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https://issues.apache.org/jira/browse/SPARK-22038?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon resolved SPARK-22038.
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Resolution: Invalid
> spark 2.1.1 ml.LogisticRegression with large feature set cause Kryo
> serialization failed: Buffer overflow
> ---------------------------------------------------------------------------------------------------------
>
> Key: SPARK-22038
> URL: https://issues.apache.org/jira/browse/SPARK-22038
> Project: Spark
> Issue Type: Question
> Components: ML
> Affects Versions: 2.1.1
> Reporter: wuhaibo
>
> I try to train a big model.
> I have 40 millions instances and 50 millions feature set, and it is sparse.
> I am using 40 executors with 20 GB each + driver with 40 GB. The number of
> data partitions is 5000, the treeAggregate depth is 4, the
> spark.kryoserializer.buffer.max is 2016m, the spark.driver.maxResultSize is
> 40G.
> The execution fails with the following messages:
> +WARN TaskSetManager: Lost task 2.1 in stage 25.0 (TID 1415,
> Blackstone064183, executor 15): org.apache.spark.SparkException: Kryo
> serialization failed: Buffer overflow. Available: 3, required: 8
> Serialization trace:
> currMin (org.apache.spark.mllib.stat.MultivariateOnlineSummarizer). To avoid
> this, increase spark.kryoserializer.buffer.max value.
> at
> org.apache.spark.serializer.KryoSerializerInstance.serialize(KryoSerializer.scala:315)
> at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:364)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:748)
> +
> I know that spark.kryoserializer.buffer.max limit 2g and can not increase.
> I have already try increasing partition num to 10000 and treeAggregate depth
> to 200, it still failed with same error message.
> And I try use java serializer without kryoserializer, it failed with oom:
> WARN TaskSetManager: Lost task 5.0 in stage 32.0 (TID 15701,
> Blackstone065188, executor 4): +java.lang.OutOfMemoryError
> at
> java.io.ByteArrayOutputStream.hugeCapacity(ByteArrayOutputStream.java:123)
> at java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:117)
> at
> java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayOutputStream.java:93)
> at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:153)
> at
> org.apache.spark.util.ByteBufferOutputStream.write(ByteBufferOutputStream.scala:41)
> at
> java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1877)
> at
> java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1786)
> at
> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1189)
> at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348)
> at
> org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:43)
> at
> org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:100)
> at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:364)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:748)+
> Any advice?
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