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) >>>>>>>> >>>>>> >>> >
