[
https://issues.apache.org/jira/browse/SPARK-24578?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wenbo Zhao updated SPARK-24578:
-------------------------------
Description:
After Spark 2.3, we observed lots of errors like the following in some of our
production job
{code:java}
18/06/15 20:59:42 ERROR TransportRequestHandler: Error sending result
ChunkFetchSuccess{streamChunkId=StreamChunkId{streamId=91672904003,
chunkIndex=0},
buffer=org.apache.spark.storage.BlockManagerManagedBuffer@783a9324} to
/172.22.18.7:60865; closing connection
java.io.IOException: Broken pipe
at sun.nio.ch.FileDispatcherImpl.write0(Native Method)
at sun.nio.ch.SocketDispatcher.write(SocketDispatcher.java:47)
at sun.nio.ch.IOUtil.writeFromNativeBuffer(IOUtil.java:93)
at sun.nio.ch.IOUtil.write(IOUtil.java:65)
at sun.nio.ch.SocketChannelImpl.write(SocketChannelImpl.java:471)
at
org.apache.spark.network.protocol.MessageWithHeader.writeNioBuffer(MessageWithHeader.java:156)
at
org.apache.spark.network.protocol.MessageWithHeader.copyByteBuf(MessageWithHeader.java:142)
at
org.apache.spark.network.protocol.MessageWithHeader.transferTo(MessageWithHeader.java:123)
at
io.netty.channel.socket.nio.NioSocketChannel.doWriteFileRegion(NioSocketChannel.java:355)
at
io.netty.channel.nio.AbstractNioByteChannel.doWrite(AbstractNioByteChannel.java:224)
at
io.netty.channel.socket.nio.NioSocketChannel.doWrite(NioSocketChannel.java:382)
at
io.netty.channel.AbstractChannel$AbstractUnsafe.flush0(AbstractChannel.java:934)
at
io.netty.channel.nio.AbstractNioChannel$AbstractNioUnsafe.flush0(AbstractNioChannel.java:362)
at
io.netty.channel.AbstractChannel$AbstractUnsafe.flush(AbstractChannel.java:901)
at
io.netty.channel.DefaultChannelPipeline$HeadContext.flush(DefaultChannelPipeline.java:1321)
at
io.netty.channel.AbstractChannelHandlerContext.invokeFlush0(AbstractChannelHandlerContext.java:776)
at
io.netty.channel.AbstractChannelHandlerContext.invokeFlush(AbstractChannelHandlerContext.java:768)
at
io.netty.channel.AbstractChannelHandlerContext.flush(AbstractChannelHandlerContext.java:749)
at
io.netty.channel.ChannelOutboundHandlerAdapter.flush(ChannelOutboundHandlerAdapter.java:115)
at
io.netty.channel.AbstractChannelHandlerContext.invokeFlush0(AbstractChannelHandlerContext.java:776)
at
io.netty.channel.AbstractChannelHandlerContext.invokeFlush(AbstractChannelHandlerContext.java:768)
at
io.netty.channel.AbstractChannelHandlerContext.flush(AbstractChannelHandlerContext.java:749)
at io.netty.channel.ChannelDuplexHandler.flush(ChannelDuplexHandler.java:117)
at
io.netty.channel.AbstractChannelHandlerContext.invokeFlush0(AbstractChannelHandlerContext.java:776)
at
io.netty.channel.AbstractChannelHandlerContext.invokeFlush(AbstractChannelHandlerContext.java:768)
at
io.netty.channel.AbstractChannelHandlerContext.flush(AbstractChannelHandlerContext.java:749)
at
io.netty.channel.DefaultChannelPipeline.flush(DefaultChannelPipeline.java:983)
at io.netty.channel.AbstractChannel.flush(AbstractChannel.java:248)
at
io.netty.channel.nio.AbstractNioByteChannel$1.run(AbstractNioByteChannel.java:284)
at
io.netty.util.concurrent.AbstractEventExecutor.safeExecute(AbstractEventExecutor.java:163)
at
io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:403)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:463)
at
io.netty.util.concurrent.SingleThreadEventExecutor$5.run(SingleThreadEventExecutor.java:858)
at
io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:138)
{code}
Here is a small reproducible for a small cluster of 2 executors (say host-1 and
host-2) each with 8 cores. Here, the memory of driver and executors are not an
import factor here as long as it is big enough, say 20G.
