It's also important to note that in many (most?) IO frameworks (gRPC, finagle, etc), asynchronous IO is typically completely non-blocking, so there generally won't be a large number of threads waiting for IO to complete. (netty uses a small pool of threads for the Event Loop Group for example).
But in general I agree with Reuven, runners should not count threads in use in other thread pools for IO for the purpose of autoscaling (or most kinds of accounting). On Thu, Jan 24, 2019 at 12:54 PM Reuven Lax <re...@google.com> wrote: > As Steve said, the main rationale for this is so that asynchronous IOs (or > in general, asynchronous remote calls) call be made. To some degree this > addresses Scott's concern: the asynchronous threads should be, for the most > part, simply waiting for IOs to complete; the reason to do the waiting > asynchronously is so that the main threadpool does not become blocked, > causing the pipeline to become IO bound. A runner like Dataflow should not > be tracking these threads for the purpose of autoscaling, as adding more > workers will (usually) not cause these calls to complete any faster. > > Reuven > > On Thu, Jan 24, 2019 at 7:28 AM Steve Niemitz <sniem...@apache.org> wrote: > >> I think I agree with a lot of what you said here, I'm just going to >> restate my initial use-case to try to make it more clear as well. >> >> From my usage of beam, I feel like the big benefit of async DoFns would >> be to allow batched IO to be implemented more simply inside a DoFn. Even >> in the Beam SDK itself, there are a lot of IOs that batch up IO operations >> in ProcessElement and wait for them to complete in FinishBundle ([1][2], >> etc). From my experience, things like error handling, emitting outputs as >> the result of an asynchronous operation completing (in the correct window, >> with the correct timestamp, etc) get pretty tricky, and it would be great >> for the SDK to provide support natively for it. >> >> It's also probably good to point out that really only DoFns that do IO >> should be asynchronous, normal CPU bound DoFns have no reason to be >> asynchronous. >> >> A really good example of this is an IO I had written recently for >> Bigtable, it takes an input PCollection of ByteStrings representing row >> keys, and returns a PCollection of the row data from bigtable. Naively >> this could be implemented by simply blocking on the Bigtable read inside >> the ParDo, however this would limit throughput substantially (even assuming >> an avg read latency is 1ms, thats still only 1000 QPS / instance of the >> ParDo). My implementation batches many reads together (as they arrive at >> the DoFn), executes them once the batch is big enough (or some time >> passes), and then emits them once the batch read completes. Emitting them >> in the correct window and handling errors gets tricky, so this is certainly >> something I'd love the framework itself to handle. >> >> I also don't see a big benefit of making a DoFn receive a future, if all >> a user is ever supposed to do is attach a continuation to it, that could >> just as easily be done by the runner itself, basically just invoking the >> entire ParDo as a continuation on the future (which then assumes the runner >> is even representing these tasks as futures internally). >> >> Making the DoFn itself actually return a future could be an option, even >> if the language itself doesn't support something like `await`, you could >> still implement it yourself in the DoFn, however, it seems like it'd be a >> strange contrast to the non-async version, which returns void. >> >> [1] >> https://github.com/apache/beam/blob/428dbaf2465292e9e53affd094e4b38cc1d754e5/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigtable/BigtableIO.java#L720 >> [2] >> https://github.com/apache/beam/blob/428dbaf2465292e9e53affd094e4b38cc1d754e5/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/pubsub/PubsubIO.java#L1080 >> >> >> On Thu, Jan 24, 2019 at 8:43 AM Robert Bradshaw <rober...