Thanks Fabian,
Thanks a lot for your feedback, and very important and necessary design
reminders!
Yes, your are right! Spark is the specified grouping columns displayed
before 1.3, but the grouping columns are implicitly passed in spark1.4 and
later. The reason for changing this behavior is that due to the user
feedback. Although implicit delivery will have the drawbacks you mentioned,
this approach is really convenient for the user.
I agree that grouping on windows we have to pay attention to the handling
of the window's properties, because we may introduce new window property.
So, from the points of view, We delay the processing of the window
property, ie: we pass the complex type 'w on the tableAPI, and provide
different property method operations in the SELECT according to the type of
'w, such as: 'w.start, 'w.end, 'w.xxx , in the TableAPI will limit and
verify the attribute operations that 'w has. An example is as follows:
tab.window(Tumble ... as 'w)
.groupBy('w, 'k1, 'k2) // 'w should be a group key.
.flatAgg(tableAgg('a)).as('w, 'k1, 'k2, 'col1, 'col2) // 'w is
composite field
.select('k1, 'col1, 'w.rowtime as 'ts, 'w.xxx as 'xx) // In select we
will limit and verify that ’w.xx is allowed
I am not sure if I fully understand your concerns, if there any understand
are mistakes, please correct me. Any feedback is appreciate！
Bests,
Jincheng
Fabian Hueske <fhue...@gmail.com> 于2018年11月22日周四 下午10:13写道：
> Hi all,
>
> First of all, it is correct that the flatMap(Expression*) and
> flatAggregate(Expression*) methods would mix scalar and table values.
> This would be a new concept that is not present in the current API.
> From my point of view, the semantics are quite clear, but I understand that
> others are more careful and worry about future extensions.
>
> I am fine with going for single expression arguments for map() and
> flatMap(). We can later expand them to Expression* if we feel the need and
> are more comfortable about the implications.
> Whenever, a time attribute needs to be forwarded, users can fall back to
> join(TableFunction) as Xiaowei mentioned.
> So we restrict the usability of the new methods but don't lose
> functionality and don't prevent future extensions.
>
> The aggregate() and flatAggregate() case is more difficult because implicit
> forwarding of grouping fields cannot be changed later without breaking the
> API.
> There are other APIs (e.g., Spark) that also implicitly forward the
> grouping columns. So this is not uncommon.
> However, I personally don't like that approach, because it is implicit and
> introduces a new behavior that is not present in the current API.
>
> One thing to consider here is the handling of grouping on windows.
> If I understood Xiaowei correctly, a composite field that is named like the
> window alias (e.g., 'w) would be implicitly added to the result of
> aggregate() or flatAggregate().
> The composite field would have fields like (start, end, rowtime) or (start,
> end, proctime) depending on the window type.
> If we would ever introduce a fourth window property, we might break
> existing queries.
> Is this something that we should worry about?
>
> Best,
> Fabian
>
> Am Do., 22. Nov. 2018 um 14:03 Uhr schrieb Piotr Nowojski <
> pi...@data-artisans.com>:
>
> > Hi Jincheng,
> >
> > #1) ok, got it.
> >
> > #3)
> > > From points of my view I we can using
> > > `Expression`, and after the discussion decided to use Expression*, then
> > > improve it. In any case, we can use Expression, and there is an
> > opportunity
> > > to become Expression* (compatibility). If we use Expression* directly,
> it
> > > is difficult for us to become Expression, which will break the
> > > compatibility between versions. What do you think?
> >
> > I don’t think that’s the case here. If we start with single param
> > `flatMap(Expression)`, it will need implicit columns to be present in the
> > result, which:
> >
> > a) IMO it brakes SQL convention (that’s why I’m against this)
> > b) we can not later easily introduce `flatMap(Expression*)` without those
> > implicit columns, without braking the compatibility or at least without
> > making `flatMap(Expression*)` and `flatMap(Expression)` terribly
> > inconsistent.
> >
> > To elaborate on (a). It’s not nice if our own API is inconsistent and it
> > sometimes behaves one way and sometimes another way:
> >
> > table.groupBy(‘k).select(scalarAggregateFunction(‘v)) => single column
> > result, just the output of `scalarAggregateFunction`
> > vs
> > table.groupBy(‘k).flatAggregate(tableAggregateFunction(‘v)) => both
> result
> > of `tableAggregateFunction` plus key (and an optional window context ?)
> >
> > Thus I think we have to now decide which way we want to jump, since later
> > will be too late. Or again, am I missing something? :)
> >
> > Piotrek
> >
> > > On 22 Nov 2018, at 02:07, jincheng sun <sunjincheng...@gmail.com>
> wrote:
> > >
> > > Hi Piotrek，
> > > #1）We have unbounded and bounded group window aggregate, for unbounded
> > case
> > > we should early fire the result with retract message, we can not using
> > > watermark, because unbounded aggregate never finished. (for improvement
> > we
> > > can introduce micro-batch in feature), for bounded window we never
> > support
> > > early fire, so we do not need retract.
> > > #3) About validation of `table.select(F(‘a).unnest(), ‘b,
> > > G(‘c).unnest())/table.flatMap(F(‘a), ‘b, scalarG(‘c))` Fabian had
> > mentioned
> > > above, please look at the prior mail. For `table.flatMap(F(‘a), ‘b,
> > > scalarG(‘c))` that we concerned, i.e.: we should discuss the issue of
> > > `Expression*` vs `Expression`. From points of my view I we can using
> > > `Expression`, and after the discussion decided to use Expression*, then
> > > improve it. In any case, we can use Expression, and there is an
> > opportunity
> > > to become Expression* (compatibility). If we use Expression* directly,
> it
> > > is difficult for us to become Expression, which will break the
> > > compatibility between versions. What do you think?
> > >
> > > If there anything not clearly, welcome any feedback！Agains，thanks for
> > share
> > > your thoughts！
> > >
> > > Thanks，
> > > Jincheng
> > >
> > > Piotr Nowojski <pi...@data-artisans.com> 于2018年11月21日周三 下午9:37写道：
> > >
> > >> Hi Jincheng,
> > >>
> > >>> #1） No，watermark solves the issue of the late event. Here, the
> > >> performance
> > >>> problem is caused by the update emit mode. i.e.: When current
> > calculation
> > >>> result is output, the previous calculation result needs to be
> > retracted.
