Hi everyone, Timo just raised a good point in the vote thread. I copied the feedback here:
> Timo: 1) I think we should not offer 2 different kinds of syntax that do the same thing. We should deprecate the old syntax. 2) We should have session windows in the new syntax as well to give users a complete migration path. 3) We should investigate if we can remove the additional `FROM TABLE(...)` syntax. As far as I see it in other examples from Oracle 18c, the additional `TABLE()` syntax is not necessary anymore for polymorphic table functions. Here are my comments: 1) I'm not sure about this. If we are going to drop the old syntax, this will break lots of existing SQL jobs. Upgrading SQL jobs is not as easy as Table API jobs, We should be as cautious as possible for this. Besides, if we want to deprecate old syntax, we must provide equal functionality first, and the new syntax doesn't support propagate time attributes. A possible solution can be to generate one more column "window_time" for window TVFs. The value of "window_time" would always be "window_end - 1" and has the time attribute type. Users can propagate the time attribute by adding "window_time" to the "group by" and "join on" clauses with the "window_start", "window_end" together. 2) I will add session window syntax to the FLIP later. 3) I like the simplified syntax "FROM tumble(input, rowtime, interval '1' minute)". However, the polymorphic table function syntax introduced in SQL standard 2016 [1] requires the TABLE() and DESCRIPTOR() syntax (see Chapter-8 “Invocation”). Therefore, I think it's safe to support the standard syntax first, and can explore whether we can extend the syntax to make TABLE() and DESCRIPTOR() keywords optional. Note that, Calcite parser currently doesn't support the simplified syntax, and this definitely needs to be discussed in the Calcite community. Actually, there has been a discussion about this [2], and Julian said: > Standard SQL doesn’t allow functions in the FROM clause. I think it’s because tables and functions are in different namespaces (and therefore there could be a table and a function with the same name). So you need to use the TABLE keyword to indicate that you are using a function as a table. Best, Jark [1]: https://standards.iso.org/ittf/PubliclyAvailableStandards/c069776_ISO_IEC_TR_19075-7_2017.zip [2]: https://lists.apache.org/x/thread.html/4a91632b1c780ef9d67311f90fce626582faae7d30a134a768c3d324@%3Cdev.calcite.apache.org%3E On Sat, 10 Oct 2020 at 17:59, Jark Wu <imj...@gmail.com> wrote: > Hi everyone, > > Thanks everyone for this healthy discussion. I think we have addressed all > the concerns. I would continue with a voting. > If you have any new objections, feel free to let me know. > > Best, > Jark > > On Sat, 10 Oct 2020 at 17:54, Jark Wu <imj...@gmail.com> wrote: > >> Hi Jingsong, >> >> That's a good question. I did have searched a lot and didn't find any >> system that provides such an out-of-box function. >> I guess the reason is that in the traditional batch systems, this feature >> is supported by the over window and they don't need to invent a >> new function/syntax for this. >> For streaming systems, we are the first one to propose this new window. >> >> However, I think CUMULATE is a good name. Because almost all the >> databases call such scenarios as "cumulative window", e.g. Snowflake[1], >> SQL Server [2], Postgres [3]. >> Thus we choose "cumulative" as the base name, but use the verb form >> "cumulate" because other window function names are also verbs, e.g. tumble, >> hop. >> >> I hope this can address your concern. >> >> Best, >> Jark >> >> [1]: >> https://docs.snowflake.com/en/sql-reference/functions-analytic.html#cumulative-window-frame-examples >> [2]: >> https://docs.microsoft.com/en-us/sql/t-sql/queries/select-over-clause-transact-sql?view=sql-server-ver15#c-producing-a-moving-average-and-cumulative-total >> [3]: >> https://popsql.com/learn-sql/postgresql/how-to-calculate-cumulative-sum-running-total-in-postgresql >> >> On Sat, 10 Oct 2020 at 17:26, Jingsong Li <jingsongl...