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From Radu Tudoran <radu.tudo...@huawei.com>
Subject RE: [DISCUSS] Development of SQL OVER / Table API Row Windows for streaming tables
Date Thu, 26 Jan 2017 17:02:35 GMT
Thanks for this redesign Fabian,


I am interested in "- FLINK-5654: processing time OVER RANGE x PRECEDING"

However, I though the issue number is 
https://issues.apache.org/jira/browse/FLINK-5654
am I wrong?

As you proposed  I will move the discussion about your remark in the comment section for this
issue.


-----Original Message-----
From: Fabian Hueske [mailto:fhueske@gmail.com] 
Sent: Thursday, January 26, 2017 3:14 PM
To: dev@flink.apache.org
Subject: Re: [DISCUSS] Development of SQL OVER / Table API Row Windows for streaming tables

Hi everybody,

I created the following JIRAs:

- FLINK-5653: processing time OVER ROWS x PRECEDING
- FLINK-5654: processing time OVER RANGE x PRECEDING
- FLINK-5655: event time OVER RANGE x PRECEDING

- FLINK-5656: processing time OVER ROWS UNBOUNDED PRECEDING
- FLINK-5657: processing time OVER RANGE UNBOUNDED PRECEDING
- FLINK-5658: event time OVER RANGE UNBOUNDED PRECEDING

Let's move the discussions about the design of the runtime operators to these issues.

Since some of you have already started working on some of the issues, it would be good if
you could pick the ones you plan to work on.
If there are overlapping interests, it would be great to collaborate, e.g, design, coding,
testing, code review.

A few more comments:

@Shaoxuan: You are right, we can implement the processing time windows with ProcessFunction
as well. A GlobalWindow is essentially a FIFO queue of arriving records. With custom triggers
and evictors, we could implement the functionality of the processing time OVER windows. We
could ask Aljoscha (he knows every detail of Flink's window framework) if a ProcessFunction
has more optimization potential than a GlobalWindow.

@Jark: That's a good point. We need logic to compute non-retractable aggregation functions
as well.

@Radu: So far we had very coarse grained DataStreamRelNodes (e.g., DataStreamAggregate implements
tumbling, sliding, and session windows for processing and event time). However, it might make
sense to start implementing more fine-grained DataStreamRelNodes.

I'll go ahead and modify the previous JIRAs about SlideRow, TumbleRow, and SessionRow to explicitly
address the Table API and how they relate to the new JIRAs.

Best,
Fabian

2017-01-26 7:05 GMT+01:00 Jark Wu <wuchong.wc@alibaba-inc.com>:

