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*
>>
>>
>> -----------------------------------------------------------------------------------
>>