@gmail.com> wrote: >> >>> +1 for voting. Thanks Jark for driving. >>> >>> +1 for TVF, It has been put forward by theory and supported by calcite. >>> It >>> will greatly enhance the window related operations. >>> >>> My personal feeling is that after TVF, the following operations can be >>> similar to the traditional batch SQL, as long as the window related >>> attributes are included in the key. >>> >>> I am not sure about the CUMULATE window, yes, It's a common requirement, >>> Is >>> there any more evidence (other systems) to prove this word ("CUMULATE") >>> is >>> appropriate. >>> >>> Best, >>> Jingsong >>> >>> On Sat, Oct 10, 2020 at 3:43 PM Jark Wu <imj...@gmail.com> wrote: >>> >>> > Hi Pengcheng, >>> > >>> > IIUC, the "stream operators" you mean is the non-time operators or >>> called >>> > regular operators, such as regular join, regular aggregate. >>> > But you may misunderstand me, only the time operators can't be applied >>> > after the new window operators, because of missing time attributes. >>> > The regular operators can still be applied after the new window >>> operators. >>> > >>> > Regarding using window TVFs to re-assign event-time and watermarks, >>> I'm not >>> > sure about this. >>> > Because assigning watermark requires to define the watermark strategy, >>> > however, the window TVF doesn't provide such ability. >>> > Polymorphic table functions are table functions which just append >>> > additional columns and convert N rows into M rows, it can't touch meta >>> > information. >>> > >>> > Best, >>> > Jark >>> > >>> > On Sat, 10 Oct 2020 at 15:41, Jark Wu <imj...@gmail.com> wrote: >>> > >>> > > Hi Danny, >>> > > >>> > > Thanks for the hint about named params syntax, I added examples with >>> > named >>> > > params in the FLIP. >>> > > >>> > > Best, >>> > > Jark >>> > > >>> > > >>> > > On Sat, 10 Oct 2020 at 15:03, Pengcheng Liu < >>> pengchengliucr...@gmail.com >>> > > >>> > > wrote: >>> > > >>> > >> Hi, Jark, >>> > >> >>> > >> I've got some different opinions there, I think it's a very >>> common >>> > use >>> > >> case to use >>> > >> window operators in combination with streaming operators(even >>> those >>> > >> time operators). >>> > >> (e.g. for some tables, users only care data within a period, but >>> for >>> > >> other tables, they may >>> > >> want the whole historical data). >>> > >> The pipeline may looks like this: >>> > >> window join -> dimension table join -> stream aggregate -> stream >>> > sort >>> > >> >>> > >> Just as what you said, the key clause can be used to distinguish >>> > >> whether a operator should >>> > >> be translated to a window operator or a streaming operator. >>> > >> >>> > >> Also, as I've mentioned before, 1) for time operator after window >>> > >> aggregation, the auxiliary function >>> > >> which is used to access time attribute column can be actually >>> > replaced >>> > >> with (window_end -1). >>> > >> Actually, we only just need to make the results of the upstream >>> > >> contains a time column whose >>> > >> range is within (window_start, window_end), and thus the >>> downstream >>> > >> time operators can work on it >>> > >> (driving by the original watermark in the source). 