@google.com> >> wrote: >> >>> If I understand correctly, the end goal is to process input elements >>> of a DoFn asynchronously. Were I to do this naively, I would implement >>> DoFns that simply take and receive [Serializable?]CompletionStages as >>> element types, followed by a DoFn that adds a callback to emit on >>> completion (possibly via a queue to avoid being-on-the-wrong-thread >>> issues) and whose finalize forces all completions. This would, of >>> course, interact poorly with processing time tracking, fusion breaks, >>> watermark tracking, counter attribution, window propagation, etc. so >>> it is desirable to make it part of the system itself. >>> >>> Taking a OutputReceiver<CompletionStage<OutputT>> seems like a decent >>> API. The invoking of the downstream process could be chained onto >>> this, with all the implicit tracking and tracing set up correctly. >>> Taking a CompletionStage as input means a DoFn would not have to >>> create its output CompletionStage ex nihilo and possibly allow for >>> better chaining (depending on the asynchronous APIs used). >>> >>> Even better might be to simply let the invocation of all >>> DoFn.process() methods be asynchronous, but as Java doesn't offer an >>> await primitive to relinquish control in the middle of a function body >>> this might be hard. >>> >>> I think for correctness, completion would have to be forced at the end >>> of each bundle. If your bundles are large enough, this may not be that >>> big of a deal. In this case you could also start executing subsequent >>> bundles while waiting for prior ones to complete. >>> >>> >>> >>> >>> On Wed, Jan 23, 2019 at 11:58 PM Bharath Kumara Subramanian >>> <codin.mart...@gmail.com> wrote: >>> >> >>> >> I'd love to see something like this as well. Also +1 to >>> process(@Element InputT element, @Output >>> OutputReceiver<CompletionStage<OutputT>>). I don't know if there's much >>> benefit to passing a future in, since the framework itself could hook up >>> the process function to complete when the future completes. >>> > >>> > >>> > One benefit we get by wrapping the input with CompletionStage is to >>> mandate[1] users to chain their processing logic to the input future; >>> thereby, ensuring asynchrony for the most part. However, it is still >>> possible for users to go out of their way and write blocking code. >>> > >>> > Although, I am not sure how counter intuitive it is for the runners to >>> wrap the input element into a future before passing it to the user code. >>> > >>> > Bharath >>> > >>> > [1] CompletionStage interface does not define methods for initially >>> creating, forcibly completing normally or exceptionally, probing completion >>> status or results, or awaiting completion of a stage. Implementations of >>> CompletionStage may provide means of achieving such effects, as appropriate >>> > >>> > >>> > On Wed, Jan 23, 2019 at 11:31 AM Kenneth Knowles <k...@apache.org> >>> wrote: >>> >> >>> >> I think your concerns are valid but i want to clarify about "first >>> class async APIs". Does "first class" mean that it is a well-encapsulated >>> abstraction? or does it mean that the user can more or less do whatever >>> they want? These are opposite but both valid meanings for "first class", to >>> me. >>> >> >>> >> I would not want to encourage users to do explicit multi-threaded >>> programming or control parallelism. Part of the point of Beam is to gain >>> big data parallelism without explicit multithreading. I see asynchronous >>> chaining of futures (or their best-approximation in your language of >>> choice) as a highly disciplined way of doing asynchronous dependency-driven >>> computation that is nonetheless conceptually, and readably, straight-line >>> code. Threads are not required nor the only way to execute this code. In >>> fact you might often want to execute without threading for a reference >>> implementation to provide canonically correct results. APIs that leak >>> lower-level details of threads are asking for trouble. >>> >> >>> >> One of our other ideas was to provide a dynamic parameter of type >>> ExecutorService. The SDK harness (pre-portability: the runner) would >>> control and observe parallelism while the user could simply register tasks. >>> Providing a future/promise API is even more disciplined. >>> >> >>> >> Kenn >>> >> >>> >> On Wed, Jan 23, 2019 at 10:35 AM Scott Wegner <sc...@apache.org> >>> wrote: >>> >>> >>> >>> A related question is how to make execution observable such that a >>> runner can make proper scaling decisions. Runners decide how to schedule >>> bundles within and across multiple worker instances, and can use >>> information about execution to make dynamic scaling decisions. First-class >>> async APIs seem like they would encourage DoFn authors to implement their >>> own parallelization, rather than deferring to the runner that should be >>> more capable of providing the right level of parallelism. >>> >>> >>> >>> In the Dataflow worker harness, we estimate execution time to >>> PTransform steps by sampling execution time on the execution thread and >>> attributing it to the currently invoked method. This approach is fairly >>> simple and possible because we assume that execution happens within the >>> thread controlled by the runner. Some DoFn's already implement their own >>> async logic and break this assumption; I would expect more if we make async >>> built into the DoFn APIs. >>> >>> >>> >>> So: this isn't an argument against async APIs, but rather: does this >>> break execution observability, and are there other lightweight mechanisms >>> for attributing execution time of async work? >>> >>> >>> >>> On Tue, Jan 22, 2019 at 7:08 PM Kenneth Knowles <k...