CODEWITHSUNDEEP
apache-sparkspark-streamingapache-spark-1.4
user3646174
user3646174

Reputation: 45

Spark: DecoderException: java.lang.OutOfMemoryError

I am running a Spark streaming application on a cluster with 3 worker nodes. Once in a while jobs are failing due to the following exception:

Job aborted due to stage failure: Task 0 in stage 4508517.0 failed 4 times, most recent failure: Lost task 0.3 in stage 4508517.0 (TID 1376191, 172.31.47.126): io.netty.handler.codec.DecoderException: java.lang.OutOfMemoryError
at io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:153)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:333)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:319)
at io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:787)
at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:130)
at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:511)
at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:468)
at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:382)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)
at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:116)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.OutOfMemoryError
at sun.misc.Unsafe.allocateMemory(Native Method)
at java.nio.DirectByteBuffer.<init>(DirectByteBuffer.java:127)
at java.nio.ByteBuffer.allocateDirect(ByteBuffer.java:306)
at io.netty.buffer.PoolArena$DirectArena.newUnpooledChunk(PoolArena.java:440)
at io.netty.buffer.PoolArena.allocateHuge(PoolArena.java:187)
at io.netty.buffer.PoolArena.allocate(PoolArena.java:165)
at io.netty.buffer.PoolArena.reallocate(PoolArena.java:277)
at io.netty.buffer.PooledByteBuf.capacity(PooledByteBuf.java:108)
at io.netty.buffer.AbstractByteBuf.ensureWritable(AbstractByteBuf.java:251)
at io.netty.buffer.AbstractByteBuf.writeBytes(AbstractByteBuf.java:849)
at io.netty.buffer.AbstractByteBuf.writeBytes(AbstractByteBuf.java:841)
at io.netty.buffer.AbstractByteBuf.writeBytes(AbstractByteBuf.java:831)
at io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:146)
... 10 more

I am submitting the job in client mode without any special parameters. Both master and workers have 15 g of memory. Spark Version is 1.4.0.

Is this solvable by tuning configuration?

Upvotes: 1

Views: 1986

Answers (1)

lhaferkamp
lhaferkamp

Reputation: 771

I'm facing the same problem and found out that its probably caused by a memory leak in netty version 4.0.23.Final which is used by Spark 1.4 (see https://github.com/netty/netty/issues/3837)

It is solved at least in Spark 1.5.0 (see https://issues.apache.org/jira/browse/SPARK-8101) which uses netty 4.0.29.Final.

So an upgrade to the latest Spark version should solve the problem. I will try it the next days.

Additionally Spark Jobserver in the current version forces netty 4.0.23.Final, so it needs a fix too.

EDIT: I upgraded to Spark 1.6 with netty 4.0.29.Final but still getting a direct buffer OOM using Spark Jobserver.

Upvotes: 1

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