Glad to know some progress. Where are you updating your state here? I
couldn't find any flink managed state here.
I suggested updating the flink managed state using onTimer over an interval
equal to the checkpoint interval.
In your case since you do throttling, it helped to maintain the fixed rate
per slot. Before the rate was sporadic.
It's definitely an IO bottleneck.
So now you can think of decoupling stateless scanning and stateful joins.
For example you can keep a stateless scan as separate flink job and keep
its output in some Kafka kind of store.
>From there you start your stateful joins. This would help focussing on your
stateful job in much better fashion
Regards
Bhaskar
On Sat, Jun 20, 2020 at 4:49 AM Jeff Henrikson <jehenri...@gmail.com> wrote:
> Bhaskar,
>
> Based on your idea of limiting input to get better checkpoint behavior,
> I made a ProcessFunction that constraints to a number of events per
> second per slot per input. I do need to do some stateless input
> scanning before joins. The stateless part needs to be fast and does no
> impact snapshots. So I inserted the throttling after the input
> preprocessing but before the stateful transformations. There is a
> significant difference of snapshot throughput (often 5x or larger) when
> I change the throttle between 200 and 300 events per second (per slot
> per input).
>
> Hope the throttling keeps being effective as I keep the job running longer.
>
> Odd. But likely a very effective way out of my problem.
>
> I wonder what drives it . . . Thread contention? IOPS contention?
>
> See ProcessFunction code below.
>
> Many thanks!
>
>
> Jeff
>
>
>
> import org.apache.flink.streaming.api.functions.ProcessFunction
> import org.apache.flink.util.Collector
>
> // Set eventsPerSecMax to -1 to disable the throttle
> // TODO: Actual number of events can be slightly larger
> // TODO: Remove pause correlation with system clock
>
> case class Throttler[T](eventsPerSecMax : Double) extends
> ProcessFunction[T,T] {
> var minutePrev = 0
> var numEvents = 0
> def minutes() = {
> val ms = System.currentTimeMillis()
> (ms / 1000 / 60).toInt
> }
> def increment() = {
> val m = minutes()
> if(m != minutePrev) {
> numEvents = 0
> }
> numEvents += 1
> }
> def eps() = {
> numEvents/60.0
> }
> override def processElement(x: T, ctx: ProcessFunction[T, T]#Context,
> out: Collector[T]): Unit = {
> increment()
> if(eventsPerSecMax > 0 && eps() > eventsPerSecMax) {
> Thread.sleep(1000L)
> }
> out.collect(x)
> }
> }
>
> On 6/19/20 9:16 AM, Jeff Henrikson wrote:
> > Bhaskar,
> >
> > Thank you for your thoughtful points.
> >
> > > I want to discuss more on points (1) and (2)
> > > If we take care of them rest will be good
> > >
> > > Coming to (1)
> > >
> > > Please try to give reasonable checkpoint interval time for every job.
> > > Minum checkpoint interval recommended by flink community is 3 minutes
> > > I thin you should give minimum 3 minutes checkpoint interval for all
> >
> > I have spent very little time testing with checkpoint intervals of under
> > 3 minutes. I frequently test with intervals of 5 minutes and of 30
> > minutes. I also test with checkpoint intervals such as 60 minutes, and
> > never (manual only). In terms of which exceptions get thrown, I don't
> > see much difference between 5/30/60, I don't see a lot of difference.
> >
> > Infinity (no checkpoint internal) seems to be an interesting value,
> > because before crashing, it seems to process around twice as much state
> > as with any finite checkpoint interval. The largest savepoints I have
> > captured have been manually triggered using the /job/:jobid/stop REST
> > API. I think it helps for the snapshot to be synchronous.
> >
> > One curiosity about the /job/:jobid/stop command is that from time of
> > the command, it often takes many minutes for the internal processing to
> > stop.
> >
> > Another curiosity about /job/:jobid/stop command is that sometimes
> > following a completed savepoint, the cluster goes back to running!
> >
> > > Coming to (2)
> > >
> > > What's your input data rate?
> >
> > My application involves what I will call "main" events that are enriched
> > by "secondary" events. While the secondary events have several
> > different input streams, data types, and join keys, I will estimate the
> > secondary events all together. My estimate for input rate is as follows:
> >
> > 50M "main" events
> > 50 secondary events for each main event, for a
> > total of around 2.5B input events
> > 8 nodes
> > 20 hours
> >
> > Combining these figures, we can estimate:
> >
> > 50000000*50/8/20/3600 = 4340 events/second/node
> >
> > I don't see how to act on your advice for (2). Maybe your idea is that
> > during backfill/bootstrap, I artificially throttle the inputs to my
> > application?
