Honestly I can't say whether 256 partitions is enough to trigger the performance issues in 2.3.0 but I'd definitely recommend upgrading as soon as you can, just in case. On a related note, how many instances, with how many threads, are you running? 256 partitions with several subtopologies will result in a large number of tasks so make sure you're parallelizing as much as possible.
That said, I may have misread your topology the first time around -- are you saying that you load data from topic2, join it with some other data, then write back to the same topic (topic2)? Having cycles in your topology is not really supported, as stream processing should generally be a DAG -- when you introduce a cycle things often don't behave the way you might expect or want. Is there a particular reason you need to do this? > the delay from sending a message on topic1 to the point where messages received on topic2 are passing the join Also, can you explain what you mean by this? That is, can you explain how you are determining when messages are "passing the join" -- are you just reading from topic2? Are you distinguishing the "new" data, written by Streams, from the "original data" that is written to this topic from elsewhere? On Thu, Oct 10, 2019 at 3:05 AM Petter Arvidsson <petter.arvids...@relayr.io> wrote: > Hi Sophie, > > Thank you for your response. > > I tested the proposed setting for CACHE_MAX_BYTES_BUFFERING_CONFIG and it > seem to not significantly change the behavior of the application. The > latency remains very similar. The documentation states the following > regarding CACHE_MAX_BYTES_BUFFERING_CONFIG and COMMIT_INTERVAL_MS_CONFIG: > > --- > To enable caching but still have an upper bound on how long records will be > cached, you can set the commit interval. In this example, it is set to 1000 > milliseconds: > > Properties props = new Properties(); > // Enable record cache of size 10 MB. > props.put(StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG, 10 * 1024 * > 1024L); > // Set commit interval to 1 second. > props.put(StreamsConfig.COMMIT_INTERVAL_MS_CONFIG, 1000); > --- > > Which made me believe that the COMMIT_INTERVAL_MS_CONFIG would already > "override" the CACHE_MAX_BYTES_BUFFERING_CONFIG and provide an upper bound > of the latency of 1s per processing step by flushing buffers every second. > Is this the case or does these two configuration values interact in some > other way? > > We are using 256 partitions for all our topics. Is this to be considered a > very high partition count? Do you think we might be affected by the bug in > 2.3.0? > > Thank you for the help! > > Best regards, > Petter > > On Wed, Oct 9, 2019 at 7:33 PM Sophie Blee-Goldman <sop...@confluent.io> > wrote: > > > Hi Petter, > > > > I'd recommend turning off caching by setting > > p.put(StreamsConfig.CACHE_MAX_BYTES_BUFFERING, 0); > > > > 2.3.0 also has some known performance issues that will be fixed in 2.3.1, > > but they > > shouldn't be noticeable if you turn caching off and aren't > reading/writing > > to topics > > with a very high partition count. These are fixed in 2.3.1 which should > be > > released > > soon for you to upgrade, but the caching is likely the main reason for > the > > latency you see. > > > > I'd also note that Streams, and Kafka in general, is typically tuned for > > high > > throughput rather than low latency, so I wouldn't be too concerned about > > a large latency unless that is a specific requirement. > > > > Cheers, > > Sophie > > > > On Wed, Oct 9, 2019 at 6:05 AM Petter Arvidsson < > > petter.arvids...@relayr.io> > > wrote: > > > > > Hi, > > > > > > I have a fairly simple kafka streams application that read messages > from > > > two topics. The problem I am facing is that the delay between sending > > > events to the streams application and it producing results is very high > > (as > > > in several minutes). My question is: how can I make this latency > smaller? > > > > > > The streams is doing the following: > > > ktable1 = topic1 > > > -> (filter out messages using flatMap) > > > -> groupBy (with new key, adds internal rekeying topic) > > > -> aggregate (in memory store backed by internal compacted topic) > > > > > > ktabe2 = topic2 > > > -> (rekey to same key as ktable1 over internal topic) > > > -> join (with ktable1) > > > -> aggregate (in memory store backed by internal compacted topic) > > > > > > ktable2.toStream.to(topic2) > > > > > > Ktable1 keep configuration that allows messages to pass through and be > > > aggregated into ktable2. Ktable2 keeps aggregates based on messages on > > > topic2. Ktable2.toStream is then used to put the aggregated messages > back > > > out on topic2. The "problem" (or misunderstanding as to how kafka > stream > > is > > > processing messages) is that the delay from sending a message on topic1 > > to > > > the point where messages received on topic2 are passing the join is > > several > > > minutes. With the settings I have (see below) on a not that heavily > > loaded > > > system, I would assume the latency would be a couple of seconds (based > on > > > the COMMIT_INTERVAL_MS_CONFIG). > > > > > > I use the following settings (as well as settings for bootstrap > servers, > > > application id and so forth): > > > p.put(StreamsConfig.COMMIT_INTERVAL_MS_CONFIG, 1000) > > > p.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest") > > > p.put(StreamsConfig.REPLICATION_FACTOR_CONFIG, 2) > > > > > > The store used for the KTables is the one returned by > > > "Stores.inMemoryKeyValueStore()". > > > > > > Kafka libraries use version "2.3.0" and the "kafka-streams-scala" > > scaladsl > > > is used to build the streams. The broker is using version "1.1.0". > > > > > > Best regards, > > > Petter > > > > > >
