Hi
Sorry to jump in late.
After read the previous email. I have such assumptions, and please correct
me if I'm wrong:
- RocksDBStateBackend with incremental checkpoint
- at least once mode
- the parallelism for the stateful operator is 8
- checkpoint may take too long to complete
- has fix rate input by using throttler.
>From the latest email, the checkpoint size grows from start to end, and the
e2e time grows also.
>From my side.
e2e checkpoint time depends on the e2e snapshot time of all operators. the
e2e snapshot time of operator depends on the ${barrier_align_time} +
${sync-snapshot-time} + ${async-snapshot-time}.
For at least once mode, you can enable debug log to track the process of
barrier align time.
You can find out which step is the bottleneck, and track one task to find
out the reason.
maybe you could try:
1. use fullsnapshot of RocksDBStateBackend(disable incremental checkpoint)
and see what the e2e time of checkpoint will be -- This wants to verify
whether there is too many "increment change" between checkpoints.
2. place the times on Heap and RocksDB, whether this will affect the
checkpoint time -- The timer on heap will affect the sync-snapshot time
3. find out whether there is io/disk problem when snapshotting?
4. find out whether there is network problem when snapshotting?
5. does upload the state using multiple threads[1] help here
[1] https://issues.apache.org/jira/browse/FLINK-11008
Best,
Congxian
Jeff Henrikson <jehenri...@gmail.com> 于2020年6月21日周日 上午2:46写道：
> Bhaskar,
>
> > Glad to know some progress.
>
> Yeah, some progress. Yet overnight run didn't look as good as I hoped.
>
> The throttling required to not crash during snapshots seems to be quite
> different from the throttling required to crash not during snapshots.
> So the lowest common denominator is quite a large performance penalty.
>
> What's worse, the rate of input that makes the snapshot performance go
> from good to bad seems to change significantly as the state size grows.
> Here is checkpoint history from an overnight run.
>
> Parameters:
>
> - 30 minutes minimum between snapshots
> - incremental snapshot mode
> - inputs throttled to 100 events per sec per input per slot,
> which is around 1/4 of the unthrottled throughput
>
> Checkpoint history:
>
> ID Status Acknowledged Trigger Time Latest
> Acknowledgement End to End
> Duration State Size Buffered During Alignment
> 12 COMPLETED 304/304 8:52:22 10:37:18 1h 44m
> 55s 60.5 GB 0 B
> 11 COMPLETED 304/304 6:47:03 8:22:19 1h 35m 16s
> 53.3 GB 0 B
> 10 COMPLETED 304/304 5:01:20 6:17:00 1h 15m 39s
> 41.0 GB 0 B
> 9 COMPLETED 304/304 3:47:43 4:31:19 43m 35s 34.1 GB 0 B
> 8 COMPLETED 304/304 2:40:58 3:17:42 36m 43s 27.8 GB 0 B
> 7 COMPLETED 304/304 1:39:15 2:10:57 31m 42s 23.1 GB 0 B
> 6 COMPLETED 304/304 0:58:02 1:09:13 11m 11s 17.4 GB 0 B
> 5 COMPLETED 304/304 0:23:27 0:28:01 4m 33s 14.3 GB 0 B
> 4 COMPLETED 304/304 23:52:29 23:53:26
> 56s 12.7 GB 0 B
> 3 COMPLETED 304/304 23:20:59 23:22:28 1m
> 29s 10.8 GB 0 B
> 2 COMPLETED 304/304 22:46:17 22:50:58 4m
> 40s 7.40 GB 0 B
>
> As you can see, GB/minute varies drastically. GB/minute also varies
> drastically with full checkpoint mode.
>
> I'm pleased that it hasn't crashed yet. Yet I'm concerned that with the
> checkpoint GB/minute getting so slow, it will crash soon.
>
> I'm really wishing state.backend.async=false worked for
> RocksDbStateBackend.
>
> I'm also wondering if my throttler would improve if I just connected to
> the REST api to ask if any checkpoint is in progress, and then paused
> inputs accordingly. Effectively state.backend.async=false via hacked
> application code.