> > >>
> > >> Hmm, yes I missed this. For time-windowed cases (some
> > >> aggregate/flatAggregate cases) emitting only on watermark should solve
> > the
> > >> problem. For non time windowed cases it would reduce the amount of
> > >> retractions, right? Or am I still missing something?
> > >>
> > >>> #3）I still hope to keep the simplicity that select only support
> > projected
> > >>> scalar, we can hardly tell the semantics of tab.select(flatmap('a),
> 'b,
> > >>> flatmap('d)).
> > >>
> > >> table.select(F(‘a).unnest(), ‘b, G(‘c).unnest())
> > >>
> > >> Could be rejected during some validation phase. On the other hand:
> > >>
> > >> table.select(F(‘a).unnest(), ‘b, scalarG(‘c))
> > >> or
> > >> table.flatMap(F(‘a), ‘b, scalarG(‘c))
> > >>
> > >> Could work and be more or less a syntax sugar for cross apply.
> > >>
> > >> Piotrek
> > >>
> > >>> On 21 Nov 2018, at 12:16, jincheng sun <sunjincheng...@gmail.com>
> > wrote:
> > >>>
> > >>> Hi shaoxuan & Hequn,
> > >>>
> > >>> Thanks for your suggestion，I'll file the JIRAs later.
> > >>> We can prepare PRs while continuing to move forward the ongoing
> > >> discussion.
> > >>>
> > >>> Regards，
> > >>> Jincheng
> > >>>
> > >>> jincheng sun <sunjincheng...@gmail.com> 于2018年11月21日周三 下午7:07写道：
> > >>>
> > >>>> Hi Piotrek,
> > >>>> Thanks for your feedback, and thanks for share your thoughts!
> > >>>>
> > >>>> #1） No，watermark solves the issue of the late event. Here, the
> > >> performance
> > >>>> problem is caused by the update emit mode. i.e.: When current
> > >> calculation
> > >>>> result is output, the previous calculation result needs to be
> > retracted.
> > >>>> #2） As I mentioned above we should continue the discussion until we
> > >> solve
> > >>>> the problems raised by Xiaowei and Fabian.
> > >>>> #3）I still hope to keep the simplicity that select only support
> > >> projected
> > >>>> scalar, we can hardly tell the semantics of tab.select(flatmap('a),
> > 'b,
> > >>>> flatmap('d)).
> > >>>>
> > >>>> Thanks,
> > >>>> Jincheng
> > >>>>
> > >>>> Piotr Nowojski <pi...@data-artisans.com> 于2018年11月21日周三 下午5:24写道：
> > >>>>
> > >>>>> Hi,
> > >>>>>
> > >>>>> 1.
> > >>>>>
> > >>>>>> In fact, in addition to the design of APIs, there will be various
> > >>>>>> performance optimization details, such as: table Aggregate
> function
> > >>>>>> emitValue will generate multiple calculation results, in extreme
> > >> cases,
> > >>>>>> each record will trigger a large number of retract messages, this
> > will
> > >>>>> have
> > >>>>>> poor performance
> > >>>>>
> > >>>>> Can this be solved/mitigated by emitting the results only on
> > >> watermarks?
> > >>>>> I think that was the path that we decided to take both for Temporal
> > >> Joins
> > >>>>> and upsert stream conversion. I know that this increases the
> latency
> > >> and
> > >>>>> there is a place for a future global setting/user preference “emit
> > the
> > >> data
> > >>>>> ASAP mode”, but emitting only on watermarks seems to me as a
> > >> better/more
> > >>>>> sane default.
> > >>>>>
> > >>>>> 2.
> > >>>>>
> > >>>>> With respect to the API discussion and implicit columns. The
> problem
> > >> for
> > >>>>> me so far is I’m not sure if I like the additionally complexity of
> > >>>>> `append()` solution, while implicit columns are definitely not in
> the
> > >>>>> spirit of SQL. Neither joins nor aggregations add extra unexpected
> > >> columns
> > >>>>> to the result without asking. This definitely can be confusing for
> > the
> > >>>>> users since it brakes the convention. Thus I would lean towards
> > >> Fabian’s
> > >>>>> proposal of multi-argument `map(Expression*)` from those 3 options.
> > >>>>>
> > >>>>> 3.
> > >>>>>
> > >>>>> Another topic is that I’m not 100% convinced that we should be
> adding
> > >> new
> > >>>>> api functions for `map`,`aggregate`,`flatMap` and `flatAggregate`.
> I
> > >> think
> > >>>>> the same could be achieved by changing
> > >>>>>
> > >>>>> table.map(F('x))
> > >>>>>
> > >>>>> into
> > >>>>>
> > >>>>> table.select(F('x)).unnest()
> > >>>>> or
> > >>>>> table.select(F('x).unnest())
> > >>>>>
> > >>>>> Where `unnest()` means unnest row/tuple type into a columnar table.
> > >>>>>
> > >>>>> table.flatMap(F('x))
> > >>>>>
> > >>>>> Could be on the other hand also handled by
> > >>>>>
> > >>>>> table.select(F('x))
> > >>>>>
> > >>>>> By correctly deducing that F(x) is a multi row output function
> > >>>>>
> > >>>>> Same might apply to `aggregate(F('x))`, but this maybe could be
> > >> replaced
> > >>>>> by:
> > >>>>>
> > >>>>> table.groupBy(…).select(F('x).unnest())
> > >>>>>
> > >>>>> Adding scalar functions should also be possible:
> > >>>>>
> > >>>>> table.groupBy('k).select(F('x).unnest(), ‘k)
> > >>>>>
> > >>>>> Maybe such approach would allow us to implement the same features
> in
> > >> the
> > >>>>> SQL as well?
> > >>>>>
> > >>>>> Piotrek
> > >>>>>
> > >>>>>> On 21 Nov 2018, at 09:43, Hequn Cheng <chenghe...@gmail.com>
> wrote:
> > >>>>>>
> > >>>>>> Hi,
> > >>>>>>
> > >>>>>> Thank you all for the great proposal and discussion!
> > >>>>>> I also prefer to move on to the next step, so +1 for opening the
> > JIRAs
> > >>>>> to
> > >>>>>> start the work.
> > >>>>>> We can have more detailed discussion there. Btw, we can start with
> > >> JIRAs
> > >>>>>> which we have agreed on.