> Hi Fabian,
>
> I completely aggree with the six JIRAs and different runtime 
> implementations.
> And I also aggree with @shaoxuan's proposal can work for both 
> processing time and event time.
>
> Hi Shaoxuan,
>
> I really like the idea you proposed that using retraction to decrease 
> computation.
> It's a great optimization for incremental aggregation (only one 
> reduced value is kept).
> But may not work for non-incremental aggregation (e.g. max, min, and
> median) which
> needs to buffer all the records in the group & window, and recalculate 
> all the records when retraction happen. That means we will get a worse 
> performance for non-incremental  aggregations when using retraction 
> optimization here.
>
> IMO, we still need a general design for OVER window as following:
>
> 1. we buffer records in a list state (maybe sorted) for each group 2. 
> when an over window is up to trigger, create an accumulator and 
> accumulate all
>     the records in the boundary of the list state.
> 3. emit the aggregate result and delete the accumulator.
>
> And the retraction mechanism that keeps the accumulator for the whole 
> life without deleting, could be implemented as an optimization on it 
> for increamental aggregations.
>
> Regards, Jark
>
>
> > 在 2017年1月26日,上午11:42,Shaoxuan Wang <wshaoxuan@gmail.com> 写道:
> >
> > Yes Fabian,
> > I will complete my design with more thorough thoughts. BTW, I think 
> > the incremental aggregate (the key point I suggested is to eliminate 
> > state
> per
> > each window) I proposed should work for both processing time and 
> > event time. It just does not need a sorted state for the processing 
> > time scenarios. (Need to verify).
> >
> > Regards,
> > Shaoxuan
> >
> >
> > On Wed, Jan 25, 2017 at 5:55 PM, Fabian Hueske <fhueske@gmail.com>
> wrote:
> >
> >> Hi everybody,
> >>
> >> thanks for the great discussions so far. It's awesome to see so 
> >> much interest in this topic!
> >>
> >> First, I'd like to comment on the development process for this 
> >> feature
> and
> >> later on the design of the runtime:
> >>
> >> Dev Process
> >> ----
> >> @Shaoxuan, I completely agree with you. We should first come up 
> >> with
> good
> >> designs for the runtime operators of the different window types. 
> >> Once we have that, we can start implementing the operators and 
> >> integrate them
> with
> >> Calcite's optimization. This will be an intermediate step and as a 
> >> byproduct give us support for SQL OVER windows. Once this is done, 
> >> we
> can
> >> extend the Table API and translate the Table API calls into the 
> >> same RelNodes as Calcite's SQL parser does.
> >>
> >> Runtime Design
> >> ----
> >> I think it makes sense to distinguish the different types of OVER
> windows
> >> because they have different requirements which result in different
> runtime
> >> implementations (with different implementation complexity and
> performance).
> >> In a previous mail I proposed to split the support for OVER windows 
> >> into the following subtasks:
> >>
> >> # bounded PRECEDING
> >> - OVER ROWS for processing time
> >>  - does not require sorted state (data always arrives in processing 
> >> time
> >> order)
> >>  - no need to consider retraction (processing time is never late)
> >>  - defines windows on row count.
> >>  - A GlobalWindow with evictor + trigger might be the best
> implementation
> >> (basically the same as DataStream.countWindow(long, long). We need 
> >> to
> add
> >> timeouts to clean up state for non-used keys though.
> >>
> >> - OVER RANGE for processing time
> >>  - does not require sorted state (data always arrives in processing 
> >> time
> >> order)
> >>  - no need to consider retraction (processing time is never late)
> >>  - defines windows on row count
> >>  - I think this could also be implemented with a GlobalWindow with
> evictor
> >> + trigger (need to verify)
> >>
> >> - OVER RANGE for event time
> >>  - need for sorted state (late data possible)
> >>  - IMO, a ProcessFunction gives us the most flexibility in adding 
> >> later features (retraction, update rate, etc.)
> >>  - @Shaoxuan, you sketched a good design. Would you like to 
> >> continue
> with
> >> a design proposal?
> >>
> >> # UNBOUNDED PRECEDING
> >> Similar considerations apply for the UNBOUNDED PRECEDING cases of 
> >> the
> above
> >> window types.
> >>
> >> If we all agree that the separation into six JIRAs 
> >> (bounded/unbounded * row-pt/range-pt/ range-et) makes sense, I 
