I mean the new mode is very much in the Dataset not the DStream API (although you can use the Dataset API with the old modes too).
On Sun, Mar 25, 2018 at 9:11 PM, Reuven Lax <re...@google.com> wrote: > But this new mode isn't a semantic change, right? It's moving away from > micro batches into something that looks a lot like what Flink does - > continuous processing with asynchronous snapshot boundaries. > > On Sun, Mar 25, 2018 at 9:01 PM Thomas Weise <t...@apache.org> wrote: > >> Hopefully the new "continuous processing mode" in Spark will enable SDF >> implementation (and real streaming)? >> >> Thanks, >> Thomas >> >> >> On Sat, Mar 24, 2018 at 3:22 PM, Holden Karau <hol...@pigscanfly.ca> >> wrote: >> >>> >>> On Sat, Mar 24, 2018 at 1:23 PM Eugene Kirpichov <kirpic...@google.com> >>> wrote: >>> >>>> >>>> >>>> On Fri, Mar 23, 2018, 11:17 PM Holden Karau <hol...@pigscanfly.ca> >>>> wrote: >>>> >>>>> On Fri, Mar 23, 2018 at 7:00 PM Eugene Kirpichov <kirpic...@google.com> >>>>> wrote: >>>>> >>>>>> On Fri, Mar 23, 2018 at 6:49 PM Holden Karau <hol...@pigscanfly.ca> >>>>>> wrote: >>>>>> >>>>>>> On Fri, Mar 23, 2018 at 6:20 PM Eugene Kirpichov < >>>>>>> kirpic...@google.com> wrote: >>>>>>> >>>>>>>> On Fri, Mar 23, 2018 at 6:12 PM Holden Karau <hol...@pigscanfly.ca> >>>>>>>> wrote: >>>>>>>> >>>>>>>>> On Fri, Mar 23, 2018 at 5:58 PM Eugene Kirpichov < >>>>>>>>> kirpic...@google.com> wrote: >>>>>>>>> >>>>>>>>>> Reviving this thread. I think SDF is a pretty big risk for Spark >>>>>>>>>> runner streaming. Holden, is it correct that Spark appears to have >>>>>>>>>> no way >>>>>>>>>> at all to produce an infinite DStream from a finite RDD? Maybe we can >>>>>>>>>> somehow dynamically create a new DStream for every initial >>>>>>>>>> restriction, >>>>>>>>>> said DStream being obtained using a Receiver that under the hood >>>>>>>>>> actually >>>>>>>>>> runs the SDF? (this is of course less efficient than a timer-capable >>>>>>>>>> runner >>>>>>>>>> would do, and I have doubts about the fault tolerance) >>>>>>>>>> >>>>>>>>> So on the streaming side we could simply do it with a fixed number >>>>>>>>> of levels on DStreams. It’s not great but it would work. >>>>>>>>> >>>>>>>> Not sure I understand this. Let me try to clarify what SDF demands >>>>>>>> of the runner. Imagine the following case: a file contains a list of >>>>>>>> "master" Kafka topics, on which there are published additional Kafka >>>>>>>> topics >>>>>>>> to read. >>>>>>>> >>>>>>>> PCollection<String> masterTopics = TextIO.read().from( >>>>>>>> masterTopicsFile) >>>>>>>> PCollection<String> nestedTopics = masterTopics.apply(ParDo( >>>>>>>> ReadFromKafkaFn)) >>>>>>>> PCollection<String> records = nestedTopics.apply(ParDo( >>>>>>>> ReadFromKafkaFn)) >>>>>>>> >>>>>>>> This exemplifies both use cases of a streaming SDF that emits >>>>>>>> infinite output for every input: >>>>>>>> - Applying it to a finite set of inputs (in this case to the result >>>>>>>> of reading a text file) >>>>>>>> - Applying it to an infinite set of inputs (i.e. having an >>>>>>>> unbounded number of streams being read concurrently, each of the >>>>>>>> streams >>>>>>>> themselves is unbounded too) >>>>>>>> >>>>>>>> Does the multi-level solution you have in mind work for this case? >>>>>>>> I suppose the second case is harder, so we can focus on that. >>>>>>>> >>>>>>> So none of those are a splittabledofn right? >>>>>>> >>>>>> Not sure what you mean? ReadFromKafkaFn in these examples is a >>>>>> splittable DoFn and we're trying to figure out how to make Spark run it. >>>>>> >>>>>> >>>>> Ah ok, sorry I saw that and for some reason parsed them as old style >>>>> DoFns in my head. >>>>> >>>>> To effectively allow us to union back into the “same” DStream we’d >>>>> have to end up using Sparks queue streams (or their equivalent custom >>>>> source because of some queue stream limitations), which invites some >>>>> reliability challenges. This might be at the point where I should send a >>>>> diagram/some sample code since it’s a bit convoluted. >>>>> >>>>> The more I think about the jumps required to make the “simple” union >>>>> approach work, the more it seems just using the statemapping for steaming >>>>> is probably more reasonable. Although the state tracking in Spark can be >>>>> somewhat expensive so it would probably make sense to benchmark to see if >>>>> it meets our needs. >>>>> >>>> So the problem is, I don't think this can be made to work using >>>> mapWithState. It doesn't allow a mapping function that emits infinite >>>> output for an input element, directly or not. >>>> >>> So, provided there is an infinite input (eg pick a never ending queue >>> stream), and each call produces a finite output, we would have an infinite >>> number of calls. >>> >>>> >>>> Dataflow and Flink, for example, had timer support even before SDFs, >>>> and