Hi Shammon, Thanks for pushing the topic further. I'm not sure how this new proposal is supposed to be working? How should timestamp barrier interplay with event time and watermarks? Or is timestamp barrier supposed to completely replace watermarks?
> stateful and temporal operators should align them (records) according to their timestamp field. Are you proposing that all of the inputs to stateful operators would have to be sorted? > There're three states in a table for specific transaction : PreCommit, Commit and Snapshot Can you explain why do you need those 3 states? Why can committed records be rolled back? >> 10. Have you considered proposing a general consistency mechanism instead >> of restricting it to TableStore+ETL graphs? For example, it seems to me to >> be possible and valuable to define instead the contract that sources/sinks >> need to implement in order to participate in globally consistent snapshots. > > A general consistency mechanism is cool! In my mind, the overall > `consistency system` consists of three components: Streaming & Batch ETL, > Streaming & Batch Storage and MetaService. MetaService is decoupled from > Storage Layer, but it stores consistency information in persistent storage. > It can be started as an independent node or a component in a large Flink > cluster. In the FLIP we use TableStore as the Storage Layer. As you > mentioned, we plan to implement specific source and sink on the TableStore > in the first phase, and may consider other storage in the future I'm not sure if I follow. Generally speaking, why do we need MetaService at all? Why can we only support writes to and reads from TableStore, and not any source/sink that implements some specific interface? Best, Piotrek niedz., 29 sty 2023 o 12:11 Shammon FY <zjur...@gmail.com> napisał(a): > Hi @Vicky > > Thank you for your suggestions about consistency and they're very nice to > me! > > I have updated the examples and consistency types[1] in FLIP. In general, I > regard the Timestamp Barrier processing as a transaction and divide the > data consistency supported in FLIP into three types > > 1. Read Uncommitted: Read data from tables even when a transaction is not > committed. > 2. Read Committed: Read data from tables according to the committed > transaction. > 3. Repeatable Read: Read data from tables according to the committed > transaction in snapshots. > > You can get more information from the updated FLIP. Looking forward to your > feedback, THX > > > [1] > > https://cwiki.apache.org/confluence/display/FLINK/FLIP-276%3A+Data+Consistency+of+Streaming+and+Batch+ETL+in+Flink+and+Table+Store#FLIP276:DataConsistencyofStreamingandBatchETLinFlinkandTableStore-DataConsistencyType > > Best, > Shammon > > > On Sat, Jan 28, 2023 at 4:42 AM Vasiliki Papavasileiou > <vpapavasile...@confluent.io.invalid> wrote: > > > Hi Shammon, > > > > > > Thank you for opening this FLIP which is very interesting and such an > > important feature to add to the Flink ecosystem. I have a couple of > > suggestions/questions: > > > > > > > > - > > > > Consistency is a very broad term with different meanings. There are > many > > variations between the two extremes of weak and strong consistency > that > > tradeoff latency for consistency. https://jepsen.io/consistency It > > would > > be great if we could devise an approach that allows the user to choose > > which consistency level they want to use for a query. > > > > > > Example: In your figure where you have a DAG, assume a user queries only > > Table1 for a specific key. Then, a failure happens and the table restores > > from a checkpoint. The user issues the same query, looking up the same > key. > > What value does she see? With monotonic-reads, the system guarantees that > > she will only see the same or newer values but not older, hence will not > > experience time-travel. This is a very useful property for a system to > have > > albeit it is at the weaker-end of consistency guarantees. But it is a > good > > stepping stone. > > > > > > Another example, assume the user queries Table1 for key K1 and gets the > > value V11. Then, she queries Table2 that is derived from Table1 for the > > same key, K1, that returns value V21. What is the relationship between > V21 > > and V11? Is V21 derived from V11 or can it be an older value V1 (the > > previous value of K1)? What if value V21 is not yet in table Table2? What > > should she see when she queries Table1? Should she see the key V11 or > not? > > Should the requirement be that a record is not visible in any of the > tables > > in a DAG unless it is available in all of them? > > > > > > > > - > > > > It would we good to have a set of examples with consistency anomalies > > that can happen (like the examples above) and what consistency levels > we > > want the system to offer to prevent them. > > Moreover, for each such example, it would be good to have a > description > > of how the approach (Timestamp Barriers) will work in practice to > > prevent > > such anomalies. > > > > > > Thank you, > > Vicky > > > > > > On Fri, Jan 27, 2023 at 4:46 PM John Roesler <vvcep...