It is read all per key and window and not just read all (this still won't scale with hot keys in the global window). The GBK preceding the StatefulParDo will guarantee that you are processing all the values for a specific key and window at any given time. Is there a specific window/trigger that is missing that you feel would remove the need for you to use StatefulParDo?
On Mon, May 20, 2019 at 12:54 PM Jan Lukavský <je...@seznam.cz> wrote: > Hi Lukasz, > > > Today, if you must have a strict order, you must guarantee that your > StatefulParDo implements the necessary "buffering & sorting" into state. > > Yes, no problem with that. But this whole discussion started, because > *this doesn't work on batch*. You simply cannot first read everything from > distributed storage and then buffer it all into memory, just to read it > again, but sorted. That will not work. And even if it would, it would be a > terrible waste of resources. > > Jan > On 5/20/19 8:39 PM, Lukasz Cwik wrote: > > > > On Mon, May 20, 2019 at 8:24 AM Jan Lukavský <je...@seznam.cz> wrote: > >> This discussion brings many really interesting questions for me. :-) >> >> > I don't see batch vs. streaming as part of the model. One can have >> microbatch, or even a runner that alternates between different modes. >> >> Although I understand motivation of this statement, this project name is >> "Apache Beam: An advanced unified programming model". What does the >> model unify, if "streaming vs. batch" is not part of the model? >> >> Using microbatching, chaining of batch jobs, or pure streaming are >> exactly the "runtime conditions/characteristics" I refer to. All these >> define several runtime parameters, which in turn define how well/badly >> will the pipeline perform and how many resources might be needed. From >> my point of view, pure streaming should be the most resource demanding >> (if not, why bother with batch? why not run everything in streaming >> only? what will there remain to "unify"?). >> >> > Fortunately, for batch, only the state for a single key needs to be >> preserved at a time, rather than the state for all keys across the range >> of skew. Of course if you have few or hot keys, one can still have >> issues (and this is not specific to StatefulDoFns). >> >> Yes, but here is still the presumption that my stateful DoFn can >> tolerate arbitrary shuffling of inputs. Let me explain the use case in >> more detail. >> >> Suppose you have input stream consisting of 1s and 0s (and some key for >> each element, which is irrelevant for the demonstration). Your task is >> to calculate in running global window the actual number of changes >> between state 0 and state 1 and vice versa. When the state doesn't >> change, you don't calculate anything. If input (for given key) would be >> (tN denotes timestamp N): >> >> t1: 1 >> >> t2: 0 >> >> t3: 0 >> >> t4: 1 >> >> t5: 1 >> >> t6: 0 >> >> then the output should yield (supposing that default state is zero): >> >> t1: (one: 1, zero: 0) >> >> t2: (one: 1, zero: 1) >> >> t3: (one: 1, zero: 1) >> >> t4: (one: 2, zero: 1) >> >> t5: (one: 2, zero: 1) >> >> t6: (one: 2, zero: 2) >> >> How would you implement this in current Beam semantics? >> > > I think your saying here that I know that my input is ordered in a > specific way and since I assume the order when writing my pipeline I can > perform this optimization. But there is nothing preventing a runner from > noticing that your processing in the global window with a specific type of > trigger and re-ordering your inputs/processing to get better performance > (since you can't use an AfterWatermark trigger for your pipeline in > streaming for the GlobalWindow). > > Today, if you must have a strict order, you must guarantee that your > StatefulParDo implements the necessary "buffering & sorting" into state. I > can see why you would want an annotation that says I must have timestamp > ordered elements, since it makes writing certain StatefulParDos much > easier. StatefulParDo is a low-level function, it really is the "here you > go and do whatever you need to but here be dragons" function while > windowing and triggering is meant to keep many people from writing > StatefulParDo in the first place. > > >> > Pipelines that fail in the "worst case" batch scenario are likely to >> degrade poorly (possibly catastrophically) when the watermark falls >> behind in streaming mode as well. >> >> But the worst case is defined by input of size (available resources + >> single byte) -> pipeline fail. Although it could have finished, given >> the right conditions. >> >> > This might be reasonable, implemented by default by buffering >> everything and releasing elements as the watermark (+lateness) advances, >> but would likely lead to inefficient (though *maybe* easier to reason >> about) code. >> >> Sure, the pipeline will be less efficient, because it would have to >> buffer and sort the inputs. But at least it will produce correct results >> in cases where updates to state are order-sensitive. >> >> > Would it be roughly equivalent to GBK + FlatMap(lambda (key, values): >> [(key, value) for value in values])? >> >> I'd say roughly yes, but difference would be in the trigger. The trigger >> should ideally fire as soon as watermark (+lateness) crosses element >> with lowest timestamp in the buffer. Although this could be somehow >> emulated by fixed trigger each X millis. >> >> > Or is the underlying desire just to be able to hint to the runner >> that the code may perform better (e.g. require less resources) as skew >> is reduced (and hence to order by timestamp iff it's cheap)? >> >> No, the sorting would have to be done in streaming case as well. That is >> an imperative of the unified model. I think it is possible to sort by >> timestamp only in batch case (and do it for *all* batch stateful pardos >> without annotation), or introduce annotation, but then make the same >> guarantees for streaming case as well. >> >> Jan >> >> On 5/20/19 4:41 PM, Robert Bradshaw wrote: >> > On Mon, May 20, 2019 at 1:19 PM Jan Lukavský <je...