a timer can set another timer and thus end up doing an infinite amount >>>> of work in a fault tolerant way - so SDF could be implemented on top of >>>> that. But AFAIK spark doesn't have a similar feature, hence my concern. >>>> >>> So we can do an inifinite queue stream which would allow us to be >>> triggered at each interval and handle our own persistence. >>> >>>> >>>> >>>>> But these still are both DStream based rather than Dataset which we >>>>> might want to support (depends on what direction folks take with the >>>>> runners). >>>>> >>>>> If we wanted to do this in the dataset world looking at a custom >>>>> sink/source would also be an option, (which is effectively what a custom >>>>> queue stream like thing for dstreams requires), but the datasource APIs >>>>> are >>>>> a bit influx so if we ended up doing things at the edge of what’s allowed >>>>> there’s a good chance we’d have to rewrite it a few times. >>>>> >>>>> >>>>>>> Assuming that we have a given dstream though in Spark we can get the >>>>>>> underlying RDD implementation for each microbatch and do our work >>>>>>> inside of >>>>>>> that. >>>>>>> >>>>>>>> >>>>>>>> >>>>>>>>> >>>>>>>>> More generally this does raise an important question if we want to >>>>>>>>> target datasets instead of rdds/DStreams in which case i would need >>>>>>>>> to do >>>>>>>>> some more poking. >>>>>>>>> >>>>>>>>> >>>>>>>>>> On Wed, Mar 14, 2018 at 10:26 PM Reuven Lax <re...@google.com> >>>>>>>>>> wrote: >>>>>>>>>> >>>>>>>>>>> How would timers be implemented? By outputing and reprocessing, >>>>>>>>>>> the same way you proposed for SDF? >>>>>>>>>>> >>>>>>>>>> i mean the timers could be inside the mappers within the system. >>>>>>>>> Could use a singleton so if a partition is re-executed it doesn’t end >>>>>>>>> up as >>>>>>>>> a straggler. >>>>>>>>> >>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> On Wed, Mar 14, 2018 at 7:25 PM Holden Karau < >>>>>>>>>>> hol...@pigscanfly.ca> wrote: >>>>>>>>>>> >>>>>>>>>>>> So the timers would have to be in our own code. >>>>>>>>>>>> >>>>>>>>>>>> On Wed, Mar 14, 2018 at 5:18 PM Eugene Kirpichov < >>>>>>>>>>>> kirpic...@google.com> wrote: >>>>>>>>>>>> >>>>>>>>>>>>> Does Spark have support for timers? (I know it has support for >>>>>>>>>>>>> state) >>>>>>>>>>>>> >>>>>>>>>>>>> On Wed, Mar 14, 2018 at 4:43 PM Reuven Lax <re...@google.com> >>>>>>>>>>>>> wrote: >>>>>>>>>>>>> >>>>>>>>>>>>>> Could we alternatively use a state mapping function to keep >>>>>>>>>>>>>> track of the computation so far instead of outputting V each >>>>>>>>>>>>>> time? (also >>>>>>>>>>>>>> the progress so far is probably of a different type R rather >>>>>>>>>>>>>> than V). >>>>>>>>>>>>>> >>>>>>>>>>>>>> >>>>>>>>>>>>>> On Wed, Mar 14, 2018 at 4:28 PM Holden Karau < >>>>>>>>>>>>>> hol...@pigscanfly.ca> wrote: >>>>>>>>>>>>>> >>>>>>>>>>>>>>> So we had a quick chat about what it would take to add >>>>>>>>>>>>>>> something like SplittableDoFns to Spark. I'd done some sketchy >>>>>>>>>>>>>>> thinking >>>>>>>>>>>>>>> about this last year but didn't get very far. >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> My back-of-the-envelope design was as follows: >>>>>>>>>>>>>>> For input type T >>>>>>>>>>>>>>> Output type V >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> Implement a mapper which outputs type (T, V) >>>>>>>>>>>>>>> and if the computation finishes T will be populated >>>>>>>>>>>>>>> otherwise V will be >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> For determining how long to run we'd up to either K seconds >>>>>>>>>>>>>>> or listen for a signal on a port >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> Once we're done running we take the result and filter for >>>>>>>>>>>>>>> the ones with T and V into seperate collections re-run until >>>>>>>>>>>>>>> finished >>>>>>>>>>>>>>> and then union the results >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> This is maybe not a great design but it was minimally >>>>>>>>>>>>>>> complicated and I figured terrible was a good place to start >>>>>>>>>>>>>>> and improve >>>>>>>>>>>>>>> from. >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> Let me know your thoughts, especially the parts where this >>>>>>>>>>>>>>> is worse than I remember because its been awhile since I >>>>>>>>>>>>>>> thought about this. >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> -- >>>>>>>>>>>>>>> Twitter: https://twitter.com/holdenkarau >>>>>>>>>>>>>>> >>>>>>>>>>>>>> -- >>>>>>>>>>>> Twitter: https://twitter.com/holdenkarau >>>>>>>>>>>> >>>>>>>>>>> -- >>>>>>>>> Twitter: https://twitter.com/holdenkarau >>>>>>>>> >>>>>>>> -- >>>>>>> Twitter: https://twitter.com/holdenkarau >>>>>>> >>>>>> -- >>>>> Twitter: https://twitter.com/holdenkarau >>>>> >>>> -- >>> Twitter: https://twitter.com/holdenkarau >>> >> >> -- Twitter: https://twitter.com/holdenkarau