@apache.org> > wrote: > > > > > Hello Shammon and all, > > > > > > Thanks for this FLIP! I've been working toward this kind of global > > > consistency across large scale data infrastructure for a long time, and > > > it's fantastic to see a high-profile effort like this come into play. > > > > > > I have been lurking in the discussion for a while and delaying my > > response > > > while I collected my thoughts. However, I've realized at some point, > > > delaying more is not as useful as just asking a few questions, so I'm > > sorry > > > if some of this seems beside the point. I'll number these to not > collide > > > with prior discussion points: > > > > > > 10. Have you considered proposing a general consistency mechanism > instead > > > of restricting it to TableStore+ETL graphs? For example, it seems to me > > to > > > be possible and valuable to define instead the contract that > > sources/sinks > > > need to implement in order to participate in globally consistent > > snapshots. > > > > > > 11. It seems like this design is assuming that the "ETL Topology" under > > > the envelope of the consistency model is a well-ordered set of jobs, > but > > I > > > suspect this is not the case for many organizations. It may be > > > aspirational, but I think the gold-standard here would be to provide an > > > entire organization with a consistency model spanning a loosely coupled > > > ecosystem of jobs and data flows spanning teams and systems that are > > > organizationally far apart. > > > > > > I realize that may be kind of abstract. Here's some examples of what's > on > > > my mind here: > > > > > > 11a. Engineering may operate one Flink cluster, and some other org, > like > > > Finance may operate another. In most cases, those are separate domains > > that > > > don't typically get mixed together in jobs, but some people, like the > > CEO, > > > would still benefit from being able to make a consistent query that > spans > > > arbitrary contexts within the business. How well can a feature like > this > > > transcend a single Flink infrastructure? Does it make sense to > consider a > > > model in which snapshots from different domains can be composable? > > > > > > 11b. Some groups may have a relatively stable set of long-running jobs, > > > while others (like data science, skunkworks, etc) may adopt a more > > > experimental, iterative approach with lots of jobs entering and exiting > > the > > > ecosystem over time. It's still valuable to have them participate in > the > > > consistency model, but it seems like the consistency system will have > to > > > deal with more chaos than I see in the design. For example, how can > this > > > feature tolerate things like zombie jobs (which are registered in the > > > system, but fail to check in for a long time, and then come back > later). > > > > > > 12. I didn't see any statements about patterns like cycles in the ETL > > > Topology. I'm aware that there are fundamental constraints on how well > > > cyclic topologies can be supported by a distributed snapshot algorithm. > > > However, there are a range of approaches/compromises that we can apply > to > > > cyclic topologies. At the very least, we can state that we will detect > > > cycles and produce a warning, etc. > > > > > > 13. I'm not sure how heavily you're waiting the query syntax part of > the > > > proposal, so please feel free to defer this point. It looked to me like > > the > > > proposal assumes people want to query either the latest consistent > > snapshot > > > or the latest inconsistent state. However, it seems like there's a > > > significant opportunity to maintain a manifest of historical snapshots > > and > > > allow people to query as of old points in time. That can be valuable > for > > > individuals answering data questions, building products, and crucially > > > supporting auditability use cases. To that latter point, it seems nice > to > > > provide not only a mechanism to query arbitrary snapshots, but also to > > > define a TTL/GC model that allows users to keep hourly snapshots for N > > > hours, daily snapshots for N days, weekly snapshots for N weeks, and > the > > > same for monthly, quarterly, and yearly snapshots. > > > > > > Ok, that's all I have for now :) I'd also like to understand some > > > lower-level details, but I wanted to get these high-level