[jira] [Commented] (SPARK-4846) When the vocabulary size is large, Word2Vec may yield "OutOfMemoryError: Requested array size exceeds VM limit"

["Apache Spark (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-4846?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14246337#comment-14246337 ] 

Apache Spark commented on SPARK-4846:
-------------------------------------

User 'jinntrance' has created a pull request for this issue:
https://github.com/apache/spark/pull/3697

> When the vocabulary size is large, Word2Vec may yield "OutOfMemoryError: Requested array size exceeds VM limit"
> ---------------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-4846
>                 URL: https://issues.apache.org/jira/browse/SPARK-4846
>             Project: Spark
>          Issue Type: Bug
>          Components: MLlib
>    Affects Versions: 1.1.0
>         Environment: Use Word2Vec to process a corpus(sized 3.5G) with one partition.
> The corpus contains about 300 million words and its vocabulary size is about 10 million.
>            Reporter: Joseph Tang
>            Priority: Critical
>
> Exception in thread "Driver" java.lang.reflect.InvocationTargetException
>     at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
>     at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
>     at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
>     at java.lang.reflect.Method.invoke(Method.java:606)
>     at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:162)
> Caused by: java.lang.OutOfMemoryError: Requested array size exceeds VM limit 
>     at java.util.Arrays.copyOf(Arrays.java:2271)
>     at java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:113)
>     at java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayOutputStream.java:93)
>     at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:140)
>     at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1870)
>     at java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1779)
>     at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1186)
>     at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:347)
>     at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:42)
>     at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:73)
>     at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:164)
>     at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:158)
>     at org.apache.spark.SparkContext.clean(SparkContext.scala:1242)
>     at org.apache.spark.rdd.RDD.mapPartitionsWithIndex(RDD.scala:610)
>     at org.apache.spark.mllib.feature.Word2Vec$$anonfun$fit$1.apply$mcVI$sp(Word2Vec.scala:291)
>     at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:141)
>     at org.apache.spark.mllib.feature.Word2Vec.fit(Word2Vec.scala:290)



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