> > >>>>>>
> > >>>>>> Best,
> > >>>>>> Hequn
> > >>>>>>
> > >>>>>> On Tue, Nov 20, 2018 at 11:38 PM Shaoxuan Wang <
> wshaox...@gmail.com
> > >
> > >>>>> wrote:
> > >>>>>>
> > >>>>>>> +1. I agree that we should open the JIRAs to start the work. We
> may
> > >>>>>>> have better ideas on the flavor of the interface when
> > >> implement/review
> > >>>>>>> the code.
> > >>>>>>>
> > >>>>>>> Regards,
> > >>>>>>> shaoxuan
> > >>>>>>>
> > >>>>>>>
> > >>>>>>> On 11/20/18, jincheng sun <sunjincheng...@gmail.com> wrote:
> > >>>>>>>> Hi all,
> > >>>>>>>>
> > >>>>>>>> Thanks all for the feedback.
> > >>>>>>>>
> > >>>>>>>> @Piotr About not using abbreviations naming, +1，I like
> > >>>>>>>> your proposal！Currently both DataSet and DataStream API are
> using
> > >>>>>>>> `aggregate`,
> > >>>>>>>> BTW，I find other language also not using abbreviations
> naming，such
> > >> as
> > >>>>> R.
> > >>>>>>>>
> > >>>>>>>> Sometimes the interface of the API is really difficult to
> perfect,
> > >> we
> > >>>>>>> need
> > >>>>>>>> to spend a lot of time thinking and feedback from a large number
> > of
> > >>>>>>> users,
> > >>>>>>>> and constantly improve, but for backward compatibility issues,
> we
> > >>>>> have to
> > >>>>>>>> adopt the most conservative approach when designing the API(Of
> > >>>>> course, I
> > >>>>>>> am
> > >>>>>>>> more in favor of developing more rich features, when we discuss
> > >>>>> clearly).
> > >>>>>>>> Therefore, I propose to divide the function implementation of
> > >>>>>>>> map/faltMap/agg/flatAgg into basic functions of JIRAs and JIRAs
> > that
> > >>>>>>>> support time attributes and groupKeys. We can develop the
> features
> > >>>>> which
> > >>>>>>>> we have already agreed on the design. And we will continue to
> > >> discuss
> > >>>>>>> the
> > >>>>>>>> uncertain design.
> > >>>>>>>>
> > >>>>>>>> In fact, in addition to the design of APIs, there will be
> various
> > >>>>>>>> performance optimization details, such as: table Aggregate
> > function
> > >>>>>>>> emitValue will generate multiple calculation results, in extreme
> > >>>>> cases,
> > >>>>>>>> each record will trigger a large number of retract messages,
> this
> > >> will
> > >>>>>>> have
> > >>>>>>>> poor performance，so we will also optimize the interface design,
> > such
> > >>>>> as
> > >>>>>>>> adding the emitWithRetractValue interface (I have updated the
> > google
> > >>>>> doc)
> > >>>>>>>> to allow the user to optionally perform incremental
> calculations,
> > >> thus
> > >>>>>>>> avoiding a large number of retracts. Details like this are
> > difficult
> > >>>>> to
> > >>>>>>>> fully discuss in the mail list, so I recommend creating
> JIRAs/FLIP
> > >>>>> first,
> > >>>>>>>> we develop designs that have been agreed upon and continue to
> > >> discuss
> > >>>>>>>> non-deterministic designs! What do you think? @Fabian & Piotr &
> > >>>>> XiaoWei
> > >>>>>>>>
> > >>>>>>>> Best,
> > >>>>>>>> Jincheng
> > >>>>>>>>
> > >>>>>>>> Xiaowei Jiang <xiaow...@gmail.com> 于2018年11月19日周一 上午12:07写道：
> > >>>>>>>>
> > >>>>>>>>> Hi Fabian & Piotr, thanks for the feedback!
> > >>>>>>>>>
> > >>>>>>>>> I appreciate your concerns, both on timestamp attributes as
> well
> > as
> > >>>>> on
> > >>>>>>>>> implicit group keys. At the same time, I'm also concerned with
> > the
> > >>>>>>>>> proposed
> > >>>>>>>>> approach of allowing Expression* as parameters, especially for
> > >>>>>>>>> flatMap/flatAgg. So far, we never allowed a scalar expression
> to
> > >>>>> appear
> > >>>>>>>>> together with table expressions. With the Expression* approach,
> > >> this
> > >>>>>>> will
> > >>>>>>>>> happen for the parameters to flatMap/flatAgg. I'm a bit
> concerned
> > >> on
> > >>>>> if
> > >>>>>>>>> we
> > >>>>>>>>> fully understand the consequences when we try to extend our
> > system
> > >> in
> > >>>>>>> the
> > >>>>>>>>> future. I would be extra cautious in doing this. To avoid
> this, I
> > >>>>> think
> > >>>>>>>>> an
> > >>>>>>>>> implicit group key for flatAgg is safer. For flatMap, if users
> > want
> > >>>>> to
> > >>>>>>>>> keep
> > >>>>>>>>> the rowtime column, he can use crossApply/join instead. So we
> are
> > >> not
> > >>>>>>>>> losing any real functionality here.
> > >>>>>>>>>
> > >>>>>>>>> Also a clarification on the following example:
> > >>>>>>>>> tab.window(Tumble ... as 'w)
> > >>>>>>>>> .groupBy('w, 'k1, 'k2) // 'w should be a group key.
> > >>>>>>>>> .flatAgg(tableAgg('a)).as('w, 'k1, 'k2, 'col1, 'col2)
> > >>>>>>>>> .select('k1, 'col1, 'w.rowtime as 'rtime)
> > >>>>>>>>> If we did not have the select clause in this example, we will
> > have
> > >>>>> 'w as
> > >>>>>>>>> a
> > >>>>>>>>> regular column in the output. It should not magically
> disappear.
> > >>>>>>>>>
> > >>>>>>>>> The concern is not as strong for Table.map/Table.agg because we
> > are
> > >>>>> not
> > >>>>>>>>> mixing scalar and table expressions. But we also want to be a
> bit
> > >>>>>>>>> consistent with these methods. If we used implicit group keys
> for
> > >>>>>>>>> Table.flatAgg, we probably should do the same for Table.agg.
> Now
> > we
> > >>>>> only
> > >>>>>>>>> have to choose what to do with Table.map. I can see good
> > arguments
> > >>>>> from
> > >>>>>>>>> both sides. But starting with a single Expression seems safer
> > >> because
> > >>>>>>>>> that
> > >>>>>>>>> we can always extend to Expression* in the future.