{code:java}
val n = 100000000
val df0 = sc.parallelize(1 to n).toDF
val df = df0.withColumn("x0", rand()).withColumn("x0", rand()
).withColumn("x1", rand()
).withColumn("x2", rand()
).withColumn("x3", rand()
).withColumn("x4", rand()
).withColumn("x5", rand()
).withColumn("x6", rand()
).withColumn("x7", rand()
).withColumn("x8", rand()
).withColumn("x9", rand())
df.cache; df.count
(1 to 10).toArray.par.map { i => println(i);
df.groupBy("x1").agg(count("value")).show() }
{code}
In the above example, we generate a random DataFrame of size around 7G; cache
it and then perform a parallel DataFrame operations by using `array.par.map`.
Because of the parallel computation, with high possibility, some task could be
scheduled to a host-2 where it needs to read the cache block data from host-1.
This follows the code path of
[https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/storage/BlockManager.scala#L691]
and then tries to transfer a big block (~ 500MB) of cache block from host-1 to
host-2. Often, this big transfer makes the cluster suffer time out issue (it
will retry 3 times, each with 120s timeout, and then do recompute to put the
cache block into the local MemoryStore).
We couldn't to reproduce the same issue in Spark 2.2.1. From the log of Spark
2.2.1, we found that
{code:java}
18/06/16 17:23:47 DEBUG BlockManager: Getting local block rdd_3_0
18/06/16 17:23:47 TRACE BlockInfoManager: Task 0 trying to acquire read lock
for rdd_3_0
18/06/16 17:23:47 DEBUG BlockManager: Block rdd_3_0 was not found
18/06/16 17:23:47 DEBUG BlockManager: Getting remote block rdd_3_0
18/06/16 17:23:47 DEBUG BlockManager: Block rdd_3_0 not found
18/06/16 17:23:47 TRACE BlockInfoManager: Task 0 trying to put rdd_3_0
18/06/16 17:23:47 TRACE BlockInfoManager: Task 0 trying to acquire read lock
for rdd_3_0
18/06/16 17:23:47 TRACE BlockInfoManager: Task 0 trying to acquire write lock
for rdd_3_0
18/06/16 17:23:47 TRACE BlockInfoManager: Task 0 acquired write lock for
rdd_3_0
18/06/16 17:23:58 INFO MemoryStore: Block rdd_3_0 stored as values in memory
(estimated size 538.2 MB, free 11.1 GB)
{code}
That is, when a task is scheduled to a host-2 where it needs to read the cache
block rdd_3_0 data from host-1, the endpoint of `master.getLocations(..)` ( see
[https://github.com/apache/spark/blob/v2.2.1/core/src/main/scala/org/apache/spark/storage/BlockManager.scala#L622])
reports a remote cache block is not found and triggered the recompute.
-I believe this behavior change is introduced by this change set
[https://github.com/apache/spark/commit/e1960c3d6f380b0dfbba6ee5d8ac6da4bc29a698#diff-2b643ea78c1add0381754b1f47eec132]-
We have two questions here
# what is the right behavior, should we re-compute or should we transfer block
from remote?
# if we should transfer from remote, why the performance is so bad for cache
block?
was:
After Spark 2.3, we observed lots of errors like the following in some of our
production job
{code:java}
18/06/15 20:59:42 ERROR TransportRequestHandler: Error sending result
ChunkFetchSuccess{streamChunkId=StreamChunkId{streamId=91672904003,
chunkIndex=0},
buffer=org.apache.spark.storage.BlockManagerManagedBuffer@783a9324} to
/172.22.18.7:60865; closing connection
java.io.IOException: Broken pipe
at sun.nio.ch.FileDispatcherImpl.write0(Native Method)
at sun.nio.ch.SocketDispatcher.write(SocketDispatcher.java:47)
at sun.nio.ch.IOUtil.writeFromNativeBuffer(IOUtil.java:93)
at sun.nio.ch.IOUtil.write(IOUtil.java:65)
at sun.nio.ch.SocketChannelImpl.write(SocketChannelImpl.java:471)
at
org.apache.spark.network.protocol.MessageWithHeader.writeNioBuffer(MessageWithHeader.java:156)
at
org.apache.spark.network.protocol.MessageWithHeader.copyByteBuf(MessageWithHeader.java:142)
at
