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From "Matthias J. Sax" <matth...@confluent.io>
Subject Re: kafka stream ktable with suppress operator
Date Sun, 10 Nov 2019 09:18:28 GMT
Sorry for the late reply.

> You mentioned that, reduce() could use RocksDB as stores by default
> while suppress() is in memory. Is that the reason that reduce() has
> both -repartition and -changelog topics while suppress() only has
> -changelog topic?

No. The repartitioning would happen if you change the record key before
the `reduce()`. Both stores will have a changelog topic (changelogs are
for in-memory and RocksDB stores alike).

> And will that be related to the shutdown hook? If I don't provide
> shutdown hook and perform a redeployment, will it cause above issue?

Well, as long as you call `KafakStreams#close()` when you stop your
instance, it should be fine. However, even if you do not call close(),
the application should not rebalance forever.

> 3) You mentioned reduce() is using RocksDB by default, but I also
> noticed offset has been set to zero for reduce changelog topic. Is it
> wrong?

If you don't use "exactly-once" a checkpoint file should be written on a
regular basis. If you do use "exaclty-once" the checkpoint file is only
written in a clean shutdown (ie, when you call `close()`).

However, I am wondering who you deploy your application? If you use
Kubernetes and don't use stateful sets, RocksDB does not help because a
new volume would be attached to the POD on restart. For this case,
stateful sets should help to avoid reading the full changelog topic.
(You might also consider using "static group membership" for this case).

Overall it's hard to say, and I am wondering a little bit, why
rebalancing happens in 5 minutes intervals. This may indicate that it's
correlated to metadata refresh. But hard to say why.


