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From Jeff Henrikson <jehenri...@gmail.com>
Subject Re: Trouble with large state
Date Sat, 20 Jun 2020 18:46:00 GMT
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 <jehenrik27@gmail.com 
> <mailto:jehenrik27@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
>     <jehenrik27@gmail.com <mailto:jehenrik27@gmail.com>
>      >> <mailto:jehenrik27@gmail.com <mailto:jehenrik27@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
>     <victtim@gmail.com <mailto:victtim@gmail.com>
>      >>     <mailto:victtim@gmail.com <mailto:victtim@gmail.com>>
>      >>      > <mailto:victtim@gmail.com <mailto:victtim@gmail.com>
>     <mailto:victtim@gmail.com <mailto:victtim@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
>     <myasuka@live.com <mailto:myasuka@live.com>
>      >>     <mailto:myasuka@live.com <mailto:myasuka@live.com>>
>      >>      >     <mailto:myasuka@live.com <mailto:myasuka@live.com>
>     <mailto:myasuka@live.com <mailto:myasuka@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 <jehenrik27@gmail.com
>     <mailto:jehenrik27@gmail.com>
>      >>     <mailto:jehenrik27@gmail.com <mailto:jehenrik27@gmail.com>>
>      >>      >         <mailto:jehenrik27@gmail.com
>     <mailto:jehenrik27@gmail.com>
>      >> <mailto:jehenrik27@gmail.com <mailto:jehenrik27@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
>      >>      >
>      >>
> 


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