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 <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 > >> >
