Hi Etienne, I was drafting a proposal about @OnWindowExpiration when this email arrived. I thought I would try to quickly unblock you by responding with a TL;DR: you can achieve your goals with state & timers as they currently exist. You'll set a timer for window.maxTimestamp().plus(allowedLateness) precisely - when this timer fires, you are guaranteed that the input watermark has exceeded this point (so all new data is droppable) while the output timestamp is held to this point (so you can safely output into the window).
@OnWindowExpiration is (1) a convenience to save you from needing a handle on the allowed lateness (not a problem in your case) and (2) actually meaningful and potentially less expensive to implement in the absence of state (this is why it needs a design discussion at all, really). Caveat: these APIs are new and not supported in every runner and windowing configuration. Kenn On Thu, Jan 26, 2017 at 1:48 AM, Etienne Chauchot <echauc...@gmail.com> wrote: > Hi, > > I have started to implement this ticket. For now it is implemented as a > PTransform that simply does ParDo.of(new DoFn) and all the processing > related to batching is done in the DoFn. > > I'm starting to deal with windows and bundles (starting to take a look at > the State API to process trans-bundles, more questions about this to come). > My comments/questions are inline: > > > Le 17/01/2017 à 18:41, Ben Chambers a écrit : > >> We should start by understanding the goals. If elements are in different >> windows can they be out in the same batch? If they have different >> timestamps what timestamp should the batch have? >> > > Regarding timestamps: currently design is as so: the transform does not > group elements in the PCollection, so the "batch" does not exist as an > element in the PCollection. There is only a user defined function > (perBatchFn) that gets called when batchSize elements have been processed. > This function takes an ArrayList as parameter. So elements keep their > original timestamps > > > Regarding windowing: I guess that if elements are not in the same window, > they are not expected to be in the same batch. > I'm just starting to work on these subjects, so I might lack a bit of > information; > what I am currently thinking about is that I need a way to know in the > DoFn that the window has expired so that I can call the perBatchFn even if > batchSize is not reached. This is the @OnWindowExpiration callback that > Kenneth mentioned in an email about bundles. > Lets imagine that we have a collection of elements artificially > timestamped every 10 seconds (for simplicity of the example) and a fixed > windowing of 1 minute. Then each window contains 6 elements. If we were to > buffer the elements by batches of 5 elements, then for each window we > expect to get 2 batches (one of 5 elements, one of 1 element). For that to > append, we need a @OnWindowExpiration on the DoFn where we call perBatchFn > > As a composite transform this will likely require a group by key which may >> affect performance. Maybe within a dofn is better. >> > Yes, the processing is done with a DoFn indeed. > >> Then it could be some annotation or API that informs the runner. Should >> batch sizes be fixed in the annotation (element count or size) or should >> the user have some method that lets them decide when to process a batch >> based on the contents? >> > For now, the user passes batchSize as an argument to BatchParDo.via() it > is a number of elements. But batch based on content might be useful for the > user. Give hint to the runner might be more flexible for the runner. Thanks. > >> Another thing to think about is whether this should be connected to the >> ability to run parts of the bundle in parallel. >> > Yes! > >> Maybe each batch is an RPC >> and you just want to start an async RPC for each batch. Then in addition >> to >> start the final RPC in finishBundle, you also need to wait for all the >> RPCs >> to complete. >> > Actually, currently each batch processing is whatever the user wants > (perBatchFn user defined function). If the user decides to issue an async > RPC in that function (call with the arrayList of input elements), IMHO he > is responsible for waiting for the response in that method if he needs the > response, but he can also do a send and forget, depending on his use case. > > Besides, I have also included a perElementFn user function to allow the > user to do some processing on the elements before adding them to the batch > (example use case: convert a String to a DTO object to invoke an external > service) > > Etienne > > On Tue, Jan 17, 2017, 8:48 AM Etienne Chauchot<echauc...@gmail.com> >> wrote: >> >> Hi JB, >> >> I meant jira vote but discussion on the ML works also :) >> >> As I understand the need (see stackoverflow links in jira ticket) the >> aim is to avoid the user having to code the batching logic in his own >> DoFn.processElement() and DoFn.finishBundle() regardless of the bundles. >> For example, possible use case is to batch a call to an external service >> (for performance). >> >> I was thinking about providing a PTransform that implements the batching >> in its own DoFn and that takes user defined functions for customization. >> >> Etienne >> >> Le 17/01/2017 à 17:30, Jean-Baptiste Onofré a écrit : >> >>> Hi >>> >>> I guess you mean discussion on the mailing list about that, right ? >>> >>> AFAIR the idea is to provide a utility class to deal with >>> >> pooling/batching. However not sure it's required as with @StartBundle etc >> in DoFn and batching depends of the end user "logic". >> >>> Regards >>> JB >>> >>> On Jan 17, 2017, 08:26, at 08:26, Etienne Chauchot<echauc...@gmail.com> >>> >> wrote: >> >>> Hi all, >>>> >>>> I have started to work on this ticket >>>> https://issues.apache.org/jira/browse/BEAM-135 >>>> >>>> As there where no vote since March 18th, is the issue still >>>> relevant/needed? >>>> >>>> Regards, >>>> >>>> Etienne >>>> >>> >
