kafka-users mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From David Espinosa <espi...@gmail.com>
Subject Re: Recommended max number of topics (and data separation)
Date Tue, 30 Jan 2018 21:38:37 GMT
Hi Andrey,
My topics are replicated with a replicated factor equals to the number of
nodes, 3 in this test.
Didn't know about the kip-227.
The problems I see at 70k topics coming from ZK are related to any
operation where ZK has to retrieve topics metadata. Just listing topics at
50K or 60k you will experience a big delay in the response. I have no more
details about these problems, but is easy to reproduce the latency in the
topics list request.
Thanks me for pointing me to this parameter,  vm.max_map_count, it wasn't
on my radar. Could you tell me what value you use?
The other way around about topic naming, I think the longer the topic names
are the sooner jute.maxbuffer overflows.
David


2018-01-30 4:40 GMT+01:00 Andrey Falko <afalko@salesforce.com>:

> On Sun, Jan 28, 2018 at 8:45 AM, David Espinosa <espixxl@gmail.com> wrote:
> > Hi Monty,
> >
> > I'm also planning to use a big amount of topics in Kafka, so recently I
> > made a test within a 3 nodes kafka cluster where I created 100k topics
> with
> > one partition. Sent 1M messages in total.
>
> Are your topic partitions replicated?
>
> > These are my conclusions:
> >
> >    - There is not any limitation on kafka regarding the number of topics
> >    but on Zookeeper and in the system where Kafka nodes is allocated.
>
> There are also the problems being addressed in KIP-227:
> https://cwiki.apache.org/confluence/display/KAFKA/KIP-
> 227%3A+Introduce+Incremental+FetchRequests+to+Increase+
> Partition+Scalability
>
> >    - Zookeeper will start having problems from 70k topics, which can be
> >    solved modifying a buffer parameter on the JVM (-Djute.maxbuffer).
> >    Performance is reduced.
>
> What kind of problems do you see at 70k topics? If performance is
> reduced w/ modifying jute.maxbuffer, won't that effect the performance
> of kafka interms of how long it takes to recover from broker failure,
> creating/deleting topics, producing and consuming?
>
> >    - Open file descriptors of the system are equivalent to [number of
> >    topics]X[number of partitions per topic]. Set to 128k in my test to
> avoid
> >    problems.
> >    - System needs a big amount of memory for page caching.
>
> I also had to tune vm.max_map_count much higher.
>
> >
> > So, after creating 100k with the required setup (system+JVM) but seeing
> > problems at 70k, I feel safe by not creating more than 50k, and always
> will
> > have Zookeeper as my first suspect if a problem comes. I think with
> proper
> > resources (memory) and system setup (open file descriptors), you don't
> have
> > any real limitation regarding partitions.
>
> I can confirm the 50k number. After about 40k-45k topics, I start
> seeing slow down in consume offset commit latencies that eclipse 50ms.
> Hopefully KIP-227 will alleviate that problem and leave ZK as the last
> remaining hurdle. I'm testing with 3x replication per partition and 10
> brokers.
>
> > By the way, I used long topic names (about 30 characters), which can be
> > important for ZK.
>
> I'd like to learn more about this, are you saying that long topic
> names would improve ZK performance because that relates to bumping up
> jute.maxbuffer?
>
> > Hope this information is of your help.
> >
> > David
> >
> > 2018-01-28 2:22 GMT+01:00 Monty Hindman <montyhindman@gmail.com>:
> >
> >> I'm designing a system and need some more clarity regarding Kafka's
> >> recommended limits on the number of topics and/or partitions. At a high
> >> level, our system would work like this:
> >>
> >> - A user creates a job X (X is a UUID).
> >> - The user uploads data for X to an input topic: X.in.
> >> - Workers process the data, writing results to an output topic: X.out.
> >> - The user downloads the data from X.out.
> >>
> >> It's important for the system that data for different jobs be kept
> >> separate, and that input and output data be kept separate. By
> "separate" I
> >> mean that there needs to be a reasonable way for users and the system's
> >> workers to query for the data they need (by job-id and by
> input-vs-output)
> >> and not get the data they don't need.
> >>
> >> Based on expected usage and our data retention policy, we would not
> expect
> >> to need more than 12,000 active jobs at any one time -- in other words,
> >> 24,000 topics. If we were to have 5 partitions per topic (our cluster
> has 5
> >> brokers), that would imply 120,000 partitions. [These number refer only
> to
> >> main/primary partitions, not any replicas that might exist.]
> >>
> >> Those numbers seem to be far larger than the suggested limits I see
> online.
> >> For example, the Kafka FAQ on these matters seems to imply that the most
> >> relevant limit is the number of partitions (rather than topics) and
> sort of
> >> implies that 10,000 partitions might be a suggested guideline (
> >> https://goo.gl/fQs2md). Also implied is that systems should use fewer
> >> topics and instead partition the data within topics if further
> separation
> >> is needed (the FAQ entry uses the example of partitioning by user ID,
> which
> >> is roughly analogous to job ID in my use case).
> >>
> >> The guidance in the FAQ is unclear to me:
> >>
> >> - Does the suggested limit of 10,000 refer to the total number of
> >> partitions (ie, main partitions plus any replicas) or just the main
> >> partitions?
> >>
> >> - If the most important limitation is number of partitions (rather than
> >> number of topics), how does the suggested strategy of using fewer topics
> >> and then partitioning by some other attribute (ie job ID) help at all?
> >>
> >> - Is my use case just a bad fit for Kafka? Or, is there a way for us to
> use
> >> Kafka while still supporting the kinds of query patterns that we need
> (ie,
> >> by job ID and by input-vs-output)?
> >>
> >> Thanks in advance for any guidance.
> >>
> >> Monty
> >>
>

Mime
  • Unnamed multipart/alternative (inline, None, 0 bytes)
View raw message