Hi Robert, Just a quick update: The issue has been resolved in the latest Maven 0.10-SNAPSHOT dependency.
Cheers, Max On Wed, Sep 30, 2015 at 3:19 PM, Robert Schmidtke <ro.schmid...@gmail.com> wrote: > Hi Max, > > thanks for your quick reply. I found the relevant code and commented it out > for testing, seems to be working. Happily waiting for the fix. Thanks again. > > Robert > > On Wed, Sep 30, 2015 at 1:42 PM, Maximilian Michels <m...@apache.org> wrote: >> >> Hi Robert, >> >> This is a regression on the current master due to changes in the way >> Flink calculates the memory and sets the maximum direct memory size. >> We introduced these changes when we merged support for off-heap >> memory. This is not a problem in the way Flink deals with managed >> memory, just -XX:MaxDirectMemorySize is set too low. By default the >> maximum direct memory is only used by the network stack. The network >> library we use, allocates more direct memory than we expected. >> >> We'll push a fix to the master as soon as possible. Thank you for >> reporting and thanks for your patience. >> >> Best regards, >> Max >> >> On Wed, Sep 30, 2015 at 1:31 PM, Robert Schmidtke >> <ro.schmid...@gmail.com> wrote: >> > Hi everyone, >> > >> > I'm constantly running into OutOfMemoryErrors and for the life of me I >> > cannot figure out what's wrong. Let me describe my setup. I'm running >> > the >> > current master branch of Flink on YARN (Hadoop 2.7.0). My job is an >> > unfinished implementation of TPC-H Q2 >> > >> > (https://github.com/robert-schmidtke/flink-benchmarks/blob/master/xtreemfs-flink-benchmark/src/main/java/org/xtreemfs/flink/benchmark/TPCH2Benchmark.java), >> > I run on 8 machines (1 for JM, the other 7 for TMs) with 64G of memory >> > per >> > machine. This is what I believe to be the relevant section of my >> > yarn_site.xml: >> > >> > >> > <property> >> > <name>yarn.nodemanager.resource.memory-mb</name> >> > <value>57344</value> >> > </property> >> > <!-- >> > <property> >> > <name>yarn.scheduler.minimum-allocation-mb</name> >> > <value>8192</value> >> > </property> >> > --> >> > <property> >> > <name>yarn.scheduler.maximum-allocation-mb</name> >> > <value>55296</value> >> > </property> >> > >> > <property> >> > <name>yarn.nodemanager.vmem-check-enabled</name> >> > <value>false</value> >> > </property> >> > >> > >> > And this is how I submit the job: >> > >> > >> > $FLINK_HOME/bin/flink run -m yarn-cluster -yjm 16384 -ytm 32768 -yn 7 >> > ..... >> > >> > >> > The TMs happily report: >> > >> > ..... >> > 11:50:15,577 INFO org.apache.flink.yarn.appMaster.YarnTaskManagerRunner >> > - JVM Options: >> > 11:50:15,577 INFO org.apache.flink.yarn.appMaster.YarnTaskManagerRunner >> > - -Xms24511m >> > 11:50:15,577 INFO org.apache.flink.yarn.appMaster.YarnTaskManagerRunner >> > - -Xmx24511m >> > 11:50:15,577 INFO org.apache.flink.yarn.appMaster.YarnTaskManagerRunner >> > - -XX:MaxDirectMemorySize=65m >> > ..... >> > >> > >> > I've tried various combinations of YARN and Flink options, to no avail. >> > I >> > always end up with the following stacktrace: >> > >> > >> > >> > 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:745) >> > 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.UnpooledUnsafeDirectByteBuf.allocateDirect(UnpooledUnsafeDirectByteBuf.java:108) >> > at >> > >> > io.netty.buffer.UnpooledUnsafeDirectByteBuf.capacity(UnpooledUnsafeDirectByteBuf.java:157) >> > 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 >> > >> > >> > I always figured that running into OOMEs with Flink would be quite hard >> > to >> > achieve, however I'm wondering what's going wrong now. Seems to be >> > related >> > to the Direct Memory? Why are you limiting it in the JVM options at all? >> > Is >> > there a special place where I can safely increase the size / remove the >> > option altogether for unboundedness? >> > >> > A note on the data sizes, I used a scaling factor 1000 for the dbgen >> > command >> > of TPC-H, which effectively means the following. Each table is split in >> > 7 >> > chunks (one local to each TM), each chunk of the part.tbl is 734M, each >> > chunk of supplier.tbl is 43M, each chunk of partsupp.tbl is 3.6G. These >> > are >> > not excessive amounts of data, however the query (at least my >> > implementation) involves joins (the one in line 249 causing the OOME) >> > and >> > maybe there are some network issues? >> > >> > Maybe you can point me into the right direction, thanks a bunch. Cheers. >> > >> > Robert > > > > > -- > My GPG Key ID: 336E2680