org.apache.spark.network.protocol.MessageWithHeader.transferTo(MessageWithHeader.java:123)
at
io.netty.channel.socket.nio.NioSocketChannel.doWriteFileRegion(NioSocketChannel.java:355)
at
io.netty.channel.nio.AbstractNioByteChannel.doWrite(AbstractNioByteChannel.java:224)
at
io.netty.channel.socket.nio.NioSocketChannel.doWrite(NioSocketChannel.java:382)
at
io.netty.channel.AbstractChannel$AbstractUnsafe.flush0(AbstractChannel.java:934)
at
io.netty.channel.nio.AbstractNioChannel$AbstractNioUnsafe.flush0(AbstractNioChannel.java:362)
at
io.netty.channel.AbstractChannel$AbstractUnsafe.flush(AbstractChannel.java:901)
at
io.netty.channel.DefaultChannelPipeline$HeadContext.flush(DefaultChannelPipeline.java:1321)
at
io.netty.channel.AbstractChannelHandlerContext.invokeFlush0(AbstractChannelHandlerContext.java:776)
at
io.netty.channel.AbstractChannelHandlerContext.invokeFlush(AbstractChannelHandlerContext.java:768)
at
io.netty.channel.AbstractChannelHandlerContext.flush(AbstractChannelHandlerContext.java:749)
at
io.netty.channel.ChannelOutboundHandlerAdapter.flush(ChannelOutboundHandlerAdapter.java:115)
at
io.netty.channel.AbstractChannelHandlerContext.invokeFlush0(AbstractChannelHandlerContext.java:776)
at
io.netty.channel.AbstractChannelHandlerContext.invokeFlush(AbstractChannelHandlerContext.java:768)
at
io.netty.channel.AbstractChannelHandlerContext.flush(AbstractChannelHandlerContext.java:749)
at io.netty.channel.ChannelDuplexHandler.flush(ChannelDuplexHandler.java:117)
at
io.netty.channel.AbstractChannelHandlerContext.invokeFlush0(AbstractChannelHandlerContext.java:776)
at
io.netty.channel.AbstractChannelHandlerContext.invokeFlush(AbstractChannelHandlerContext.java:768)
at
io.netty.channel.AbstractChannelHandlerContext.flush(AbstractChannelHandlerContext.java:749)
at
io.netty.channel.DefaultChannelPipeline.flush(DefaultChannelPipeline.java:983)
at io.netty.channel.AbstractChannel.flush(AbstractChannel.java:248)
at
io.netty.channel.nio.AbstractNioByteChannel$1.run(AbstractNioByteChannel.java:284)
at
io.netty.util.concurrent.AbstractEventExecutor.safeExecute(AbstractEventExecutor.java:163)
at
io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:403)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:463)
at
io.netty.util.concurrent.SingleThreadEventExecutor$5.run(SingleThreadEventExecutor.java:858)
at
io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:138)
{code}
Here is a small reproducible for a small cluster of 2 executors (say host-1 and
host-2) each with 8 cores. Here, the memory of driver and executors are not an
import factor here as long as it is big enough, say 20G.
{code:java}
val n = 100000000
val df0 = sc.parallelize(1 to n).toDF
val df = df0.withColumn("x0", rand()).withColumn("x0", rand()
).withColumn("x1", rand()
).withColumn("x2", rand()
).withColumn("x3", rand()
).withColumn("x4", rand()
).withColumn("x5", rand()
).withColumn("x6", rand()
).withColumn("x7", rand()
).withColumn("x8", rand()
).withColumn("x9", rand())
df.cache; df.count
(1 to 10).toArray.par.map { i => println(i);
df.groupBy("x1").agg(count("value")).show() }
{code}
In the above example, we generate a random DataFrame of size around 7G; cache
it and then perform a parallel DataFrame operations by using `array.par.map`.
Because of the parallel computation, with high possibility, some task could be
scheduled to a host-2 where it needs to read the cache block data from host-1.
This follows the code path of
[https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/storage/BlockManager.scala#L691]
and then tries to transfer a big block (~ 500MB) of cache block from host-1 to
host-2. Often, this big transfer makes the cluster suffer time out issue (it
will retry 3 times, each with 120s timeout, and then do recompute to put the
cache block into the local MemoryStore).