> > >>>>>>>>>
> > >>>>>>>>> While thinking about this problem, it appears that we may need
> > more
> > >>>>> work
> > >>>>>>>>> in
> > >>>>>>>>> our handling of watermarks for SQL/Table API. Our current way
> of
> > >>>>>>>>> propagating the watermarks from source all the way to sink
> might
> > >> not
> > >>>>> be
> > >>>>>>>>> optimal. For example, after a tumbling window, the watermark
> can
> > >>>>>>> actually
> > >>>>>>>>> be advanced to just before the expiring of next window. I think
> > >> that
> > >>>>> in
> > >>>>>>>>> general, each operator may need to generate new watermarks
> > instead
> > >> of
> > >>>>>>>>> simply propagating them. Once we accept that watermarks may
> > change
> > >>>>>>> during
> > >>>>>>>>> the execution, it appears that the timestamp columns may also
> > >>>>> change, as
> > >>>>>>>>> long as we have some way to associate watermark with it. My
> > >>>>> intuition is
> > >>>>>>>>> that once we have a through solution for the watermark issue,
> we
> > >> may
> > >>>>> be
> > >>>>>>>>> able to solve the problem we encountered for Table.map in a
> > cleaner
> > >>>>> way.
> > >>>>>>>>> But this is a complex issue which deserves a discussion on its
> > own.
> > >>>>>>>>>
> > >>>>>>>>> Regards,
> > >>>>>>>>> Xiaowei
> > >>>>>>>>>
> > >>>>>>>>>
> > >>>>>>>>> On Fri, Nov 16, 2018 at 12:34 AM Piotr Nowojski <
> > >>>>>>> pi...@data-artisans.com>
> > >>>>>>>>> wrote:
> > >>>>>>>>>
> > >>>>>>>>>> Hi,
> > >>>>>>>>>>
> > >>>>>>>>>> Isn’t the problem of multiple expressions limited only to
> > >> `flat***`
> > >>>>>>>>>> functions and to be more specific only to having two (or more)
> > >>>>>>>>>> different
> > >>>>>>>>>> table functions passed as an expressions?
> > `.flatAgg(TableAggA('a),
> > >>>>>>>>>> scalarFunction1(‘b), scalarFunction2(‘c))` seems to be well
> > >> defined
> > >>>>>>>>>> (duplicate result of every scalar function to every record. Or
> > am
> > >> I
> > >>>>>>>>> missing
> > >>>>>>>>>> something?
> > >>>>>>>>>>
> > >>>>>>>>>> Another remark, I would be in favour of not using
> abbreviations
> > >> and
> > >>>>>>>>> naming
> > >>>>>>>>>> `agg` -> `aggregate`, `flatAgg` -> `flatAggregate`.
> > >>>>>>>>>>
> > >>>>>>>>>> Piotrek
> > >>>>>>>>>>
> > >>>>>>>>>>> On 15 Nov 2018, at 14:15, Fabian Hueske <fhue...@gmail.com>
> > >> wrote:
> > >>>>>>>>>>>
> > >>>>>>>>>>> Hi Jincheng,
> > >>>>>>>>>>>
> > >>>>>>>>>>> I said before, that I think that the append() method is
> better
> > >> than
> > >>>>>>>>>>> implicitly forwarding keys, but still, I believe it adds
> > >>>>> unnecessary
> > >>>>>>>>>> boiler
> > >>>>>>>>>>> plate code.
> > >>>>>>>>>>>
> > >>>>>>>>>>> Moreover, I haven't seen a convincing argument why
> > >> map(Expression*)
> > >>>>>>>>>>> is
> > >>>>>>>>>>> worse than map(Expression). In either case we need to do all
> > >> kinds
> > >>>>>>> of
> > >>>>>>>>>>> checks to prevent invalid use of functions.
> > >>>>>>>>>>> If the method is not correctly used, we can emit a good error
> > >>>>>>> message
> > >>>>>>>>> and
> > >>>>>>>>>>> documenting map(Expression*) will be easier than
> > >>>>>>>>>> map(append(Expression*)),
> > >>>>>>>>>>> in my opinion.
> > >>>>>>>>>>> I think we should not add unnessary syntax unless there is a
> > good
> > >>>>>>>>> reason
> > >>>>>>>>>>> and to be honest, I haven't seen this reason yet.
> > >>>>>>>>>>>
> > >>>>>>>>>>> Regarding the groupBy.agg() method, I think it should behave
> > just
> > >>>>>>>>>>> like
> > >>>>>>>>>> any
> > >>>>>>>>>>> other method, i.e., not do any implicit forwarding.
> > >>>>>>>>>>> Let's take the example of the windowed group by, that you
> > posted
> > >>>>>>>>> before.
> > >>>>>>>>>>>
> > >>>>>>>>>>> tab.window(Tumble ... as 'w)
> > >>>>>>>>>>> .groupBy('w, 'k1, 'k2) // 'w should be a group key.
> > >>>>>>>>>>> .agg(agg('a)).as('w, 'k1, 'k2, 'col1, 'col2)
> > >>>>>>>>>>> .select('k1, 'col1, 'w.rowtime as 'rtime)
> > >>>>>>>>>>>
> > >>>>>>>>>>> What happens if 'w.rowtime is not selected? What is the data
> > type
> > >>>>> of
> > >>>>>>>>> the
> > >>>>>>>>>>> field 'w in the resulting Table? Is it a regular field at all
> > or
> > >>>>>>> just
> > >>>>>>>>>>> a
> > >>>>>>>>>>> system field that disappears if it is not selected?
> > >>>>>>>>>>>
> > >>>>>>>>>>> IMO, the following syntax is shorter, more explicit, and
> better
> > >>>>>>>>>>> aligned
> > >>>>>>>>>>> with the regular window.groupBy.select aggregations that are
> > >>>>>>>>>>> supported
> > >>>>>>>>>>> today.
> > >>>>>>>>>>>
> > >>>>>>>>>>> tab.window(Tumble ... as 'w)
> > >>>>>>>>>>> .groupBy('w, 'k1, 'k2) // 'w should be a group key.
