Hi Ufuk,
thanks for quick reply.
Actually I had a little time to try both things.
1) helped only temporarily - it just took a bit longer to saturate the
pool. After few minutes, periodically all kafka threads were waiting for
bufferPool.
2) This seemed to help. I also reduced checkpoint interval - on rocks we
had 5min, now I tried 30s. .
I attach throughput metrics - the former (around 18) is with increased
heap & buffers, the latter (around 22) is with FileSystemStateBackend.
My state is few GB large - during the test it reached around 2-3GB. I
must admit I was quite impressed that checkpointing to HDFS using
FileSystem took only about 6-7s (with occasional spikes to 12-13s, which
can be seen on metrcs - didn't check if it was caused by hdfs or sth else).
Now I looked at logs from 18 and seems like checkpointing rocksdb took
around 2-3minutes:
2016-04-20 17:47:33,439 [Checkpoint Timer] INFO
org.apache.flink.runtime.checkpoint.CheckpointCoordinator -
Triggering checkpoint 6 @ 1461167253439
2016-04-20 17:49:54,196 [flink-akka.actor.default-dispatcher-147] INFO
org.apache.flink.runtime.checkpoint.CheckpointCoordinator - Completed
checkpoint 6 (in 140588 ms)
- however I don't see any threads dumping state in threadStacks...
I guess I'll have to add some metrics around state invocations to see
where is the problem with rocksDB... I'll write if I find anything, but
that won't be today I think...
Btw - I was looking at FS state and I wonder would it be feasible to
make variant of this state using immutable map (probably some scala one)
to be able to do async checkpoints.
Then synchronous part would be essentially free - just taking the state
map and materializing it asynchronously.
Of course, that would work only for immutable state - but this is often
the case when writing in scala. WDYT?
thanks,
maciek
On 20/04/2016 16:28, Ufuk Celebi wrote:
Could be different things actually, including the parts of the network
you mentioned.
1)
Regarding the TM config:
- It can help to increase the number of network buffers (you can go
ahead and give it 4 GB, e.g. 134217728 buffers a 32 KB)
- In general, you have way more memory available than you actually
give to Flink. I would increase the 20 GB heap size.
As a first step you could address these two points and re-run your job.
2)
As a follow-up you could also work with the FileSystemStateBackend,
which keeps state in memory (on-heap) and writes checkpoints to files.
This would help in checking how much RocksDB is slowing things down.
I'm curious about the results. Do you think you will have time to try this?
– Ufuk
On Wed, Apr 20, 2016 at 3:45 PM, Maciek Próchniak <m...@touk.pl> wrote:
Hi,
I'm running my flink job on one rather large machine (20 cores with
hyperthreading, 120GB RAM). Task manager has 20GB heap allocated.
It does more or less:
read csv from kafka -> keyBy one of the fields -> some custom state
processing.
Kafka topic has 24 partitions, so my parallelism is also 24
After some tweaks and upgrading to 1.0.2-rc3 (as I use RocksDB state
backend) I reached a point when throughput is ~120-150k/s.
One the same kafka and machine I reached > 500k/s with simple filtering job,
so I wanted to see what's the bottleneck.
