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 > > > > ======================== > > > > > > > > > >