>
> > Where are you updating your state here? I
> > couldn't find any flink managed state here.
>
> The only updates to state I make are through the built-in
> DataStream.cogroup. A unit test (without RocksDB loaded) of the way I
> use .cogroup shows exactly two ways that .cogroup calls an
> implementation of AppendingState.add. I summarize those below.
>
> The two AppendingState subclasses invoked are HeapListState and
> HeapReducingState. Neither have a support attribute on them, such as
> MapState's @PublicEvolving.
>
> > I suggested updating the flink managed state using onTimer over an
> > interval equal to the checkpoint interval.
>
> So the onTimer method, with interval set to the checkpoint interval.
> Interesting.
>
> It looks like the closest subclass for my use case use would be either
> KeyedCoProcessFunction. Let me see if I understand concretely the idea:
>
> 1) between checkpoints, read join input and write join output, by
> loading any state reads from external state, but buffering all state
> changes in memory in some kind of data structure.
>
> 2) whenever a checkpoint arrived or the memory consumed by buffered
> writes gets too big, flush the writes to state.
>
> Is that the gist of the idea about .onTimer?
>
>
> Jeff
>
>
>
> There are two paths from .coGroup to AppendingState.add
>
> path 1 of 2: .coGroup to HeapListState
>
> add:90, HeapListState {org.apache.flink.runtime.state.heap}
> processElement:203, EvictingWindowOperator
> {org.apache.flink.streaming.runtime.operators.windowing}
> processElement:164, StreamOneInputProcessor
> {org.apache.flink.streaming.runtime.io}
> processInput:143, StreamOneInputProcessor
> {org.apache.flink.streaming.runtime.io}
>
>
>
> org.apache.flink.streaming.runtime.operators.windowing.EvictingWindowOperator#processElement
>
> (windowAssigner is an instance of GlobalWindows)
>
> @Override
> public void processElement(StreamRecord<IN> element)
> throws Exception {
> final Collection<W> elementWindows =
> windowAssigner.assignWindows(
> element.getValue(),
> element.getTimestamp(),
> windowAssignerContext);
>
> //if element is handled by none of assigned
> elementWindows
> boolean isSkippedElement = true;
>
> final K key =
> this.<K>getKeyedStateBackend().getCurrentKey();
>
> if (windowAssigner instanceof
> MergingWindowAssigner) {
> . . .
> } else {
> for (W window : elementWindows) {
>
> // check if the window is already
> inactive
> if (isWindowLate(window)) {
> continue;
> }
> isSkippedElement = false;
>
>
> evictingWindowState.setCurrentNamespace(window);
> evictingWindowState.add(element);
>
> =>
>
> org.apache.flink.runtime.state.heap.HeapListState#add:
> @Override
> public void add(V value) {
> Preconditions.checkNotNull(value, "You
> cannot add
> null to a ListState.");
>
> final N namespace = currentNamespace;
>
> final StateTable<K, N, List<V>> map =
> stateTable;
> List<V> list = map.get(namespace);
>
> if (list == null) {
> list = new ArrayList<>();
> map.put(namespace, list);
> }
> list.add(value);
> }
>
> path 2 of 2: .coGroup to HeapReducingState
>
> add:95, HeapReducingState
> {org.apache.flink.runtime.state.heap}
> onElement:49, CountTrigger
> {org.apache.flink.streaming.api.windowing.triggers}
> onElement:898, WindowOperator$Context
> {org.apache.flink.streaming.runtime.operators.windowing}
> processElement:210, EvictingWindowOperator
> {org.apache.flink.streaming.runtime.operators.windowing}
> processElement:164, StreamOneInputProcessor
> {org.apache.flink.streaming.runtime.io}
> processInput:143, StreamOneInputProcessor
> {org.apache.flink.streaming.runtime.io}
>
> @Override
> public void processElement(StreamRecord<IN> element)
> throws
> Exception {
> final Collection<W> elementWindows =
> windowAssigner.assignWindows(
> element.getValue(),
> element.getTimestamp(),
> windowAssignerContext);
>
> //if element is handled by none of assigned
> elementWindows
> boolean isSkippedElement = true;
>
> final K key =
> this.<K>getKeyedStateBackend().getCurrentKey();
>
> if (windowAssigner instanceof
> MergingWindowAssigner) {
> . . .