We couldn't to reproduce the same issue in Spark 2.2.1. From the log of Spark
2.2.1, we found that
{code:java}
18/06/16 17:23:47 DEBUG BlockManager: Getting local block rdd_3_0
18/06/16 17:23:47 TRACE BlockInfoManager: Task 0 trying to acquire read lock
for rdd_3_0
18/06/16 17:23:47 DEBUG BlockManager: Block rdd_3_0 was not found
18/06/16 17:23:47 DEBUG BlockManager: Getting remote block rdd_3_0
18/06/16 17:23:47 DEBUG BlockManager: Block rdd_3_0 not found
18/06/16 17:23:47 TRACE BlockInfoManager: Task 0 trying to put rdd_3_0
18/06/16 17:23:47 TRACE BlockInfoManager: Task 0 trying to acquire read lock
for rdd_3_0
18/06/16 17:23:47 TRACE BlockInfoManager: Task 0 trying to acquire write lock
for rdd_3_0
18/06/16 17:23:47 TRACE BlockInfoManager: Task 0 acquired write lock for
rdd_3_0
18/06/16 17:23:58 INFO MemoryStore: Block rdd_3_0 stored as values in memory
(estimated size 538.2 MB, free 11.1 GB)
{code}
That is, when a task is scheduled to a host-2 where it needs to read the cache
block rdd_3_0 data from host-1, the endpoint of `master.getLocations(..)` ( see
[https://github.com/apache/spark/blob/v2.2.1/core/src/main/scala/org/apache/spark/storage/BlockManager.scala#L622])
reports a remote cache block is not found and triggered the recompute.
I believe this behavior change is introduced by this change set
[https://github.com/apache/spark/commit/e1960c3d6f380b0dfbba6ee5d8ac6da4bc29a698#diff-2b643ea78c1add0381754b1f47eec132]
We have two questions here
# what is the right behavior, should we re-compute or should we transfer block
from remote?
# if we should transfer from remote, why the performance is so bad for cache
block?
> Reading remote cache block behavior changes and causes timeout issue
> --------------------------------------------------------------------
>
> Key: SPARK-24578
> URL: https://issues.apache.org/jira/browse/SPARK-24578
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 2.3.0, 2.3.1
> Reporter: Wenbo Zhao
> Priority: Major
>
> After Spark 2.3, we observed lots of errors like the following in some of our
> production job
> {code:java}
> 18/06/15 20:59:42 ERROR TransportRequestHandler: Error sending result
> ChunkFetchSuccess{streamChunkId=StreamChunkId{streamId=91672904003,
> chunkIndex=0},
> buffer=org.apache.spark.storage.BlockManagerManagedBuffer@783a9324} to
> /172.22.18.7:60865; closing connection
> java.io.IOException: Broken pipe
> at sun.nio.ch.FileDispatcherImpl.write0(Native Method)
> at sun.nio.ch.SocketDispatcher.write(SocketDispatcher.java:47)
> at sun.nio.ch.IOUtil.writeFromNativeBuffer(IOUtil.java:93)
> at sun.nio.ch.IOUtil.write(IOUtil.java:65)
> at sun.nio.ch.SocketChannelImpl.write(SocketChannelImpl.java:471)
> at
> org.apache.spark.network.protocol.MessageWithHeader.writeNioBuffer(MessageWithHeader.java:156)
> at
> org.apache.spark.network.protocol.MessageWithHeader.copyByteBuf(MessageWithHeader.java:142)
> at
> org.apache.spark.network.protocol.MessageWithHeader.transferTo(MessageWithHeader.java:123)
> at
> io.netty.channel.socket.nio.NioSocketChannel.doWriteFileRegion(NioSocketChannel.java:355)
> at
> io.netty.channel.nio.AbstractNioByteChannel.doWrite(AbstractNioByteChannel.java:224)
> at
> io.netty.channel.socket.nio.NioSocketChannel.doWrite(NioSocketChannel.java:382)
> at
> io.netty.channel.AbstractChannel$AbstractUnsafe.flush0(AbstractChannel.java:934)
> at
> io.netty.channel.nio.AbstractNioChannel$AbstractNioUnsafe.flush0(AbstractNioChannel.java:362)
> at
> io.netty.channel.AbstractChannel$AbstractUnsafe.flush(AbstractChannel.java:901)
> at
> io.netty.channel.DefaultChannelPipeline$HeadContext.flush(DefaultChannelPipeline.java:1321)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeFlush0(AbstractChannelHandlerContext.java:776)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeFlush(AbstractChannelHandlerContext.java:768)
> at
> io.netty.channel.AbstractChannelHandlerContext.flush(AbstractChannelHandlerContext.java:749)
> at
> io.netty.channel.ChannelOutboundHandlerAdapter.flush(ChannelOutboundHandlerAdapter.java:115)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeFlush0(AbstractChannelHandlerContext.java:776)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeFlush(AbstractChannelHandlerContext.java:768)
> at
> io.netty.channel.AbstractChannelHandlerContext.flush(AbstractChannelHandlerContext.java:749)
> at io.netty.channel.ChannelDuplexHandler.flush(ChannelDuplexHandler.java:117)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeFlush0(AbstractChannelHandlerContext.java:776)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeFlush(AbstractChannelHandlerContext.java:768)
> at
> io.netty.channel.AbstractChannelHandlerContext.flush(AbstractChannelHandlerContext.java:749)
> at
> io.netty.channel.DefaultChannelPipeline.flush(DefaultChannelPipeline.java:983)
> at io.netty.channel.AbstractChannel.flush(AbstractChannel.java:248)
> at
> io.netty.channel.nio.AbstractNioByteChannel$1.run(AbstractNioByteChannel.java:284)
> at
> io.netty.util.concurrent.AbstractEventExecutor.safeExecute(AbstractEventExecutor.java:163)
> at
> io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:403)
> at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:463)
> at
> io.netty.util.concurrent.SingleThreadEventExecutor$5.run(SingleThreadEventExecutor.java:858)
> at
> io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:138)
> {code}
>
> Here is a small reproducible for a small cluster of 2 executors (say host-1
> and host-2) each with 8 cores. Here, the memory of driver and executors are
> not an import factor here as long as it is big enough, say 20G.
