lang.OutOfMemoryError: Java heap space >> >>>>>>> at java.util.Arrays.copyOfRange(Arrays.java:2694) >> >>>>>>> at java.lang.String.<init>(String.java:203) >> >>>>>>> at java.nio.HeapCharBuffer.toString(HeapCharBuffer.java:561) >> >>>>>>> at java.nio.CharBuffer.toString(CharBuffer.java:1201) >> >>>>>>> at org.apache.hadoop.io.Text.decode(Text.java:350) >> >>>>>>> at org.apache.hadoop.io.Text.decode(Text.java:327) >> >>>>>>> at org.apache.hadoop.io.Text.toString(Text.java:254) >> >>>>>>> at >> org.apache.spark.SparkContext$$anonfun$textFile$1.apply(SparkContext.scala:349) >> >>>>>>> at >> org.apache.spark.SparkContext$$anonfun$textFile$1.apply(SparkContext.scala:349) >> >>>>>>> at >> scala.collection.Iterator$$anon$11.next(Iterator.scala:328) >> >>>>>>> at >> scala.collection.Iterator$$anon$12.next(Iterator.scala:357) >> >>>>>>> at >> scala.collection.Iterator$class.foreach(Iterator.scala:727) >> >>>>>>> at >> scala.collection.AbstractIterator.foreach(Iterator.scala:1157) >> >>>>>>> at >> org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:242) >> >>>>>>> at >> org.apache.spark.api.python.PythonRDD$$anon$2.run(PythonRDD.scala:85) >> >>>>>>> >> >>>>>>> There are other exceptions, but I think they all stem from the >> above, >> >>>>>>> eg. org.apache.spark.SparkException: Error sending message to >> >>>>>>> BlockManagerMaster >> >>>>>>> >> >>>>>>> Let me know if there are other settings I should try, or if I >> should >> >>>>>>> try a newer snapshot. >> >>>>>>> >> >>>>>>> Thanks again! >> >>>>>>> >> >>>>>>> >> >>>>>>> On Mon, Mar 24, 2014 at 9:35 AM, Matei Zaharia < >> matei.zaha...@gmail.com> wrote: >> >>>>>>>> Hey Jim, >> >>>>>>>> >> >>>>>>>> In Spark 0.9 we added a "batchSize" parameter to PySpark that >> makes it group multiple objects together before passing them between Java >> and Python, but this may be too high by default. Try passing batchSize=10 >> to your SparkContext constructor to lower it (the default is 1024). Or even >> batchSize=1 to match earlier versions. >> >>>>>>>> >> >>>>>>>> Matei >> >>>>>>>> >> >>>>>>>> On Mar 21, 2014, at 6:18 PM, Jim Blomo <jim.bl...@gmail.com> >> wrote: >> >>>>>>>> >> >>>>>>>>> Hi all, I'm wondering if there's any settings I can use to >> reduce the >> >>>>>>>>> memory needed by the PythonRDD when computing simple stats. I >> am >> >>>>>>>>> getting OutOfMemoryError exceptions while calculating count() >> on big, >> >>>>>>>>> but not absurd, records. It seems like PythonRDD is trying to >> keep >> >>>>>>>>> too many of these records in memory, when all that is needed is >> to >> >>>>>>>>> stream through them and count. Any tips for getting through >> this >> >>>>>>>>> workload? >> >>>>>>>>> >> >>>>>>>>> >> >>>>>>>>> Code: >> >>>>>>>>> session = sc.textFile('s3://...json.gz') # ~54GB of compressed >> data >> >>>>>>>>> >> >>>>>>>>> # the biggest individual text line is ~3MB >> >>>>>>>>> parsed = session.map(lambda l: l.split("\t",1)).map(lambda >> (y,s): >> >>>>>>>>> (loads(y), loads(s))) >> >>>>>>>>> parsed.persist(StorageLevel.MEMORY_AND_DISK) >> >>>>>>>>> >> >>>>>>>>> parsed.count() >> >>>>>>>>> # will never finish: executor.Executor: Uncaught exception will >> FAIL >> >>>>>>>>> all executors >> >>>>>>>>> >> >>>>>>>>> Incidentally the whole app appears to be killed, but this error >> is not >> >>>>>>>>> propagated to the shell. >> >>>>>>>>> >> >>>>>>>>> Cluster: >> >>>>>>>>> 15 m2.xlarges (17GB memory, 17GB swap, >> spark.executor.memory=10GB) >> >>>>>>>>> >> >>>>>>>>> Exception: >> >>>>>>>>> java.lang.OutOfMemoryError: Java heap space >> >>>>>>>>> at >> org.apache.spark.api.python.PythonRDD$$anon$1.read(PythonRDD.scala:132) >> >>>>>>>>> at >> org.apache.spark.api.python.PythonRDD$$anon$1.next(PythonRDD.scala:120) >> >>>>>>>>> at >> org.apache.spark.api.python.PythonRDD$$anon$1.next(PythonRDD.scala:113) >> >>>>>>>>> at >> scala.collection.Iterator$class.foreach(Iterator.scala:727) >> >>>>>>>>> at >> org.apache.spark.api.python.PythonRDD$$anon$1.foreach(PythonRDD.scala:113) >> >>>>>>>>> at >> scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48) >> >>>>>>>>> at >> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103) >> >>>>>>>>> at >> org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:94) >> >>>>>>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:220) >> >>>>>>>>> at >> org.apache.spark.api.python.PythonRDD$$anon$2.run(PythonRDD.scala:85) >> >>>>>>>> >> >>>>>> >> >>> >> > >> > >