> } else {
> for (W window : elementWindows) {
>
> // check if the window is already
> inactive
> if (isWindowLate(window)) {
> continue;
> }
> isSkippedElement = false;
>
>
> evictingWindowState.setCurrentNamespace(window);
> evictingWindowState.add(element);
>
> triggerContext.key = key;
> triggerContext.window = window;
> evictorContext.key = key;
> evictorContext.window = window;
>
> TriggerResult triggerResult =
> triggerContext.onElement(element);
>
> =>
> public TriggerResult onElement(StreamRecord<IN>
> element)
> throws Exception {
> return
> trigger.onElement(element.getValue(),
> element.getTimestamp(), window, this);
>
> =>
>
> @Override
> public TriggerResult onElement(Object element, long
> timestamp,
> W window, TriggerContext ctx) throws Exception {
> ReducingState<Long> count =
> ctx.getPartitionedState(stateDesc);
> count.add(1L);
>
> =>
>
> org.apache.flink.runtime.state.heap.HeapReducingState#add
> @Override
> public void add(V value) throws IOException {
>
> if (value == null) {
>
>
>
> On 6/19/20 8:22 PM, Vijay Bhaskar wrote:
> > Glad to know some progress. Where are you updating your state here? I
> > couldn't find any flink managed state here.
> > I suggested updating the flink managed state using onTimer over an
> > interval equal to the checkpoint interval.
> >
> > In your case since you do throttling, it helped to maintain the fixed
> > rate per slot. Before the rate was sporadic.
> > It's definitely an IO bottleneck.
> >
> > So now you can think of decoupling stateless scanning and stateful joins.
> > For example you can keep a stateless scan as separate flink job and keep
> > its output in some Kafka kind of store.
> >
> > From there you start your stateful joins. This would help focussing on
> > your stateful job in much better fashion
> >
> > Regards
> > Bhaskar
> >
> >
> >
> >
> > On Sat, Jun 20, 2020 at 4:49 AM Jeff Henrikson <jehenri...@gmail.com
> > <mailto:jehenri...@gmail.com>> wrote:
> >
> > Bhaskar,
> >
> > Based on your idea of limiting input to get better checkpoint
> behavior,
> > I made a ProcessFunction that constraints to a number of events per
> > second per slot per input. I do need to do some stateless input
> > scanning before joins. The stateless part needs to be fast and does
> no
> > impact snapshots. So I inserted the throttling after the input
> > preprocessing but before the stateful transformations. There is a
> > significant difference of snapshot throughput (often 5x or larger)
> when
> > I change the throttle between 200 and 300 events per second (per slot
> > per input).
> >
> > Hope the throttling keeps being effective as I keep the job running
> > longer.
> >
> > Odd. But likely a very effective way out of my problem.
> >
> > I wonder what drives it . . . Thread contention? IOPS contention?
> >
> > See ProcessFunction code below.
> >
> > Many thanks!
> >
> >
> > Jeff
> >
> >
> >
> > import org.apache.flink.streaming.api.functions.ProcessFunction
> > import org.apache.flink.util.Collector
> >
> > // Set eventsPerSecMax to -1 to disable the throttle
> > // TODO: Actual number of events can be slightly larger
> > // TODO: Remove pause correlation with system clock
> >
> > case class Throttler[T](eventsPerSecMax : Double) extends
> > ProcessFunction[T,T] {
> > var minutePrev = 0
> > var numEvents = 0
> > def minutes() = {
> > val ms = System.currentTimeMillis()
> > (ms / 1000 / 60).toInt
> > }
> > def increment() = {
> > val m = minutes()
> > if(m != minutePrev) {
> > numEvents = 0
> > }
> > numEvents += 1
> > }
> > def eps() = {
> > numEvents/60.0
> > }
> > override def processElement(x: T, ctx: ProcessFunction[T,
> > T]#Context,
> > out: Collector[T]): Unit = {
> > increment()
> > if(eventsPerSecMax > 0 && eps() > eventsPerSecMax) {
> > Thread.sleep(1000L)
> > }
> > out.collect(x)
> > }
> > }
> >
> > On 6/19/20 9:16 AM, Jeff Henrikson wrote:
> > > Bhaskar,
> > >
> > > Thank you for your thoughtful points.
