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From Alessandro Tagliapietra <tagliapietra.alessan...@gmail.com>
Subject Re: Reducing streams startup bandwidth usage
Date Sun, 08 Dec 2019 20:54:09 GMT
It seems that even with caching enabled, after a while the sent bytes stil
go up

[image: Screen Shot 2019-12-08 at 12.52.31 PM.png]

you can see the deploy when I've enabled caching but it looks like it's
still a temporary solution.

--
Alessandro Tagliapietra


On Sat, Dec 7, 2019 at 10:08 AM Alessandro Tagliapietra <
tagliapietra.alessandro@gmail.com> wrote:

> Could be, but since we have a limite amount of input keys (~30), windowing
> generates new keys but old ones are never touched again since the data per
> key is in order, I assume it shouldn't be a big deal for it to handle 30
> keys
> I'll have a look at cache metrics and see if something pops out
>
> Thanks
>
> --
> Alessandro Tagliapietra
>
>
> On Sat, Dec 7, 2019 at 10:02 AM John Roesler <vvcephei@apache.org> wrote:
>
>> Hmm, that’s a good question. Now that we’re talking about caching, I
>> wonder if the cache was just too small. It’s not very big by default.
>>
>> On Sat, Dec 7, 2019, at 11:16, Alessandro Tagliapietra wrote:
>> > Ok I'll check on that!
>> >
>> > Now I can see that with caching we went from 3-4MB/s to 400KB/s, that
>> will
>> > help with the bill.
>> >
>> > Last question, any reason why after a while the regular windowed stream
>> > starts sending every update instead of caching?
>> > Could it be because it doesn't have any more memory available? Any other
>> > possible reason?
>> >
>> > Thank you so much for your help
>> >
>> > --
>> > Alessandro Tagliapietra
>> >
>> >
>> > On Sat, Dec 7, 2019 at 9:14 AM John Roesler <vvcephei@apache.org>
>> wrote:
>> >
>> > > Ah, yes. Glad you figured it out!
>> > >
>> > > Caching does not reduce EOS guarantees at all. I highly recommend
>> using
>> > > it. You might even want to take a look at the caching metrics to make
>> sure
>> > > you have a good hit ratio.
>> > >
>> > > -John
>> > >
>> > > On Sat, Dec 7, 2019, at 10:51, Alessandro Tagliapietra wrote:
>> > > > Never mind I've found out I can use `.withCachingEnabled` on the
>> store
>> > > > builder to achieve the same thing as the windowing example as
>> > > > `Materialized.as` turns that on by default.
>> > > >
>> > > > Does caching in any way reduces the EOS guarantees?
>> > > >
>> > > > --
>> > > > Alessandro Tagliapietra
>> > > >
>> > > >
>> > > > On Sat, Dec 7, 2019 at 1:12 AM Alessandro Tagliapietra <
>> > > > tagliapietra.alessandro@gmail.com> wrote:
>> > > >
>> > > > > Seems my journey with this isn't done just yet,
>> > > > >
>> > > > > This seems very complicated to me but I'll try to explain it as
>> best I
>> > > can.
>> > > > > To better understand the streams network usage I've used
>> prometheus
>> > > with
>> > > > > the JMX exporter to export kafka metrics.
>> > > > > To check the amount of data we use I'm looking at the increments
>> > > > > of kafka_producer_topic_metrics_byte_total and
>> > > > > kafka_producer_producer_topic_metrics_record_send_total,
>> > > > >
>> > > > > Our current (before the change mentioned above) code looks like
>> this:
>> > > > >
>> > > > > // This transformers just pairs a value with the previous one
>> storing
>> > > the
>> > > > > temporary one in a store
>> > > > > val pairsStream = metricStream
>> > > > >   .transformValues(ValueTransformerWithKeySupplier {
>> PairTransformer()
>> > > },
>> > > > > "LastValueStore")
>> > > > >   .filter { _, value: MetricSequence? -> value != null }
>> > > > >
>> > > > > // Create a store to store suppressed windows until a new one is
>> > > received
>> > > > > val suppressStoreSupplier =
>> > > > >
>> > >
>> Stores.keyValueStoreBuilder(Stores.persistentKeyValueStore("suppress-store"),
>> > > > > ......