> >
> > 100% of my application state is due to .cogroup, which manages a
> > HeapListState on its own. I cannot think of any controls for changing
> > how .cogroup handles internal state per se. I will paste below the
> > Flink code path that .cogroup uses to update its internal state when it
> > runs my application.
> >
> > The only control I can think of with .cogroup that indirectly impacts
> > internal state is delayed triggering.
> >
> > Currently I use a trigger on every event, which I understand creates a
> > suboptimal number of events. I previously experimented with delayed
> > triggering, but I did not get good results.
> >
> > Just now I tried again ContinuousProcessingTimeTrigger of 30 seconds,
> > with rocksdb.timer-service.factory: heap, and a 5 minute checkpoint
> > interval. The first checkpoint failed, which has been rare when I use
> > all the same parameters except for triggering on every event. So it
> > looks worse not better.
> >
> > Thanks again,
> >
> >
> > Jeff Henrikson
> >
> >
> >
> >
> > On 6/18/20 11:21 PM, Vijay Bhaskar wrote:
> >> Thanks for the reply.
> >> I want to discuss more on points (1) and (2)
> >> If we take care of them rest will be good
> >>
> >> Coming to (1)
> >>
> >> Please try to give reasonable checkpoint interval time for every job.
> >> Minum checkpoint interval recommended by flink community is 3 minutes
> >> I thin you should give minimum 3 minutes checkpoint interval for all
> >>
> >> Coming to (2)
> >>
> >> What's your input data rate?
> >> For example you are seeing data at 100 msg/sec, For each message if
> >> there is state changing and you are updating the state with RocksDB,
> >> it's going to
> >> create 100 rows in 1 second at RocksDb end, On the average if 50
> >> records have changed each second, even if you are using RocksDB
> >> differentialstate = true,
> >> there is no use. Because everytime 50% is new rows getting added. So
> >> the best bet is to update records with RocksDB only once in your
> >> checkpoint interval.
> >> Suppose your checkpoint interval is 5 minutes. If you update RocksDB
> >> state once in 5 minutes, then the rate at which new records added to
> >> RocksDB will be 1 record/5min.
> >> Whereas in your original scenario, 30000 records added to rocksDB in 5
> >> min. You can save 1:30000 ratio of records in addition to RocksDB.
> >> Which will save a huge
> >> redundant size addition to RocksDB. Ultimately your state is driven
> >> by your checkpoint interval. From the input source you will go back 5
> >> min back and read the state, similarly from RocksDB side
> >> also you can have a state update once in 5 min should work. Otherwise
> >> even if you add state there is no use.
> >>
> >> Regards
> >> Bhaskar
> >>
> >> Try to update your RocksDB state in an interval equal to the
> >> checkpoint interval. Otherwise in my case many times what's observed is
> >> state size grows unnecessarily.
> >>
> >> On Fri, Jun 19, 2020 at 12:42 AM Jeff Henrikson <jehenri...@gmail.com
> >> <mailto:jehenri...@gmail.com>> wrote:
> >>
> >> Vijay,
> >>
> >> Thanks for your thoughts. Below are answers to your questions.
> >>
> >> > 1. What's your checkpoint interval?
> >>
> >> I have used many different checkpoint intervals, ranging from 5
> >> minutes
> >> to never. I usually setMinPasueBetweenCheckpoints to the same
> >> value as
> >> the checkpoint interval.
> >>
> >> > 2. How frequently are you updating the state into RocksDB?
> >>
> >> My understanding is that for .cogroup:
> >>
> >> - Triggers control communication outside the operator
> >> - Evictors control cleanup of internal state
> >> - Configurations like write buffer size control the frequency of
> >> state change at the storage layer
> >> - There is no control for how frequently the window state
> >> updates at
> >> the layer of the RocksDB api layer.
> >>
> >> Thus, the state update whenever data is ingested.
> >>
> >> > 3. How many task managers are you using?
> >>
> >> Usually I have been running with one slot per taskmanager. 28GB of
> >> usable ram on each node.
> >>
> >> > 4. How much data each task manager handles while taking the
> >> checkpoint?
> >>
> >> Funny you should ask. I would be okay with zero.
> >>
> >> The application I am replacing has a latency of 36-48 hours, so if I
> >> had
> >> to fully stop processing to take every snapshot synchronously, it
> >> might
> >> be seen as totally acceptable, especially for initial bootstrap.
