Thanks!

Also are there any producer optimizations anyone can think of in this
scenario?



On Mon, Dec 21, 2020 at 8:58 AM Joris Peeters <joris.mg.peet...@gmail.com>
wrote:

> I'd probably just do it by experiment for your concrete data.
>
> Maybe generate a few million synthetic data rows, and for-each-batch insert
> them into a dev DB, with an outer grid search over various candidate batch
> sizes. You're looking to optimise for flat-out rows/s, so whichever batch
> size wins (given a fixed number of total rows) is near-optimal.
> You can repeat the exercise with N simultaneous threads to inspect how
> batch sizes and multiple partitions P would interact (which might well be
> sublinear in P in case of e.g. transactions etc).
>
> On Mon, Dec 21, 2020 at 4:48 PM Yana K <yanak1...@gmail.com> wrote:
>
> > Thanks Haruki and Joris.
> >
> > Haruki:
> > Thanks for the detailed calculations. Really appreciate it. What tool/lib
> > is used to load test kafka?
> > So we've one consumer group and running 7 instances of the application -
> > that should be good enough - correct?
> >
> > Joris:
> > Great point.
> > DB insert is a bottleneck (and hence moved it to its own layer) - and we
> > are batching but wondering what is the best way to calculate the batch
> > size.
> >
> > Thanks,
> > Yana
> >
> > On Mon, Dec 21, 2020 at 1:39 AM Joris Peeters <
> joris.mg.peet...@gmail.com>
> > wrote:
> >
> > > Do you know why your consumers are so slow? 12E6msg/hour is 3333msg/s,
> > > which is not very high from a Kafka point-of-view. As you're doing
> > database
> > > inserts, I suspect that is where the bottleneck lies.
> > >
> > > If, for example, you're doing a single-row insert in a SQL DB for every
> > > message then this would incur a lot of overhead. Yes, you can somewhat
> > > alleviate that by parallellising - i.e. increasing the partition count
> -
> > > but it is also worth looking at batch inserts, if you aren't yet. Say,
> > each
> > > consumer waits for 1000 messages or 5 seconds to have passed (whichever
> > > comes first) and then does a single bulk insert of the msgs it has
> > > received, followed by a manual commit.
> > >
> > > [A] you might already be doing this and [B] your DB of choice might not
> > > support bulk inserts (although most do), but otherwise I'd expect this
> to
> > > work a lot better than increasing the partition count.
> > >
> > > On Mon, Dec 21, 2020 at 8:10 AM Haruki Okada <ocadar...@gmail.com>
> > wrote:
> > >
> > > > About load test:
> > > > I think it'd be better to monitor per-message process latency and
> > > estimate
> > > > required partition count based on it because it determines the max
> > > > throughput per single partition.
> > > > - Say you have to process 12 million messages/hour = 3333
> messages/sec
> > .
> > > > - If you have 7 partitions (thus 7 parallel consumers at maximum),
> > single
> > > > consumer should process 3333 / 7 = 476 messages/sec
> > > > - It means, process latency per single message should be lower than
> 2.1
> > > > milliseconds (1000 / 476)
> > > >   => If you have 14 partitions, it becomes 4.2 milliseconds
> > > >
> > > > So required partition count can be calculated by per-message process
> > > > latency. (I think Spring-Kafka can be easily integrated with
> prometheus
> > > so
> > > > you can use it to measure that)
> > > >
> > > > About increasing instance count:
> > > > - It depends on current system resource usage.
> > > >   * If the system resource is not so busy (likely because the
> consumer
> > > just
> > > > almost waits DB-write to return), you don't need to increase consumer
> > > > instances
> > > >   * But I think you should make sure that single consumer instance
> > isn't
> > > > assigned multiple partitions to fully parallelize consumption across
> > > > partitions. (If I remember correctly,
> > ConcurrentMessageListenerContainer
> > > > has a property to configure the concurrency)
> > > >
> > > > 2020年12月21日(月) 15:51 Yana K <yanak1...@gmail.com>:
> > > >
> > > > > So as the next step I see to increase the partition of the 2nd
> topic
> > -
> > > > do I
> > > > > increase the instances of the consumer from that or keep it at 7?
> > > > > Anything else (besides researching those libs)?
> > > > >
> > > > > Are there any good tools for load testing kafka?