> > >>>>>>>>>>> .agg('w.rowtime as 'rtime, 'k1, 'k2, agg('a))
> > >>>>>>>>>>>
> > >>>>>>>>>>>
> > >>>>>>>>>>> Best, Fabian
> > >>>>>>>>>>>
> > >>>>>>>>>>> Am Mi., 14. Nov. 2018 um 08:37 Uhr schrieb jincheng sun <
> > >>>>>>>>>>> sunjincheng...@gmail.com>:
> > >>>>>>>>>>>
> > >>>>>>>>>>>> Hi Fabian/Xiaowei,
> > >>>>>>>>>>>>
> > >>>>>>>>>>>> I am very sorry for my late reply! Glad to see your reply,
> and
> > >>>>>>>>>>>> sounds
> > >>>>>>>>>>>> pretty good！
> > >>>>>>>>>>>> I agree that the approach with append() which can clearly
> > >> defined
> > >>>>>>>>>>>> the
> > >>>>>>>>>>>> result schema is better which Fabian mentioned.
> > >>>>>>>>>>>> In addition and append() and also contains non-time
> > attributes,
> > >>>>>>>>>>>> e.g.:
> > >>>>>>>>>>>>
> > >>>>>>>>>>>> tab('name, 'age, 'address, 'rowtime)
> > >>>>>>>>>>>> tab.map(append(udf('name), 'address, 'rowtime).as('col1,
> > 'col2,
> > >>>>>>>>>>>> 'address, 'rowtime)
> > >>>>>>>>>>>> .window(Tumble over 5.millis on 'rowtime as 'w)
> > >>>>>>>>>>>> .groupBy('w, 'address)
> > >>>>>>>>>>>>
> > >>>>>>>>>>>> In this way the append() is very useful, and the behavior is
> > >> very
> > >>>>>>>>>> similar
> > >>>>>>>>>>>> to withForwardedFields() in DataSet.
> > >>>>>>>>>>>> So +1 to using append() approach for the map()&flatmap()!
> > >>>>>>>>>>>>
> > >>>>>>>>>>>> But how about the agg() and flatAgg()? In agg/flatAgg case I
> > >> agree
> > >>>>>>>>>>>> Xiaowei's approach that define the keys to be implied in the
> > >>>>> result
> > >>>>>>>>>> table
> > >>>>>>>>>>>> and appears at the beginning, for example as follows:
> > >>>>>>>>>>>> tab.window(Tumble ... as 'w)
> > >>>>>>>>>>>> .groupBy('w, 'k1, 'k2) // 'w should be a group key.
> > >>>>>>>>>>>> .agg(agg('a)).as('w, 'k1, 'k2, 'col1, 'col2)
> > >>>>>>>>>>>> .select('k1, 'col1, 'w.rowtime as 'rtime)
> > >>>>>>>>>>>>
> > >>>>>>>>>>>> What to you think? @Fabian @Xiaowei
> > >>>>>>>>>>>>
> > >>>>>>>>>>>> Thanks,
> > >>>>>>>>>>>> Jincheng
> > >>>>>>>>>>>>
> > >>>>>>>>>>>> Fabian Hueske <fhue...@gmail.com> 于2018年11月9日周五 下午6:35写道：
> > >>>>>>>>>>>>
> > >>>>>>>>>>>>> Hi Jincheng,
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>> Thanks for the summary!
> > >>>>>>>>>>>>> I like the approach with append() better than the implicit
> > >>>>>>>>>>>>> forwarding
> > >>>>>>>>>> as
> > >>>>>>>>>>>> it
> > >>>>>>>>>>>>> clearly indicates which fields are forwarded.
> > >>>>>>>>>>>>> However, I don't see much benefit over the
> > flatMap(Expression*)
> > >>>>>>>>>> variant,
> > >>>>>>>>>>>> as
> > >>>>>>>>>>>>> we would still need to analyze the full expression tree to
> > >> ensure
> > >>>>>>>>> that
> > >>>>>>>>>> at
> > >>>>>>>>>>>>> most (or exactly?) one Scalar / TableFunction is used.
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>> Best,
> > >>>>>>>>>>>>> Fabian
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>> Am Do., 8. Nov. 2018 um 19:25 Uhr schrieb jincheng sun <
> > >>>>>>>>>>>>> sunjincheng...@gmail.com>:
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>>> Hi all,
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>> We are discussing very detailed content about this
> proposal.
> > >> We
> > >>>>>>>>>>>>>> are
> > >>>>>>>>>>>>> trying
> > >>>>>>>>>>>>>> to design the API in many aspects (functionality,
> > >> compatibility,
> > >>>>>>>>> ease
> > >>>>>>>>>>>> of
> > >>>>>>>>>>>>>> use, etc.). I think this is a very good process. Only
> such a
> > >>>>>>>>> detailed
> > >>>>>>>>>>>>>> discussion, In order to develop PR more clearly and
> smoothly
> > >> in
> > >>>>>>>>>>>>>> the
> > >>>>>>>>>>>> later
> > >>>>>>>>>>>>>> stage. I am very grateful to @Fabian and @Xiaowei for
> > >> sharing a
> > >>>>>>>>>>>>>> lot
> > >>>>>>>>>> of
> > >>>>>>>>>>>>>> good ideas.
> > >>>>>>>>>>>>>> About the definition of method signatures I want to share
> my
> > >>>>>>>>>>>>>> points
> > >>>>>>>>>>>> here
> > >>>>>>>>>>>>>> which I am discussing with fabian in google doc (not yet
> > >>>>>>>>>>>>>> completed)，
> > >>>>>>>>>> as
> > >>>>>>>>>>>>>> follows:
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>> Assume we have a table:
> > >>>>>>>>>>>>>> val tab = util.addTable[(Long, String)]("MyTable", 'long,
> > >>>>>>> 'string,
> > >>>>>>>>>>>>>> 'proctime.proctime)
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>> Approach 1:
> > >>>>>>>>>>>>>> case1: Map follows Source Table
> > >>>>>>>>>>>>>> val result =
> > >>>>>>>>>>>>>> tab.map(udf('string)).as('proctime, 'col1, 'col2)//
> proctime
> > >>>>>>>>> implied
> > >>>>>>>>>>>> in
> > >>>>>>>>>>>>>> the output
> > >>>>>>>>>>>>>> .window(Tumble over 5.millis on 'proctime as 'w)
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>> case2: FatAgg follows Window (Fabian mentioned above)
> > >>>>>>>>>>>>>> val result =
> > >>>>>>>>>>>>>> tab.window(Tumble ... as 'w)
> > >>>>>>>>>>>>>> .groupBy('w, 'k1, 'k2) // 'w should be a group key.