@google.com> >>> wrote: >>> >>>> >>> >>>> When executed over the portable APIs, it will be primarily the Java >>> SDK harness that makes all of these decisions. If we wanted runners to have >>> some insight into it we would have to add it to the Beam model protos. I >>> don't have any suggestions there, so I would leave it out of this >>> discussion until there's good ideas. We could learn a lot by trying it out >>> just in the SDK harness. >>> >>>> >>> >>>> Kenn >>> >>>> >>> >>>> On Tue, Jan 22, 2019 at 6:12 PM Xinyu Liu <xinyuliu...@gmail.com> >>> wrote: >>> >>>>> >>> >>>>> I don't have a strong opinion on the resolution of the futures >>> regarding to @FinishBundle invocation. Leaving it to be unspecified does >>> give runners more room to implement it with their own support. >>> >>>>> >>> >>>>> Optimization is also another great point. Fuse seems pretty >>> complex to me too if we need to find a way to chain the resulting future >>> into the next transform, or leave the async transform as a standalone stage >>> initially? >>> >>>>> >>> >>>>> Btw, I was counting the number of replies before we hit the >>> portability. Seems after 4 replies fuse finally showed up :). >>> >>>>> >>> >>>>> Thanks, >>> >>>>> Xinyu >>> >>>>> >>> >>>>> >>> >>>>> On Tue, Jan 22, 2019 at 5:42 PM Kenneth Knowles <k...@google.com> >>> wrote: >>> >>>>>> >>> >>>>>> >>> >>>>>> >>> >>>>>> On Tue, Jan 22, 2019, 17:23 Reuven Lax <re...@google.com wrote: >>> >>>>>>> >>> >>>>>>> >>> >>>>>>> >>> >>>>>>> On Tue, Jan 22, 2019 at 5:08 PM Xinyu Liu <xinyuliu...@gmail.com> >>> wrote: >>> >>>>>>>> >>> >>>>>>>> @Steve: it's good to see that this is going to be useful in >>> your use cases as well. Thanks for sharing the code from Scio! I can see in >>> your implementation that waiting for the future completion is part of the >>> @FinishBundle. We are thinking of taking advantage of the underlying runner >>> async support so the user-level code won't need to implement this logic, >>> e.g. Samza has an AsyncSteamTask api that provides a callback to invoke >>> after future completion[1], and Flink also has AsyncFunction api [2] which >>> provides a ResultFuture similar to the API we discussed. >>> >>>>>>> >>> >>>>>>> >>> >>>>>>> Can this be done correctly? What I mean is that if the process >>> dies, can you guarantee that no data is lost? Beam currently guarantees >>> this for FinishBundle, but if you use an arbitrary async framework this >>> might not be true. >>> >>>>>> >>> >>>>>> >>> >>>>>> What a Beam runner guarantees is that *if* the bundle is >>> committed, *then* finishbundle has run. So it seems just as easy to say >>> *if* a bundle is committed, *then* every async result has been resolved. >>> >>>>>> >>> >>>>>> If the process dies the two cases should be naturally analogous. >>> >>>>>> >>> >>>>>> But it raises the question of whether they should be resolved >>> prior to finishbundle, after, or unspecified. I lean toward unspecified. >>> >>>>>> >>> >>>>>> That's for a single ParDo. Where this could get complex is >>> optimizing fused stages for greater asynchrony. >>> >>>>>> >>> >>>>>> Kenn >>> >>>>>> >>> >>>>>>> >>> >>>>>>>> >>> >>>>>>>> A simple use case for this is to execute a Runnable >>> asynchronously in user's own executor. The following code illustrates >>> Kenn's option #2, with a very simple single-thread pool being the executor: >>> >>>>>>>> >>> >>>>>>>> new DoFn<InputT, OutputT>() { >>> >>>>>>>> @ProcessElement >>> >>>>>>>> public void process(@Element InputT element, @Output >>> OutputReceiver<CompletionStage<OutputT>> outputReceiver) { >>> >>>>>>>> CompletableFuture<OutputT> future = >>> CompletableFuture.supplyAsync( >>> >>>>>>>> () -> someOutput, >>> >>>>>>>> Executors.newSingleThreadExecutor()); >>> >>>>>>>> outputReceiver.output(future); >>> >>>>>>>> } >>> >>>>>>>> } >>> >>>>>>>> >>> >>>>>>>> The neat thing about this API is that the user can choose their >>> own async framework and we only expect the output to be a CompletionStage. >>> >>>>>>>> >>> >>>>>>>> >>> >>>>>>>> For the implementation of bundling, can we compose a >>> CompletableFuture from each element in the bundle, e.g. >>> CompletableFuture.allOf(...), and then invoke @FinishBundle when this >>> future is complete? Seems this might work. >>> >>>>>>>> >>> >>>>>>>> Thanks, >>> >>>>>>>> Xinyu >>> >>>>>>>> >>> >>>>>>>> >>> >>>>>>>> [1] >>> https://samza.apache.org/learn/documentation/1.0.0/api/javadocs/org/apache/samza/task/AsyncStreamTask.html >>> >>>>>>>> [2] >>> https://ci.apache.org/projects/flink/flink-docs-release-1.7/dev/stream/operators/asyncio.html >>> >>>>>>>> >>> >>>>>>>> On Tue, Jan 22, 2019 at 2:45 PM Steve Niemitz < >>> sniem...