>> > > > >
>> > > > > // Window and aggregate data in 1 minute intervals
>> > > > > val aggregatedStream = pairsStream
>> > > > >   .groupByKey()
>> > > > >   .windowedBy<TimeWindow>(TimeWindows.of(Duration.ofMinutes(1)))
>> > > > >   .aggregate(
>> > > > >           { MetricSequenceList(ArrayList()) },
>> > > > >           { key, value, aggregate ->
>> > > > >               aggregate.getRecords().add(value)
>> > > > >               aggregate
>> > > > >           },
>> > > > >           Materialized.`as`<String, MetricSequenceList,
>> > > WindowStore<Bytes,
>> > > > >
>> > >
>> ByteArray>>("aggregate-store").withKeySerde(Serdes.String()).withValueSerde(Settings.getValueSpecificavroSerde())
>> > > > >   )
>> > > > >   .toStream()
>> > > > >   .flatTransform(TransformerSupplier {
>> > > > >       // This transformer basically waits until a new window is
>> > > received
>> > > > > to emit the previous one
>> > > > >   }, "suppress-store")
>> > > > >   .map { sensorId: String, suppressedOutput: SuppressedOutput ->
>> > > > >       .... etc ....
>> > > > >
>> > > > >
>> > > > > Basically:
>> > > > >  - all data goes through LastValueStore store that stores each
>> message
>> > > and
>> > > > > emits a pair with the previous one
>> > > > >  - the aggregate-store is used to store the per-window list of
>> > > messages in
>> > > > > the aggregate method
>> > > > >  - the suppress store is used to store each received window which
>> is
>> > > > > emitted only after a newer one is received
>> > > > >
>> > > > > What I'm experiencing is that:
>> > > > >  - during normal execution, the streams app sends to the lastvalue
>> > > store
>> > > > > changelog topic 5k messages/min, the aggregate and suppress store
>> > > changelog
>> > > > > topics only about 100
>> > > > >  - at some point (after many hours of operation), the streams app
>> > > starts
>> > > > > sending to the aggregate and suppress store changelog topic the
>> same
>> > > amount
>> > > > > of messages going through the lastvaluestore
>> > > > >  - if I restart the streams app it goes back to the initial
>> behavior
>> > > > >
>> > > > > You can see the behavior in this graph https://imgur.com/dJcUNSf
>> > > > > You can also see that after a restart everything goes back to
>> normal
>> > > > > levels.
>> > > > > Regarding other metrics, process latency increases, poll latency
>> > > > > decreases, poll rate decreases, commit rate stays the same while
>> commit
>> > > > > latency increases.
>> > > > >
>> > > > > Now, I've these questions:
>> > > > >  - why isn't the aggregate/suppress store changelog topic
>> throughput
>> > > the
>> > > > > same as the LastValueStore? Shouldn't every time it aggregates
>> send a
>> > > > > record to the changelog?
>> > > > >  - is the windowing doing some internal caching like not sending
>> every
>> > > > > aggregation record until the window time is passed? (if so, where
>> can I
>> > > > > find that code since I would like to use that also for our new
>> > > > > implementation)
>> > > > >
>> > > > > Thank you in advance
>> > > > >
>> > > > > --
>> > > > > Alessandro Tagliapietra
>> > > > >
>> > > > >
>> > > > > On Wed, Dec 4, 2019 at 7:57 AM John Roesler <vvcephei@apache.org>
>> > > wrote:
>> > > > >
>> > > > >> Oh, good!
>> > > > >>
>> > > > >> On Tue, Dec 3, 2019, at 23:29, Alessandro Tagliapietra wrote:
>> > > > >> > Testing on staging shows that a restart on exception is much
>> faster
>> > > and
>> > > > >> the
>> > > > >> > stream starts right away which I think means we're reading way
>> less
>> > > data
>> > > > >> > than before!
>> > > > >> >
>> > > > >> > What I was referring to is that, in Streams, the keys for
>> window
>> > > > >> > > aggregation state is actually composed of both the window
>> itself
>> > > and
>> > > > >> the
>> > > > >> > > key. In the DSL, it looks like "Windowed<K>". That results
>> in the
>> > > > >> store
>> > > > >> > > having a unique key per window for each K, which is why we
>> need
>> > > > >> retention
>> > > > >> > > as well as compaction for our changelogs. But for you, if
>> you just
>> > > > >> make the
>> > > > >> > > key "K", then compaction alone should do the trick.
>> > > > >> >
>> > > > >> > Yes we had compact,delete as cleanup policy but probably it
>> still
>> > > had a
>> > > > >> too
>> > > > >> > long retention value, also the rocksdb store is probably much
>> > > faster now
>> > > > >> > having only one key per key instead of one key per window per
>> key.
>> > > > >> >
>> > > > >> > Thanks a lot for helping! I'm now going to setup a
>> prometheus-jmx
>> > > > >> > monitoring so we can keep better track of what's going on :)
>> > > > >> >
>> > > > >> > --
>> > > > >> > Alessandro Tagliapietra
>> > > > >> >
>> > > > >> >
>> > > > >> > On Tue, Dec 3, 2019 at 9:12 PM John Roesler <
>> vvcephei@apache.org>
>> > > > >> wrote:
>> > > > >> >
>> > > > >> > > Oh, yeah, I remember that conversation!
