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From Davies Liu <dav...@databricks.com>
Subject Re: NegativeArraySizeException in pyspark when loading an RDD pickleFile
Date Wed, 28 Jan 2015 18:01:16 GMT
HadoopRDD will try to split the file as 64M partitions in size, so you
got 1916+ partitions.
(assume 100k per row, they are 80G in size).

I think it has very small chance that one object or one batch will be
bigger than 2G.
Maybe there are a bug when it split the pickled file, could you create
a RDD for each
file, then see which file is cause the issue (maybe some of them)?

On Wed, Jan 28, 2015 at 1:30 AM, Rok Roskar <rokroskar@gmail.com> wrote:
> hi, thanks for the quick answer -- I suppose this is possible, though I
> don't understand how it could come about. The largest individual RDD
> elements are ~ 1 Mb in size (most are smaller) and the RDD is composed of
> 800k of them. The file is saved in 134 parts, but is being read in using
> some 1916+ partitions (I don't know why actually -- how does this number
> come about?). How can I check if any objects/batches are exceeding 2Gb?
>
> Thanks,
>
> Rok
>
>
> On Tue, Jan 27, 2015 at 7:55 PM, Davies Liu <davies@databricks.com> wrote:
>>
>> Maybe it's caused by integer overflow, is it possible that one object
>> or batch bigger than 2G (after pickling)?
>>
>> On Tue, Jan 27, 2015 at 7:59 AM, rok <rokroskar@gmail.com> wrote:
>> > I've got an dataset saved with saveAsPickleFile using pyspark -- it
>> > saves
>> > without problems. When I try to read it back in, it fails with:
>> >
>> > Job aborted due to stage failure: Task 401 in stage 0.0 failed 4 times,
>> > most
>> > recent failure: Lost task 401.3 in stage 0.0 (TID 449,
>> > e1326.hpc-lca.ethz.ch): java.lang.NegativeArraySizeException:
>> >
>> > org.apache.hadoop.io.BytesWritable.setCapacity(BytesWritable.java:119)
>> >
>> > org.apache.hadoop.io.BytesWritable.setSize(BytesWritable.java:98)
>> >
>> > org.apache.hadoop.io.BytesWritable.readFields(BytesWritable.java:153)
>> >
>> >
>> > org.apache.hadoop.io.serializer.WritableSerialization$WritableDeserializer.deserialize(WritableSerialization.java:67)
>> >
>> >
>> > org.apache.hadoop.io.serializer.WritableSerialization$WritableDeserializer.deserialize(WritableSerialization.java:40)
>> >
>> >
>> > org.apache.hadoop.io.SequenceFile$Reader.deserializeValue(SequenceFile.java:1875)
>> >
>> >
>> > org.apache.hadoop.io.SequenceFile$Reader.getCurrentValue(SequenceFile.java:1848)
>> >
>> >
>> > org.apache.hadoop.mapred.SequenceFileRecordReader.getCurrentValue(SequenceFileRecordReader.java:103)
>> >
>> >
>> > org.apache.hadoop.mapred.SequenceFileRecordReader.next(SequenceFileRecordReader.java:78)
>> >
>> > org.apache.spark.rdd.HadoopRDD$$anon$1.getNext(HadoopRDD.scala:219)
>> >
>> > org.apache.spark.rdd.HadoopRDD$$anon$1.getNext(HadoopRDD.scala:188)
>> >
>> > org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:71)
>> >
>> >
>> > org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
>> >         scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
>> >
>> >
>> > org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:330)
>> >
>> >
>> > org.apache.spark.api.python.PythonRDD$WriterThread$$anonfun$run$1.apply$mcV$sp(PythonRDD.scala:209)
>> >
>> >
>> > org.apache.spark.api.python.PythonRDD$WriterThread$$anonfun$run$1.apply(PythonRDD.scala:184)
>> >
>> >
>> > org.apache.spark.api.python.PythonRDD$WriterThread$$anonfun$run$1.apply(PythonRDD.scala:184)
>> >
>> > org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1311)
>> >
>> >
>> > org.apache.spark.api.python.PythonRDD$WriterThread.run(PythonRDD.scala:183)
>> >
>> >
>> > Not really sure where to start looking for the culprit -- any
>> > suggestions
>> > most welcome. Thanks!
>> >
>> > Rok
>> >
>> >
>> >
>> >
>> > --
>> > View this message in context:
>> > http://apache-spark-user-list.1001560.n3.nabble.com/NegativeArraySizeException-in-pyspark-when-loading-an-RDD-pickleFile-tp21395.html
>> > Sent from the Apache Spark User List mailing list archive at Nabble.com.
>> >
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>
>

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