If all it takes is bumping these numbers up a bit, that seems like a reasonable thing to do ASAP. (I would argue that perhaps they shouldn't be static, e.g. it might be preferable to start emitting results right away, but use larger batches for the steady state if there are performance benefits.)
That being said, it sounds like there's something deeper going on here. We should also verify that this performance impact is limited to the direct runner. On Wed, Dec 16, 2020 at 1:36 PM Steve Niemitz <sniem...@apache.org> wrote: > I tried changing my build locally to 10 seconds and 10,000 elements but it > didn't seem to make much of a difference, it still takes a few minutes for > elements to begin actually showing up to downstream stages from the Pubsub > read. I can see elements being emitted > from OutputAndTimeBoundedSplittableProcessElementInvoker, and bundles being > committed by ParDoEvaluator.finishBundle, but after that, they seem to just > kind of disappear somewhere. > > On Wed, Dec 16, 2020 at 4:18 PM Boyuan Zhang <boyu...@google.com> wrote: > >> Making it as the PipelineOptions was my another proposal but it might >> take some time to do so. On the other hand, tuning the number into >> something acceptable is low-hanging fruit. >> >> On Wed, Dec 16, 2020 at 12:48 PM Ismaël Mejía <ieme...@gmail.com> wrote: >> >>> It sounds reasonable. I am wondering also on the consequence of these >>> parameters for other runners (where it is every 10 seconds or 10000 >>> elements) + their own configuration e.g. checkpointInterval, >>> checkpointTimeoutMillis and minPauseBetweenCheckpoints for Flink. It >>> is not clear for me what would be chosen now in this case. >>> >>> I know we are a bit anti knobs but maybe it makes sense to make this >>> configurable via PipelineOptions at least for Direct runner. >>> >>> On Wed, Dec 16, 2020 at 7:29 PM Boyuan Zhang <boyu...@google.com> wrote: >>> > >>> > I agree, Ismael. >>> > >>> > From my current investigation, the performance overhead should majorly >>> come from the frequency of checkpoint in >>> OutputAndTimeBoundedSplittableProcessElementinvoker[1], which is hardcoded >>> in the DirectRunner(every 1 seconds or 100 elements)[2]. I believe >>> configuring these numbers on DirectRunner should improve reported cases so >>> far. My last proposal was to change the number to every 5 seconds or 10000 >>> elements. What do you think? >>> > >>> > [1] >>> https://github.com/apache/beam/blob/master/runners/core-java/src/main/java/org/apache/beam/runners/core/OutputAndTimeBoundedSplittableProcessElementInvoker.java >>> > [2] >>> https://github.com/apache/beam/blob/3bb232fb098700de408f574585dfe74bbaff7230/runners/direct-java/src/main/java/org/apache/beam/runners/direct/SplittableProcessElementsEvaluatorFactory.java#L178-L181 >>> > >>> > On Wed, Dec 16, 2020 at 9:02 AM Ismaël Mejía <ieme...@gmail.com> >>> wrote: >>> >> >>> >> I can guess that the same issues mentioned here probably will affect >>> >> the usability for people trying Beam's interactive SQL on Unbounded IO >>> >> too. >>> >> >>> >> We should really take into account that the performance of the SDF >>> >> based path should be as good or better than the previous version >>> >> before considering its removal (--experiments=use_deprecated_read) and >>> >> probably have consensus when this happens. >>> >> >>> >> >>> >> On Fri, Dec 11, 2020 at 11:33 PM Boyuan Zhang <boyu...@google.com> >>> wrote: >>> >> > >>> >> > > From what I've seen, the direct runner initiates a checkpoint >>> after every element output. >>> >> > That seems like the 1 second limit kicks in before the output >>> reaches 100 elements. >>> >> > >>> >> > I think the original purpose for DirectRunner to use a small limit >>> on issuing checkpoint requests is for exercising SDF better in a small data >>> set. But it brings overhead on a larger set owing to too many checkpoints. >>> It would be ideal to make this limit configurable from pipeline but the >>> easiest approach is that we figure out a number for most common cases. Do >>> you think we raise the limit to 1000 elements or every 5 seconds will help? >>> >> > >>> >> > On Fri, Dec 11, 2020 at 2:22 PM Steve Niemitz <sniem...@apache.org> >>> wrote: >>> >> >> >>> >> >> From what I've seen, the direct runner initiates a checkpoint >>> after every element output. >>> >> >> >>> >> >> On Fri, Dec 11, 2020 at 5:19 PM Boyuan Zhang <boyu...@google.com> >>> wrote: >>> >> >>> >>> >> >>> 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? >>> >> >>>> > >>>> >>> >> >>>> > >>> >>> >> >>>> > >>> >>