2) for time >>> > >> operator after other window operators, >>> > >> the downstream time operators can access the time column directly >>> > from >>> > >> it's input. >>> > >> >>> > >> One more thoughts there, maybe the window TVFs can re-assign >>> > >> timestamps and watermarks, so >>> > >> that in some case when the watermark can not be retrieved from >>> source >>> > >> directly(may needs some >>> > >> conversions), the watermark can still be assigned dynamically in >>> the >>> > >> SQL(use the time column as >>> > >> the watermark column) and thus make it work. I think this can >>> save >>> > >> much time to revise the event >>> > >> time column in some cases(this is a real demand in our production >>> > >> environment). >>> > >> >>> > >> I strongly suggest that we should support the combination usage >>> of >>> > >> window operators and >>> > >> streaming operators. And I think we can achieve this with little >>> > work. >>> > >> >>> > >> Best, >>> > >> Pengcheng >>> > >> >>> > >> >>> > >> Jark Wu <imj...@gmail.com> 于2020年10月10日周六 下午1:45写道： >>> > >> >>> > >>> Hi Benchao, >>> > >>> >>> > >>> That's a good question. >>> > >>> >>> > >>> IMO, the new windowed operators and the current time operators are >>> two >>> > >>> different sets of functions, >>> > >>> just like time operators and non-time operators are two different >>> sets >>> > of >>> > >>> functions. >>> > >>> I think it's fine if we don't support integrating them, just like >>> time >>> > >>> operators can't be applied on non-windowed aggregate. >>> > >>> If users want to use time operators in the whole pipeline, then >>> he/she >>> > >>> can >>> > >>> use the grouped window aggregates instead of the window TVFs. >>> > >>> >>> > >>> The key idea of window TVF is that all the operators in the >>> pipeline >>> > are >>> > >>> based on the **windows**. >>> > >>> In terms of syntax, if the key clause (e.g. group by, partitioned >>> by, >>> > >>> join >>> > >>> on, order by) contains window_start and window_end, >>> > >>> it can be translated into windowed operators. >>> > >>> Thus, we will have windowed CEP, windowed sort, windowed over >>> aggregate >>> > >>> in >>> > >>> the future to make it possible to build a windowed pipeline. >>> > >>> >>> > >>> But I think we can elaborate the integration more in the future if >>> > users >>> > >>> need it. Actually, I don't fully understand the scenario of >>> integrating >>> > >>> window TVF and time operators at this point. >>> > >>> For example, interval join an input stream and a window join >>> result. I >>> > >>> don't see why it can't be expressed by nested window join and why >>> users >>> > >>> have to use interval join here. >>> > >>> Maybe we can wait for more inputs from users when the window TVF is >>> > >>> released and we can elaborate it again. >>> > >>> >>> > >>> Best, >>> > >>> Jark >>> > >>> >>> > >>> On Sat, 10 Oct 2020 at 12:01, 刘 芃成 <pengchengliucr...@gmail.com> >>> > wrote: >>> > >>> >>> > >>> > Hi, Benchao, >>> > >>> > I think I got your point, actually, in current >>> implementation >>> > >>> for >>> > >>> > group window aggregation, the value of time attributes(e.g. >>> > >>> > TUMBLE_ROWTIME/TUMBLE_PROCTIME) is calculated as (window_end – >>> 1), >>> > so I >>> > >>> > think we can just use it directly if you need this. But I think >>> this >>> > >>> time >>> > >>> > attributes is mainly suggested to use in case of cascaded window >>> > >>> operations. >>> > >>> > Regarding the example you provided, I think the semantics of the >>> SQL >>> > in >>> > >>> > your example which doing interval join(e.g. with TUMBLE_ROWTIME) >>> > after >>> > >>> > window aggregation is not clear in the current implementation, >>> and I >>> > >>> think >>> > >>> > that’s a strong reason why we need the new TVFs syntax. >>> > >>> > With the new syntax, users should understand which time >>> column >>> > to >>> > >>> > use and how to generate it when doing interval join and etc. >>> > >>> > >>> > >>> > Best, >>> > >>> > Pengcheng >>> > >>> > >>> > >>> > 发件人: Benchao Li <libenc...