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From "Jark Wu" <wuchong...@alibaba-inc.com>
Subject Re: [DISCUSS] Development of SQL OVER / Table API Row Windows for streaming tables
Date Thu, 26 Jan 2017 06:05:01 GMT
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
>>>>>> 
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>>>>>> Hansaallee 205, 40549 Düsseldorf, Germany, www.huawei.com
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>>>>>> 
>>>>>> 
>>>>>> -----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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