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)
>>
