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From "Matthias J. Sax" <mj...@informatik.hu-berlin.de>
Subject Re: Rework of the window-join semantics
Date Tue, 07 Apr 2015 09:50:32 GMT
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.


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 <ktzoumas@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 <sewen@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.fora@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.marton@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 <
>> aljoscha@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.marton@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 <gyfora@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
>>>> equal
>>>>>> size
>>>>>>>> on both streams. The reason for this is that we now take
>> window
>>> of
>>>>>> each
>>>>>>>> of the two streams and do joins over these pairs. This would
>> a
>>>>>> blocking
>>>>>>>> operation if the windows are not closed at exactly the same
>>>> (and
>>>>>> since
>>>>>>>> we dont want this we only allow time windows)
>>>>>>>> I talked with Peter who came up with the initial idea of
>>>>> 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
>> of
>>>> the
>>>>>>>> streams, we join this element with the current window(this
>> window
>>> is
>>>>>>>> constantly updated) of the other stream. This is non-blocking
>>> 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
>> 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

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