> > > > >
> > > > > On Sun, Dec 20, 2020 at 7:23 PM Haruki Okada <ocadar...@gmail.com>
> > > > wrote:
> > > > >
> > > > > > It depends on how you manually commit offsets.
> > > > > > Auto-commit does commits offsets in async manner basically, so as
> > > long
> > > > as
> > > > > > you do manual-commit in the same way,  there should be no much
> > > > > difference.
> > > > > >
> > > > > > And, generally offset-commit mode doesn't make much difference in
> > > > > > performance regardless manual/auto or async/sync unless
> > offset-commit
> > > > > > latency takes significant amount in processing time (e.g. you
> > commit
> > > > > > offsets synchronously in every poll() loop).
> > > > > >
> > > > > > 2020年12月21日(月) 11:08 Yana K <yanak1...@gmail.com>:
> > > > > >
> > > > > > > Thank you so much Marina and Haruka.
> > > > > > >
> > > > > > > Marina's response:
> > > > > > > - When you say " if you are sure there is no room for perf
> > > > optimization
> > > > > > of
> > > > > > > the processing itself :" - do you mean code level
> optimizations?
> > > Can
> > > > > you
> > > > > > > please explain?
> > > > > > > - On the second topic you say " I'd say at least 40" - is this
> > > based
> > > > on
> > > > > > 12
> > > > > > > million records / hour?
> > > > > > > -  "if you can change the incoming topic" - I don't think it is
> > > > > possible
> > > > > > :(
> > > > > > > -  "you could artificially achieve the same by adding one more
> > step
> > > > > > > (service) in your pipeline" - this is the next thing - but I
> want
> > > to
> > > > be
> > > > > > > sure this will help, given we've to maintain one more layer
> > > > > > >
> > > > > > > Haruka's response:
> > > > > > > - "One possible solution is creating an intermediate topic" - I
> > > > already
> > > > > > did
> > > > > > > it
> > > > > > > - I'll look at Decaton - thx
> > > > > > >
> > > > > > > Is there any thoughts on the auto commit vs manual commit - if
> it
> > > can
> > > > > > > better the performance while consuming?
> > > > > > >
> > > > > > > Yana
> > > > > > >
> > > > > > >
> > > > > > >
> > > > > > > On Sat, Dec 19, 2020 at 7:01 PM Haruki Okada <
> > ocadar...@gmail.com>
> > > > > > wrote:
> > > > > > >
> > > > > > > > Hi.
> > > > > > > >
> > > > > > > > Yeah, Spring-Kafka does processing messages sequentially, so
> > the
> > > > > > consumer
> > > > > > > > throughput would be capped by database latency per single
> > > process.
> > > > > > > > One possible solution is creating an intermediate topic (or
> > > > altering
> > > > > > > source
> > > > > > > > topic) with much more partitions as Marina suggested.
> > > > > > > >
> > > > > > > > I'd like to suggest another solution, that is multi-threaded
> > > > > processing
> > > > > > > per
> > > > > > > > single partition.
> > > > > > > > Decaton (https://github.com/line/decaton) is a library to
> > > achieve
> > > > > it.
> > > > > > > >
> > > > > > > > Also confluent has published a blog post about
> > parallel-consumer
> > > (
> > > > > > > >
> > > > > > > >
> > > > > > >
> > > > > >
> > > > >
> > > >
> > >
> >
> https://www.confluent.io/blog/introducing-confluent-parallel-message-processing-client/
> > > > > > > > )
> > > > > > > > for that purpose, but it seems it's still in the BETA stage.