@apache.org> >>> > >>> > 日期: 2020年10月10日 星期六 上午11:02 >>> > >>> > 收件人: pengcheng Liu <pengchengliucr...@gmail.com> >>> > >>> > 抄送: dev <dev@flink.apache.org> >>> > >>> > 主题: Re: [DISCUSS] FLIP-145: Support SQL windowing table-valued >>> > function >>> > >>> > >>> > >>> > Hi pengcheng, >>> > >>> > >>> > >>> > Thanks for your response. >>> > >>> > I knew that the original time attribute column will be retained >>> after >>> > >>> the >>> > >>> > TVF, >>> > >>> > what I'm questioning is how do we get the time attribute column >>> after >>> > >>> > Aggregation. >>> > >>> > Your answer did not remove my doubts about this. >>> > >>> > >>> > >>> > It's ok if we did not plan to integrate new TVF aggregate with >>> old >>> > >>> "time >>> > >>> > attribute scenarios" >>> > >>> > listed in my previous email in this FLIP. However it's good to >>> > >>> elaborate >>> > >>> > more on this, and >>> > >>> > leave it to the future plan. >>> > >>> > >>> > >>> > pengcheng Liu <pengchengliucr...@gmail.com<mailto: >>> > >>> > pengchengliucr...@gmail.com>> 于2020年10月10日周六 上午10:45写道： >>> > >>> > Hi，Benchao, >>> > >>> > In TVFs, the time attributes is just passed through from >>> parent >>> > >>> rels, >>> > >>> > and the TVFs just add two >>> > >>> > additional window attributes(i.e. window_start & window_end). >>> > >>> Also, I >>> > >>> > think the time columns can be not only a time attribute >>> > >>> > with type of `TimeIndicatorType` but also a regular column >>> with >>> > >>> type >>> > >>> > of `Timestamp`. >>> > >>> > >>> > >>> > For cascaded window operations, we can use >>> > window_start/window_end >>> > >>> of >>> > >>> > the previous window result directly to >>> > >>> > indicate operating on the same window, or use new DESCRIPTOR >>> > >>> column >>> > >>> > to assign new windows, in case of the change of >>> > >>> > the time column(e.g. in some case, the original timestamp is >>> > >>> > inaccurate and need some conversion to be used). >>> > >>> > >>> > >>> > You can check the definition or signature of these TVFs in >>> the >>> > >>> FLIP. >>> > >>> > e.g. >>> > >>> > SELECT * FROM TABLE( >>> > >>> > TUMBLE(TABLE Bid, DESCRIPTOR(bidtime), INTERVAL '10' MINUTES)) >>> > >>> > In the example, the `bidtime` is the time attribute column, >>> which >>> > >>> is >>> > >>> > the first operand of the DESCRIPTOR function. >>> > >>> > >>> > >>> > +1 start voting. >>> > >>> > >>> > >>> > Benchao Li <libenc...@apache.org<mailto:libenc...@apache.org>> >>> > >>> > 于2020年10月10日周六 上午10:08写道： >>> > >>> > Hi Jark, >>> > >>> > >>> > >>> > 2 & 3 sounds good to me. >>> > >>> > >>> > >>> > Regarding time attribute, >>> > >>> > I still have some questions, I knew it's easy to support cascaded >>> > >>> window >>> > >>> > aggregate using new TVFs. >>> > >>> > However there are some other places where need time attribute: >>> > >>> > - CEP >>> > >>> > - interval join >>> > >>> > - order by >>> > >>> > - over window >>> > >>> > If there is no time attribute column, how do we integrate these >>> old >>> > >>> > features with the new TVFs. >>> > >>> > E.g. >>> > >>> > StreamA -> new window aggregate -> interval join -> Sink >>> > >>> > / >>> > >>> > StreamB ----------------------------------- >>> > >>> > >>> > >>> > >>> > >>> > Jark Wu <imj...