>> > > > >> > >
>> > > > >> > > Yes, then, I agree, if you're only storing state of the most
>> > > recent
>> > > > >> window
>> > > > >> > > for each key, and the key you use for that state is actually
>> the
>> > > key
>> > > > >> of the
>> > > > >> > > records, then an aggressive compaction policy plus your
>> custom
>> > > > >> transformer
>> > > > >> > > seems like a good way forward.
>> > > > >> > >
>> > > > >> > > What I was referring to is that, in Streams, the keys for
>> window
>> > > > >> > > aggregation state is actually composed of both the window
>> itself
>> > > and
>> > > > >> the
>> > > > >> > > key. In the DSL, it looks like "Windowed<K>". That results
>> in the
>> > > > >> store
>> > > > >> > > having a unique key per window for each K, which is why we
>> need
>> > > > >> retention
>> > > > >> > > as well as compaction for our changelogs. But for you, if
>> you just
>> > > > >> make the
>> > > > >> > > key "K", then compaction alone should do the trick.
>> > > > >> > >
>> > > > >> > > And yes, if you manage the topic yourself, then Streams won't
>> > > adjust
>> > > > >> the
>> > > > >> > > retention time. I think it might validate that the retention
>> > > isn't too
>> > > > >> > > short, but I don't remember offhand.
>> > > > >> > >
>> > > > >> > > Cheers, and let me know how it goes!
>> > > > >> > > -John
>> > > > >> > >
>> > > > >> > > On Tue, Dec 3, 2019, at 23:03, Alessandro Tagliapietra wrote:
>> > > > >> > > > Hi John,
>> > > > >> > > >
>> > > > >> > > > afaik grace period uses stream time
>> > > > >> > > >
>> > > > >> > >
>> > > > >>
>> > >
>> https://kafka.apache.org/21/javadoc/org/apache/kafka/streams/kstream/Windows.html
>> > > > >> > > > which is
>> > > > >> > > > per partition, unfortunately we process data that's not in
>> sync
>> > > > >> between
>> > > > >> > > > keys so each key needs to be independent and a key can
>> have much
>> > > > >> older
>> > > > >> > > > data
>> > > > >> > > > than the other.
>> > > > >> > > >
>> > > > >> > > > Having a small grace period would probably close old
>> windows
>> > > sooner
>> > > > >> than
>> > > > >> > > > expected. That's also why in my use case a custom store
>> that
>> > > just
>> > > > >> stores
>> > > > >> > > > the last window data for each key might work better. I had
>> the
>> > > same
>> > > > >> issue
>> > > > >> > > > with suppression and it has been reported here
>> > > > >> > > > https://issues.apache.org/jira/browse/KAFKA-8769
>> > > > >> > > > Oh I just saw that you're the one that helped me on slack
>> and
>> > > > >> created the
>> > > > >> > > > issue (thanks again for that).
>> > > > >> > > >
>> > > > >> > > > The behavior that you mention about streams setting
>> changelog
>> > > > >> retention
>> > > > >> > > > time is something they do on creation of the topic when the
>> > > broker
>> > > > >> has
>> > > > >> > > auto
>> > > > >> > > > creation enabled? Because we're using confluent cloud and
>> I had
>> > > to
>> > > > >> create
>> > > > >> > > > it manually.
>> > > > >> > > > Regarding the change in the recovery behavior, with compact
>> > > cleanup
>> > > > >> > > policy
>> > > > >> > > > shouldn't the changelog only keep the last value? That
>> would
>> > > make
>> > > > >> the
>> > > > >> > > > recovery faster and "cheaper" as it would only need to
>> read a
>> > > single
>> > > > >> > > value
>> > > > >> > > > per key (if the cleanup just happened) right?
>> > > > >> > > >
>> > > > >> > > > --
>> > > > >> > > > Alessandro Tagliapietra
>> > > > >> > > >
>> > > > >> > > >
>> > > > >> > > > On Tue, Dec 3, 2019 at 8:51 PM John Roesler <
>> > > vvcephei@apache.org>
>> > > > >> wrote:
>> > > > >> > > >
>> > > > >> > > > > Hey Alessandro,
>> > > > >> > > > >
>> > > > >> > > > > That sounds also like it would work. I'm wondering if it
>> would
>> > > > >> actually
>> > > > >> > > > > change what you observe w.r.t. recovery behavior, though.
>> > > Streams
>> > > > >> > > already
>> > > > >> > > > > sets the retention time on the changelog to equal the
>> > > retention
>> > > > >> time
>> > > > >> > > of the
>> > > > >> > > > > windows, for windowed aggregations, so you shouldn't be
>> > > loading a
>> > > > >> lot
>> > > > >> > > of
>> > > > >> > > > > window data for old windows you no longer care about.