> >> would suggest to move the discussions about the design of the 
> >> implementation to the individual
> JIRAs.
> >>
> >> What do think?
> >>
> >> Best, Fabian
> >>
> >> 2017-01-25 9:19 GMT+01:00 Shaoxuan Wang <wshaoxuan@gmail.com>:
> >>
> >>> Hi Liuxinchun,
> >>> I am not sure where did you get the inception: anyone has 
> >>> suggested "to process Event time window in Sliding Row Window". If 
> >>> you were referring
> >> my
> >>> post, there may be some misunderstanding there. I think you were 
> >>> asking
> >> the
> >>> similar question as Hongyuhong. I have just replied to him. Please
> take a
> >>> look and let me know if that makes sense to you. "Retraction" is 
> >>> an important building block to compute correct incremental results 
> >>> in streaming. It is another big topic, we should discuss this in 
> >>> another thread.
> >>>
> >>> Regards,
> >>> Shaoxuan
> >>>
> >>>
> >>>
> >>> On Wed, Jan 25, 2017 at 3:44 PM, liuxinchun 
> >>> <liuxinchun@huawei.com>
> >> wrote:
> >>>
> >>>> I don't think it is a good idea to process Event time window in
> Sliding
> >>>> Row Window. In Sliding Time window, when an element is late, we 
> >>>> can
> >>> trigger
> >>>> the recalculation of the related windows. And the sliding period 
> >>>> is coarse-gained, We only need to recalculate size/sliding number 
> >>>> of
> >>> windows.
> >>>> But in Sliding Row Window, the calculation is triggered when 
> >>>> every
> >>> element
> >>>> is coming. The sliding period is becoming fine-gained. When an 
> >>>> element
> >> is
> >>>> late, there are so many "windows" are influenced. Even if we 
> >>>> store all
> >>> the
> >>>> raw data, the computation is very large.
> >>>>
> >>>> I think if it is possible to set a standard to sliding Event Time 
> >>>> Row Window, When certain elements are late, we can only 
> >>>> recalculate
> partial
> >>>> windows and permit some error. For example, we can only 
> >>>> recalculate
> the
> >>>> windows end in range between (lateElement.timestamp - leftDelta, 
> >>>> lateElement.timestamp] and those windows begin in range between 
> >>>> [lateElement.timestamp, lateElement.timestamp + rightDelta).
> >>>> ////////////////////////////////////////////////////////////
> >>>> //////////////////////////
> >>>> Hi everyone,
> >>>> Thanks for this great discussion, and glad to see more and more 
> >>>> people
> >>> are
> >>>> interested on stream SQL & tableAPI.
> >>>>
> >>>> IMO, the key problems for Over window design are the SQL 
> >>>> semantics and
> >>> the
> >>>> runtime design. I totally agree with Fabian that we should skip 
> >>>> the
> >>> design
> >>>> of TumbleRows and SessionRows windows for now, as they are not 
> >>>> well
> >>> defined
> >>>> in SQL semantics.
> >>>>
> >>>> Runtime design is the most crucial part we are interested in and 
> >>>> volunteered to contribute into. We have thousands of machines 
> >>>> running
> >>> flink
> >>>> streaming jobs. The costs in terms of CPU, memory, and state are 
> >>>> the
> >>> vital
> >>>> factors that we have to taken into account. We have been working 
> >>>> on
> the
> >>>> design of OVER window in the past months, and planning to send 
> >>>> out a detailed design doc to DEV quite soon. But since Fabian 
> >>>> started a good discussion on OVER window, I would like to share 
> >>>> our ideas/thoughts
> >> about
> >>>> the runtime design for OVER window.
> >>>>
> >>>>   1. As SunJincheng pointed out earlier, sliding window does not 
> >>>> work
> >>> for
> >>>>   unbounded preceding, we need alternative approach for unbound 
> >>>> over window.
> >>>>   2. Though sliding window may work for some cases of bounded window,
> >>>>   it is not very efficient thereby should not be used for production.
> >> To
> >>>> the
> >>>>   best of my understanding, the current runtime implementation of
> >>> sliding
> >>>>   window has not leveraged the concepts of state Panes yet. This 
> >>>> means that
> >>>>   if we use sliding window for OVER window,  there will be a 
> >>>> backend
> >>> state
> >>>>   created per each group (partition by) and each row, and 
> >>>> whenever a
> >> new
> >>>>   record arrives, it will be accumulated to all the existing 
> >>>> windows
> >>> that
> >>>> has
> >>>>   not been closed. This would cause quite a lot of overhead in 
> >>>> terms
> >> of
> >>>> both
> >>>>   CPU and memory&state.
> >>>>   3. Fabian has mentioned an approach of leveraging “ProcessFunction”
> >>> and
> >>>>   a “sortedState”. I like this idea. The design details on this

