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From Matei Zaharia <matei.zaha...@gmail.com>
Subject Re: pySpark memory usage
Date Fri, 28 Mar 2014 03:48:01 GMT
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.blomo@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.zaharia@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.blomo@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)
>> 


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