>> > > > >> > > > >
>> > > > >> > > > > Have you set the "grace period" on your window
>> definition? By
>> > > > >> default,
>> > > > >> > > it
>> > > > >> > > > > is set to 24 hours, but you can set it as low as you
>> like.
>> > > E.g.,
>> > > > >> if you
>> > > > >> > > > > want to commit to having in-order data only, then you
>> can set
>> > > the
>> > > > >> grace
>> > > > >> > > > > period to zero. This _should_ let the broker clean up the
>> > > > >> changelog
>> > > > >> > > records
>> > > > >> > > > > as soon as the window ends.
>> > > > >> > > > >
>> > > > >> > > > > Of course, the log cleaner doesn't run all the time, so
>> > > there's
>> > > > >> some
>> > > > >> > > extra
>> > > > >> > > > > delay in which "expired" data would still be visible in
>> the
>> > > > >> changelog,
>> > > > >> > > but
>> > > > >> > > > > it would actually be just the same as if you manage the
>> store
>> > > > >> yourself.
>> > > > >> > > > >
>> > > > >> > > > > Hope this helps!
>> > > > >> > > > > -John
>> > > > >> > > > >
>> > > > >> > > > > On Tue, Dec 3, 2019, at 22:22, Alessandro Tagliapietra
>> wrote:
>> > > > >> > > > > > Thanks John for the explanation,
>> > > > >> > > > > >
>> > > > >> > > > > > I thought that with EOS enabled (which we have) it
>> would in
>> > > the
>> > > > >> worst
>> > > > >> > > > > case
>> > > > >> > > > > > find a valid checkpoint and start the restore from
>> there
>> > > until
>> > > > >> it
>> > > > >> > > reached
>> > > > >> > > > > > the last committed status, not completely from
>> scratch. What
>> > > > >> you say
>> > > > >> > > > > > definitely makes sense now.
>> > > > >> > > > > > Since we don't really need old time windows and we
>> ensure
>> > > data
>> > > > >> is
>> > > > >> > > ordered
>> > > > >> > > > > > when processed I think I"ll just write a custom
>> transformer
>> > > to
>> > > > >> keep
>> > > > >> > > only
>> > > > >> > > > > > the last window, store intermediate aggregation
>> results in
>> > > the
>> > > > >> store
>> > > > >> > > and
>> > > > >> > > > > > emit a new value only when we receive data belonging
>> to a
>> > > new
>> > > > >> window.
>> > > > >> > > > > > That with a compact only changelog topic should keep
>> the
>> > > rebuild
>> > > > >> > > data to
>> > > > >> > > > > > the minimum as it would have only the last value for
>> each
>> > > key.
>> > > > >> > > > > >
>> > > > >> > > > > > Hope that makes sense
>> > > > >> > > > > >
>> > > > >> > > > > > Thanks again
>> > > > >> > > > > >
>> > > > >> > > > > > --
>> > > > >> > > > > > Alessandro Tagliapietra
>> > > > >> > > > > >
>> > > > >> > > > > >
>> > > > >> > > > > > On Tue, Dec 3, 2019 at 3:04 PM John Roesler <
>> > > > >> vvcephei@apache.org>
>> > > > >> > > wrote:
>> > > > >> > > > > >
>> > > > >> > > > > > > Hi Alessandro,
>> > > > >> > > > > > >
>> > > > >> > > > > > > To take a stab at your question, maybe it first
>> doesn't
>> > > find
>> > > > >> it,
>> > > > >> > > but
>> > > > >> > > > > then
>> > > > >> > > > > > > restores some data, writes the checkpoint, and then
>> later
>> > > on,
>> > > > >> it
>> > > > >> > > has to
>> > > > >> > > > > > > re-initialize the task for some reason, and that's
>> why it
>> > > does
>> > > > >> > > find a
>> > > > >> > > > > > > checkpoint then?
>> > > > >> > > > > > >
>> > > > >> > > > > > > More to the heart of the issue, if you have EOS
>> enabled,
>> > > > >> Streams
>> > > > >> > > _only_
>> > > > >> > > > > > > records the checkpoint when the store is in a
>> > > known-consistent
>> > > > >> > > state.
>> > > > >> > > > > For
>> > > > >> > > > > > > example, if you have a graceful shutdown, Streams
>> will
>> > > flush
>> > > > >> all
>> > > > >> > > the
>> > > > >> > > > > > > stores, commit all the transactions, and then write
>> the
>> > > > >> checkpoint
>> > > > >> > > > > file.
>> > > > >> > > > > > > Then, on re-start, it will pick up from that
>> checkpoint.
>> > > > >> > > > > > >
>> > > > >> > > > > > > But as soon as it starts processing records, it
>> removes
>> > > the
>> > > > >> > > checkpoint
>> > > > >> > > > > > > file, so if it crashes while it was processing,
>> there is
>> > > no
>> > > > >> > > checkpoint
>> > > > >> > > > > file
>> > > > >> > > > > > > there, and it will have to restore from the
>> beginning of
>> > > the
>> > > > >> > > changelog.