> >>>> are
> >> not
> >>>> quite
> >>>>   clear yet. So I would like to add more thoughts on this. Regardless
> >>>>   which dataStream API we are going to use (it is very likely 
> >>>> that we
> >>> need
> >>>>   a new API), we should come out with an optimal approach. The 
> >>>> purpose
> >>> of
> >>>>   grouping window and over window is to partition the data, such 
> >>>> that
> >> we
> >>>> can
> >>>>   generate the aggregate results. So when we talk about the 
> >>>> design of
> >>> OVER
> >>>>   window, we have to think about the aggregates. As we proposed 
> >>>> in our recent
> >>>>   UDAGG doc https://goo.gl/6ntclB,  the user defined accumulator 
> >>>> will
> >>> be
> >>>>   stored in the aggregate state. Besides accumulator, we have 
> >>>> also introduced
> >>>>   a retract API for UDAGG. With aggregate accumulator and retract
> >> API, I
> >>>> am
> >>>>   proposing a runtime approach to implement the OVER window as
> >>> followings.
> >>>>   4.
> >>>>      - We first implement a sorted state interface
> >>>>      - Per each group, we just create one sorted state. When a 
> >>>> new
> >>> record
> >>>>      arrives, it will insert into this sorted state, in the 
> >>>> meanwhile
> >> it
> >>>> will be
> >>>>      accumulated to the aggregate accumulator.
> >>>>      - For over window, we keep the aggregate accumulator for the
> >> entire
> >>>>      job lifelong time. This is different than the case where we
> >> delete
> >>>> the
> >>>>      accumulator for each group/window when a grouping-window is
> >>> finished.
> >>>>      - When an over window is up to trigger, we grab the
> >>>>      previous accumulator from the state and accumulate values 
> >>>> onto it with all
> >>>>      the records till the upperBoundary of the current window, 
> >>>> and retract all
> >>>>      the out of scope records till its lowerBoundary. We emit the
> >>>>      aggregate result and save the accumulator for the next window.
> >>>>
> >>>>
> >>>> Hello Fabian,
> >>>> I would suggest we should first start working on runtime design 
> >>>> of
> over
> >>>> window and aggregate. Once we have a good design there, one can 
> >>>> easily
> >>> add
> >>>> the support for SQL as well as tableAPI. What do you think?
> >>>>
> >>>> Regards,
> >>>> Shaoxuan
> >>>>
> >>>> On Tue, Jan 24, 2017 at 10:42 PM, Fabian Hueske 
> >>>> <fhueske@gmail.com>
> >>> wrote:
> >>>>
> >>>>> Hi Radu,
> >>>>>
> >>>>> thanks for your comments!
> >>>>>
> >>>>> Yes, my intention is to open new JIRA issues to structure the 
> >>>>> development process. Everybody is very welcome to pick up issues