@gmail.com<mailto:imj...@gmail.com>> >>> 于2020年10月9日周五 >>> > >>> > 下午11:51写道： >>> > >>> > Hi Benchao, >>> > >>> > >>> > >>> > 1) time attribute >>> > >>> > Yes. We don't need time attribute auxiliary function. Because >>> the new >>> > >>> > window operations are all based on the >>> > >>> > window_start and window_end columns instead of on the time >>> > >>> attributes. So >>> > >>> > we don't need to propagate time attributes. >>> > >>> > Cascaded window aggregate can be expressed by simply GROUP BY the >>> > >>> > window_start and window_end of the previous window result. >>> > >>> > I have added a cascaded window aggregate example in the Tumbling >>> > Window >>> > >>> > section in the FLIP. >>> > >>> > If you want to define proctime window aggregate, the time column >>> in >>> > TVF >>> > >>> > should be a proctime attribute field (or PROCTIME() function). >>> > >>> > >>> > >>> > 2) batch support >>> > >>> > Yes. The proposed syntax/API are unified for batch and streaming. >>> > Batch >>> > >>> > support is in the plan, but may not have enough time to catch up >>> > 1.12. >>> > >>> > >>> > >>> > 3) support `grouping sets` >>> > >>> > This is not included in the FLIP, but I think it's great if we >>> can >>> > >>> support >>> > >>> > `grouping sets`. >>> > >>> > The existing window impl doesn't support this because we convert >>> the >>> > >>> > LogicalAggregate into WindowAggregate in the beginning, >>> > >>> > the expand grouping sets rule can't be applied in this situation. >>> > >>> > Fortunately, with the new window impl, the conversion to >>> > >>> WindowAggregate >>> > >>> > will happen at the end, so I think the expand rule can be >>> > >>> > applied and support this feature naturally. >>> > >>> > Therefore, IMO, we don't need to include this feature in this >>> FLIP to >>> > >>> avoid >>> > >>> > the FLIP being too large. >>> > >>> > This can be a follow-up issue (maybe just add tests and docs) >>> after >>> > the >>> > >>> > FLIP. >>> > >>> > >>> > >>> > Best, >>> > >>> > Jark >>> > >>> > >>> > >>> > >>> > >>> > On Fri, 9 Oct 2020 at 19:09, 刘 芃成 <pengchengliucr...@gmail.com >>> > <mailto: >>> > >>> > pengchengliucr...@gmail.com>> wrote: >>> > >>> > >>> > >>> > > Hi，Benchao, >>> > >>> > > Welcome to join the discussion, yes, this new syntax >>> can >>> > >>> make SQL >>> > >>> > > more clear and simpler. >>> > >>> > > For your first question, the `window_start` and >>> > `window_end` >>> > >>> > > columns will be added automatically, >>> > >>> > > so we don't need to use auxiliary group functions to >>> infer >>> > or >>> > >>> > > access the window properties. >>> > >>> > > >>> > >>> > > For the `grouping sets` on TVFs, I think it's >>> interesting >>> > if >>> > >>> we >>> > >>> > > can support it, as we already supported `grouping sets` >>> > >>> > > on streaming aggregates in blink planner. But I'm not >>> sure >>> > >>> if it >>> > >>> > > will be included into this FLIP. >>> > >>> > > >>> > >>> > > cc @Jark Wu >>> > >>> > > >>> > >>> > > Best, >>> > >>> > > Pengcheng >>> > >>> > > >>> > >>> > > >>> > >>> > > 在 2020/10/9 下午5:25，“Benchao Li”<libenc...@apache.org<mailto: >>> > >>> > libenc...