@seznam.cz> wrote: >> >> Hi Robert, >> >> >> >> yes, I think you rephrased my point - although no *explicit* guarantees >> >> of ordering are given in either mode, there is *implicit* ordering in >> >> streaming case that is due to nature of the processing - the difference >> >> between watermark and timestamp of elements flowing through the >> pipeline >> >> are generally low (too high difference leads to the overbuffering >> >> problem), but there is no such bound on batch. >> > Fortunately, for batch, only the state for a single key needs to be >> > preserved at a time, rather than the state for all keys across the >> > range of skew. Of course if you have few or hot keys, one can still >> > have issues (and this is not specific to StatefulDoFns). >> > >> >> As a result, I see a few possible solutions: >> >> >> >> - the best and most natural seems to be extension of the model, so >> >> that it defines batch as not only "streaming pipeline executed in batch >> >> fashion", but "pipeline with at least as good runtime characteristics >> as >> >> in streaming case, executed in batch fashion", I really don't think >> that >> >> there are any conflicts with the current model, or that this could >> >> affect performance, because the required sorting (as pointed by >> >> Aljoscha) is very probably already done during translation of stateful >> >> pardos. Also note that this definition only affects user defined >> >> stateful pardos >> > I don't see batch vs. streaming as part of the model. One can have >> > microbatch, or even a runner that alternates between different modes. >> > The model describes what the valid outputs are given a (sometimes >> > partial) set of inputs. It becomes really hard to define things like >> > "as good runtime characteristics." Once you allow any >> > out-of-orderedness, it is not very feasible to try and define (and >> > more cheaply implement) a "upper bound" of acceptable >> > out-of-orderedness. >> > >> > Pipelines that fail in the "worst case" batch scenario are likely to >> > degrade poorly (possibly catastrophically) when the watermark falls >> > behind in streaming mode as well. >> > >> >> - another option would be to introduce annotation for DoFns (e.g. >> >> @RequiresStableTimeCharacteristics), which would result in the sorting >> >> in batch case - but - this extension would have to ensure the sorting >> in >> >> streaming mode also - it would require definition of allowed lateness, >> >> and triggger (essentially similar to window) >> > This might be reasonable, implemented by default by buffering >> > everything and releasing elements as the watermark (+lateness) >> > advances, but would likely lead to inefficient (though *maybe* easier >> > to reason about) code. Not sure about the semantics of triggering >> > here, especially data-driven triggers. Would it be roughly equivalent >> > to GBK + FlatMap(lambda (key, values): [(key, value) for value in >> > values])? >> > >> > Or is the underlying desire just to be able to hint to the runner that >> > the code may perform better (e.g. require less resources) as skew is >> > reduced (and hence to order by timestamp iff it's cheap)? >> > >> >> - last option would be to introduce these "higher order guarantees" >> in >> >> some extension DSL (e.g. Euphoria), but that seems to be the worst >> >> option to me >> >> >> >> I see the first two options quite equally good, although the letter one >> >> is probably more time consuming to implement. But it would bring >> >> additional feature to streaming case as well. >> >> >> >> Thanks for any thoughts. >> >> >> >> Jan >> >> >> >> On 5/20/19 12:41 PM, Robert Bradshaw wrote: >> >>> On Fri, May 17, 2019 at 4:48 PM Jan Lukavský <je...@seznam.cz> wrote: >> >>>> Hi Reuven, >> >>>> >> >>>>> How so? AFAIK stateful DoFns work just fine in batch runners. >> >>>> Stateful ParDo works in batch as far, as the logic inside the state >> works for absolutely unbounded out-of-orderness of elements. That basically >> (practically) can work only for cases, where the order of input elements >> doesn't matter. But, "state" can refer to "state machine", and any time you >> have a state machine involved, then the ordering of elements would matter. >> >>> No guarantees on order are provided in *either* streaming or batch >> >>> mode by the model. However, it is the case that in order to make >> >>> forward progress most streaming runners attempt to limit the amount of >> >>> out-of-orderedness of elements (in terms of event time vs. processing >> >>> time) to make forward progress, which in turn could help cap the >> >>> amount of state that must be held concurrently, whereas a batch runner >> >>> may not allow any state to be safely discarded until the whole >> >>> timeline from infinite past to infinite future has been observed. >> >>> >> >>> Also, as pointed out, state is not preserved "batch to batch" in >> batch mode. >> >>> >> >>> >> >>> On Thu, May 16, 2019 at 3:59 PM Maximilian Michels <m...@apache.org> >> wrote: >> >>> >> >>>>> batch semantics and streaming semantics differs only in that I >> can have GlobalWindow with default trigger on batch and cannot on stream >> >>>> You can have a GlobalWindow in streaming with a default trigger. You >> >>>> could define additional triggers that do early firings. And you could >> >>>> even trigger the global window by advancing the watermark to +inf. >> >>> IIRC, as a pragmatic note, we prohibited global window with default >> >>> trigger on unbounded PCollections in the SDK because this is more >> >>> likely to be user error than an actual desire to have no output until >> >>> drain. But it's semantically valid in the model. >> >
