Perfect! I am eager to see what you came up with! On Sat, Apr 18, 2015 at 2:00 PM, Gyula Fóra <gyula.f...@gmail.com> wrote:
> Hey all, > > We have spent some time with Asterios, Paris and Jonas to finalize the > windowing semantics (both the current features and the window join), and I > think we made very have come up with a very clear picture. > > We will write down the proposed semantics and publish it to the wiki next > week. > > Cheers, > Gyula > > On Thu, Apr 16, 2015 at 5:50 PM, Asterios Katsifodimos < > asterios.katsifodi...@tu-berlin.de> wrote: > > > As far as I see in [1], Peter's/Gyula's suggestion is what Infosphere > > Streams does: symmetric hash join. > > > > From [1]: > > "When a tuple is received on an input port, it is inserted into the > window > > corresponding to the input port, which causes the window to trigger. As > > part of the trigger processing, the tuple is compared against all tuples > > inside the window of the opposing input port. If the tuples match, then > an > > output tuple will be produced for each match. If at least one output was > > generated, a window punctuation will be generated after all the outputs." > > > > Cheers, > > Asterios > > > > [1] > > > > > http://www-01.ibm.com/support/knowledgecenter/#!/SSCRJU_3.2.1/com.ibm.swg.im.infosphere.streams.spl-standard-toolkit-reference.doc/doc/join.html > > > > > > > > On Thu, Apr 9, 2015 at 1:30 PM, Matthias J. Sax < > > mj...@informatik.hu-berlin.de> wrote: > > > > > Hi Paris, > > > > > > thanks for the pointer to the Naiad paper. That is quite interesting. > > > > > > The paper I mentioned [1], does not describe the semantics in detail; > it > > > is more about the implementation for the stream-joins. However, it uses > > > the same semantics (from my understanding) as proposed by Gyula. > > > > > > -Matthias > > > > > > [1] Kang, Naughton, Viglas. "Evaluationg Window Joins over Unbounded > > > Streams". VLDB 2002. > > > > > > > > > > > > On 04/07/2015 12:38 PM, Paris Carbone wrote: > > > > Hello Matthias, > > > > > > > > Sure, ordering guarantees are indeed a tricky thing, I recall having > > > that discussion back in TU Berlin. Bear in mind thought that > DataStream, > > > our abstract data type, represents a *partitioned* unbounded sequence > of > > > events. There are no *global* ordering guarantees made whatsoever in > that > > > model across partitions. If you see it more generally there are many > > “race > > > conditions” in a distributed execution graph of vertices that process > > > multiple inputs asynchronously, especially when you add joins and > > > iterations into the mix (how do you deal with reprocessing “old” tuples > > > that iterate in the graph). Btw have you checked the Naiad paper [1]? > > > Stephan cited a while ago and it is quite relevant to that discussion. > > > > > > > > Also, can you cite the paper with the joining semantics you are > > > referring to? That would be of good help I think. > > > > > > > > Paris > > > > > > > > [1] https://users.soe.ucsc.edu/~abadi/Papers/naiad_final.pdf > > > > > > > > <https://users.soe.ucsc.edu/~abadi/Papers/naiad_final.pdf> > > > > > > > > <https://users.soe.ucsc.edu/~abadi/Papers/naiad_final.pdf> > > > > On 07 Apr 2015, at 11:50, Matthias J. Sax < > > mj...@informatik.hu-berlin.de > > > <mailto:mj...@informatik.hu-berlin.de>> wrote: > > > > > > > > Hi @all, > > > > > > > > please keep me in the loop for this work. I am highly interested and > I > > > > want to help on it. > > > > > > > > My initial thoughts are as follows: > > > > > > > > 1) Currently, system timestamps are used and the suggested approach > can > > > > be seen as state-of-the-art (there is actually a research paper using > > > > the exact same join semantic). Of course, the current approach is > > > > inherently non-deterministic. The advantage is, that there is no > > > > overhead in keeping track of the order of records and the latency > > should > > > > be very low. (Additionally, state-recovery is simplified. Because, > the > > > > processing in inherently non-deterministic, recovery can be done with > > > > relaxed guarantees). > > > > > > > > 2) The user should be able to "switch on" deterministic processing, > > > > ie, records are timestamped (either externally when generated, or > > > > timestamped at the sources). Because deterministic processing adds > some > > > > overhead, the user should decide for it actively. > > > > In this case, the order must be preserved in each re-distribution > step > > > > (merging is sufficient, if order is preserved within each incoming > > > > channel). Furthermore, deterministic processing can be achieved by > > sound > > > > window semantics (and there is a bunch of them). Even for > > > > single-stream-windows it's a tricky problem; for join-windows it's > even > > > > harder. From my point of view, it is less important which semantics > are > > > > chosen; however, the user must be aware how it works. The most tricky > > > > part for deterministic processing, is to deal with duplicate > timestamps > > > > (which cannot be avoided). The timestamping for (intermediate) result > > > > tuples, is also an important question to be answered. > > > > > > > > > > > > -Matthias > > > > > > > > > > > > On 04/07/2015 11:37 AM, Gyula Fóra wrote: > > > > Hey, > > > > > > > > I agree with Kostas, if we define the exact semantics how this works, > > > this > > > > is not more ad-hoc than any other stateful operator with multiple > > inputs. > > > > (And I don't think any other system support something similar) > > > > > > > > We need to make some design choices that are similar to the issues we > > had > > > > for windowing. We need to chose how we want to evaluate the windowing > > > > policies (global or local) because that affects what kind of policies > > can > > > > be parallel, but I can work on these things. > > > > > > > > I think this is an amazing feature, so I wouldn't necessarily rush > the > > > > implementation for 0.9 though. > > > > > > > > And thanks for helping writing these down. > > > > > > > > Gyula > > > > > > > > On Tue, Apr 7, 2015 at 11:11 AM, Kostas Tzoumas <ktzou...@apache.org > > > <mailto:ktzou...@apache.org>> wrote: > > > > > > > > Yes, we should write these semantics down. I volunteer to help. > > > > > > > > I don't think that this is very ad-hoc. The semantics are basically > the > > > > following. Assuming an arriving element from the left side: > > > > (1) We find the right-side matches > > > > (2) We insert the left-side arrival into the left window > > > > (3) We recompute the left window > > > > We need to see whether right window re-computation needs to be > > triggered > > > as > > > > well. I think that this way of joining streams is also what the > > symmetric > > > > hash join algorithms were meant to support. > > > > > > > > Kostas > > > > > > > > > > > > On Tue, Apr 7, 2015 at 10:49 AM, Stephan Ewen <se...@apache.org > > <mailto: > > > se...@apache.org>> wrote: > > > > > > > > Is the approach of joining an element at a time from one input > against > > a > > > > window on the other input not a bit arbitrary? > > > > > > > > This just joins whatever currently happens to be the window by the > time > > > > the > > > > single element arrives - that is a bit non-predictable, right? > > > > > > > > As a more general point: The whole semantics of windowing and when > they > > > > are > > > > triggered are a bit ad-hoc now. It would be really good to start > > > > formalizing that a bit and > > > > put it down somewhere. Users need to be able to clearly understand > and > > > > how > > > > to predict the output. > > > > > > > > > > > > > > > > On Fri, Apr 3, 2015 at 12:10 PM, Gyula Fóra <gyula.f...@gmail.com > > > <mailto:gyula.f...@gmail.com>> > > > > wrote: > > > > > > > > I think it should be possible to make this compatible with the > > > > .window().every() calls. Maybe if there is some trigger set in > "every" > > > > we > > > > would not join that stream 1 by 1 but every so many elements. The > > > > problem > > > > here is that the window and every in this case are very-very > different > > > > than > > > > the normal windowing semantics. The window would define the join > window > > > > for > > > > each element of the other stream while every would define how often I > > > > join > > > > This stream with the other one. > > > > > > > > We need to think to make this intuitive. > > > > > > > > On Fri, Apr 3, 2015 at 11:23 AM, Márton Balassi < > > > > balassi.mar...