It turns out that quite often all of kafka threads are stuck waiting for
buffer from pool:
"Thread-6695" #7466 daemon prio=5 os_prio=0 tid=0x00007f77fd80d000
nid=0x8118 in Object.wait() [0x00007f7ad54d9000]
java.lang.Thread.State: TIMED_WAITING (on object monitor)
at java.lang.Object.wait(Native Method)
at
org.apache.flink.runtime.io.network.buffer.LocalBufferPool.requestBuffer(LocalBufferPool.java:163)
- locked <0x00000002eade3890> (a java.util.ArrayDeque)
at
org.apache.flink.runtime.io.network.buffer.LocalBufferPool.requestBufferBlocking(LocalBufferPool.java:133)
at
org.apache.flink.runtime.io.network.api.writer.RecordWriter.emit(RecordWriter.java:92)
- locked <0x00000002eb73cbd0> (a
org.apache.flink.runtime.io.network.api.serialization.SpanningRecordSerializer)
at
org.apache.flink.streaming.runtime.io.StreamRecordWriter.emit(StreamRecordWriter.java:86)
at
org.apache.flink.streaming.runtime.io.RecordWriterOutput.collect(RecordWriterOutput.java:78)
at
org.apache.flink.streaming.runtime.io.RecordWriterOutput.collect(RecordWriterOutput.java:39)
at
org.apache.flink.streaming.api.operators.TimestampedCollector.collect(TimestampedCollector.java:51)
at
org.apache.flink.streaming.api.scala.DataStream$$anon$6$$anonfun$flatMap$1.apply(DataStream.scala:541)
at
org.apache.flink.streaming.api.scala.DataStream$$anon$6$$anonfun$flatMap$1.apply(DataStream.scala:541)
at scala.collection.immutable.List.foreach(List.scala:381)
at
org.apache.flink.streaming.api.scala.DataStream$$anon$6.flatMap(DataStream.scala:541)
at
org.apache.flink.streaming.api.operators.StreamFlatMap.processElement(StreamFlatMap.java:48)
at
org.apache.flink.streaming.runtime.tasks.OperatorChain$ChainingOutput.collect(OperatorChain.java:309)
at
org.apache.flink.streaming.runtime.tasks.OperatorChain$ChainingOutput.collect(OperatorChain.java:297)
at
org.apache.flink.streaming.api.operators.StreamFilter.processElement(StreamFilter.java:38)
at
org.apache.flink.streaming.runtime.tasks.OperatorChain$ChainingOutput.collect(OperatorChain.java:309)
at
org.apache.flink.streaming.runtime.tasks.OperatorChain$ChainingOutput.collect(OperatorChain.java:297)
at
org.apache.flink.streaming.api.operators.StreamFilter.processElement(StreamFilter.java:38)
at
org.apache.flink.streaming.runtime.tasks.OperatorChain$ChainingOutput.collect(OperatorChain.java:309)
at
org.apache.flink.streaming.runtime.tasks.OperatorChain$ChainingOutput.collect(OperatorChain.java:297)
at
org.apache.flink.streaming.api.operators.StreamMap.processElement(StreamMap.java:39)
at
org.apache.flink.streaming.runtime.tasks.OperatorChain$ChainingOutput.collect(OperatorChain.java:309)
at
org.apache.flink.streaming.runtime.tasks.OperatorChain$ChainingOutput.collect(OperatorChain.java:297)
at
org.apache.flink.streaming.api.operators.StreamMap.processElement(StreamMap.java:39)
at
org.apache.flink.streaming.runtime.tasks.OperatorChain$ChainingOutput.collect(OperatorChain.java:309)
at
org.apache.flink.streaming.runtime.tasks.OperatorChain$ChainingOutput.collect(OperatorChain.java:297)
at
org.apache.flink.streaming.api.operators.StreamSource$ManualWatermarkContext.collect(StreamSource.java:318)
- locked <0x00000002eaf3eb50> (a java.lang.Object)
at
org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer09$ConsumerThread.run(FlinkKafkaConsumer09.java:473)
- locked <0x00000002eaf3eb50> (a java.lang.Object)
This seems a bit weird for me, as most of state processing threads are idle:
"My custom function -> (Sink: Unnamed, Map) (19/24)" #7353 daemon prio=5
os_prio=0 tid=0x00007f7a7400e000 nid=0x80a7 waiting on condition
[0x00007f7bee8ed000]
java.lang.Thread.State: TIMED_WAITING (parking)
at sun.misc.Unsafe.park(Native Method)
- parking to wait for <0x00000002eb840c38> (a
java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
at
java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215)
at
java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:2078)
at
java.util.concurrent.LinkedBlockingQueue.poll(LinkedBlockingQueue.java:467)
at
org.apache.flink.runtime.io.network.partition.consumer.SingleInputGate.getNextBufferOrEvent(SingleInputGate.java:415)
at
org.apache.flink.streaming.runtime.io.BarrierBuffer.getNextNonBlocked(BarrierBuffer.java:108)
at
org.apache.flink.streaming.runtime.io.StreamInputProcessor.processInput(StreamInputProcessor.java:175)
at
org.apache.flink.streaming.runtime.tasks.OneInputStreamTask.run(OneInputStreamTask.java:65)
at
org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:225)
at org.apache.flink.runtime.taskmanager.Task.run(Task.java:559)
at java.lang.Thread.run(Thread.java:745)
I tried with using more network buffers, but I doesn't seem to change
anything - and if I understand correctly
https://ci.apache.org/projects/flink/flink-docs-master/setup/config.html#configuring-the-network-buffers
I should not need more than 24^2 * 4 of them...
Does anybody encountered such problem? Or maybe it's just normal for such
case...
thanks,
maciek