> >>>>> and discuss the design proposals.
> >>>>> At the moment I see the following six issues to start with:
> >>>>>
> >>>>> - streaming SQL OVER ROW for processing time
> >>>>>  - bounded PRECEDING
> >>>>>  - unbounded PRECEDING
> >>>>>
> >>>>> - streaming SQL OVER RANGE for processing time
> >>>>>  - bounded PRECEDING
> >>>>>  - unbounded PRECEDING
> >>>>>
> >>>>> - streaming SQL OVER RANGE for event time
> >>>>>  - bounded PRECEDING
> >>>>>  - unbounded PRECEDING
> >>>>>
> >>>>> For each of these windows we need corresponding translation 
> >>>>> rules and execution code.
> >>>>>
> >>>>> Subsequent JIRAs would be
> >>>>> - extending the Table API for supported SQL windows
> >>>>> - add support for FOLLOWING
> >>>>> - etc.
> >>>>>
> >>>>> Regarding the requirement for a sorted state. I am not sure if 
> >>>>> the OVER windows should be implemented using Flink's DataStream

> >>>>> window
> >>>> framework.
> >>>>> We need a good design document to figure out what is the best 
> >>>>> approach. A ProcessFunction with a sorted state might be a good
> >>> solution
> >>>> as well.
> >>>>>
> >>>>> Best, Fabian
> >>>>>
> >>>>>
> >>>>> 2017-01-24 10:41 GMT+01:00 Radu Tudoran <radu.tudoran@huawei.com>:
> >>>>>
> >>>>>> Hi all,
> >>>>>>
> >>>>>> Thanks for starting these discussion - it is very useful.
> >>>>>> It does make sense indeed to refactor all these and coordinate

> >>>>>> a
> >> bit
> >>>>>> the efforts not to have overlapping implementations and
> >> incompatible
> >>>>> solutions.
> >>>>>>
> >>>>>> If you close the 3 jira issues you mentioned - do you plan to

> >>>>>> redesign them and open new ones? Do you need help from our side

> >>>>>> -
> >> we
> >>>>>> can also pick the redesign of some of these new jira issues.