> > >
> > > > I want to discuss more on points (1) and (2)
> > > > If we take care of them rest will be good
> > > >
> > > > Coming to (1)
> > > >
> > > > Please try to give reasonable checkpoint interval time for
> > every job.
> > > > Minum checkpoint interval recommended by flink community is 3
> > minutes
> > > > I thin you should give minimum 3 minutes checkpoint interval
> > for all
> > >
> > > I have spent very little time testing with checkpoint intervals
> > of under
> > > 3 minutes. I frequently test with intervals of 5 minutes and of
> 30
> > > minutes. I also test with checkpoint intervals such as 60
> > minutes, and
> > > never (manual only). In terms of which exceptions get thrown, I
> > don't
> > > see much difference between 5/30/60, I don't see a lot of
> difference.
> > >
> > > Infinity (no checkpoint internal) seems to be an interesting
> value,
> > > because before crashing, it seems to process around twice as much
> > state
> > > as with any finite checkpoint interval. The largest savepoints I
> > have
> > > captured have been manually triggered using the /job/:jobid/stop
> > REST
> > > API. I think it helps for the snapshot to be synchronous.
> > >
> > > One curiosity about the /job/:jobid/stop command is that from
> > time of
> > > the command, it often takes many minutes for the internal
> > processing to
> > > stop.
> > >
> > > Another curiosity about /job/:jobid/stop command is that sometimes
> > > following a completed savepoint, the cluster goes back to running!
> > >
> > > > Coming to (2)
> > > >
> > > > What's your input data rate?
> > >
> > > My application involves what I will call "main" events that are
> > enriched
> > > by "secondary" events. While the secondary events have several
> > > different input streams, data types, and join keys, I will
> > estimate the
> > > secondary events all together. My estimate for input rate is as
> > follows:
> > >
> > > 50M "main" events
> > > 50 secondary events for each main event, for a
> > > total of around 2.5B input events
> > > 8 nodes
> > > 20 hours
> > >
> > > Combining these figures, we can estimate:
> > >
> > > 50000000*50/8/20/3600 = 4340 events/second/node
> > >
> > > I don't see how to act on your advice for (2). Maybe your idea
> > is that
> > > during backfill/bootstrap, I artificially throttle the inputs to
> my
> > > application?
> > >
> > > 100% of my application state is due to .cogroup, which manages a
> > > HeapListState on its own. I cannot think of any controls for
> > changing
> > > how .cogroup handles internal state per se. I will paste below
> the
> > > Flink code path that .cogroup uses to update its internal state
> > when it
> > > runs my application.
> > >
> > > The only control I can think of with .cogroup that indirectly
> > impacts
> > > internal state is delayed triggering.
> > >
> > > Currently I use a trigger on every event, which I understand
> > creates a
> > > suboptimal number of events. I previously experimented with
> delayed
> > > triggering, but I did not get good results.
> > >
> > > Just now I tried again ContinuousProcessingTimeTrigger of 30
> > seconds,
> > > with rocksdb.timer-service.factory: heap, and a 5 minute
> checkpoint
> > > interval. The first checkpoint failed, which has been rare when
> > I use
> > > all the same parameters except for triggering on every event. So
> it
> > > looks worse not better.
> > >
> > > Thanks again,
> > >
> > >
> > > Jeff Henrikson
> > >
> > >
> > >
> > >
> > > On 6/18/20 11:21 PM, Vijay Bhaskar wrote:
> > >> Thanks for the reply.
