Sounds good to me.
By the next release, we should have also phased out the old Kafka Source,
which is one of the most common and most problematic users of Union State
On Wed, May 20, 2020 at 11:12 AM Yu Li <car...@gmail.com> wrote:
> +1 on improving Union State implementation.
>
> I think the concerns raised around union state is valid, meanwhile jobs
> with 200 parallelism on the source operator could be regarded as "large
> job".
>
> To compromise, I suggest we split the improvements of the issue into 3
> steps:
>
> 1. Increase `state.backend.fs.memory-threshold` from 1K to 20K (which will
> at most increase the memory cost on JM side by 200*200*20K=800MB)
> 2. Improve the union state implementation
> 3. Further increase `state.backend.fs.memory-threshold` higher
>
> What do you think? Thanks.
>
> Best Regards,
> Yu
>
>
> On Sat, 16 May 2020 at 23:15, Yun Tang <myas...@live.com> wrote:
>
> > If we cannot get rid of union state, I think we should introduce memory
> > control on the serialized TDDs when deploying
> > tasks instead of how union state is implemented when assign state in
> > StateAssignmentOperation.
> > The duplicated TaskStateSnapshot would not really increase much memory as
> > the ByteStreamStateHandle's are
> > actually share the same reference until they are serialized.
> >
> > When talking about the estimated memory footprint, I previously think
> that
> > depends on the pool size of future executor (HardWare#getNumberCPUCores).
> > However, with the simple program below, I found the async submit task
> logic
> > make the number of existing RemoteRpcInvocation in JM at the same time
> > larger than the HardWare#getNumberCPUCores.
> > Take below program for example, we have 200 parallelism of source and the
> > existing RemoteRpcInvocation in JM at the same time could be nearly 200
> > while our pool size of future executor is only 96. I think if we could
> > clear the serialized data in RemoteRpcInvocation as soon as possible, we
> > might mitigate this problem greatly.
> >
> > Simple program which used union state to reproduce the memory footprint
> > problem: one sub-task of the total union state is 100KB bytes array, and
> > 200 sub-tasks in total could lead to more than 100KB * 200 * 200 = 3.8GB
> > memory for all union state.
> >
> > public class Program {
> > private static final Logger LOG =
> > LoggerFactory.getLogger(Program.class);
> >
> > public static void main(String[] args) throws Exception {
> > final StreamExecutionEnvironment env =
> > StreamExecutionEnvironment.getExecutionEnvironment();
> > env.enableCheckpointing(60 * 1000L);
> > env.addSource(new MySource()).setParallelism(200).print();
> > env.execute("Mock program");
> > }
> >
> > private static class MySource extends
> > RichParallelSourceFunction<Integer> implements CheckpointedFunction {
> > private static final ListStateDescriptor<byte[]> stateDescriptor =
> > new ListStateDescriptor<>("list-1", byte[].class);
> > private ListState<byte[]> unionListState;
> > private volatile boolean running = true;
> > @Override
> > public void snapshotState(FunctionSnapshotContext context) throws
> > Exception {
> > unionListState.clear();
> > byte[] array = new byte[100 * 1024];
> > ThreadLocalRandom.current().nextBytes(array);
> > unionListState.add(array);
> > }
> >
> > @Override
> > public void initializeState(FunctionInitializationContext context)
> > throws Exception {
> > if (context.isRestored()) {
> > unionListState =
> > context.getOperatorStateStore().getUnionListState(stateDescriptor);
> > List<byte[]> collect =
> > StreamSupport.stream(unionListState.get().spliterator(),
> > false).collect(Collectors.toList());
> > LOG.info("union state Collect size: {}.", collect.size());
> > } else {
> > unionListState =
> > context.getOperatorStateStore().getUnionListState(stateDescriptor);
> > }
> > }
> >
> > @Override
> > public void run(SourceContext<Integer> ctx) throws Exception {
> > while (running) {
> > synchronized (ctx.getCheckpointLock()) {
> > ctx.collect(ThreadLocalRandom.current().nextInt());
> > }
> > Thread.sleep(100);
> > }
> > }
> >
> > @Override
> > public void cancel() {
> > running = false;
> > }
> > }
> > }
> >
> > Best
> > Yun Tang
> > ________________________________
> > From: Stephan Ewen <se...@apache.org>
> > Sent: Saturday, May 16, 2020 18:56
> > To: dev <dev@flink.apache.org>
> > Cc: Till Rohrmann <trohrm...@apache.org>; Piotr Nowojski <
> > pi...@ververica.com>
> > Subject: Re: [DISCUSS] increase "state.backend.fs.memory-threshold" from
> > 1K to 100K
> >
> > Okay, thank you for all the feedback.
