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From shijinkui <shijin...@huawei.com>
Subject 答复: [DISCUSS] Development of SQL OVER / Table API Row Windows for streaming tables
Date Mon, 06 Feb 2017 02:24:08 GMT
hi,Fabian, sunjincheng

Today is the first workday of 2017 in China. When we come back, I found the SQL issues had
been assigned between New Year... 
Yuhong Hong is interest in FLINK-5657. She had implemented it before. Can we reconsider to
assign FLINK-5657 to her?

Thanks 
Jinkui Shi

[1] https://issues.apache.org/jira/browse/FLINK-4557

-----邮件原件-----
发件人: Fabian Hueske [mailto:fhueske@gmail.com] 
发送时间: 2017年1月25日 17:55
收件人: dev@flink.apache.org
主题: Re: [DISCUSS] Development of SQL OVER / Table API Row Windows for streaming tables

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,
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> > > > please notify the
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> > > > 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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