Hi Antonio, Thanks for the details! Which version of Beam SDK are you using? And are you using --experiments=beam_fn_api with DirectRunner to launch your pipeline?
For ReadFromKafkaDoFn.processElement(), it will take a Kafka topic+partition as input element and a KafkaConsumer will be assigned to this topic+partition then poll records continuously. The Kafka consumer will resume reading and return from the process fn when - There are no available records currently(this is a feature of SDF which calls SDF self-initiated checkpoint) - The OutputAndTimeBoundedSplittableProcessElementInvoker issues checkpoint request to ReadFromKafkaDoFn for getting partial results. The checkpoint frequency for DirectRunner is every 100 output records or every 1 seconds. It seems like either the self-initiated checkpoint or DirectRunner issued checkpoint gives you the performance regression since there is overhead when rescheduling residuals. In your case, it's more like that the checkpoint behavior of OutputAndTimeBoundedSplittableProcessElementInvoker gives you 200 elements a batch. I want to understand what kind of performance regression you are noticing? Is it slower to output the same amount of records? On Fri, Dec 11, 2020 at 1:31 PM Antonio Si <antonio...@gmail.com> wrote: > Hi Boyuan, > > This is Antonio. I reported the KafkaIO.read() performance issue on the > slack channel a few days ago. > > I am not sure if this is helpful, but I have been doing some debugging on > the SDK KafkaIO performance issue for our pipeline and I would like to > provide some observations. > > It looks like in my case the ReadFromKafkaDoFn.processElement() was > invoked within the same thread and every time kafaconsumer.poll() is > called, it returns some records, from 1 up to 200 records. So, it will > proceed to run the pipeline steps. Each kafkaconsumer.poll() takes about > 0.8ms. So, in this case, the polling and running of the pipeline are > executed sequentially within a single thread. So, after processing a batch > of records, it will need to wait for 0.8ms before it can process the next > batch of records again. > > Any suggestions would be appreciated. > > Hope that helps. > > Thanks and regards, > > Antonio. > > On 2020/12/04 19:17:46, Boyuan Zhang <boyu...@google.com> wrote: > > Opened https://issues.apache.org/jira/browse/BEAM-11403 for tracking. > > > > On Fri, Dec 4, 2020 at 10:52 AM Boyuan Zhang <boyu...@google.com> wrote: > > > > > Thanks for the pointer, Steve! I'll check it out. The execution paths > for > > > UnboundedSource and SDF wrapper are different. It's highly possible > that > > > the regression either comes from the invocation path for SDF wrapper, > or > > > the implementation of SDF wrapper itself. > > > > > > On Fri, Dec 4, 2020 at 6:33 AM Steve Niemitz <sniem...@apache.org> > wrote: > > > > > >> Coincidentally, someone else in the ASF slack mentioned [1] yesterday > > >> that they were seeing significantly reduced performance using > KafkaIO.Read > > >> w/ the SDF wrapper vs the unbounded source. They mentioned they were > using > > >> flink 1.9. > > >> > > >> https://the-asf.slack.com/archives/C9H0YNP3P/p1607057900393900 > > >> > > >> On Thu, Dec 3, 2020 at 1:56 PM Boyuan Zhang <boyu...@google.com> > wrote: > > >> > > >>> Hi Steve, > > >>> > > >>> I think the major performance regression comes from > > >>> OutputAndTimeBoundedSplittableProcessElementInvoker[1], which will > > >>> checkpoint the DoFn based on time/output limit and use timers/state > to > > >>> reschedule works. > > >>> > > >>> [1] > > >>> > https://github.com/apache/beam/blob/master/runners/core-java/src/main/java/org/apache/beam/runners/core/OutputAndTimeBoundedSplittableProcessElementInvoker.java > > >>> > > >>> On Thu, Dec 3, 2020 at 9:40 AM Steve Niemitz <sniem...@apache.org> > > >>> wrote: > > >>> > > >>>> I have a pipeline that reads from pubsub, does some aggregation, and > > >>>> writes to various places. Previously, in older versions of beam, > when > > >>>> running this in the DirectRunner, messages would go through the > pipeline > > >>>> almost instantly, making it very easy to debug locally, etc. > > >>>> > > >>>> However, after upgrading to beam 2.25, I noticed that it could take > on > > >>>> the order of 5-10 minutes for messages to get from the pubsub read > step to > > >>>> the next step in the pipeline (deserializing them, etc). The > subscription > > >>>> being read from has on the order of 100,000 elements/sec arriving > in it. > > >>>> > > >>>> Setting --experiments=use_deprecated_read fixes it, and makes the > > >>>> pipeline behave as it did before. > > >>>> > > >>>> It seems like the SDF implementation in the DirectRunner here is > > >>>> causing some kind of issue, either buffering a very large amount of > data > > >>>> before emitting it in a bundle, or something else. Has anyone else > run > > >>>> into this? > > >>>> > > >>> > > >