>> > > > >> > > > > > >
>> > > > >> > > > > > > This design is there on purpose, because otherwise we
>> > > cannot
>> > > > >> > > actually
>> > > > >> > > > > > > guarantee correctness... For example, if you are
>> > > maintaining a
>> > > > >> > > count
>> > > > >> > > > > > > operation, and we process an input record i,
>> increment the
>> > > > >> count
>> > > > >> > > and
>> > > > >> > > > > write
>> > > > >> > > > > > > it to the state store, and to the changelog topic.
>> But we
>> > > > >> crash
>> > > > >> > > before
>> > > > >> > > > > we
>> > > > >> > > > > > > commit that transaction. Then, the write to the
>> changelog
>> > > > >> would be
>> > > > >> > > > > aborted,
>> > > > >> > > > > > > and we would re-process record i . However, we've
>> already
>> > > > >> updated
>> > > > >> > > the
>> > > > >> > > > > local
>> > > > >> > > > > > > state store, so when we increment it again, it
>> results in
>> > > > >> > > > > double-counting
>> > > > >> > > > > > > i. The key point here is that there's no way to do an
>> > > atomic
>> > > > >> > > operation
>> > > > >> > > > > > > across two different systems (local state store and
>> the
>> > > > >> changelog
>> > > > >> > > > > topic).
>> > > > >> > > > > > > Since we can't guarantee that we roll back the
>> incremented
>> > > > >> count
>> > > > >> > > when
>> > > > >> > > > > the
>> > > > >> > > > > > > changelog transaction is aborted, we can't keep the
>> local
>> > > > >> store
>> > > > >> > > > > consistent
>> > > > >> > > > > > > with the changelog.
>> > > > >> > > > > > >
>> > > > >> > > > > > > After a crash, the only way to ensure the local
>> store is
>> > > > >> consistent
>> > > > >> > > > > with
>> > > > >> > > > > > > the changelog is to discard the entire thing and
>> rebuild
>> > > it.
>> > > > >> This
>> > > > >> > > is
>> > > > >> > > > > why we
>> > > > >> > > > > > > have an invariant that the checkpoint file only
>> exists
>> > > when we
>> > > > >> > > _know_
>> > > > >> > > > > that
>> > > > >> > > > > > > the local store is consistent with the changelog, and
>> > > this is
>> > > > >> why
>> > > > >> > > > > you're
>> > > > >> > > > > > > seeing so much bandwidth when re-starting from an
>> unclean
>> > > > >> shutdown.
>> > > > >> > > > > > >
>> > > > >> > > > > > > Note that it's definitely possible to do better than
>> this,
>> > > > >> and we
>> > > > >> > > would
>> > > > >> > > > > > > very much like to improve it in the future.
>> > > > >> > > > > > >
>> > > > >> > > > > > > Thanks,
>> > > > >> > > > > > > -John
>> > > > >> > > > > > >
>> > > > >> > > > > > > On Tue, Dec 3, 2019, at 16:16, Alessandro
>> Tagliapietra
>> > > wrote:
>> > > > >> > > > > > > > Hi John,
>> > > > >> > > > > > > >
>> > > > >> > > > > > > > thanks a lot for helping, regarding your message:
>> > > > >> > > > > > > >  - no we only have 1 instance of the stream
>> application,
>> > > > >> and it
>> > > > >> > > > > always
>> > > > >> > > > > > > > re-uses the same state folder
>> > > > >> > > > > > > >  - yes we're seeing most issues when restarting not
>> > > > >> gracefully
>> > > > >> > > due
>> > > > >> > > > > > > exception
>> > > > >> > > > > > > >
>> > > > >> > > > > > > > I've enabled trace logging and filtering by a
>> single
>> > > state
>> > > > >> store
>> > > > >> > > the
>> > > > >> > > > > > > > StoreChangelogReader messages are:
>> > > > >> > > > > > > >
>> > > > >> > > > > > > > Added restorer for changelog
>> > > > >> > > > > sensors-stream-aggregate-store-changelog-0
>> > > > >> > > > > > > > Added restorer for changelog
>> > > > >> > > > > sensors-stream-aggregate-store-changelog-1
>> > > > >> > > > > > > > Added restorer for changelog
>> > > > >> > > > > sensors-stream-aggregate-store-changelog-2
>> > > > >> > > > > > > > Did not find checkpoint from changelog
>> > > > >> > > > > > > > sensors-stream-aggregate-store-changelog-2 for
>> store
>> > > > >> > > aggregate-store,
>> > > > >> > > > > > > > rewinding to beginning.
