Hello,
I did have an off-heap memory leak in my streaming application, due to :
https://issues.apache.org/jira/browse/HADOOP-12007.
Now that I use the CodecPool to close that leak, I get under load the following
error :
org.apache.flink.runtime.io.network.netty.exception.LocalTransportException:
java.lang.OutOfMemoryError: Direct buffer memory
at
org.apache.flink.runtime.io.network.netty.PartitionRequestClientHandler.exceptionCaught(PartitionRequestClientHandler.java:153)
at
io.netty.channel.AbstractChannelHandlerContext.invokeExceptionCaught(AbstractChannelHandlerContext.java:246)
at
io.netty.channel.AbstractChannelHandlerContext.fireExceptionCaught(AbstractChannelHandlerContext.java:224)
at
io.netty.channel.ChannelInboundHandlerAdapter.exceptionCaught(ChannelInboundHandlerAdapter.java:131)
at
io.netty.channel.AbstractChannelHandlerContext.invokeExceptionCaught(AbstractChannelHandlerContext.java:246)
at
io.netty.channel.AbstractChannelHandlerContext.fireExceptionCaught(AbstractChannelHandlerContext.java:224)
at
io.netty.channel.ChannelInboundHandlerAdapter.exceptionCaught(ChannelInboundHandlerAdapter.java:131)
at
io.netty.channel.AbstractChannelHandlerContext.invokeExceptionCaught(AbstractChannelHandlerContext.java:246)
at
io.netty.channel.AbstractChannelHandlerContext.notifyHandlerException(AbstractChannelHandlerContext.java:737)
at
io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:310)
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$2.run(SingleThreadEventExecutor.java:112)
at java.lang.Thread.run(Thread.java:744)
Caused by: io.netty.handler.codec.DecoderException: java.lang.OutOfMemoryError:
Direct buffer memory
at
io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:234)
at
io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
... 9 more
Caused by: java.lang.OutOfMemoryError: Direct buffer memory
at java.nio.Bits.reserveMemory(Bits.java:658)
at java.nio.DirectByteBuffer.<init>(DirectByteBuffer.java:123)
at java.nio.ByteBuffer.allocateDirect(ByteBuffer.java:306)
at
io.netty.buffer.PoolArena$DirectArena.newUnpooledChunk(PoolArena.java:651)
at io.netty.buffer.PoolArena.allocateHuge(PoolArena.java:237)
at io.netty.buffer.PoolArena.allocate(PoolArena.java:215)
at io.netty.buffer.PoolArena.reallocate(PoolArena.java:358)
at io.netty.buffer.PooledByteBuf.capacity(PooledByteBuf.java:111)
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$1.cumulate(ByteToMessageDecoder.java:92)
at
io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:228)
... 10 more
But the JVM Heap is ok (monitored by JVisualVM) and the memory size of the JVM
process is half what it was with the memory leak when Yarn killed the container.
Note that I have added a “PartitionBy” in my stream process before the sink and
my app is no longer a simple “mapper style” app.
Do you known the cause of the error and how to correct it ?
Best regards,
Arnaud
De : LINZ, Arnaud
Envoyé : vendredi 13 novembre 2015 15:49
À : '[email protected]' <[email protected]>
Objet : RE: Crash in a simple "mapper style" streaming app likely due to a
memory leak ?
Hi Robert,
Thanks, it works with 50% -- at least way past the previous crash point.
In my opinion (I lack real metrics), the part that uses the most memory is the
M2 mapper, instantiated once per slot.
The most complex part is the Sink (it does use a lot of hdfs files, flushing
threads etc.) ; but I expect the “RichSinkFunction” to be instantiated only
once per slot ? I’m really surprised by that memory usage, I will try using a
monitoring app on the yarn jvm to understand.
How do I set this yarn.heap-cutoff-ratio parameter for a specific application
? I don’t want to modify the “root-protected” flink-conf.yaml for all the users
& flink jobs with that value.
Regards,
Arnaud
De : Robert Metzger [mailto:[email protected]]
Envoyé : vendredi 13 novembre 2015 15:16
À : [email protected]<mailto:[email protected]>
Objet : Re: Crash in a simple "mapper style" streaming app likely due to a
memory leak ?
Hi Arnaud,
can you try running the job again with the configuration value of
"yarn.heap-cutoff-ratio" set to 0.5.
As you can see, the container has been killed because it used more than 12 GB :
"12.1 GB of 12 GB physical memory used;"
You can also see from the logs, that we limit the JVM Heap space to 9.2GB:
"java -Xms9216m -Xmx9216m"
In an ideal world, we would tell the JVM to limit its memory usage to 12 GB,
but sadly, the heap space is not the only memory the JVM is allocating. Its
allocating direct memory, and other stuff outside. Therefore, we use only 75%
of the container memory to the heap.
In your case, I assume that each JVM is having multiple HDFS clients, a lot of
local threads etc.... that's why the memory might not suffice.
With a cutoff ratio of 0.5, we'll only use 6 GB for the heap.
That value might be a bit too high .. but I want to make sure that we first
identify the issue.
If the job is running with 50% cutoff, you can try to reduce it again towards
25% (that's the default value, unlike the documentation says).
I hope that helps.
Regards,
Robert
On Fri, Nov 13, 2015 at 2:58 PM, LINZ, Arnaud
<[email protected]<mailto:[email protected]>> wrote:
Hello,
I use the brand new 0.10 version and I have problems running a streaming
execution. My topology is linear : a custom source SC scans a directory and
emits hdfs file names ; a first mapper M1 opens the file and emits its lines ;
a filter F filters lines ; another mapper M2 transforms them ; and a
mapper/sink M3->SK stores them in HDFS.
SC->M1->F->M2->M3->SK
The M2 transformer uses a bit of RAM because when it opens it loads a 11M row
static table inside a hash map to enrich the lines. I use 55 slots on Yarn,
using 11 containers of 12Gb x 5 slots
To my understanding, I should not have any memory problem since each record is
independent : no join, no key, no aggregation, no window => it’s a simple flow
mapper, with RAM simply used as a buffer. However, if I submit enough input
data, I systematically crash my app with “Connection unexpectedly closed by
remote task manager” exception, and the first error in YARN log shows that “a
container is running beyond physical memory limits”.
If I increase the container size, I simply need to feed in more data to get the
crash happen.
Any idea?
Greetings,
Arnaud
_________________________________
Exceptions in Flink dashboard detail :
Root Exception :
org.apache.flink.runtime.io.network.netty.exception.RemoteTransportException:
Connection unexpectedly closed by remote task manager
'bt1shli6/172.21.125.31:33186<http://172.21.125.31:33186>'. This might indicate
that the remote task manager was lost.
at
org.apache.flink.runtime.io.network.netty.PartitionRequestClientHandler.channelInactive(PartitionRequestClientHandler.java:119)
(…)
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