@apache.org> wrote: >>> >>>>>>>>> >>> >>>>>>>>> I'd love to see something like this as well. Also +1 to >>> process(@Element InputT element, @Output >>> OutputReceiver<CompletionStage<OutputT>>). I don't know if there's much >>> benefit to passing a future in, since the framework itself could hook up >>> the process function to complete when the future completes. >>> >>>>>>>>> >>> >>>>>>>>> I feel like I've spent a bunch of time writing very similar >>> "kick off a future in ProcessElement, join it in FinishBundle" code, and >>> looking around beam itself a lot of built-in transforms do it as well. >>> Scio provides a few AsyncDoFn implementations [1] but it'd be great to see >>> this as a first-class concept in beam itself. Doing error handling, >>> concurrency, etc correctly can be tricky. >>> >>>>>>>>> >>> >>>>>>>>> [1] >>> https://github.com/spotify/scio/blob/master/scio-core/src/main/java/com/spotify/scio/transforms/BaseAsyncDoFn.java >>> >>>>>>>>> >>> >>>>>>>>> On Tue, Jan 22, 2019 at 5:39 PM Kenneth Knowles < >>> k...@google.com> wrote: >>> >>>>>>>>>> >>> >>>>>>>>>> If the input is a CompletionStage<InputT> then the output >>> should also be a CompletionStage<OutputT>, since all you should do is async >>> chaining. We could enforce this by giving the DoFn an >>> OutputReceiver(CompletionStage<OutputT>). >>> >>>>>>>>>> >>> >>>>>>>>>> Another possibility that might be even more robust against >>> poor future use could be process(@Element InputT element, @Output >>> OutputReceiver<CompletionStage<OutputT>>). In this way, the process method >>> itself will be async chained, rather than counting on the user to do the >>> right thing. >>> >>>>>>>>>> >>> >>>>>>>>>> We should see how these look in real use cases. The way that >>> processing is split between @ProcessElement and @FinishBundle might >>> complicate things. >>> >>>>>>>>>> >>> >>>>>>>>>> Kenn >>> >>>>>>>>>> >>> >>>>>>>>>> On Tue, Jan 22, 2019 at 12:44 PM Xinyu Liu < >>> xinyuliu...@gmail.com> wrote: >>> >>>>>>>>>>> >>> >>>>>>>>>>> Hi, guys, >>> >>>>>>>>>>> >>> >>>>>>>>>>> As more users try out Beam running on the SamzaRunner, we >>> got a lot of asks for an asynchronous processing API. There are a few >>> reasons for these asks: >>> >>>>>>>>>>> >>> >>>>>>>>>>> The users here are experienced in asynchronous programming. >>> With async frameworks such as Netty and ParSeq and libs like async jersey >>> client, they are able to make remote calls efficiently and the libraries >>> help manage the execution threads underneath. Async remote calls are very >>> common in most of our streaming applications today. >>> >>>>>>>>>>> Many jobs are running on a multi-tenancy cluster. Async >>> processing helps for less resource usage and fast computation (less context >>> switch). >>> >>>>>>>>>>> >>> >>>>>>>>>>> I asked about the async support in a previous email thread. >>> The following API was mentioned in the reply: >>> >>>>>>>>>>> >>> >>>>>>>>>>> new DoFn<InputT, OutputT>() { >>> >>>>>>>>>>> @ProcessElement >>> >>>>>>>>>>> public void process(@Element CompletionStage<InputT> >>> element, ...) { >>> >>>>>>>>>>> element.thenApply(...) >>> >>>>>>>>>>> } >>> >>>>>>>>>>> } >>> >>>>>>>>>>> >>> >>>>>>>>>>> We are wondering whether there are any discussions on this >>> API and related docs. It is awesome that you guys already considered having >>> DoFn to process asynchronously. Out of curiosity, this API seems to create >>> a CompletionState out of the input element (probably using framework's >>> executor) and then allow user to chain on it. To us, it seems more >>> convenient if the DoFn output a CompletionStage<OutputT> or pass in a >>> CompletionStage<OutputT> to invoke upon completion. >>> >>>>>>>>>>> >>> >>>>>>>>>>> We would like to discuss further on the async API and >>> hopefully we will have a great support in Beam. Really appreciate the >>> feedback! >>> >>>>>>>>>>> >>> >>>>>>>>>>> Thanks, >>> >>>>>>>>>>> Xinyu >>> >>> >>> >>> >>> >>> >>> >>> -- >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> Got feedback? tinyurl.com/swegner-feedback >>> >>