> > >>>>>>>>>>>>>> .flatAgg(tabAgg('a)).as('k1, 'k2, 'w, 'col1, 'col2)
> > >>>>>>>>>>>>>> .select('k1, 'col1, 'w.rowtime as 'rtime)
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>> Approach 2: Similar to Fabian‘s approach, which the result
> > >>>>> schema
> > >>>>>>>>>> would
> > >>>>>>>>>>>>> be
> > >>>>>>>>>>>>>> clearly defined, but add a built-in append UDF. That make
> > >>>>>>>>>>>>>> map/flatmap/agg/flatAgg interface only accept one
> > Expression.
> > >>>>>>>>>>>>>> val result =
> > >>>>>>>>>>>>>> tab.map(append(udf('string), 'long, 'proctime)) as ('col1,
> > >>>>>>>>>>>>>> 'col2,
> > >>>>>>>>>>>>>> 'long, 'proctime)
> > >>>>>>>>>>>>>> .window(Tumble over 5.millis on 'proctime as 'w)
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>> Note: Append is a special UDF for built-in that can pass
> > >> through
> > >>>>>>>>>>>>>> any
> > >>>>>>>>>>>>>> column.
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>> So, May be we can defined the as table.map(Expression)
> > >> first,
> > >>>>>>> If
> > >>>>>>>>>>>>>> necessary, we can extend to table.map(Expression*) in the
> > >>>>> future
> > >>>>>>>>>>>>>> ?
> > >>>>>>>>>> Of
> > >>>>>>>>>>>>>> course, I also hope that we can do more perfection in this
> > >>>>>>>>>>>>>> proposal
> > >>>>>>>>>>>>> through
> > >>>>>>>>>>>>>> discussion.
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>> Thanks,
> > >>>>>>>>>>>>>> Jincheng
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>> Xiaowei Jiang <xiaow...@gmail.com> 于2018年11月7日周三
> 下午11:45写道：
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> Hi Fabian,
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> I think that the key question you raised is if we allow
> > extra
> > >>>>>>>>>>>>> parameters
> > >>>>>>>>>>>>>> in
> > >>>>>>>>>>>>>>> the methods map/flatMap/agg/flatAgg. I can see why
> allowing
> > >>>>> that
> > >>>>>>>>> may
> > >>>>>>>>>>>>>> appear
> > >>>>>>>>>>>>>>> more convenient in some cases. However, it might also
> cause
> > >>>>> some
> > >>>>>>>>>>>>>> confusions
> > >>>>>>>>>>>>>>> if we do that. For example, do we allow multiple UDFs in
> > >> these
> > >>>>>>>>>>>>>> expressions?
> > >>>>>>>>>>>>>>> If we do, the semantics may be weird to define, e.g. what
> > >> does
> > >>>>>>>>>>>>>>> table.groupBy('k).flatAgg(TableAggA('a), TableAggB('b))
> > mean?
> > >>>>>>>>>>>>>>> Even
> > >>>>>>>>>>>>> though
> > >>>>>>>>>>>>>>> not allowing it may appear less powerful, but it can make
> > >>>>> things
> > >>>>>>>>> more
> > >>>>>>>>>>>>>>> intuitive too. In the case of agg/flatAgg, we can define
> > the
> > >>>>>>> keys
> > >>>>>>>>> to
> > >>>>>>>>>>>> be
> > >>>>>>>>>>>>>>> implied in the result table and appears at the beginning.
> > You
> > >>>>>>> can
> > >>>>>>>>>>>> use a
> > >>>>>>>>>>>>>>> select method if you want to modify this behavior. I
> think
> > >> that
> > >>>>>>>>>>>>>> eventually
> > >>>>>>>>>>>>>>> we will have some API which allows other expressions as
> > >>>>>>>>>>>>>>> additional
> > >>>>>>>>>>>>>>> parameters, but I think it's better to do that after we
> > >>>>>>> introduce
> > >>>>>>>>> the
> > >>>>>>>>>>>>>>> concept of nested tables. A lot of things we suggested
> here
> > >> can
> > >>>>>>>>>>>>>>> be
> > >>>>>>>>>>>>>>> considered as special cases of that. But things are much
> > >>>>> simpler
> > >>>>>>>>>>>>>>> if
> > >>>>>>>>>>>> we
> > >>>>>>>>>>>>>>> leave that to later.
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> Regards,
> > >>>>>>>>>>>>>>> Xiaowei
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> On Wed, Nov 7, 2018 at 5:18 PM Fabian Hueske <
> > >>>>> fhue...@gmail.com
> > >>>>>>>>
> > >>>>>>>>>>>>> wrote:
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>> Hi,
> > >>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>> * Re emit:
> > >>>>>>>>>>>>>>>> I think we should start with a well understood semantics
> > of
> > >>>>>>> full
> > >>>>>>>>>>>>>>>> replacement. This is how the other agg functions work.
> > >>>>>>>>>>>>>>>> As was said before, there are open questions regarding
> an
> > >>>>>>> append
> > >>>>>>>>>>>> mode
> > >>>>>>>>>>>>>>>> (checkpointing, whether supporting retractions or not
> and
> > if
> > >>>>>>> yes
> > >>>>>>>>>>>> how
> > >>>>>>>>>>>>> to
> > >>>>>>>>>>>>>>>> declare them, ...).
> > >>>>>>>>>>>>>>>> Since this seems to be an optimization, I'd postpone it.
> > >>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>> * Re grouping keys:
> > >>>>>>>>>>>>>>>> I don't think we should automatically add them because
> the
> > >>>>>>>>>>>>>>>> result
> > >>>>>>>>>>>>>> schema
> > >>>>>>>>>>>>>>>> would not be intuitive.
> > >>>>>>>>>>>>>>>> Would they be added at the beginning of the tuple or at
> > the
> > >>>>>>> end?
> > >>>>>>>>>>>> What
> > >>>>>>>>>>>>>>>> metadata fields of windows would be added? In which
> order
> > >>>>> would
> > >>>>>>>>>>>> they
> > >>>>>>>>>>>>> be
> > >>>>>>>>>>>>>>>> added?
> > >>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>> However, we could support syntax like this:
> > >>>>>>>>>>>>>>>> val t: Table = ???
> > >>>>>>>>>>>>>>>> t
> > >>>>>>>>>>>>>>>> .window(Tumble ... as 'w)
> > >>>>>>>>>>>>>>>> .groupBy('a, 'b)
> > >>>>>>>>>>>>>>>> .flatAgg('b, 'a, myAgg(row('*)), 'w.end as 'wend,
> > 'w.rowtime
> > >>>>>>> as
> > >>>>>>>>>>>>>> 'rtime)
> > >>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>> The result schema would be clearly defined as [b, a, f1,
> > f2,
> > >>>>>>>>>>>>>>>> ...,
> > >>>>>>>>>>>> fn,
> > >>>>>>>>>>>>>>> wend,
> > >>>>>>>>>>>>>>>> rtime]. (f1, f2, ...fn) are the result attributes of the
> > >> UDF.
