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From "Sital Kedia (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-15074) Spark shuffle service bottlenecked while fetching large amount of intermediate data
Date Mon, 02 May 2016 22:32:12 GMT
Sital Kedia created SPARK-15074:
-----------------------------------

             Summary: Spark shuffle service bottlenecked while fetching large amount of intermediate
data
                 Key: SPARK-15074
                 URL: https://issues.apache.org/jira/browse/SPARK-15074
             Project: Spark
          Issue Type: Improvement
          Components: Shuffle
    Affects Versions: 1.6.1
            Reporter: Sital Kedia


While running a job which produces more than 90TB of intermediate data, we find that about
10-15% of the reducer execution time is being spent in shuffle fetch. Jstack of the shuffle
service reveals that most of the time the shuffle service is reading the index files generated
by the mapper. 

java.lang.Thread.State: RUNNABLE
	at java.io.FileInputStream.readBytes(Native Method)
	at java.io.FileInputStream.read(FileInputStream.java:255)
	at java.io.DataInputStream.readFully(DataInputStream.java:195)
	at java.io.DataInputStream.readLong(DataInputStream.java:416)
	at org.apache.spark.network.shuffle.ExternalShuffleBlockResolver.getSortBasedShuffleBlockData(ExternalShuffleBlockResolver.java:277)
	at org.apache.spark.network.shuffle.ExternalShuffleBlockResolver.getBlockData(ExternalShuffleBlockResolver.java:190)
	at org.apache.spark.network.shuffle.ExternalShuffleBlockHandler.handleMessage(ExternalShuffleBlockHandler.java:85)
	at org.apache.spark.network.shuffle.ExternalShuffleBlockHandler.receive(ExternalShuffleBlockHandler.java:72)
	at org.apache.spark.network.server.TransportRequestHandler.processRpcRequest(TransportRequestHandler.java:149)
	at org.apache.spark.network.server.TransportRequestHandler.handle(TransportRequestHandler.java:102)
	at org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:104)
	at org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:51)
	at io.netty.channel.SimpleChannelInboundHandler.channelRead(SimpleChannelInboundHandler.java:105)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
	at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
	at io.netty.handler.timeout.IdleStateHandler.channelRead(IdleStateHandler.java:266)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
	at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
	at io.netty.handler.codec.MessageToMessageDecoder.channelRead(MessageToMessageDecoder.java:103)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
	at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
	at org.apache.spark.network.util.TransportFrameDecoder.channelRead(TransportFrameDecoder.java:86)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
	at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
	at io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:846)
	at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:131)
	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:111)
	at java.lang.Thread.run(Thread.java:745)


The issue is that for each shuffle fetch, we reopen the same index file again and read it.
It would be much efficient, if we can avoid opening the same file multiple times and cache
the data. We can use an LRU cache to save the index file information. This way we can also
limit the number of entries in the cache so that we don't blow up the memory indefinitely.






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