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 >>