> >
> > So we should definitely work on getting rid of the Union State, or at
> least
> > change the way it is implemented (avoid duplicate serializer snapshots).
> >
> > Can you estimate from which size of the cluster on the JM heap usage
> > becomes critical (if we increased the threshold to 100k, or maybe 50k) ?
> >
> >
> > On Sat, May 16, 2020 at 8:10 AM Congxian Qiu <qcx978132...@gmail.com>
> > wrote:
> >
> > > Hi,
> > >
> > > Overall, I agree with increasing this value. but the default value set
> to
> > > 100K maybe something too large from my side.
> > >
> > > I want to share some more information from my side.
> > >
> > > The small files problem is indeed a problem many users may encounter in
> > > production env. The states(Keyed state and Operator state) can become
> > small
> > > files in DFS, but increase the value of
> > `state.backend.fs.memory-threshold`
> > > may encounter the JM OOM problem as Yun said previously.
> > > We've tried increase this value in our production env, but some
> > connectors
> > > which UnionState prevent us to do this, the memory consumed by these
> jobs
> > > can be very large (in our case, thousands of parallelism, set
> > > `state.backend.fs.memory-threshold` to 64K, will consume 10G+ memory
> for
> > > JM), so in the end, we use the solution proposed in FLINK-11937[1] for
> > both
> > > keyed state and operator state.
> > >
> > >
> > > [1] https://issues.apache.org/jira/browse/FLINK-11937
> > > Best,
> > > Congxian
> > >
> > >
> > > Yun Tang <myas...@live.com> 于2020年5月15日周五 下午9:09写道：
> > >
> > > > Please correct me if I am wrong, "put the increased value into the
> > > default
> > > > configuration" means
> > > > we will update that in default flink-conf.yaml but still leave the
> > > default
> > > > value of `state.backend.fs.memory-threshold`as previously?
> > > > It seems I did not get the point why existing setups with existing
> > > configs
> > > > will not be affected.
> > > >
> > > > The concern I raised is because one of our large-scale job with 1024
> > > > parallelism source of union state meet the JM OOM problem when we
> > > increase
> > > > this value.
> > > > I think if we introduce memory control when serializing TDD
> > > asynchronously
> > > > [1], we could be much more confident to increase this configuration
> as
> > > the
> > > > memory footprint
> > > > expands at that time by a lot of serialized TDDs.
> > > >
> > > >
> > > > [1]
> > > >
> > >
> >
> https://github.com/apache/flink/blob/32bd0944d0519093c0a4d5d809c6636eb3a7fc31/flink-runtime/src/main/java/org/apache/flink/runtime/executiongraph/Execution.java#L752
> > > >
> > > > Best
> > > > Yun Tang
> > > >
> > > > ________________________________
> > > > From: Stephan Ewen <se...@apache.org>
> > > > Sent: Friday, May 15, 2020 16:53
> > > > To: dev <dev@flink.apache.org>
> > > > Cc: Till Rohrmann <trohrm...@apache.org>; Piotr Nowojski <
> > > > pi...@ververica.com>
> > > > Subject: Re: [DISCUSS] increase "state.backend.fs.memory-threshold"
> > from
> > > > 1K to 100K
> > > >
> > > > I see, thanks for all the input.