> >>>>>> For example we already
> >>>>> have
> >>>>>> an implementation for this and we can help with the design.
> >>>>>> Nevertheless, let's coordinate the effort.
> >>>>>>
> >>>>>> Regarding the support for the different types of window - I

> >>>>>> think the
> >>>>> best
> >>>>>> option is to split the implementation in small units. We can

> >>>>>> easily do
> >>>>> this
> >>>>>> from the transformation rule class and with this each 
> >>>>>> particular type of window (session/sliding/sliderows/processing

> >>>>>> time/...)
> >> will
> >>>>>> have a clear implementation and a corresponding architecture

> >>>>>> within
> >>>> the jira issue?
> >>>>> What
> >>>>>> do you think about such a granularity?
> >>>>>>
> >>>>>> Regarding the issue of " Q4: The implementaion of SlideRows

> >>>>>> still need a custom operator that collects records in a 
> >>>>>> priority queue ordered by the "rowtime", which is similar to

> >>>>>> the design we discussed in FLINK-4697, right? "
> >>>>>> Why would you need this operator? The window buffer can act
to 
> >>>>>> some
> >>>>> extent
> >>>>>> as a priority queue as long as the trigger and evictor is set

> >>>>>> to work
> >>>>> based
> >>>>>> on the rowtime - or maybe I am missing something... Can you

> >>>>>> please
> >>>>> clarify
> >>>>>> this.
> >>>>>>
> >>>>>>
> >>>>>> Dr. Radu Tudoran
> >>>>>> Senior Research Engineer - Big Data Expert IT R&D Division
> >>>>>>
> >>>>>>
> >>>>>> HUAWEI TECHNOLOGIES Duesseldorf GmbH European Research Center

> >>>>>> Riesstrasse 25, 80992 München
> >>>>>>
> >>>>>> E-mail: radu.tudoran@huawei.com
> >>>>>> Mobile: +49 15209084330
> >>>>>> Telephone: +49 891588344173
> >>>>>>
> >>>>>> HUAWEI TECHNOLOGIES Duesseldorf GmbH Hansaallee 205, 40549 
> >>>>>> Düsseldorf, Germany, www.huawei.com Registered Office: 
> >>>>>> Düsseldorf, Register Court Düsseldorf, HRB
> >> 56063,
> >>>>>> Managing Director: Bo PENG, Wanzhou MENG, Lifang CHEN Sitz der

> >>>>>> Gesellschaft: Düsseldorf, Amtsgericht Düsseldorf, HRB
> >> 56063,
> >>>>>> Geschäftsführer: Bo PENG, Wanzhou MENG, Lifang CHEN This e-mail

> >>>>>> and its attachments contain confidential information
> >> from
> >>>>>> HUAWEI, which is intended only for the person or entity whose
> >> address
> >>>> is
> >>>>>> listed above. Any use of the information contained herein in

> >>>>>> any
> >> way
> >>>>>> (including, but not limited to, total or partial disclosure,
> >>>>> reproduction,
> >>>>>> or dissemination) by persons other than the intended 
> >>>>>> recipient(s)
> >> is
> >>>>>> prohibited. If you receive this e-mail in error, please notify

> >>>>>> the
> >>>> sender
> >>>>>> by phone or email immediately and delete it!
> >>>>>>
> >>>>>>
> >>>>>> -----Original Message-----
> >>>>>> From: Jark Wu [mailto:wuchong.wc@alibaba-inc.com]
> >>>>>> Sent: Tuesday, January 24, 2017 6:53 AM
> >>>>>> To: dev@flink.apache.org
> >>>>>> Subject: Re: [DISCUSS] Development of SQL OVER / Table API Row
> >>> Windows
> >>>>> for
> >>>>>> streaming tables
> >>>>>>
> >>>>>> Hi Fabian,
> >>>>>>
> >>>>>> Thanks for bringing up this discussion and the nice approach
to
> >> avoid
> >>>>>> overlapping contributions.
> >>>>>>
> >>>>>> All of these make sense to me. But I have some questions.
> >>>>>>
> >>>>>> Q1: If I understand correctly, we will not support TumbleRows