> > >>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>> * Re Multi-staged evaluation:
> > >>>>>>>>>>>>>>>> I think this should be an optimization that can be
> applied
> > >> if
> > >>>>>>>>>>>>>>>> the
> > >>>>>>>>>>>> UDF
> > >>>>>>>>>>>>>>>> implements the merge() method.
> > >>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>> Best, Fabian
> > >>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>> Am Mi., 7. Nov. 2018 um 08:01 Uhr schrieb Shaoxuan Wang
> <
> > >>>>>>>>>>>>>>>> wshaox...@gmail.com
> > >>>>>>>>>>>>>>>>> :
> > >>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>> Hi xiaowei,
> > >>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>> Yes, I agree with you that the semantics of
> > >>>>>>>>>>>> TableAggregateFunction
> > >>>>>>>>>>>>>> emit
> > >>>>>>>>>>>>>>>> is
> > >>>>>>>>>>>>>>>>> much more complex than AggregateFunction. The
> fundamental
> > >>>>>>>>>>>>> difference
> > >>>>>>>>>>>>>> is
> > >>>>>>>>>>>>>>>>> that TableAggregateFunction emits a "table" while
> > >>>>>>>>>>>> AggregateFunction
> > >>>>>>>>>>>>>>>> outputs
> > >>>>>>>>>>>>>>>>> (a column of) a "row". In the case of AggregateFunction
> > it
> > >>>>>>> only
> > >>>>>>>>>>>> has
> > >>>>>>>>>>>>>> one
> > >>>>>>>>>>>>>>>>> mode which is “replacing” (complete update). But for
> > >>>>>>>>>>>>>>>>> TableAggregateFunction, it could be incremental (only
> > emit
> > >>>>> the
> > >>>>>>>>>>>> new
> > >>>>>>>>>>>>>>>> updated
> > >>>>>>>>>>>>>>>>> results) update or complete update (always emit the
> > entire
> > >>>>>>>>>>>>>>>>> table
> > >>>>>>>>>>>>> when
> > >>>>>>>>>>>>>>>>> “emit" is triggered). From the performance
> perspective,
> > we
> > >>>>>>>>>>>>>>>>> might
> > >>>>>>>>>>>>>> want
> > >>>>>>>>>>>>>>> to
> > >>>>>>>>>>>>>>>>> use incremental update. But we need review and design
> > this
> > >>>>>>>>>>>>> carefully,
> > >>>>>>>>>>>>>>>>> especially taking into account the cases of the
> failover
> > >>>>>>>>>>>>>>>>> (instead
> > >>>>>>>>>>>>> of
> > >>>>>>>>>>>>>>> just
> > >>>>>>>>>>>>>>>>> back-up the ACC it may also needs to remember the emit
> > >>>>> offset)
> > >>>>>>>>>>>> and
> > >>>>>>>>>>>>>>>>> retractions, as the semantics of TableAggregateFunction
> > >> emit
> > >>>>>>>>>>>>>>>>> are
> > >>>>>>>>>>>>>>>> different
> > >>>>>>>>>>>>>>>>> than other UDFs. TableFunction also emits a table, but
> it
> > >>>>> does
> > >>>>>>>>>>>> not
> > >>>>>>>>>>>>>> need
> > >>>>>>>>>>>>>>>> to
> > >>>>>>>>>>>>>>>>> worry this due to the nature of stateless.
> > >>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>> Regards,
> > >>>>>>>>>>>>>>>>> Shaoxuan
> > >>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>> On Tue, Nov 6, 2018 at 7:16 PM Xiaowei Jiang
> > >>>>>>>>>>>>>>>>> <xiaow...@gmail.com
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> wrote:
> > >>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>> Hi Jincheng,
> > >>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>> Thanks for adding the public interfaces! I think that
> > >> it's a
> > >>>>>>>>>>>> very
> > >>>>>>>>>>>>>>> good
> > >>>>>>>>>>>>>>>>>> start. There are a few points that we need to have
> more
> > >>>>>>>>>>>>>> discussions.
> > >>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>> - TableAggregateFunction - this is a very complex
> beast,
> > >>>>>>>>>>>>>>> definitely
> > >>>>>>>>>>>>>>>>> the
> > >>>>>>>>>>>>>>>>>> most complex user defined objects we introduced so
> far.
> > I
> > >>>>>>>>>>>>> think
> > >>>>>>>>>>>>>>>> there
> > >>>>>>>>>>>>>>>>>> are
> > >>>>>>>>>>>>>>>>>> quite some interesting questions here. For example, do
> > we
> > >>>>>>>>>>>>> allow
> > >>>>>>>>>>>>>>>>>> multi-staged TableAggregate in this case? What is the
> > >>>>>>>>>>>>> semantics
> > >>>>>>>>>>>>>> of
> > >>>>>>>>>>>>>>>>>> emit? Is
> > >>>>>>>>>>>>>>>>>> it amendments to the previous output, or replacing
> it? I
> > >>>>>>>>>>>> think
> > >>>>>>>>>>>>>>> that
> > >>>>>>>>>>>>>>>>> this
> > >>>>>>>>>>>>>>>>>> subject itself is worth a discussion to make sure we
> get
> > >>>>>>> the
> > >>>>>>>>>>>>>>> details
> > >>>>>>>>>>>>>>>>>> right.
> > >>>>>>>>>>>>>>>>>> - GroupedTable.agg - does the group keys automatically
> > >>>>>>>>>>>> appear
> > >>>>>>>>>>>>> in
> > >>>>>>>>>>>>>>> the
> > >>>>>>>>>>>>>>>>>> output? how about the case of windowing aggregation?
> > >>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>> Regards,
> > >>>>>>>>>>>>>>>>>> Xiaowei
> > >>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>> On Tue, Nov 6, 2018 at 6:25 PM jincheng sun <
> > >>>>>>>>>>>>>>> sunjincheng...@gmail.com>
> > >>>>>>>>>>>>>>>>>> wrote:
> > >>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>> Hi, Xiaowei,
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>> Thanks for bring up the discuss of Table API
> > Enhancement
> > >>>>>>>>>>>>> Outline
> > >>>>>>>>>>>>>> !
