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From Shaoxuan Wang <wshaox...@gmail.com>
Subject Re: [DISCUSS] Development of SQL OVER / Table API Row Windows for streaming tables
Date Mon, 06 Feb 2017 15:04:32 GMT
 Sorry for the late response.

Hi Jark,
Thanks for raising a good question - my proposal “may not work for
non-incremental aggregation (e.g. max, min, and median)”, but I have
some different opinions.
Yes, I have proposed a concept of “accumulate on getValue” in my UDAGG
proposal https://goo.gl/6ntclB. But after giving this some thorough
thought, I think this concept is unfortunately not necessary. All
aggregates should be suitable for incremental aggregate (even for max, min
and median). One can choose to accumulate all records at the same time when
the window is completed. But it will still execute the accumulate method to
update the accumulator state for each record. The way it executes
accumulate function to accumulate each record already implies that
the aggregation is incremental. Whether it is accumulated once at each
record arrival (incremental) or accumulated all records when the window is
completed (non-incremental), really does not matter in terms of the
correctness and the complexity. On the other hand, the non-incremental
approach will introduce CPU jitter and latency overhead, so I would like to
propose to always apply incremental mode for all streaming aggregations.

Fabian,
Yes, my proposal will not work for the aggregate if it does not have
retract function. But I believe we can always implement the retract
functions (as it is just the opposite operation to accumulate, i.e. the
retract will exist if an accumulate exists) for all aggregates. If this
makes sense to all of you, I would like to propose that UDAGG should be
forced to provision the retract function.

What do you think? I have updated my UDAGG proposal and UDAGG Jira, we can
move the discussion there if you think that is more appropriate.

Regards,
Shaoxuan


On Thu, Jan 26, 2017 at 10:14 PM, Fabian Hueske <fhueske@gmail.com> wrote:

> 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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