>> > > > >> > > > > > > > Did not find checkpoint from changelog
>> > > > >> > > > > > > > sensors-stream-aggregate-store-changelog-1 for
>> store
>> > > > >> > > aggregate-store,
>> > > > >> > > > > > > > rewinding to beginning.
>> > > > >> > > > > > > > Did not find checkpoint from changelog
>> > > > >> > > > > > > > sensors-stream-aggregate-store-changelog-0 for
>> store
>> > > > >> > > aggregate-store,
>> > > > >> > > > > > > > rewinding to beginning.
>> > > > >> > > > > > > > No checkpoint found for task 0_2 state store
>> > > aggregate-store
>> > > > >> > > > > changelog
>> > > > >> > > > > > > > sensors-stream-aggregate-store-changelog-2 with EOS
>> > > turned
>> > > > >> on.
>> > > > >> > > > > > > > Reinitializing the task and restore its state from
>> the
>> > > > >> beginning.
>> > > > >> > > > > > > > No checkpoint found for task 0_1 state store
>> > > aggregate-store
>> > > > >> > > > > changelog
>> > > > >> > > > > > > > sensors-stream-aggregate-store-changelog-1 with EOS
>> > > turned
>> > > > >> on.
>> > > > >> > > > > > > > Reinitializing the task and restore its state from
>> the
>> > > > >> beginning.
>> > > > >> > > > > > > > No checkpoint found for task 0_0 state store
>> > > aggregate-store
>> > > > >> > > > > changelog
>> > > > >> > > > > > > > sensors-stream-aggregate-store-changelog-0 with EOS
>> > > turned
>> > > > >> on.
>> > > > >> > > > > > > > Reinitializing the task and restore its state from
>> the
>> > > > >> beginning.
>> > > > >> > > > > > > > Found checkpoint 709937 from changelog
>> > > > >> > > > > > > > sensors-stream-aggregate-store-changelog-2 for
>> store
>> > > > >> > > aggregate-store.
>> > > > >> > > > > > > > Restoring partition
>> > > > >> sensors-stream-aggregate-store-changelog-2
>> > > > >> > > from
>> > > > >> > > > > > > offset
>> > > > >> > > > > > > > 709937 to endOffset 742799
>> > > > >> > > > > > > > Found checkpoint 3024234 from changelog
>> > > > >> > > > > > > > sensors-stream-aggregate-store-changelog-1 for
>> store
>> > > > >> > > aggregate-store.
>> > > > >> > > > > > > > Restoring partition
>> > > > >> sensors-stream-aggregate-store-changelog-1
>> > > > >> > > from
>> > > > >> > > > > > > offset
>> > > > >> > > > > > > > 3024234 to endOffset 3131513
>> > > > >> > > > > > > > Found checkpoint 14514072 from changelog
>> > > > >> > > > > > > > sensors-stream-aggregate-store-changelog-0 for
>> store
>> > > > >> > > aggregate-store.
>> > > > >> > > > > > > > Restoring partition
>> > > > >> sensors-stream-aggregate-store-changelog-0
>> > > > >> > > from
>> > > > >> > > > > > > offset
>> > > > >> > > > > > > > 14514072 to endOffset 17116574
>> > > > >> > > > > > > > Restored from
>> > > sensors-stream-aggregate-store-changelog-2 to
>> > > > >> > > > > > > aggregate-store
>> > > > >> > > > > > > > with 966 records, ending offset is 711432, next
>> starting
>> > > > >> > > position is
>> > > > >> > > > > > > 711434
>> > > > >> > > > > > > > Restored from
>> > > sensors-stream-aggregate-store-changelog-2 to
>> > > > >> > > > > > > aggregate-store
>> > > > >> > > > > > > > with 914 records, ending offset is 712711, next
>> starting
>> > > > >> > > position is
>> > > > >> > > > > > > 712713
>> > > > >> > > > > > > > Restored from
>> > > sensors-stream-aggregate-store-changelog-1 to
>> > > > >> > > > > > > aggregate-store
>> > > > >> > > > > > > > with 18 records, ending offset is 3024261, next
>> starting
>> > > > >> > > position is
>> > > > >> > > > > > > 3024262
>> > > > >> > > > > > > >
>> > > > >> > > > > > > >
>> > > > >> > > > > > > > why it first says it didn't find the checkpoint and
>> > > then it
>> > > > >> does
>> > > > >> > > > > find it?
>> > > > >> > > > > > > > It seems it loaded about  2.7M records (sum of
>> offset
>> > > > >> difference
>> > > > >> > > in
>> > > > >> > > > > the
>> > > > >> > > > > > > > "restorting partition ...." messages) right?
>> > > > >> > > > > > > > Maybe should I try to reduce the checkpoint
>> interval?