> > >> I want to discuss more on points (1) and (2)
> > >> If we take care of them rest will be good
> > >>
> > >> Coming to (1)
> > >>
> > >> Please try to give reasonable checkpoint interval time for every
> > job.
> > >> Minum checkpoint interval recommended by flink community is 3
> > minutes
> > >> I thin you should give minimum 3 minutes checkpoint interval for
> all
> > >>
> > >> Coming to (2)
> > >>
> > >> What's your input data rate?
> > >> For example you are seeing data at 100 msg/sec, For each message
> if
> > >> there is state changing and you are updating the state with
> > RocksDB,
> > >> it's going to
> > >> create 100 rows in 1 second at RocksDb end, On the average if 50
> > >> records have changed each second, even if you are using RocksDB
> > >> differentialstate = true,
> > >> there is no use. Because everytime 50% is new rows getting
> > added. So
> > >> the best bet is to update records with RocksDB only once in your
> > >> checkpoint interval.
> > >> Suppose your checkpoint interval is 5 minutes. If you update
> > RocksDB
> > >> state once in 5 minutes, then the rate at which new records
> > added to
> > >> RocksDB will be 1 record/5min.
> > >> Whereas in your original scenario, 30000 records added to
> > rocksDB in 5
> > >> min. You can save 1:30000 ratio of records in addition to
> RocksDB.
> > >> Which will save a huge
> > >> redundant size addition to RocksDB. Ultimately your state is
> > driven
> > >> by your checkpoint interval. From the input source you will go
> > back 5
> > >> min back and read the state, similarly from RocksDB side
> > >> also you can have a state update once in 5 min should work.
> > Otherwise
> > >> even if you add state there is no use.
> > >>
> > >> Regards
> > >> Bhaskar
> > >>
> > >> Try to update your RocksDB state in an interval equal to the
> > >> checkpoint interval. Otherwise in my case many times what's
> > observed is
> > >> state size grows unnecessarily.
> > >>
> > >> On Fri, Jun 19, 2020 at 12:42 AM Jeff Henrikson
> > <jehenri...@gmail.com <mailto:jehenri...@gmail.com>
> > >> <mailto:jehenri...@gmail.com <mailto:jehenri...@gmail.com>>>
> wrote:
> > >>
> > >> Vijay,
> > >>
> > >> Thanks for your thoughts. Below are answers to your
> questions.
> > >>
> > >> > 1. What's your checkpoint interval?
> > >>
> > >> I have used many different checkpoint intervals, ranging
> from 5
> > >> minutes
> > >> to never. I usually setMinPasueBetweenCheckpoints to the
> same
> > >> value as
> > >> the checkpoint interval.
> > >>
> > >> > 2. How frequently are you updating the state into
> RocksDB?
> > >>
> > >> My understanding is that for .cogroup:
> > >>
> > >> - Triggers control communication outside the operator
> > >> - Evictors control cleanup of internal state
> > >> - Configurations like write buffer size control the
> > frequency of
> > >> state change at the storage layer
> > >> - There is no control for how frequently the window state
> > >> updates at
> > >> the layer of the RocksDB api layer.
> > >>
> > >> Thus, the state update whenever data is ingested.
> > >>
> > >> > 3. How many task managers are you using?
> > >>
> > >> Usually I have been running with one slot per taskmanager.
> > 28GB of
> > >> usable ram on each node.
> > >>
> > >> > 4. How much data each task manager handles while taking
> the
> > >> checkpoint?
> > >>
> > >> Funny you should ask. I would be okay with zero.
> > >>
> > >> The application I am replacing has a latency of 36-48 hours,
> > so if I
> > >> had
> > >> to fully stop processing to take every snapshot
> > synchronously, it
> > >> might
> > >> be seen as totally acceptable, especially for initial
> > bootstrap.
> > >> Also,
> > >> the velocity of running this backfill is approximately 115x
> real
> > >> time on
> > >> 8 nodes, so the steady-state run may not exhibit the failure
> > mode in
> > >> question at all.
> > >>
> > >> It has come as some frustration to me that, in the case of
> > >> RocksDBStateBackend, the configuration key
> state.backend.async
> > >> effectively has no meaningful way to be false.