-Matthias





On 11/4/19 7:42 AM, Xiyuan Hu wrote:
> Hi Matthias,
> 
> Could you help with above issue? Or any suggestions?
> 
> Thanks a lot!
> 
> On Thu, Oct 31, 2019 at 4:00 PM Xiyuan Hu <xiyuan.huhu@gmail.com> wrote:
>>
>> Hi Matthias,
>>
>> Some additional information, after I restart the app, it went to
>> endless rebalancing. Join rate loos like below attachment. It's
>> basically rebalanced every 5 minutes. I checked into each node
>> logging. And found below warning:
>>
>> On node A:
>> 2019/10/31 10:13:46 | 2019-10-31 10:13:46,543 WARN
>> [kafka-coordinator-heartbeat-thread | XXX]
>> o.a.k.c.c.i.AbstractCoordinator [Consumer
>> clientId=XXX-StreamThread-1-consumer, groupId=XXX] This member will
>> leave the group because consumer poll timeout has expired. This means
>> the time between subsequent calls to poll() was longer than the
>> configured max.poll.interval.ms, which typically implies that the poll
>> loop is spending too much time processing messages. You can address
>> this either by increasing max.poll.interval.ms or by reducing the
>> maximum size of batches returned in poll() with max.poll.records.
>> 2019/10/31 10:13:46 | 2019-10-31 10:13:46,544 INFO
>> [kafka-coordinator-heartbeat-thread | XXX]
>> o.a.k.c.c.i.AbstractCoordinator [Consumer
>> clientId=XXX-StreamThread-1-consumer, groupId=XXX] Member
>> XXX-StreamThread-1-consumer-7307ab88-9724-4af8-99b8-5d1c3ef5294f
>> sending LeaveGroup request to coordinator xx:9092 (id: 2147483644
>> rack: null)
>> 2019/10/31 10:13:52 | 2019-10-31 10:13:52,766 INFO
>> [XXX-StreamThread-1] o.a.k.s.s.i.RocksDBTimestampedStore Opening store
>> KSTREAM-REDUCE-STATE-STORE-0000000003.1572480000000 in regular mode
>> 2019/10/31 10:13:52 | 2019-10-31 10:13:52,767 INFO
>> [XXX-StreamThread-1] o.a.k.s.p.i.StoreChangelogReader stream-thread
>> [XXX-StreamThread-1] Restoring task 1_3's state store
>> KTABLE-SUPPRESS-STATE-STORE-0000000009 from beginning of the changelog
>> XXX-KTABLE-SUPPRESS-STATE-STORE-0000000009-changelog-3
>> 2019/10/31 10:13:52 | 2019-10-31 10:13:52,794 INFO
>> [XXX-StreamThread-1] o.a.k.c.c.i.ConsumerCoordinator [Consumer
>> clientId=XXX-StreamThread-1-consumer, groupId=XXX] Revoking previously
>> assigned partitions
>> [XXX-KSTREAM-REDUCE-STATE-STORE-0000000003-repartition-3]
>> 2019/10/31 10:13:52 | 2019-10-31 10:13:52,794 INFO
>> [XXX-StreamThread-1] o.a.k.s.p.internals.StreamThread stream-thread
>> [XXX-StreamThread-1] State transition from PARTITIONS_ASSIGNED to
>> PARTITIONS_REVOKED
>>
>> While on other nodes say: 2019/10/31 10:13:47 Attempt to heartbeat
>> failed since group is rebalancing.
>>
>> If my understanding is correct, above warning caused the group
>> rebalancing? My questions are:
>> 1) Why it only happened after restart?
>> 2) Even if it rebalanced, why it keeps rebalancing like a endless
>> loop? I can't understand the behavior here.
>> 3) You mentioned reduce() is using RocksDB by default, but I also
>> noticed offset has been set to zero for reduce changelog topic. Is it
>> wrong?
>>
>> Thanks a lot for the help!
>>
>> On Thu, Oct 31, 2019 at 8:11 AM Xiyuan Hu <xiyuan.huhu@gmail.com> wrote:
>>>
>>> Hi Matthias,
>>>
>>> When I redeployment the application with the same application Id, it
>>> will cause a rebalance loop: partition revoked -> rebalance -> offset
>>> reset to zero -> partition assigned -> partition revoked.
>>>
>>> The app was running well before the redeployment, but once redeployed,
>>> it will keep rebalancing for hours and I have to switch to a new
>>> application id to stop that.
>>>
>>> You mentioned that, reduce() could use RocksDB as stores by default
>>> while suppress() is in memory. Is that the reason that reduce() has
>>> both -repartition and -changelog topics while suppress() only has
>>> -changelog topic?
>>>
>>> And will that be related to the shutdown hook? If I don't provide
>>> shutdown hook and perform a redeployment, will it cause above issue?
>>>
>>> Thanks!
>>>
>>> On Thu, Oct 31, 2019 at 5:55 AM Matthias J. Sax <matthias@confluent.io>
wrote:
>>>>
>>>> Just a follow up: currently, suppress() only supports in-memory stores
>>>> (note, that `suppress()` has it's own store).
>>>>
>>>> For the actually `reduce()` store, you can pick between RocksDB and
>>>> in-memory (default is RocksDB). Hence, if you restart an application on
