Re: Trouble with large state

[Vijay Bhaskar]

Jeff
Glad to know that you are able to progress well and issue got resolved

Regards
Bhaskar

On Tue, Jun 23, 2020 at 12:24 AM Jeff Henrikson 
wrote:

> Bhaskar,
>
> I think I am unstuck.  The performance numbers I sent after throttling
> were due to a one character error in business logic.  I think I now have
> something good enough to work with for now.  I will repost if I
> encounter further unexpected issues.
>
> Adding application-level throttling ends up resolving both my symptom of
> slow/failing checkpoints, and also my symptom of crashes during long runs.
>
> Many thanks!
>
>
> Jeff
>
>
> On 6/20/20 11:46 AM, Jeff Henrikson wrote:
> > 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:
> >
> >  IDStatusAcknowledgedTrigger TimeLatest
> > AcknowledgementEnd to End DurationState SizeBuffered During
> > Alignment
> >  12COMPLETED304/3048:52:2210:37:181h 44m 55s
> > 60.5 GB0 B
> >  11COMPLETED304/3046:47:038:22:191h 35m 16s
> > 53.3 GB0 B
> >  10COMPLETED304/3045:01:206:17:001h 15m 39s
> > 41.0 GB0 B
> >  9COMPLETED304/3043:47:434:31:1943m 35s34.1
> > GB0 B
> >  8COMPLETED304/3042:40:583:17:4236m 43s27.8
> > GB0 B
> >  7COMPLETED304/3041:39:152:10:5731m 42s23.1
> > GB0 B
> >  6COMPLETED304/3040:58:021:09:1311m 11s17.4
> > GB0 B
> >  5COMPLETED304/3040:23:270:28:014m 33s14.3
> > GB0 B
> >  4COMPLETED304/30423:52:2923:53:2656s12.7
> > GB0 B
> >  3COMPLETED304/30423:20:5923:22:281m 29s10.8
> > GB0 B
> >  2COMPLETED304/30422:46:1722:50:584m 40s7.40
> > GB0 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}
> >   

Re: Trouble with large state

[Jeff Henrikson]

Bhaskar,

I think I am unstuck.  The performance numbers I sent after throttling 
were due to a one character error in business logic.  I think I now have 
something good enough to work with for now.  I will repost if I 
encounter further unexpected issues.


Adding application-level throttling ends up resolving both my symptom of 
slow/failing checkpoints, and also my symptom of crashes during long runs.


Many thanks!


Jeff


On 6/20/20 11:46 AM, Jeff Henrikson wrote:

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 element) 
throws Exception {
     final Collection elementWindows = 
windowAssigner.assignWindows(
     element.getValue(), element.getTimesta

Re: Trouble with large state

[Congxian Qiu]

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  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  AcknowledgedTrigger TimeLatest
> Acknowledgement  End to End
> DurationState Size  Buffered During Alignment
> 12  COMPLETED   304/304 8:52:22 10:37:181h 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 23:52:2923:53:26
> 56s 12.7 GB 0 B
> 3   COMPLETED   304/304 23:20:5923:22:281m
> 29s  10.8 GB 0 B
> 2   COMPLETED   304/304 22:46:1722:50:584m
> 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 mem

Re: Trouble with large state

[Jeff Henrikson]

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 10:37:181h 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 23:52:2923:53:2656s 
12.7 GB 0 B
3   COMPLETED   304/304 23:20:5923:22:281m 29s  
10.8 GB 0 B
2   COMPLETED   304/304 22:46:1722:50:584m 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 element) 
throws Exception {
		final Collection elementWindows = 
windowAssigner.assignWindows(
element.getValue(), element.getTimestamp(), 
windowAssignerContext);


//if element is handled by none of assigned 
elementWindows
boolean isSkippedElement = true;

final K key = 
this.getKeyedStateBackend().getCurrentKey();

if (windowAssigner instanceof MergingWindowAssigner) {
. . .
} else {
for (W window : elementWindows) {

// check if the window is already 
inactive
  

Re: Trouble with large state

[Vijay Bhaskar]

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  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:
> >
> >  5000*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 ma

Re: Trouble with large state

[Jeff Henrikson]

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:

     5000*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)

Pleas

Re: Trouble with large state

[Jeff Henrikson]

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:

5000*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, 3 records added to rocksDB in 5 
min. You can save 1:3 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 > wrote:


Vijay,

Thanks for your thoughts.  Below are answers to your questions.

  > 1. What's your checkpoint interval?

I have us

Re: Trouble with large state

[Vijay Bhaskar]

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, 3 records added to rocksDB in 5 min.
You can save 1:3 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 
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//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.
> >
> > Regard

Re: Trouble with large state

[Jeff Henrikson]

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//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 > 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 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 mailto:jehenri...@gmail.com>>
*Sent:* Thursday, June 18, 2020 1:46
*To:* user 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
cu

Re: Trouble with large state

[Jeff Henrikson]

Hi Yun,

Thanks for your thoughts.  Answers to your questions:

>  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.

In the UI, under Job | Checkpoints | History, then opening the 
checkpoint detail, the checkpoints fail by not some operators not 
acknowledging.  It's always a subset of of the larger state operators 
that stop acknowledging.  The exact selection of operators that stop is 
nondeterministic.  The checkpoints frequently fail before any timeout 
that I impose on them.


>  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.


The only general pattern I have observed is:

1) Some taskmanager A throws one of the various connectivity
exceptions I listed complaining about another taskmanager B.
2) Taskmanager B shows no obvious error other than complaining
that taskmanager A has disconnected from it.

Regards,


Jeff Henrikson



On 6/17/20 9:52 PM, Yun Tang 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 
*Sent:* Thursday, June 18, 2020 1:46
*To:* user 
*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' has failed. This
  might indicate that the remote task manager has been lost.

  2) 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] has failed, address is now
  gated for [50] ms. Reason: [Association failed with
  [akka.tcp://flink@10.67.6.66:34987]] Caused by:
  [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
  

Re: Trouble with large state

[Vijay Bhaskar]

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  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  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 
>> *Sent:* Thursday, June 18, 2020 1:46
>> *To:* user 
>> *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' has failed. This
>>  might indicate that the remote task manager has been lost.
>>
>>  2) 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] has failed, address is now
>>  gated for [50] ms. Reason: [Association failed with
>>  [akka.tcp://flink@10.67.6.66:34987]] Caused by:
>>  [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

Re: Trouble with large state

[Timothy Victor]

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  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 
> *Sent:* Thursday, June 18, 2020 1:46
> *To:* user 
> *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' has failed. This
>  might indicate that the remote task manager has been lost.
>
>  2) 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] has failed, address is now
>  gated for [50] ms. Reason: [Association failed with
>  [akka.tcp://flink@10.67.6.66:34987]] Caused by:
>  [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

Re: Trouble with large state

[Yun Tang]

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 
Sent: Thursday, June 18, 2020 1:46
To: user 
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' has failed. This
 might indicate that the remote task manager has been lost.

 2) 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] has failed, address is now
 gated for [50] ms. Reason: [Association failed with
 [akka.tcp://flink@10.67.6.66:34987]] Caused by:
 [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: 1024
 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: 6400 # default 8MB
 state.backend.rocksdb.write-batch-size: 1600 # default 2MB

 web.checkpoints.history: 250