> > >>
> > >> The only way I have found in the existing code to get a
> > behavior like
> > >> synchronous snapshot is to POST to /jobs/<jobID>/stop with
> > >> drain=false
> > >> and a URL. This method of failing fast is the way that I
> > discovered
> > >> that I needed to increase transfer threads from the default.
> > >>
> > >> The reason I don't just run the whole backfill and then take
> one
> > >> snapshot is that even in the absence of checkpoints, a very
> > similar
> > >> congestion seems to take the cluster down when I am say
> > 20-30% of the
> > >> way through my backfill.
> > >>
> > >> Reloading from my largest feasible snapshot makes it
> > possible to make
> > >> another snapshot a bit larger before crash, but not by much.
> > >>
> > >> On first glance, the code change to allow
> > RocksDBStateBackend into a
> > >> synchronous snapshots mode looks pretty easy. Nevertheless,
> > I was
> > >> hoping to do the initial launch of my application without
> > needing to
> > >> modify the framework.
> > >>
> > >> Regards,
> > >>
> > >>
> > >> Jeff Henrikson
> > >>
> > >>
> > >> On 6/18/20 7:28 AM, Vijay Bhaskar wrote:
> > >> > For me this seems to be an IO bottleneck at your task
> > manager.
> > >> > I have a couple of queries:
> > >> > 1. What's your checkpoint interval?
> > >> > 2. How frequently are you updating the state into RocksDB?
> > >> > 3. How many task managers are you using?
> > >> > 4. How much data each task manager handles while taking
> the
> > >> checkpoint?
> > >> >
> > >> > For points (3) and (4) , you should be very careful. I
> > feel you
> > >> are
> > >> > stuck at this.
> > >> > You try to scale vertically by increasing more CPU and
> > memory for
> > >> each
> > >> > task manager.
> > >> > If not, try to scale horizontally so that each task
> > manager IO
> > >> gets reduces
> > >> > Apart from that check is there any bottleneck with the
> file
> > >> system.
> > >> >
> > >> > Regards
> > >> > Bhaskar
> > >> >
> > >> >
> > >> >
> > >> >
> > >> >
> > >> > On Thu, Jun 18, 2020 at 5:12 PM Timothy Victor
> > <vict...@gmail.com <mailto:vict...@gmail.com>
> > >> <mailto:vict...@gmail.com <mailto:vict...@gmail.com>>
> > >> > <mailto:vict...@gmail.com <mailto:vict...@gmail.com>
> > <mailto:vict...@gmail.com <mailto:vict...@gmail.com>>>> wrote:
> > >> >
> > >> > I had a similar problem. I ended up solving by not
> > >> relying on
> > >> > checkpoints for recovery and instead re-read my input
> > sources
> > >> (in my
> > >> > case a kafka topic) from the earliest offset and
> > rebuilding
> > >> only the
> > >> > state I need. I only need to care about the past 1
> to 2
> > >> days of
> > >> > state so can afford to drop anything older. My
> recovery
> > >> time went
> > >> > from over an hour for just the first checkpoint to
> > under 10
> > >> minutes.
> > >> >
> > >> > Tim
> > >> >
> > >> > On Wed, Jun 17, 2020, 11:52 PM Yun Tang
> > <myas...@live.com <mailto:myas...@live.com>
> > >> <mailto:myas...@live.com <mailto:myas...@live.com>>
> > >> > <mailto:myas...@live.com <mailto:myas...@live.com>
> > <mailto:myas...@live.com <mailto:myas...@live.com>>>> wrote:
> > >> >
> > >> > Hi Jeff
> > >> >
> > >> > 1. "after around 50GB of state, I stop being
> able to
> > >> reliably
> > >> > take checkpoints or savepoints. "
> > >> > What is the exact reason that job cannot
> complete
> > >> > checkpoint? Expired before completing or
> > decline by
> > >> some
> > >> > tasks? The former one is manly caused by high
> > >> back-pressure
> > >> > and the later one is mainly due to some
> internal
> > >> error.