> > > >
> > > > I agree with Yun Tang that the use of UnionState is problematic and
> can
> > > > cause issues in conjunction with this.
> > > > However, most of the large-scale users I know that also struggle with
> > > > UnionState have also increased this threshold, because with this low
> > > > threshold, they get an excess number of small files and overwhelm
> their
> > > > HDFS / S3 / etc.
> > > >
> > > > An intermediate solution could be to put the increased value into the
> > > > default configuration. That way, existing setups with existing
> configs
> > > will
> > > > not be affected, but new users / installations will have a simper
> time.
> > > >
> > > > Best,
> > > > Stephan
> > > >
> > > >
> > > > On Thu, May 14, 2020 at 9:20 PM Yun Tang <myas...@live.com> wrote:
> > > >
> > > > > Tend to be not in favor of this proposal as union state is somewhat
> > > > abused
> > > > > in several popular source connectors (e.g. kafka), and increasing
> > this
> > > > > value could lead to JM OOM when sending tdd from JM to TMs with
> large
> > > > > parallelism.
> > > > >
> > > > > After we collect union state and initialize the map list [1], we
> > > already
> > > > > have union state ready to assign. At this time, the memory
> footprint
> > > has
> > > > > not increase too much as the union state which shared across tasks
> > have
> > > > the
> > > > > same reference of ByteStreamStateHandle. However, when we send tdd
> > with
> > > > the
> > > > > taskRestore to TMs, akka will serialize those ByteStreamStateHandle
> > > > within
> > > > > tdd to increases the memory footprint. If the source have 1024
> > > > > parallelisms, and any one of the sub-task would then have
> 1024*100KB
> > > size
> > > > > state handles. The sum of total memory footprint cannot be ignored.
> > > > >
> > > > > If we plan to increase the default value of
> > > > > state.backend.fs.memory-threshold, we should first resolve the
> above
> > > > case.
> > > > > In other words, this proposal could be a trade-off, which benefit
> > > perhaps
> > > > > 99% users, but might bring harmful effects to 1% user with
> > large-scale
> > > > > flink jobs.
> > > > >
> > > > >
> > > > > [1]
> > > > >
> > > >
> > >
> >
> https://github.com/apache/flink/blob/c1ea6fcfd05c72a68739bda8bd16a2d1c15522c0/flink-runtime/src/main/java/org/apache/flink/runtime/checkpoint/RoundRobinOperatorStateRepartitioner.java#L64-L87
> > > > >
> > > > > Best
> > > > > Yun Tang
> > > > >
> > > > >
> > > > > ________________________________
> > > > > From: Yu Li <car...@gmail.com>
> > > > > Sent: Thursday, May 14, 2020 23:51
> > > > > To: Till Rohrmann <trohrm...@apache.org>
> > > > > Cc: dev <dev@flink.apache.org>; Piotr Nowojski <
> pi...@ververica.com>
> > > > > Subject: Re: [DISCUSS] increase "state.backend.fs.memory-threshold"
> > > from
> > > > > 1K to 100K
> > > > >
> > > > > TL;DR: I have some reservations but tend to be +1 for the proposal,
> > > > > meanwhile suggest we have a more thorough solution in the long run.
> > > > >
> > > > > Please correct me if I'm wrong, but it seems the root cause of the
> > > issue
> > > > is
> > > > > too many small files generated.
> > > > >
> > > > > I have some concerns for the case of session cluster [1], as well
> as
> > > > > possible issues for users at large scale, otherwise I think
> > increasing
> > > > > `state.backend.fs.memory-threshold` to 100K is a good choice, based
> > on
> > > > the
> > > > > assumption that a large portion of our users are running small jobs
> > > with
> > > > > small states.