> >>>>>> and SessionRows at the beginning. But maybe support them as
a 
> >>>>>> syntax
> >>> sugar
> >>>>> (in
> >>>>>> Table API) when the SlideRows is supported in the future. Right
?
> >>>>>>
> >>>>>> Q2: How to support SessionRows based on SlideRows ?  I don't

> >>>>>> get
> >> how
> >>> to
> >>>>>> partition on "gap-separated".
> >>>>>>
> >>>>>> Q3: Should we break down the approach into smaller tasks for
> >>> streaming
> >>>>>> tables and batch tables ?
> >>>>>>
> >>>>>> Q4: The implementaion of SlideRows still need a custom operator
> >> that
> >>>>>> collects records in a priority queue ordered by the "rowtime",
> >> which
> >>> is
> >>>>>> similar to the design we discussed in FLINK-4697, right?
> >>>>>>
> >>>>>> +1 not support for OVER ROW for event time at this point.
> >>>>>>
> >>>>>> Regards, Jark
> >>>>>>
> >>>>>>
> >>>>>>> 在 2017年1月24日,上午10:28,Hongyuhong <hongyuhong@huawei.com>
写道:
> >>>>>>>
> >>>>>>> Hi,
> >>>>>>> We are also interested in streaming sql and very willing
to
> >>>> participate
> >>>>>> and contribute.
> >>>>>>>
> >>>>>>> We are now in progress and we will also contribute to calcite

> >>>>>>> to
> >>> push
> >>>>>> forward the window and stream-join support.
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>> --------------
> >>>>>>> Sender: Fabian Hueske [mailto:fhueske@gmail.com] Send Time:
> >>>> 2017年1月24日
> >>>>>>> 5:55
> >>>>>>> Receiver: dev@flink.apache.org
> >>>>>>> Theme: Re: [DISCUSS] Development of SQL OVER / Table API
Row
> >>> Windows
> >>>>>>> for streaming tables
> >>>>>>>
> >>>>>>> Hi Haohui,
> >>>>>>>
> >>>>>>> our plan was in fact to piggy-back on Calcite and use the

> >>>>>>> TUMBLE
> >>>>>> function [1] once is it is available (CALCITE-1345 [2]).
> >>>>>>> Unfortunately, this issue does not seem to be very active,
so 
> >>>>>>> I
> >>> don't
> >>>>>> know what the progress is.
> >>>>>>>
> >>>>>>> I would suggest to move the discussion about group windows
to 
> >>>>>>> a
> >>>>> separate
> >>>>>> thread and keep this one focused on the organization of the
SQL
> >> OVER
> >>>>>> windows.
> >>>>>>>
> >>>>>>> Best,
> >>>>>>> Fabian
> >>>>>>>
> >>>>>>> [1] http://calcite.apache.org/docs/stream.html)
> >>>>>>> [2] https://issues.apache.org/jira/browse/CALCITE-1345
> >>>>>>>
> >>>>>>> 2017-01-23 22:42 GMT+01:00 Haohui Mai <ricetons@gmail.com>:
> >>>>>>>
> >>>>>>>> Hi Fabian,
> >>>>>>>>
> >>>>>>>> FLINK-4692 has added the support for tumbling window
and we 
> >>>>>>>> are excited to try it out and expose it as a SQL construct.
> >>>>>>>>
> >>>>>>>> Just curious -- what's your thought on the SQL syntax
on
> >> tumbling
> >>>>>> window?
> >>>>>>>>
> >>>>>>>> Implementation wise it might make sense to think tumbling

> >>>>>>>> window
> >>> as
> >>>> a
> >>>>>>>> special case of the sliding window.
> >>>>>>>>
> >>>>>>>> The problem I see is that the OVER construct might be
> >> insufficient
> >>>> to
> >>>>>>>> support all the use cases of tumbling windows. For example,