> > >> > 2. Have you checked what reason the remote task
> > manager
> > >> is lost?
> > >> > If the remote task manager is not crashed, it
> > might
> > >> be due
> > >> > to GC impact, I think you might need to check
> > >> task-manager
> > >> > logs and GC logs.
> > >> >
> > >> > Best
> > >> > Yun Tang
> > >> >
> > >>
> >
> ------------------------------------------------------------------------
> > >> > *From:* Jeff Henrikson <jehenri...@gmail.com
> > <mailto:jehenri...@gmail.com>
> > >> <mailto:jehenri...@gmail.com <mailto:jehenri...@gmail.com>>
> > >> > <mailto:jehenri...@gmail.com
> > <mailto:jehenri...@gmail.com>
> > >> <mailto:jehenri...@gmail.com <mailto:jehenri...@gmail.com>>>>
> > >> > *Sent:* Thursday, June 18, 2020 1:46
> > >> > *To:* user <user@flink.apache.org
> > <mailto:user@flink.apache.org>
> > >> <mailto:user@flink.apache.org
> > <mailto:user@flink.apache.org>> <mailto:user@flink.apache.org
> > <mailto:user@flink.apache.org>
> > >> <mailto:user@flink.apache.org <mailto:user@flink.apache.org
> >>>>
> > >> > *Subject:* Trouble with large state
> > >> > Hello Flink users,
> > >> >
> > >> > I have an application of around 10 enrichment
> > joins. All
> > >> events
> > >> > are
> > >> > read from kafka and have event timestamps. The
> > joins are
> > >> built
> > >> > using
> > >> > .cogroup, with a global window, triggering on
> every 1
> > >> event, plus a
> > >> > custom evictor that drops records once a newer
> > record
> > >> for the
> > >> > same ID
> > >> > has been processed. Deletes are represented by
> empty
> > >> events with
> > >> > timestamp and ID (tombstones). That way, we can
> drop
> > >> records when
> > >> > business logic dictates, as opposed to when a
> maximum
> > >> retention
> > >> > has been
> > >> > attained. The application runs
> > RocksDBStateBackend, on
> > >> > Kubernetes on
> > >> > AWS with local SSDs.
> > >> >
> > >> > Unit tests show that the joins produce expected
> > >> results. On an
> > >> > 8 node
> > >> > cluster, watermark output progress seems to
> > indicate I
> > >> should be
> > >> > able to
> > >> > bootstrap my state of around 500GB in around 1
> > day. I am
> > >> able
> > >> > to save
> > >> > and restore savepoints for the first half an hour
> > of run
> > >> time.
> > >> >
> > >> > My current trouble is that after around 50GB of
> > state,
> > >> I stop
> > >> > being able
> > >> > to reliably take checkpoints or savepoints. Some
> > time
> > >> after
> > >> > that, I
> > >> > start getting a variety of failures where the
> first
> > >> suspicious
> > >> > log event
> > >> > is a generic cluster connectivity error, such as:
> > >> >
> > >> > 1) java.io.IOException: Connecting the
> channel
> > >> failed:
> > >> > Connecting
> > >> > to remote task manager +
> > '/10.67.7.101:38955 <http://10.67.7.101:38955>
> > >> <http://10.67.7.101:38955>
> > >> > <http://10.67.7.101:38955>' has failed. This
> > >> > might indicate that the remote task manager
> has
> > >> been lost.
> > >> >
> > >> > 2) org.apache.flink.runtime.io
> > <http://org.apache.flink.runtime.io>
> > >> <http://org.apache.flink.runtime.io>.network.netty.exception
> > >> > .RemoteTransportException: Connection
> > unexpectedly
> > >> closed
> > >> > by remote
> > >> > task manager 'null'. This might indicate
> > that the
> > >> remote task
> > >> > manager was lost.
