On Tue, Jan 30, 2018 at 1:38 PM, David Espinosa <espi...@gmail.com> wrote: > 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.
AFAIK kafka doesn't do a full list as part of normal operations from ZK. If you have requirements in your consumer/producer code on doing --describe, then that would be a problem. I think that can be worked around. Based on my profiling data so far, while things are working in non-failure mode, none of the ZK functions pop up as "hot methods". > 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? I set it to the max allowable on Amzn Linux: vm.max_map_count=1215752192 > The other way around about topic naming, I think the longer the topic names > are the sooner jute.maxbuffer overflows. I see; what value(s) have you tried with and how much gain did you you see? > David > > > 2018-01-30 4:40 GMT+01:00 Andrey Falko <afa...@salesforce.com>: > >> On Sun, Jan 28, 2018 at 8:45 AM, David Espinosa <espi...@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 <montyhind...@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 >> >> >>