> > > > >
> > > > > OTOH, maybe extending the solution [2] of resolving RocksDB small
> > file
> > > > > problem (as proposed by FLINK-11937 [3]) to also support operator
> > state
> > > > > could be an alternative? We have already applied the solution in
> > > > production
> > > > > for operator state and solved the HDFS NN RPC bottleneck problem on
> > > last
> > > > > year's Singles' day.
> > > > >
> > > > > Best Regards,
> > > > > Yu
> > > > >
> > > > > [1]
> > > > >
> > > > >
> > > >
> > >
> >
> https://ci.apache.org/projects/flink/flink-docs-stable/concepts/glossary.html#flink-session-cluster
> > > > > [2]
> > > > >
> > > > >
> > > >
> > >
> >
> https://docs.google.com/document/d/1ukLfqNt44yqhDFL3uIhd68NevVdawccb6GflGNFzLcg
> > > > > <
> > > > >
> > > >
> > >
> >
> https://docs.google.com/document/d/1ukLfqNt44yqhDFL3uIhd68NevVdawccb6GflGNFzLcg/edit#heading=h.rl48knhoni0h
> > > > > >
> > > > > [3] https://issues.apache.org/jira/browse/FLINK-11937
> > > > >
> > > > >
> > > > > On Thu, 14 May 2020 at 21:45, Till Rohrmann <trohrm...@apache.org>
> > > > wrote:
> > > > >
> > > > > > I cannot say much about the concrete value but if our users have
> > > > problems
> > > > > > with the existing default values, then it makes sense to me to
> > change
> > > > it.
> > > > > >
> > > > > > One thing to check could be whether it is possible to provide a
> > > > > meaningful
> > > > > > exception in case that the state size exceeds the frame size. At
> > the
> > > > > > moment, Flink should fail with a message saying that a rpc
> message
> > > > > exceeds
> > > > > > the maximum frame size. Maybe it is also possible to point the
> user
> > > > > towards
> > > > > > "state.backend.fs.memory-threshold" if the message exceeds the
> > frame
> > > > size
> > > > > > because of too much state.
> > > > > >
> > > > > > Cheers,
> > > > > > Till
> > > > > >
> > > > > > On Thu, May 14, 2020 at 2:34 PM Stephan Ewen <se...@apache.org>
> > > wrote:
> > > > > >
> > > > > >> The parameter "state.backend.fs.memory-threshold" decides when a
> > > state
> > > > > >> will
> > > > > >> become a file and when it will be stored inline with the
> metadata
> > > (to
> > > > > >> avoid
> > > > > >> excessive amounts of small files).
> > > > > >>
> > > > > >> By default, this threshold is 1K - so every state above that
> size
> > > > > becomes
> > > > > >> a
> > > > > >> file. For many cases, this threshold seems to be too low.
> > > > > >> There is an interesting talk with background on this from Scott
> > > > Kidder:
> > > > > >> https://www.youtube.com/watch?v=gycq0cY3TZ0
> > > > > >>
> > > > > >> I wanted to discuss increasing this to 100K by default.
> > > > > >>
> > > > > >> Advantage:
> > > > > >> - This should help many users out of the box, which otherwise
> > see
> > > > > >> checkpointing problems on systems like S3, GCS, etc.
> > > > > >>
> > > > > >> Disadvantage:
> > > > > >> - For very large jobs, this increases the required heap memory
> > on
> > > > the
> > > > > JM
> > > > > >> side, because more state needs to be kept in-line when gathering
> > the
> > > > > acks
> > > > > >> for a pending checkpoint.
> > > > > >> - If tasks have a lot of states and each state is roughly at
> > this
> > > > > >> threshold, we increase the chance of exceeding the RPC frame
> size
> > > and
> > > > > >> failing the job.
> > > > > >>
> > > > > >> What do you think?
> > > > > >>
> > > > > >> Best,
> > > > > >> Stephan
> > > > > >>
> > > > > >
> > > > >
> > > >
> > >
> >
>