> > >> >
> > >> > 3) Association with remote system
> > >> > [akka.tcp://flink@10.67.6.66:34987
> > <http://flink@10.67.6.66:34987>
> > >> <http://flink@10.67.6.66:34987>
> > >> > <http://flink@10.67.6.66:34987>] has failed,
> > address is
> > >> now
> > >> > gated for [50] ms. Reason: [Association
> > failed with
> > >> > [akka.tcp://flink@10.67.6.66:34987
> > <http://flink@10.67.6.66:34987>
> > >> <http://flink@10.67.6.66:34987>
> > >> > <http://flink@10.67.6.66:34987>]] Caused by:
> > >> > [java.net <http://java.net>
> > <http://java.net>.NoRouteToHostException:
> > >> No route to host]
> > >> >
> > >> > I don't see any obvious out of memory errors on
> the
> > >> TaskManager UI.
> > >> >
> > >> > Adding nodes to the cluster does not seem to
> > increase the
> > >> maximum
> > >> > savable state size.
> > >> >
> > >> > I could enable HA, but for the time being I have
> been
> > >> leaving it
> > >> > out to
> > >> > avoid the possibility of masking deterministic
> > faults.
> > >> >
> > >> > Below are my configurations.
> > >> >
> > >> > Thanks in advance for any advice.
> > >> >
> > >> > Regards,
> > >> >
> > >> >
> > >> > Jeff Henrikson
> > >> >
> > >> >
> > >> >
> > >> > Flink version: 1.10
> > >> >
> > >> > Configuration set via code:
> > >> > parallelism=8
> > >> > maxParallelism=64
> > >> >
> setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
> > >> >
> > >> setCheckpointingMode(CheckpointingMode.AT_LEAST_ONCE)
> > >> > setTolerableCheckpointFailureNumber(1000)
> > >> > setMaxConcurrentCheckpoints(1)
> > >> >
> > >> >
> > >>
> >
>
> enableExternalizedCheckpoints(ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION)
> > >>
> > >> > RocksDBStateBackend
> > >> >
> > setPredefinedOptions(PredefinedOptions.FLASH_SSD_OPTIMIZED)
> > >> > setNumberOfTransferThreads(25)
> > >> > setDbStoragePath points to a local nvme SSD
> > >> >
> > >> > Configuration in flink-conf.yaml:
> > >> >
> > >> > jobmanager.rpc.address: localhost
> > >> > jobmanager.rpc.port: 6123
> > >> > jobmanager.heap.size: 28000m
> > >> > taskmanager.memory.process.size: 28000m
> > >> > taskmanager.memory.jvm-metaspace.size: 512m
> > >> > taskmanager.numberOfTaskSlots: 1
> > >> > parallelism.default: 1
> > >> > jobmanager.execution.failover-strategy: full
> > >> >
> > >> > cluster.evenly-spread-out-slots: false
> > >> >
> > >> > taskmanager.memory.network.fraction:
> > 0.2 #
> > >> > default 0.1
> > >> > taskmanager.memory.framework.off-heap.size:
> 2GB
> > >> > taskmanager.memory.task.off-heap.size: 2GB
> > >> >
> > taskmanager.network.memory.buffers-per-channel: 32
> > >> # default 2
> > >> > taskmanager.memory.managed.fraction: 0.4
> #
> > >> docs say
> > >> > default 0.1, but something seems to set 0.4
> > >> > taskmanager.memory.task.off-heap.size:
> > 2048MB #
> > >> > default 128M
> > >> >
> > >> > state.backend.fs.memory-threshold: 1048576
> > >> > state.backend.fs.write-buffer-size: 10240000
> > >> > state.backend.local-recovery: true
> > >> > state.backend.rocksdb.writebuffer.size: 64MB
> > >> > state.backend.rocksdb.writebuffer.count: 8
> > >> >
> > state.backend.rocksdb.writebuffer.number-to-merge: 4
> > >> >
> > state.backend.rocksdb.timer-service.factory: heap
> > >> > state.backend.rocksdb.block.cache-size:
> > 64000000 #
> > >> default 8MB
> > >> > state.backend.rocksdb.write-batch-size:
> > 16000000 #
> > >> default 2MB
> > >> >
> > >> > web.checkpoints.history: 250
> > >> >
> > >>
> >
>
>
