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From Alessandro Tagliapietra <tagliapietra.alessan...@gmail.com>
Subject Re: Reducing streams startup bandwidth usage
Date Sat, 07 Dec 2019 17:16:12 GMT
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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