>>>> the same host, it should not be necessary to reload the state from the
>>>> changelog topic if you use RocksDB.
>>>>
>>>> However, the suppress buffer must be recreated from the
>>>> suppress-changelog topics on restart atm.
>>>>
>>>> Originally, `suppress()` intended to support persistent stores as well,
>>>> but it was not implement yet. We hope to close this gap in the future.
>>>>
>>>>>>> I haven't figured out the reason, but after restart, the app
will keep
>>>>>>> reset changelog topic offset to ZERO and trigger rebalance.
>>>>
>>>> Resetting to zero would happen is the full state needs to be recovered.
>>>> However, this should not result in a rebalance. Can you elaborate on the
>>>> rebalancing issue you described?
>>>>
>>>>
>>>>
>>>> -Matthias
>>>>
>>>> On 10/28/19 5:54 PM, Alex Brekken wrote:
>>>>> I assume you're using RocksDB as your state stores... The bytes out you're
>>>>> seeing on the changelog topics is probably because they are restoring
your
>>>>> state stores.  If your state stores are in-memory, then on every
>>>>> application startup they're going to be restored from the changelog
>>>>> topics.  If your state stores are persistent (saved to disk), then a
>>>>> restore can still happen if you've lost your filesystem.  (maybe you're
>>>>> doing a state store cleanup on startup/shutdown, or have temporal storage
>>>>> such as emptyDir in k8s, for example)  So *I think* what you're seeing
is
>>>>> normal, though if you want to dig deeper there are rocksdb metrics that
can
>>>>> be exposed and will show restore related info.  Additionally, there is
a
>>>>> StateRestoreListener interface that you can implement if you'd like to
log
>>>>> some of the state store restoration details.
>>>>>
>>>>> Alex
>>>>>
>>>>> On Mon, Oct 28, 2019 at 4:41 PM Xiyuan Hu <xiyuan.huhu@gmail.com>
wrote:
>>>>>
>>>>>> Hi,
>>>>>> I'm using 2.3.1 now and having the same issue. During restarting,
I
>>>>>> noticed a lot logging like below:
>>>>>> Seeking to EARLIEST offset of partition
>>>>>> XX-KSTREAM-REDUCE-STATE-STORE-0000000014-changelog-41
>>>>>> Seeking to EARLIEST offset of partition
>>>>>> XX-KTABLE-SUPPRESS-STATE-STORE-0000000020-changelog-41
>>>>>>
>>>>>> After restarting, the bytesout of changelog topic is as high as
>>>>>> 800-900MB/s while normally, it has zero bytes out. Is this expected?
>>>>>> I haven't figured out the reason, but after restart, the app will
keep
>>>>>> reset changelog topic offset to ZERO and trigger rebalance. It seems
a
>>>>>> dead loop?
>>>>>> Rebalance -> reset to ZERO -> rebalance
>>>>>>
>>>>>> Is there any config I should set?
>>>>>>
>>>>>> Thanks!
>>>>>>
>>>>>> On Sun, Oct 27, 2019 at 11:25 PM Matthias J. Sax <matthias@confluent.io>
>>>>>> wrote:
>>>>>>>
>>>>>>> What version are you using? We fixed couple of bugs in `suppress()`
-- I
>>>>>>> would recommend to use latest 2.3.1 bug-fix release.
>>>>>>>
>>>>>>>
>>>>>>> -Matthia
>>>>>>>
>>>>>>> On 10/25/19 9:12 AM, Tao Wang wrote:
>>>>>>>> When using suppress operator with windowed Ktable, it looks
like
>>>>>> restarting the kafka stream causes the aggregated messages from the
>>>>>> SUPPRESS-STATE-STORE published again..
>>>>>>>>
>>>>>>>> Here is the sudo code .. anything I am missing or anything
can be done
>>>>>> to avoid this ..
>>>>>>>>
>>>>>>>>
>>>>>>>> KTable<Windowed<String>, String> test = <KStreamObject>
>>>>>>>> .groupByKey()
>>>>>>>>
>>>>>> .windowedBy(TimeWindows.of(Duration.ofSeconds(30)).grace(Duration.ofSeconds(3)))
>>>>>>>> .aggregate(
>>>>>>>>         ....
>>>>>>>>         ,
>>>>>>>>
>>>>>>  Materialized.<String,String,WindowStore<String,String>>as("aggregated-stream21-store")
>>>>>>>>                     .withRetention(Duration.ofMinutes(5))
>>>>>>>>                     .with(Serdes.String(), Serdes.String())
>>>>>>>>    )
>>>>>>>>
>>>>>> .suppress(Suppressed.untilWindowCloses(Suppressed.BufferConfig.unbounded()))
>>>>>>>>
>>>>>>>> .toStream()
>>>>>>>>
>>>>>>>> .to("<topic_out>")
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> So when restarting the stream app, the <topic_out>
will have
>>>>>> duplicated messages from a while back ... is this expected behavior
?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Tao Wang
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
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