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
>