@apache.org>> 写入: >>> > >>> > > >>> > >>> > > Thanks Jark for bringing this discussion, I like this FLIP >>> very >>> > >>> much. >>> > >>> > > >>> > >>> > > Especially the cumulate window, it's much like the current >>> > TUMBLE >>> > >>> > > window + >>> > >>> > > Fast Emit (which is an undocumented experimental feature), >>> > >>> however, >>> > >>> > > it's >>> > >>> > > more powerful. >>> > >>> > > >>> > >>> > > And This will make the SQL semantic more standard, >>> especially >>> > >>> for the >>> > >>> > > HOPPING window. >>> > >>> > > >>> > >>> > > Regarding time attribute, >>> > >>> > > It seems that we don't need a specific function to infer >>> the >>> > time >>> > >>> > > attribute >>> > >>> > > like >>> > >>> > > `TUMBLE_ROWTIME` / `TUMBLE_PROCTIME`. Then are >>> `window_start` >>> > and >>> > >>> > > `window_end` >>> > >>> > > column a time attribute column automatically? >>> > >>> > > - If not, what will be the time attribute of the result >>> > relation >>> > >>> of >>> > >>> > > these >>> > >>> > > TVFs? >>> > >>> > > Especially after the window aggregation. >>> > >>> > > - If yes, then how do we handle proctime? >>> > >>> > > >>> > >>> > > Regarding batch operators, >>> > >>> > > It's great to hear that we can reuse the batch operators in >>> > >>> > continuous >>> > >>> > > batch mode >>> > >>> > > as you mentioned in the FLIP. >>> > >>> > > Current window aggregate could also be used in batch mode >>> with >>> > >>> > > rowtime. Do >>> > >>> > > you plan >>> > >>> > > to support these TVFs for batch mode in this FLIP? Hence >>> the >>> > >>> > Table/SQL >>> > >>> > > is a >>> > >>> > > unified >>> > >>> > > API, it's great if we can keep the features complete both >>> in >>> > >>> > streaming >>> > >>> > > and >>> > >>> > > batch mode. >>> > >>> > > >>> > >>> > > There is one more question, I don't know whether it should >>> be >>> > >>> > > considered in >>> > >>> > > this FLIP. >>> > >>> > > Does the new window support `grouping sets`? (It's not >>> > supported >>> > >>> in >>> > >>> > old >>> > >>> > > window impl). >>> > >>> > > >>> > >>> > > Jark Wu <imj...@gmail.com<mailto:imj...@gmail.com>> >>> > >>> 于2020年10月9日周五 >>> > >>> > 下午4:14写道： >>> > >>> > > >>> > >>> > > > Hi all, >>> > >>> > > > >>> > >>> > > > I know we have a lot of discussion and development on >>> going >>> > >>> right >>> > >>> > > now but >>> > >>> > > > it would be great if we can get FLIP-145 into a votable >>> > state. >>> > >>> > > > If there are no objections, I would like to start voting >>> in >>> > the >>> > >>> > next >>> > >>> > > days. >>> > >>> > > > >>> > >>> > > > Best, >>> > >>> > > > Jark >>> > >>> > > > >>> > >>> > > > On Thu, 1 Oct 2020 at 14:29, Jark Wu <imj...@gmail.com >>> > <mailto: >>> > >>> > imj...@gmail.com>> wrote: >>> > >>> > > > >>> > >>> > > > > Hi everyone, >>> > >>> > > > > >>> > >>> > > > > I have added a section for Performance Optimization to >>> > >>> describe >>> > >>> > > how to >>> > >>> > > > > improve the performance in the short-term and long-term >>> > >>> > > > > and sketch the future performance potential under the >>> new >>> > >>> window >>> > >>> > > API. >>> > >>> > > > > Introducing the window API is just the first step, we >>> will >>> > >>> > > > > continuously improve the performance to make it >>> powerful >>> > and >>> > >>> > > useful. >>> > >>> > > > > >>> > >>> > > > > Best, >>> > >>> > > > > Jark >>> > >>> > > > > >>> > >>> > > > > On Thu, 1 Oct 2020 at 14:28, Jark Wu <imj...@gmail.com >>> > >>> <mailto: >>> > >>> > imj...@gmail.com>> wrote: >>> > >>> > > > > >>> > >>> > > > >> Hi Pengcheng, >>> > >>> > > > >> >>> > >>> > > > >> Yes, the window TVF is part of the FLIP. Welcome to >>> > >>> contribute >>> > >>> > > and join >>> > >>> > > > >> the discussion. >>> > >>> > > > >> Regarding the SESSION window aggregation, users can >>> use >>> > the >>> > >>> > > existing >>> > >>> > > > >> grouped session window function. >>> > >>> > > > >> >>> > >>> > > > >> Best, >>> > >>> > > > >> Jark >>> > >>> > > > >> >>> > >>> > > > >> On Sun, 27 Sep 2020 at 21:24, liupengcheng < >>> > >>> > > pengchengliucr...