>> > > > >> > > > > > > >
>> > > > >> > > > > > > > Regards
>> > > > >> > > > > > > >
>> > > > >> > > > > > > > --
>> > > > >> > > > > > > > Alessandro Tagliapietra
>> > > > >> > > > > > > >
>> > > > >> > > > > > > >
>> > > > >> > > > > > > > On Mon, Dec 2, 2019 at 9:18 AM John Roesler <
>> > > > >> vvcephei@apache.org
>> > > > >> > > >
>> > > > >> > > > > wrote:
>> > > > >> > > > > > > >
>> > > > >> > > > > > > > > Hi Alessandro,
>> > > > >> > > > > > > > >
>> > > > >> > > > > > > > > I'm sorry to hear that.
>> > > > >> > > > > > > > >
>> > > > >> > > > > > > > > The restore process only takes one factor into
>> > > account:
>> > > > >> the
>> > > > >> > > current
>> > > > >> > > > > > > offset
>> > > > >> > > > > > > > > position of the changelog topic is stored in a
>> local
>> > > file
>> > > > >> > > > > alongside the
>> > > > >> > > > > > > > > state stores. On startup, the app checks if the
>> > > recorded
>> > > > >> > > position
>> > > > >> > > > > lags
>> > > > >> > > > > > > the
>> > > > >> > > > > > > > > latest offset in the changelog. If so, then it
>> reads
>> > > the
>> > > > >> > > missing
>> > > > >> > > > > > > changelog
>> > > > >> > > > > > > > > records before starting processing.
>> > > > >> > > > > > > > >
>> > > > >> > > > > > > > > Thus, it would not restore any old window data.
>> > > > >> > > > > > > > >
>> > > > >> > > > > > > > > There might be a few different things going on to
>> > > explain
>> > > > >> your
>> > > > >> > > > > > > observation:
>> > > > >> > > > > > > > > * if there is more than one instance in your
>> Streams
>> > > > >> cluster,
>> > > > >> > > > > maybe the
>> > > > >> > > > > > > > > task is "flopping" between instances, so each
>> instance
>> > > > >> still
>> > > > >> > > has to
>> > > > >> > > > > > > recover
>> > > > >> > > > > > > > > state, since it wasn't the last one actively
>> > > processing
>> > > > >> it.
>> > > > >> > > > > > > > > * if the application isn't stopped gracefully, it
>> > > might
>> > > > >> not
>> > > > >> > > get a
>> > > > >> > > > > > > chance
>> > > > >> > > > > > > > > to record its offset in that local file, so on
>> > > restart it
>> > > > >> has
>> > > > >> > > to
>> > > > >> > > > > > > restore
>> > > > >> > > > > > > > > some or all of the state store from changelog.
>> > > > >> > > > > > > > >
>> > > > >> > > > > > > > > Or it could be something else; that's just what
>> comes
>> > > to
>> > > > >> mind.
>> > > > >> > > > > > > > >
>> > > > >> > > > > > > > > If you want to get to the bottom of it, you can
>> take a
>> > > > >> look at
>> > > > >> > > the
>> > > > >> > > > > > > logs,
>> > > > >> > > > > > > > > paying close attention to which tasks are
>> assigned to
>> > > > >> which
>> > > > >> > > > > instances
>> > > > >> > > > > > > after
>> > > > >> > > > > > > > > each restart. You can also look into the logs
>> from
>> > > > >> > > > > > > > >
>> > > > >> > >
>> > > `org.apache.kafka.streams.processor.internals.StoreChangelogReader`
>> > > > >> > > > > > > (might
>> > > > >> > > > > > > > > want to set it to DEBUG or TRACE level to really
>> see
>> > > > >> what's
>> > > > >> > > > > happening).
>> > > > >> > > > > > > > >
>> > > > >> > > > > > > > > I hope this helps!
>> > > > >> > > > > > > > > -John
>> > > > >> > > > > > > > >
>> > > > >> > > > > > > > > On Sun, Dec 1, 2019, at 21:25, Alessandro
>> Tagliapietra
>> > > > >> wrote:
>> > > > >> > > > > > > > > > Hello everyone,
>> > > > >> > > > > > > > > >
>> > > > >> > > > > > > > > > we're having a problem with bandwidth usage on
>> > > streams
>> > > > >> > > > > application
>> > > > >> > > > > > > > > startup,
>> > > > >> > > > > > > > > > our current setup does this:
>> > > > >> > > > > > > > > >
>> > > > >> > > > > > > > > > ...