@gmail.com<mailto:balassi.mar...@gmail.com>> > > > > wrote: > > > > > > > > That would be really neat, the problem I see there, that we do not > > > > distinguish between dataStream.window() and > > > > dataStream.window().every() > > > > currently, they both return WindowedDataStreams and TriggerPolicies > > > > of > > > > the > > > > every call do not make much sense in this setting (in fact > > > > practically > > > > the > > > > trigger is always set to count of one). > > > > > > > > But of course we could make it in a way, that we check that the > > > > eviction > > > > should be either null or count of 1, in every other case we throw an > > > > exception while building the JobGraph. > > > > > > > > On Fri, Apr 3, 2015 at 8:43 AM, Aljoscha Krettek < > > > > aljos...@apache.org<mailto:aljos...@apache.org>> > > > > wrote: > > > > > > > > Or you could define it like this: > > > > > > > > stream_A = a.window(...) > > > > stream_B = b.window(...) > > > > > > > > stream_A.join(stream_B).where().equals().with() > > > > > > > > So a join would just be a join of two WindowedDataStreamS. This > > > > would > > > > neatly move the windowing stuff into one place. > > > > > > > > On Thu, Apr 2, 2015 at 9:54 PM, Márton Balassi < > > > > balassi.mar...@gmail.com<mailto:balassi.mar...@gmail.com> > > > > > > > > wrote: > > > > Big +1 for the proposal for Peter and Gyula. I'm really for > > > > bringing > > > > the > > > > windowing and window join API in sync. > > > > > > > > On Thu, Apr 2, 2015 at 6:32 PM, Gyula Fóra <gyf...@apache.org > <mailto: > > > gyf...@apache.org>> > > > > wrote: > > > > > > > > Hey guys, > > > > > > > > As Aljoscha has highlighted earlier the current window join > > > > semantics > > > > in > > > > the streaming api doesn't follow the changes in the windowing > > > > api. > > > > More > > > > precisely, we currently only support joins over time windows of > > > > equal > > > > size > > > > on both streams. The reason for this is that we now take a > > > > window > > > > of > > > > each > > > > of the two streams and do joins over these pairs. This would be > > > > a > > > > blocking > > > > operation if the windows are not closed at exactly the same time > > > > (and > > > > since > > > > we dont want this we only allow time windows) > > > > > > > > I talked with Peter who came up with the initial idea of an > > > > alternative > > > > approach for stream joins which works as follows: > > > > > > > > Instead of pairing windows for joins, we do element against > > > > window > > > > joins. > > > > What this means is that whenever we receive an element from one > > > > of > > > > the > > > > streams, we join this element with the current window(this > > > > window > > > > is > > > > constantly updated) of the other stream. This is non-blocking on > > > > any > > > > window > > > > definitions as we dont have to wait for windows to be completed > > > > and > > > > we > > > > can > > > > use this with any of our predefined policies like Time.of(...), > > > > Count.of(...), Delta.of(....). > > > > > > > > Additionally this also allows some very flexible way of defining > > > > window > > > > joins. With this we could also define grouped windowing inside > > > > if > > > > a > > > > join. > > > > An example of this would be: Join all elements of Stream1 with > > > > the > > > > last > > > > 5 > > > > elements by a given windowkey of Stream2 on some join key. > > > > > > > > This feature can be easily implemented over the current > > > > operators, > > > > so > > > > I > > > > already have a working prototype for the simple non-grouped > > > > case. > > > > My > > > > only > > > > concern is the API, the best thing I could come up with is > > > > something > > > > like > > > > this: > > > > > > > > stream_A.join(stream_B).onWindow(windowDefA, > > > > windowDefB).by(windowKey1, > > > > windowKey2).where(...).equalTo(...).with(...) > > > > > > > > (the user can omit the "by" and "with" calls) > > > > > > > > I think this new approach would be worthy of our "flexible > > > > windowing" > > > > in > > > > contrast with the current approach. > > > > > > > > Regards, > > > > Gyula > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >
