This PR fix the problem: https://github.com/apache/spark/pull/2659
cc @josh
Davies
On Tue, Nov 11, 2014 at 7:47 PM, bliuab <bli...@cse.ust.hk> wrote:
> In spark-1.0.2, I have come across an error when I try to broadcast a quite
> large numpy array(with 35M dimension). The error information except the
> java.lang.NegativeArraySizeException error and details is listed below.
> Moreover, when broadcast a relatively smaller numpy array(30M dimension),
> everything works fine. And 30M dimension numpy array takes 230M memory
> which, in my opinion, not very large.
> As far as I have surveyed, it seems related with py4j. However, I have no
> idea how to fix this. I would be appreciated if I can get some hint.
> ------------
> py4j.protocol.Py4JError: An error occurred while calling o23.broadcast.
> Trace:
> java.lang.NegativeArraySizeException
> at py4j.Base64.decode(Base64.java:292)
> at py4j.Protocol.getBytes(Protocol.java:167)
> at py4j.Protocol.getObject(Protocol.java:276)
> at
> py4j.commands.AbstractCommand.getArguments(AbstractCommand.java:81)
> at py4j.commands.CallCommand.execute(CallCommand.java:77)
> at py4j.GatewayConnection.run(GatewayConnection.java:207)
> -------------
> And the test code is a follows:
> conf =
> SparkConf().setAppName('brodyliu_LR').setMaster('spark://10.231.131.87:5051')
> conf.set('spark.executor.memory', '4000m')
> conf.set('spark.akka.timeout', '100000')
> conf.set('spark.ui.port','8081')
> conf.set('spark.cores.max','150')
> #conf.set('spark.rdd.compress', 'True')
> conf.set('spark.default.parallelism', '300')
> #configure the spark environment
> sc = SparkContext(conf=conf, batchSize=1)
>
> vec = np.random.rand(35000000)
> a = sc.broadcast(vec)
>
>
>
>
>
>
> --
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