> > > > > > > >
> > > > > > > > 2020年12月20日(日) 11:41 Marina Popova <ppine7...@protonmail.com
> > > > > .invalid>:
> > > > > > > >
> > > > > > > > > The way I see it - you can only do a few things - if you
> are
> > > sure
> > > > > > there
> > > > > > > > is
> > > > > > > > > no room for perf optimization of the processing itself :
> > > > > > > > > 1. speed up your processing per consumer thread: which you
> > > > already
> > > > > > > tried
> > > > > > > > > by splitting your logic into a 2-step pipeline instead of
> > > 1-step,
> > > > > and
> > > > > > > > > delegating the work of writing to a DB to the second step (
> > > make
> > > > > sure
> > > > > > > > your
> > > > > > > > > second intermediate Kafka topic is created with much more
> > > > > partitions
> > > > > > to
> > > > > > > > be
> > > > > > > > > able to parallelize your work much higher - I'd say at
> least
> > > 40)
> > > > > > > > > 2. if you can change the incoming topic - I would create it
> > > with
> > > > > many
> > > > > > > > more
> > > > > > > > > partitions as well - say at least 40 or so - to parallelize
> > > your
> > > > > > first
> > > > > > > > step
> > > > > > > > > service processing more
> > > > > > > > > 3. and if you can't increase partitions for the original
> > topic
> > > )
> > > > -
> > > > > > you
> > > > > > > > > could artificially achieve the same by adding one more step
> > > > > (service)
> > > > > > > in
> > > > > > > > > your pipeline that would just read data from the original
> > > > > 7-partition
> > > > > > > > > topic1 and just push it unchanged into a new topic2 with ,
> > say
> > > 40
> > > > > > > > > partitions - and then have your other services pick up from
> > > this
> > > > > > topic2
> > > > > > > > >
> > > > > > > > >
> > > > > > > > > good luck,
> > > > > > > > > Marina
> > > > > > > > >
> > > > > > > > > Sent with ProtonMail Secure Email.
> > > > > > > > >
> > > > > > > > > ‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐
> > > > > > > > > On Saturday, December 19, 2020 6:46 PM, Yana K <
> > > > > yanak1...@gmail.com>
> > > > > > > > > wrote:
> > > > > > > > >
> > > > > > > > > > Hi
> > > > > > > > > >
> > > > > > > > > > I am new to the Kafka world and running into this scale
> > > > problem.
> > > > > I
> > > > > > > > > thought
> > > > > > > > > > of reaching out to the community if someone can help.
> > > > > > > > > > So the problem is I am trying to consume from a Kafka
> topic
> > > > that
> > > > > > can
> > > > > > > > > have a
> > > > > > > > > > peak of 12 million messages/hour. That topic is not under
> > my
> > > > > > control
> > > > > > > -
> > > > > > > > it
> > > > > > > > > > has 7 partitions and sending json payload.
> > > > > > > > > > I have written a consumer (I've used Java and
> Spring-Kafka
> > > lib)
> > > > > > that
> > > > > > > > will
> > > > > > > > > > read that data, filter it and then load it into a
> > database. I
> > > > ran
> > > > > > > into
> > > > > > > > a
> > > > > > > > > > huge consumer lag that would take 10-12hours to catch
> up. I
> > > > have
> > > > > 7
> > > > > > > > > > instances of my application running to match the 7
> > partitions
> > > > > and I
> > > > > > > am
> > > > > > > > > > using auto commit. Then I thought of splitting the write
> > > logic
> > > > > to a
> > > > > > > > > > separate layer. So now my architecture has a component
> that
> > > > reads
> > > > > > and
> > > > > > > > > > filters and produces the data to an internal topic (I've
> > > done 7
> > > > > > > > > partitions
> > > > > > > > > > but as you see it's under my control). Then a consumer
> > picks
> > > up
> > > > > > data
> > > > > > > > from
> > > > > > > > > > that topic and writes it to the database. It's better but
> > > still
> > > > > it
> > > > > > > > takes
> > > > > > > > > > 3-5hours for the consumer lag to catch up.
> > > > > > > > > > Am I missing something fundamentally? Are there any other
> > > ideas
> > > > > for
> > > > > > > > > > optimization that can help overcome this scale challenge.
> > Any
> > > > > > pointer
> > > > > > > > and
> > > > > > > > > > article will help too.
> > > > > > > > > >
> > > > > > > > > > Appreciate your help with this.
> > > > > > > > > >
> > > > > > > > > > Thanks
> > > > > > > > > > Yana
> > > > > > > > >
> > > > > > > > >
> > > > > > > > >
> > > > > > > >
> > > > > > > > --
> > > > > > > > ========================
> > > > > > > > Okada Haruki
> > > > > > > > ocadar...@gmail.com
> > > > > > > > ========================
> > > > > > > >
> > > > > > >
> > > > > >
> > > > > >
> > > > > > --
> > > > > > ========================
> > > > > > Okada Haruki
> > > > > > ocadar...@gmail.com
> > > > > > ========================
> > > > > >
> > > > >
> > > >
> > > >
> > > > --
> > > > ========================
> > > > Okada Haruki
> > > > ocadar...@gmail.com
> > > > ========================
> > > >
> > >
> >
>

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