> >>>>>>>> it
> >>> fails
> >>>>>>>> to express tumbling windows that have fractional time
units 
> >>>>>>>> (as pointed out in http://calcite.apache.org/docs/stream.html).
> >>>>>>>>
> >>>>>>>> It looks to me that the Calcite / Azure Stream Analytics
have 
> >>>>>>>> introduced a new construct (TUMBLE / TUMBLINGWINDOW)
to 
> >>>>>>>> address
> >>> this
> >>>>>> issue.
> >>>>>>>>
> >>>>>>>> Do you think it is a good idea to follow the same conventions?
> >>> Your
> >>>>>>>> ideas are appreciated.
> >>>>>>>>
> >>>>>>>> Regards,
> >>>>>>>> Haohui
> >>>>>>>>
> >>>>>>>>
> >>>>>>>> On Mon, Jan 23, 2017 at 1:02 PM Haohui Mai 
> >>>>>>>> <ricetons@gmail.com>
> >>>>> wrote:
> >>>>>>>>
> >>>>>>>>> +1
> >>>>>>>>>
> >>>>>>>>> We are also quite interested in these features and
would 
> >>>>>>>>> love
> >> to
> >>>>>>>>> participate and contribute.
> >>>>>>>>>
> >>>>>>>>> ~Haohui
> >>>>>>>>>
> >>>>>>>>> On Mon, Jan 23, 2017 at 7:31 AM Fabian Hueske <
> >> fhueske@gmail.com
> >>>>
> >>>>>> wrote:
> >>>>>>>>>
> >>>>>>>>>> Hi everybody,
> >>>>>>>>>>
> >>>>>>>>>> it seems that currently several contributors
are working on
> >> new
> >>>>>>>>>> features for the streaming Table API / SQL around
row 
> >>>>>>>>>> windows
> >>> (as
> >>>>>>>>>> defined in
> >>>>>>>>>> FLIP-11
> >>>>>>>>>> [1]) and SQL OVER-style window (FLINK-4678,
FLINK-4679,
> >>>> FLINK-4680,
> >>>>>>>>>> FLINK-5584).
> >>>>>>>>>> Since these efforts overlap quite a bit I spent
some time
> >>> thinking
> >>>>>>>>>> about how we can approach these features and
how to avoid 
> >>>>>>>>>> overlapping contributions.
> >>>>>>>>>>
> >>>>>>>>>> The challenge here is the following. Some of
the Table API 
> >>>>>>>>>> row windows
> >>>>>>>> as
> >>>>>>>>>> defined by FLIP-11 [1] are basically SQL OVER
windows while
> >>> other
> >>>>>>>>>> cannot be easily expressed as such (TumbleRows
for 
> >>>>>>>>>> row-count intervals, SessionRows).
> >>>>>>>>>> However, since Calcite already supports SQL
OVER windows, 
> >>>>>>>>>> we
> >> can
> >>>>>>>>>> reuse
> >>>>>>>> the
> >>>>>>>>>> optimization logic for some of the Table API
row windows. I
> >> also
> >>>>>>>>>> thought about the semantics of the TumbleRows
and 
> >>>>>>>>>> SessionRows windows as defined in
> >>>>>>>>>> FLIP-11 and came to the conclusion that these
are not well
> >>> defined
> >>>>>>>>>> in
> >>>>>>>>>> FLIP-11 and should rather be defined as SlideRows
windows
> >> with a
> >>>>>>>>>> special PARTITION BY clause.
> >>>>>>>>>>
> >>>>>>>>>> I propose to approach SQL OVER windows and Table
API row
> >> windows
> >>>> as
> >>>>>>>>>> follows:
> >>>>>>>>>>
> >>>>>>>>>> We start with three simple cases for SQL OVER
windows (not
> >> Table
> >>>>>>>>>> API
> >>>>>>>> yet):
> >>>>>>>>>>
> >>>>>>>>>> * OVER RANGE for event time
> >>>>>>>>>> * OVER RANGE for processing time
> >>>>>>>>>> * OVER ROW for processing time
> >>>>>>>>>>
> >>>>>>>>>> All cases fulfill the following restrictions:
> >>>>>>>>>> - All aggregations in SELECT must refer to the
same window.
> >>>>>>>>>> - PARTITION BY may not contain the rowtime attribute.
> >>>>>>>>>> - ORDER BY must be on rowtime attribute (for
event time) or
> >> on a
> >>>>>>>>>> marker function that indicates processing time.
Additional
> >> sort
> >>>>>>>>>> attributes are not supported initially.
> >>>>>>>>>> - only "BETWEEN UNBOUNDED PRECEDING AND CURRENT
ROW" and
> >>> "BETWEEN
> >>>> x
> >>>>>>>>>> PRECEDING AND CURRENT ROW" are supported.
> >>>>>>>>>>
> >>>>>>>>>> OVER ROW for event time cannot be easily supported.
With 
> >>>>>>>>>> event time, we may have late records which need
to be 
> >>>>>>>>>> injected into
> >>> the
> >>>>>>>>>> order of records.
> >>>>>>>>>> When
> >>>>>>>>>> a record in injected in to the order where a
row-count 
> >>>>>>>>>> window
> >>> has
> >>>>>>>> already
> >>>>>>>>>> been computed, this and all following windows
will change. 
> >>>>>>>>>> We
> >>>> could
> >>>>>>>> either
> >>>>>>>>>> drop the record or sent out many retraction
records. I 
> >>>>>>>>>> think
> >> it
> >>> is
> >>>>>>>>>> best
> >>>>>>>> to
> >>>>>>>>>> not open this can of worms at this point.
> >>>>>>>>>>
> >>>>>>>>>> The rational for all of the above restrictions
is to have
> >> first
> >>>>>>>>>> versions of OVER windows soon.
> >>>>>>>>>> Once we have the above cases covered we can
extend and 
> >>>>>>>>>> remove
> >>>>>>>> limitations
> >>>>>>>>>> as follows:
> >>>>>>>>>>
> >>>>>>>>>> - Table API SlideRow windows (with the same
restrictions as
> >>>> above).
> >>>>>>>>>> This will be mostly API work since the execution
part has 
> >>>>>>>>>> been
> >>>>> solved
> >>>>>> before.
> >>>>>>>>>> - Add support for FOLLOWING (except UNBOUNDED
FOLLOWING)
> >>>>>>>>>> - Add support for different windows in SELECT.
All windows
> >> must
> >>> be
> >>>>>>>>>> partitioned and ordered in the same way.
> >>>>>>>>>> - Add support for additional ORDER BY attributes
(besides
> >> time).
> >>>>>>>>>>
> >>>>>>>>>> As I said before, TumbleRows and SessionRows
windows as in
> >>> FLIP-11
> >>>>>>>>>> are
> >>>>>>>> not
> >>>>>>>>>> well defined, IMO.
> >>>>>>>>>> They can be expressed as SlideRows windows with
special 
> >>>>>>>>>> partitioning (partitioning on fixed, non-overlapping
time
> >> ranges
> >>>>>>>>>> for TumbleRows, and gap-separated, non-overlapping
time 
> >>>>>>>>>> ranges
> >>> for
> >>>>>>>>>> SessionRows) I would not start to work on those
yet.
> >>>>>>>>>>
> >>>>>>>>>> I would like to close all related JIRA issues
(FLINK-4678, 
> >>>>>>>>>> FLINK-4679, FLINK-4680, FLINK-5584) and restructure
the
> >>>> development
> >>>>>>>>>> of these
> >>>>>>>> features
> >>>>>>>>>> as outlined above with corresponding JIRA issues.
> >>>>>>>>>>
> >>>>>>>>>> What do others think? (I cc'ed the contributors
assigned to
> >> the
> >>>>>>>>>> above
> >>>>>>>> JIRA
> >>>>>>>>>> issues)
> >>>>>>>>>>
> >>>>>>>>>> Best, Fabian
> >>>>>>>>>>
> >>>>>>>>>> [1]
> >>>>>>>>>>
> >>>>>>>>>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-
> >>>>>>>> 11%3A+Table+API+Stream+Aggregations
> >>>>>>>>>>
> >>>>>>>>>
> >>>>>>>>
> >>>>>>
> >>>>>>
> >>>>>
> >>>>
> >>>
> >>
>
>
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