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Posted to issues@spark.apache.org by "Andrew Or (JIRA)" <ji...@apache.org> on 2014/11/26 04:03:12 UTC
[jira] [Commented] (SPARK-3995) [PYSPARK] PySpark's sample methods
do not work with NumPy 1.9
[ https://issues.apache.org/jira/browse/SPARK-3995?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14225636#comment-14225636 ]
Andrew Or commented on SPARK-3995:
----------------------------------
Hey [~mengxr] do we want this in branch-1.1? If so we should re-open it.
> [PYSPARK] PySpark's sample methods do not work with NumPy 1.9
> -------------------------------------------------------------
>
> Key: SPARK-3995
> URL: https://issues.apache.org/jira/browse/SPARK-3995
> Project: Spark
> Issue Type: Bug
> Components: PySpark, Spark Core
> Affects Versions: 1.1.0
> Reporter: Jeremy Freeman
> Assignee: Jeremy Freeman
> Priority: Critical
> Fix For: 1.2.0
>
>
> There is a breaking bug in PySpark's sampling methods when run with NumPy v1.9. This is the version of NumPy included with the current Anaconda distribution (v2.1); this is a popular distribution, and is likely to affect many users.
> Steps to reproduce are:
> {code:python}
> foo = sc.parallelize(range(1000),5)
> foo.takeSample(False, 10)
> {code}
> Returns:
> {code}
> PythonException: Traceback (most recent call last):
> File "/Users/freemanj11/code/spark-1.1.0-bin-hadoop1/python/pyspark/worker.py", line 79, in main
> serializer.dump_stream(func(split_index, iterator), outfile)
> File "/Users/freemanj11/code/spark-1.1.0-bin-hadoop1/python/pyspark/serializers.py", line 196, in dump_stream
> self.serializer.dump_stream(self._batched(iterator), stream)
> File "/Users/freemanj11/code/spark-1.1.0-bin-hadoop1/python/pyspark/serializers.py", line 127, in dump_stream
> for obj in iterator:
> File "/Users/freemanj11/code/spark-1.1.0-bin-hadoop1/python/pyspark/serializers.py", line 185, in _batched
> for item in iterator:
> File "/Users/freemanj11/code/spark-1.1.0-bin-hadoop1/python/pyspark/rddsampler.py", line 116, in func
> if self.getUniformSample(split) <= self._fraction:
> File "/Users/freemanj11/code/spark-1.1.0-bin-hadoop1/python/pyspark/rddsampler.py", line 58, in getUniformSample
> self.initRandomGenerator(split)
> File "/Users/freemanj11/code/spark-1.1.0-bin-hadoop1/python/pyspark/rddsampler.py", line 44, in initRandomGenerator
> self._random = numpy.random.RandomState(self._seed)
> File "mtrand.pyx", line 610, in mtrand.RandomState.__init__ (numpy/random/mtrand/mtrand.c:7397)
> File "mtrand.pyx", line 646, in mtrand.RandomState.seed (numpy/random/mtrand/mtrand.c:7697)
> ValueError: Seed must be between 0 and 4294967295
> {code}
> In PySpark's {{RDDSamplerBase}} class from {{pyspark.rddsampler}} we use:
> {code:python}
> self._seed = seed if seed is not None else random.randint(0, sys.maxint)
> {code}
> In previous versions of NumPy a random seed larger than 2 ** 32 would silently get truncated to 2 ** 32. This was fixed in a recent patch (https://github.com/numpy/numpy/commit/6b1a1205eac6fe5d162f16155d500765e8bca53c). But sampling {{(0, sys.maxint)}} often yields ints larger than 2 ** 32, which effectively breaks sampling operations in PySpark (unless the seed is set manually).
> I am putting a PR together now (the fix is very simple!).
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