@gmail.com<mailto:pengchengliucr...@gmail.com >>> > >>> > >>> > > > > >>> > >>> > > > >> wrote: >>> > >>> > > > >> >>> > >>> > > > >>> Hi Jark, >>> > >>> > > > >>> Thanks for reply, yes, I think it's a good >>> > >>> feature, it >>> > >>> > > can >>> > >>> > > > >>> improve the NRT scenarios >>> > >>> > > > >>> as you mentioned in the FLIP. Also, I think >>> it >>> > can >>> > >>> > > improve the >>> > >>> > > > >>> streaming SQL greatly, >>> > >>> > > > >>> it can support richer window operations in >>> flink >>> > >>> SQL >>> > >>> > and >>> > >>> > > bring >>> > >>> > > > >>> great convenience to users. >>> > >>> > > > >>> (we are now only supported group window in >>> > flink). >>> > >>> > > > >>> >>> > >>> > > > >>> Regarding the SESSION window, I think it's >>> > >>> especially >>> > >>> > > useful >>> > >>> > > > for >>> > >>> > > > >>> user behavior analysis(e.g. >>> > >>> > > > >>> counting user visits on a news website or >>> social >>> > >>> > > platform), but >>> > >>> > > > >>> I agree that we can keep it >>> > >>> > > > >>> out of the FLIP now to catch up 1.12. >>> > >>> > > > >>> >>> > >>> > > > >>> Recently, I've done some work on the stream >>> > planner >>> > >>> > with >>> > >>> > > the >>> > >>> > > > >>> TVFs, and I'm willing to contribute >>> > >>> > > > >>> to this part. Is it in the plan of this FLIP? >>> > >>> > > > >>> >>> > >>> > > > >>> Best, >>> > >>> > > > >>> PengchengLiu >>> > >>> > > > >>> >>> > >>> > > > >>> >>> > >>> > > > >>> 在 2020/9/26 下午11:09，“Jark Wu”<imj...@gmail.com >>> <mailto: >>> > >>> > imj...@gmail.com>> 写入: >>> > >>> > > > >>> >>> > >>> > > > >>> Hi pengcheng, >>> > >>> > > > >>> >>> > >>> > > > >>> That's great to see you also have the need of >>> window >>> > >>> join. >>> > >>> > > > >>> You are right, the windowing TVF is a powerful >>> > feature >>> > >>> > which >>> > >>> > > can >>> > >>> > > > >>> support >>> > >>> > > > >>> more operations in the future. >>> > >>> > > > >>> I think it as of the date time "partition" >>> selection >>> > in >>> > >>> > > batch SQL >>> > >>> > > > >>> jobs, >>> > >>> > > > >>> with this new syntax, I think it is possible >>> > >>> > > > >>> to migrate traditional batch SQL jobs to Flink >>> SQL >>> > by >>> > >>> > > changing a >>> > >>> > > > >>> few lines. >>> > >>> > > > >>> >>> > >>> > > > >>> Regarding the SESSION window, this is on purpose >>> to >>> > >>> keep it >>> > >>> > > out of >>> > >>> > > > >>> the >>> > >>> > > > >>> FLIP, because we want to keep the >>> > >>> > > > >>> FLIP small to catch up 1.12 and SESSION TVF is >>> rarely >>> > >>> > useful >>> > >>> > > (e.g. >>> > >>> > > > >>> session >>> > >>> > > > >>> window join?). >>> > >>> > > > >>> >>> > >>> > > > >>> Best, >>> > >>> > > > >>> Jark >>> > >>> > > > >>> >>> > >>> > > > >>> On Fri, 25 Sep 2020 at 22:59, liupengcheng < >>> > >>> > > > >>> pengchengliucr...@gmail.com<mailto: >>> > >>> pengchengliucr...@gmail.com >>> > >>> > >> >>> > >>> > > > >>> wrote: >>> > >>> > > > >>> >>> > >>> > > > >>> > Hi, Jark, >>> > >>> > > > >>> > I'm very interested in this feature, >>> and >>> > I'm >>> > >>> also >>> > >>> > > working >>> > >>> > > > >>> on this >>> > >>> > > > >>> > recently. >>> > >>> > > > >>> > I just have a glance at the FLIP, it's >>> > good, >>> > >>> but >>> > >>> > I >>> > >>> > > found >>> > >>> > > > >>> that >>> > >>> > > > >>> > there is no plan to add SESSION windows. >>> > >>> > > > >>> > Also, I think there can be more things >>> we >>> > >>> can do >>> > >>> > > based on >>> > >>> > > > >>> this new >>> > >>> > > > >>> > syntax. For example, >>> > >>> > > > >>> > - window sort support >>> > >>> > > > >>> > - window union/intersect/minus support >>> > >>> > > > >>> > - Improve dimension table join >>> > >>> > > > >>> > We can have more deep discussion on >>> this >>> > new >>> > >>> > > feature >>> > >>> > > > later >>> > >>> > > > >>> . >>> > >>> > > > >>> > I've also opened an jira that is >>> related to >>> > >>> this >>> > >>> > > feature >>> > >>> > > > >>> recently: >>> > >>> > > > >>> > >>> https://issues.apache.org/jira/browse/FLINK-18830 >>> > >>> > > > >>> > >>> > >>> > > > >>> > Best! >>> > >>> > > > >>> > PengchengLiu >>> > >>> > > > >>> > >>> > >>> > > > >>> > 在 2020/9/25 下午10:30，“Jark Wu”< >>> imj...@gmail.com >>> > >>> <mailto: >>> > >>> > imj...@gmail.com>> 写入: >>> > >>> > > > >>> > >>> > >>> > > > >>> > Hi everyone, >>> > >>> > > > >>> > >>> > >>> > > > >>> > I want to start a FLIP about supporting >>> > windowing >>> > >>> > > > table-valued >>> > >>> > > > >>> > functions >>> > >>> > > > >>> > (TVF). >>> > >>> > > > >>> > The main purpose of this FLIP is to >>> improve the >>> > >>> near >>> > >>> > > > real-time >>> > >>> > > > >>> (NRT) >>> > >>> > > > >>> > experience of Flink. >>> > >>> > > > >>> > >>> > >>> > > > >>> > FLIP-145: >>> > >>> > > > >>> > >>> > >>> > > > >>> > >>> > >>> > > > >>> >>> > >>> > > > >>> > >>> > > >>> > >>> > >>> > >>> >>> > >>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-145%3A+Support+SQL+windowing+table-valued+function >>> > >>> > > > >>> > >>> > >>> > > > >>> > We want to introduce TUMBLE, HOP, CUMULATE >>> > >>> windowing >>> > >>> > > TVFs, >>> > >>> > > > the >>> > >>> > > > >>> > CUMULATE is >>> > >>> > > > >>> > a new kind of window. >>> > >>> > > > >>> > With the windowing TVFs, we can support >>> richer >>> > >>> > > operations on >>> > >>> > > > >>> windows, >>> > >>> > > > >>> > including window join, window TopN and so >>> on. >>> > >>> > > > >>> > This makes things simple: we only need to >>> > assign >>> > >>> > > windows at >>> > >>> > > > the >>> > >>> > > > >>> > beginning >>> > >>> > > > >>> > of the query, and then apply operations >>> after >>> > >>> that >>> > >>> > like >>> > >>> > > > >>> traditional >>> > >>> > > > >>> > batch >>> > >>> > > > >>> > SQL. >>> > >>> > > > >>> > We hope it can help to reduce the learning >>> > curve >>> > >>> of >>> > >>> > > windows, >>> > >>> > > > >>> improve >>> > >>> > > > >>> > NRT >>> > >>> > > > >>> > for Flink, and attract more batch users. >>> > >>> > > > >>> > >>> > >>> > > > >>> > A simple code snippet for 10 minutes >>> tumbling >>> > >>> window >>> > >>> > > > aggregate: >>> > >>> > > > >>> > >>> > >>> > > > >>> > SELECT window_start, window_end, SUM(price) >>> > >>> > > > >>> > FROM TABLE( >>> > >>> > > > >>> > TUMBLE(TABLE Bid, DESCRIPTOR(bidtime), >>> > >>> INTERVAL >>> > >>> > > '10' >>> > >>> > > > >>> MINUTES)) >>> > >>> > > > >>> > GROUP BY window_start, window_end; >>> > >>> > > > >>> > >>> > >>> > > > >>> > I'm looking forward to your feedback. >>> > >>> > > > >>> > >>> > >>> > > > >>> > Best, >>> > >>> > > > >>> > Jark >>> > >>> > > > >>> > >>> > >>> > > > >>> > >>> > >>> > > > >>> > >>> > >>> > > > >>> >>> > >>> > > > >>> >>> > >>> > > > >>> >>> > >>> > > > >>> > >>> > > >>> > >>> > > >>> > >>> > > -- >>> > >>> > > >>> > >>> > > Best, >>> > >>> > > Benchao Li >>> > >>> > > >>> > >>> > >>> > >>> > >>> > >>> > -- >>> > >>> > >>> > >>> > Best, >>> > >>> > Benchao Li >>> > >>> > >>> > >>> > >>> > >>> > -- >>> > >>> > >>> > >>> > Best, >>> > >>> > Benchao Li >>> > >>> > >>> > >>> >>> > >> >>> > >>> >>> >>> -- >>> Best, Jingsong Lee >>> >>