> >> Also,
> >> the velocity of running this backfill is approximately 115x real
> >> time on
> >> 8 nodes, so the steady-state run may not exhibit the failure mode in
> >> question at all.
> >>
> >> It has come as some frustration to me that, in the case of
> >> RocksDBStateBackend, the configuration key state.backend.async
> >> effectively has no meaningful way to be false.
> >>
> >> The only way I have found in the existing code to get a behavior
> like
> >> synchronous snapshot is to POST to /jobs/<jobID>/stop with
> >> drain=false
> >> and a URL. This method of failing fast is the way that I discovered
> >> that I needed to increase transfer threads from the default.
> >>
> >> The reason I don't just run the whole backfill and then take one
> >> snapshot is that even in the absence of checkpoints, a very similar
> >> congestion seems to take the cluster down when I am say 20-30% of
> the
> >> way through my backfill.
> >>
> >> Reloading from my largest feasible snapshot makes it possible to
> make
> >> another snapshot a bit larger before crash, but not by much.
> >>
> >> On first glance, the code change to allow RocksDBStateBackend into a
> >> synchronous snapshots mode looks pretty easy. Nevertheless, I was
> >> hoping to do the initial launch of my application without needing to
> >> modify the framework.
> >>
> >> Regards,
> >>
> >>
> >> Jeff Henrikson
> >>
> >>
> >> On 6/18/20 7:28 AM, Vijay Bhaskar wrote:
> >> > For me this seems to be an IO bottleneck at your task manager.
> >> > I have a couple of queries:
> >> > 1. What's your checkpoint interval?
> >> > 2. How frequently are you updating the state into RocksDB?
> >> > 3. How many task managers are you using?
> >> > 4. How much data each task manager handles while taking the
> >> checkpoint?
> >> >
> >> > For points (3) and (4) , you should be very careful. I feel you
> >> are
> >> > stuck at this.
> >> > You try to scale vertically by increasing more CPU and memory for
> >> each
> >> > task manager.
> >> > If not, try to scale horizontally so that each task manager IO
> >> gets reduces
> >> > Apart from that check is there any bottleneck with the file
> >> system.
> >> >
> >> > Regards
> >> > Bhaskar
> >> >
> >> >
> >> >
> >> >
> >> >
> >> > On Thu, Jun 18, 2020 at 5:12 PM Timothy Victor <
> vict...@gmail.com
> >> <mailto:vict...@gmail.com>
> >> > <mailto:vict...@gmail.com <mailto:vict...@gmail.com>>> wrote:
> >> >
> >> > I had a similar problem. I ended up solving by not
> >> relying on
> >> > checkpoints for recovery and instead re-read my input sources
> >> (in my
> >> > case a kafka topic) from the earliest offset and rebuilding
> >> only the
> >> > state I need. I only need to care about the past 1 to 2
> >> days of
> >> > state so can afford to drop anything older. My recovery
> >> time went
> >> > from over an hour for just the first checkpoint to under 10
> >> minutes.
> >> >
> >> > Tim
> >> >
> >> > On Wed, Jun 17, 2020, 11:52 PM Yun Tang <myas...@live.com
> >> <mailto:myas...@live.com>
> >> > <mailto:myas...@live.com <mailto:myas...@live.com>>> wrote:
> >> >
> >> > Hi Jeff
> >> >
> >> > 1. "after around 50GB of state, I stop being able to
> >> reliably
> >> > take checkpoints or savepoints. "
> >> > What is the exact reason that job cannot complete
> >> > checkpoint? Expired before completing or decline by
> >> some
> >> > tasks? The former one is manly caused by high
> >> back-pressure
> >> > and the later one is mainly due to some internal
> >> error.
> >> > 2. Have you checked what reason the remote task manager
> >> is lost?
> >> > If the remote task manager is not crashed, it might
> >> be due
> >> > to GC impact, I think you might need to check
> >> task-manager
> >> > logs and GC logs.
> >> >
> >> > Best
> >> > Yun Tang
> >> >
> >>
> ------------------------------------------------------------------------
> >> > *From:* Jeff Henrikson <jehenri...@gmail.com
> >> <mailto:jehenri...@gmail.com>
> >> > <mailto:jehenri...@gmail.com
> >> <mailto:jehenri...@gmail.com>>>
> >> > *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>>>
> >> > *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>' 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>.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>] 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>]] Caused by:
> >> > [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
> >> >
> >>
>