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>> I quickly looked at the overall content, these are
> good
> > >>>>>>>>>>>>>> expressions
> > >>>>>>>>>>>>>>>> of
> > >>>>>>>>>>>>>>>>>> our
> > >>>>>>>>>>>>>>>>>>> offline discussions. But from the points of my view,
> we
> > >>>>>>>>>>>> should
> > >>>>>>>>>>>>>> add
> > >>>>>>>>>>>>>>>> the
> > >>>>>>>>>>>>>>>>>>> usage of public interfaces that we will introduce in
> > this
> > >>>>>>>>>>>>>> propose.
> > >>>>>>>>>>>>>>>>> So, I
> > >>>>>>>>>>>>>>>>>>> added the following usage description of interface
> and
> > >>>>>>>>>>>>> operators
> > >>>>>>>>>>>>>>> in
> > >>>>>>>>>>>>>>>>>>> google doc：
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>> 1. Map Operator
> > >>>>>>>>>>>>>>>>>>> Map operator is a new operator of Table, Map operator
> > >> can
> > >>>>>>>>>>>>>>> apply a
> > >>>>>>>>>>>>>>>>>>> scalar function, and can return multi-column. The
> usage
> > >> as
> > >>>>>>>>>>>>>> follows:
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>> val res = tab
> > >>>>>>>>>>>>>>>>>>> .map(fun: ScalarFunction).as(‘a, ‘b, ‘c)
> > >>>>>>>>>>>>>>>>>>> .select(‘a, ‘c)
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>> 2. FlatMap Operator
> > >>>>>>>>>>>>>>>>>>> FaltMap operator is a new operator of Table, FlatMap
> > >>>>>>>>>>>>> operator
> > >>>>>>>>>>>>>>> can
> > >>>>>>>>>>>>>>>>>> apply
> > >>>>>>>>>>>>>>>>>>> a table function, and can return multi-row. The usage
> > as
> > >>>>>>>>>>>>> follows:
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>> val res = tab
> > >>>>>>>>>>>>>>>>>>> .flatMap(fun: TableFunction).as(‘a, ‘b, ‘c)
> > >>>>>>>>>>>>>>>>>>> .select(‘a, ‘c)
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>> 3. Agg Operator
> > >>>>>>>>>>>>>>>>>>> Agg operator is a new operator of Table/GroupedTable,
> > >> Agg
> > >>>>>>>>>>>>>>>> operator
> > >>>>>>>>>>>>>>>>>> can
> > >>>>>>>>>>>>>>>>>>> apply a aggregate function, and can return
> > multi-column.
> > >>>>> The
> > >>>>>>>>>>>>>> usage
> > >>>>>>>>>>>>>>> as
> > >>>>>>>>>>>>>>>>>>> follows:
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>> val res = tab
> > >>>>>>>>>>>>>>>>>>> .groupBy(‘a) // leave groupBy-Clause out to define
> > >>>>>>>>>>>> global
> > >>>>>>>>>>>>>>>>>> aggregates
> > >>>>>>>>>>>>>>>>>>> .agg(fun: AggregateFunction).as(‘a, ‘b, ‘c)
> > >>>>>>>>>>>>>>>>>>> .select(‘a, ‘c)
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>> 4. FlatAgg Operator
> > >>>>>>>>>>>>>>>>>>> FlatAgg operator is a new operator of
> > >> Table/GroupedTable,
> > >>>>>>>>>>>>>>> FaltAgg
> > >>>>>>>>>>>>>>>>>>> operator can apply a table aggregate function, and
> can
> > >>>>>>> return
> > >>>>>>>>>>>>>>>>> multi-row.
> > >>>>>>>>>>>>>>>>>>> The usage as follows:
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>> val res = tab
> > >>>>>>>>>>>>>>>>>>> .groupBy(‘a) // leave groupBy-Clause out to define
> > >>>>>>>>>>>>> global
> > >>>>>>>>>>>>>>>> table
> > >>>>>>>>>>>>>>>>>>> aggregates
> > >>>>>>>>>>>>>>>>>>> .flatAgg(fun: TableAggregateFunction).as(‘a, ‘b,
> ‘c)
> > >>>>>>>>>>>>>>>>>>> .select(‘a, ‘c)
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>> 5. TableAggregateFunction
> > >>>>>>>>>>>>>>>>>>> The behavior of table aggregates is most like
> > >>>>>>>>>>>>>>>> GroupReduceFunction
> > >>>>>>>>>>>>>>>>>> did,
> > >>>>>>>>>>>>>>>>>>> which computed for a group of elements, and output a
> > >> group
> > >>>>>>>>>>>> of
> > >>>>>>>>>>>>>>>>> elements.
> > >>>>>>>>>>>>>>>>>>> The TableAggregateFunction can be applied on
> > >>>>>>>>>>>>>>> GroupedTable.flatAgg() .
> > >>>>>>>>>>>>>>>>> The
> > >>>>>>>>>>>>>>>>>>> interface of TableAggregateFunction has a lot of
> > content,
> > >>>>> so
> > >>>>>>>>>>>> I
> > >>>>>>>>>>>>>>> don't
> > >>>>>>>>>>>>>>>>> copy
> > >>>>>>>>>>>>>>>>>>> it here， Please look at the detail in google doc:
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>
> > >>>>>>>>>>
> > >>>>>>>>>
> > >>>>>>>
> > >>>>>
> > >>
> >
> https://docs.google.com/document/d/19rVeyqveGtV33UZt72GV-DP2rLyNlfs0QNGG0xWjayY/edit
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>> I will be very appreciate to anyone for reviewing and
> > >>>>>>>>>>>>> commenting.
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>> Best,
> > >>>>>>>>>>>>>>>>>>> Jincheng
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>
> > >>>>>>>>>>
> > >>>>>>>>>>
> > >>>>>>>>>
> > >>>>>>>>
> > >>>>>>>
> > >>>>>>>
> > >>>>>>> --
> > >>>>>>>
> > >>>>>>>
> > >>>>>
> > >>
> >
> -----------------------------------------------------------------------------------
> > >>>>>>>
> > >>>>>>> *Rome was not built in one day*
> > >>>>>>>
> > >>>>>>>
> > >>>>>>>
> > >>>>>
> > >>
> >
> -----------------------------------------------------------------------------------
> > >>>>>>>
> > >>>>>
> > >>>>>
> > >>
> > >>
> >
> >
> >
>