> {code:java}
> val n = 100000000
> val df0 = sc.parallelize(1 to n).toDF
> val df = df0.withColumn("x0", rand()).withColumn("x0", rand()
> ).withColumn("x1", rand()
> ).withColumn("x2", rand()
> ).withColumn("x3", rand()
> ).withColumn("x4", rand()
> ).withColumn("x5", rand()
> ).withColumn("x6", rand()
> ).withColumn("x7", rand()
> ).withColumn("x8", rand()
> ).withColumn("x9", rand())
> df.cache; df.count
> (1 to 10).toArray.par.map { i => println(i);
> df.groupBy("x1").agg(count("value")).show() }
> {code}
>
> In the above example, we generate a random DataFrame of size around 7G; cache
> it and then perform a parallel DataFrame operations by using `array.par.map`.
> Because of the parallel computation, with high possibility, some task could
> be scheduled to a host-2 where it needs to read the cache block data from
> host-1. This follows the code path of
> [https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/storage/BlockManager.scala#L691]
> and then tries to transfer a big block (~ 500MB) of cache block from host-1
> to host-2. Often, this big transfer makes the cluster suffer time out issue
> (it will retry 3 times, each with 120s timeout, and then do recompute to put
> the cache block into the local MemoryStore).
> We couldn't to reproduce the same issue in Spark 2.2.1. From the log of Spark
> 2.2.1, we found that
> {code:java}
> 18/06/16 17:23:47 DEBUG BlockManager: Getting local block rdd_3_0
> 18/06/16 17:23:47 TRACE BlockInfoManager: Task 0 trying to acquire read lock
> for rdd_3_0
> 18/06/16 17:23:47 DEBUG BlockManager: Block rdd_3_0 was not found
> 18/06/16 17:23:47 DEBUG BlockManager: Getting remote block rdd_3_0
> 18/06/16 17:23:47 DEBUG BlockManager: Block rdd_3_0 not found
> 18/06/16 17:23:47 TRACE BlockInfoManager: Task 0 trying to put rdd_3_0
> 18/06/16 17:23:47 TRACE BlockInfoManager: Task 0 trying to acquire read lock
> for rdd_3_0
> 18/06/16 17:23:47 TRACE BlockInfoManager: Task 0 trying to acquire write lock
> for rdd_3_0
> 18/06/16 17:23:47 TRACE BlockInfoManager: Task 0 acquired write lock for
> rdd_3_0
> 18/06/16 17:23:58 INFO MemoryStore: Block rdd_3_0 stored as values in memory
> (estimated size 538.2 MB, free 11.1 GB)
> {code}
> That is, when a task is scheduled to a host-2 where it needs to read the
> cache block rdd_3_0 data from host-1, the endpoint of
> `master.getLocations(..)` ( see
> [https://github.com/apache/spark/blob/v2.2.1/core/src/main/scala/org/apache/spark/storage/BlockManager.scala#L622])
> reports a remote cache block is not found and triggered the recompute.
> -I believe this behavior change is introduced by this change set
> [https://github.com/apache/spark/commit/e1960c3d6f380b0dfbba6ee5d8ac6da4bc29a698#diff-2b643ea78c1add0381754b1f47eec132]-
>
> We have two questions here
> # what is the right behavior, should we re-compute or should we transfer
> block from remote?
> # if we should transfer from remote, why the performance is so bad for cache
> block?
>
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