>> > > > >> > > > > > > > > > .groupByKey()
>> > > > >> > > > > > > > > >
>> > > > >> > >
>> .windowedBy<TimeWindow>(TimeWindows.of(Duration.ofMinutes(1)))
>> > > > >> > > > > > > > > > .aggregate(
>> > > > >> > > > > > > > > >         { MetricSequenceList(ArrayList()) },
>> > > > >> > > > > > > > > >         { key, value, aggregate ->
>> > > > >> > > > > > > > > >             aggregate.getRecords().add(value)
>> > > > >> > > > > > > > > >             aggregate
>> > > > >> > > > > > > > > >         },
>> > > > >> > > > > > > > > >         Materialized.`as`<String,
>> > > MetricSequenceList,
>> > > > >> > > > > > > WindowStore<Bytes,
>> > > > >> > > > > > > > > >
>> > > > >> > > > > > > > >
>> > > > >> > > > > > >
>> > > > >> > > > >
>> > > > >> > >
>> > > > >>
>> > >
>> ByteArray>>("aggregate-store").withKeySerde(Serdes.String()).withValueSerde(Settings.getValueSpecificavroSerde())
>> > > > >> > > > > > > > > > )
>> > > > >> > > > > > > > > > .toStream()
>> > > > >> > > > > > > > > > .flatTransform(TransformerSupplier {
>> > > > >> > > > > > > > > > ...
>> > > > >> > > > > > > > > >
>> > > > >> > > > > > > > > > basically in each window we append the new
>> values
>> > > and
>> > > > >> then do
>> > > > >> > > > > some
>> > > > >> > > > > > > other
>> > > > >> > > > > > > > > > logic with the array of windowed values.
>> > > > >> > > > > > > > > > The aggregate-store changelog topic
>> configuration
>> > > uses
>> > > > >> > > > > > > compact,delete as
>> > > > >> > > > > > > > > > cleanup policy and has 12 hours of retention.
>> > > > >> > > > > > > > > >
>> > > > >> > > > > > > > > > What we've seen is that on application startup
>> it
>> > > takes
>> > > > >> a
>> > > > >> > > couple
>> > > > >> > > > > > > minutes
>> > > > >> > > > > > > > > to
>> > > > >> > > > > > > > > > rebuild the state store, even if the state
>> store
>> > > > >> directory is
>> > > > >> > > > > > > persisted
>> > > > >> > > > > > > > > > across restarts. That along with an exception
>> that
>> > > > >> caused the
>> > > > >> > > > > docker
>> > > > >> > > > > > > > > > container to be restarted a couple hundreds
>> times
>> > > > >> caused a
>> > > > >> > > big
>> > > > >> > > > > > > confluent
>> > > > >> > > > > > > > > > cloud bill compared to what we usually spend
>> (1/4
>> > > of a
>> > > > >> full
>> > > > >> > > > > month in
>> > > > >> > > > > > > 1
>> > > > >> > > > > > > > > day).
>> > > > >> > > > > > > > > >
>> > > > >> > > > > > > > > > What I think is happening is that the topic is
>> > > keeping
>> > > > >> all
>> > > > >> > > the
>> > > > >> > > > > > > previous
>> > > > >> > > > > > > > > > windows even with the compacting policy
>> because each
>> > > > >> key is
>> > > > >> > > the
>> > > > >> > > > > > > original
>> > > > >> > > > > > > > > > key + the timestamp not just the key. Since we
>> don't
>> > > > >> care
>> > > > >> > > about
>> > > > >> > > > > > > previous
>> > > > >> > > > > > > > > > windows as the flatTransform after the
>> toStream()
>> > > makes
>> > > > >> sure
>> > > > >> > > > > that we
>> > > > >> > > > > > > > > don't
>> > > > >> > > > > > > > > > process old windows (a custom suppressor
>> basically)
>> > > is
>> > > > >> there
>> > > > >> > > a
>> > > > >> > > > > way to
>> > > > >> > > > > > > > > only
>> > > > >> > > > > > > > > > keep the last window so that the store
>> rebuilding
>> > > goes
>> > > > >> > > faster and
>> > > > >> > > > > > > without
>> > > > >> > > > > > > > > > rebuilding old windows too? Or should I create
>> a
>> > > custom
>> > > > >> > > window
>> > > > >> > > > > using
>> > > > >> > > > > > > the
>> > > > >> > > > > > > > > > original key as key so that the compaction
>> keeps
>> > > only
>> > > > >> the
>> > > > >> > > last
>> > > > >> > > > > window
>> > > > >> > > > > > > > > data?
>> > > > >> > > > > > > > > >
>> > > > >> > > > > > > > > > Thank you
>> > > > >> > > > > > > > > >
>> > > > >> > > > > > > > > > --
>> > > > >> > > > > > > > > > Alessandro Tagliapietra
>> > > > >> > > > > > > > > >
>> > > > >> > > > > > > > >
>> > > > >> > > > > > > >
>> > > > >> > > > > > >
>> > > > >> > > > > >
>> > > > >> > > > >
>> > > > >> > > >
>> > > > >> > >
>> > > > >> >
>> > > > >>
>> > > > >
>> > > >
>> > >
>> >
>>
>

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