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)
>