questions off > > my > > > chest. > > > > > > Thanks again for the FLIP! > > > -John > > > > > > On 2023/01/13 11:43:28 Shammon FY wrote: > > > > Hi Piotr, > > > > > > > > I discussed with @jinsong lee about `Timestamp Barrier` and `Aligned > > > > Checkpoint` for data consistency in FLIP, we think there are many > > defects > > > > indeed in using `Aligned Checkpoint` to support data consistency as > you > > > > mentioned. > > > > > > > > According to our historical discussion, I think we have reached an > > > > agreement on an important point: we finally need `Timestamp Barrier > > > > Mechanism` to support data consistency. But according to our > (@jinsong > > > lee > > > > and I) opinions, the total design and implementation based on > > 'Timestamp > > > > Barrier' will be too complex, and it's also too big in one FLIP. > > > > > > > > So we‘d like to use FLIP-276[1] as an overview design of data > > consistency > > > > in Flink Streaming and Batch ETL based on `Timestamp Barrier`. > @jinsong > > > and > > > > I hope that we can reach an agreement on the overall design in > > FLINK-276 > > > > first, and then on the basic of FLIP-276 we can create other FLIPs > with > > > > detailed design according to modules and drive them. Finally, we can > > > > support data consistency based on Timestamp in Flink. > > > > > > > > I have updated FLIP-276, deleted the Checkpoint section, and added > the > > > > overall design of `Timestamp Barrier`. Here I briefly describe the > > > modules > > > > of `Timestamp Barrier` as follows > > > > 1. Generation: JobManager must coordinate all source subtasks and > > > generate > > > > a unified timestamp barrier from System Time or Event Time for them > > > > 2. Checkpoint: Store <checkpoint, timestamp barrier> when the > timestamp > > > > barrier is generated, so that the job can recover the same timestamp > > > > barrier for the uncompleted checkpoint. > > > > 3. Replay data: Store <timestamp barrier, offset> for source when it > > > > broadcasts timestamp barrier, so that the source can replay the same > > data > > > > according to the same timestamp barrier. > > > > 4. Align data: Align data for stateful operator(aggregation, join and > > > etc.) > > > > and temporal operator(window) > > > > 5. Computation: Operator computation for a specific timestamp barrier > > > based > > > > on the results of a previous timestamp barrier. > > > > 6. Output: Operator outputs or commits results when it collects all > the > > > > timestamp barriers, including operators with data buffer or async > > > > operations. > > > > > > > > I also list the main work in Flink and Table Store in FLIP-276. > Please > > > help > > > > to review the FLIP when you're free and feel free to give any > comments. > > > > > > > > Looking forward for your feedback, THX > > > > > > > > [1] > > > > > > > > > > https://cwiki.apache.org/confluence/display/FLINK/FLIP-276%3A+Data+Consistency+of+Streaming+and+Batch+ETL+in+Flink+and+Table+Store > > > > > > > > Best, > > > > Shammon > > > > > > > > > > > > On Tue, Dec 20, 2022 at 10:01 AM Shammon FY <zjur...@gmail.com> > wrote: > > > > > > > > > Hi Piotr, > > > > > > > > > > Thanks for your syncing. I will update the FLIP later and keep this > > > > > discussion open. Looking forward to your feedback, thanks > > > > > > > > > > > > > > > Best, > > > > > Shammon > > > > > > > > > > > > > > > On Mon, Dec 19, 2022 at 10:45 PM Piotr Nowojski < > > pnowoj...@apache.org> > > > > > wrote: > > > > > > > > > >> Hi Shammon, > > > > >> > > > > >> I've tried to sync with Timo, David Moravek and Dawid Wysakowicz > > about > > > > >> this > > > > >> subject. We have only briefly chatted and exchanged some > > > thoughts/ideas, > > > > >> but unfortunately we were not able to finish the discussions > before > > > the > > > > >> holiday season/vacations. Can we get back to this topic in > January? > > > > >> > > > > >> Best, > > > > >> Piotrek > > > > >> > > > > >> pt., 16 gru 2022 o 10:53 Shammon FY <zjur...@gmail.com> > napisał(a): > > > > >> > > > > >> > Hi Piotr, > > > > >> > > > > > >> > I found there may be several points in our discussion, it will > > cause > > > > >> > misunderstanding between us when we focus on different one. I > list > > > each > > > > >> > point in our discussion as follows > > > > >> > > > > > >> > > Point 1: Is "Aligned Checkpoint" the only mechanism to > guarantee > > > data > > > > >> > consistency in the current Flink implementation, and "Watermark" > > and > > > > >> > "Aligned Checkpoint cannot do that? > > > > >> > My answer is "Yes", the "Aligned Checkpoint" is the only one due > > to > > > its > > > > >> > "Align Data" ability, we can do it in the first stage. > > > > >> > > > > > >> > > Point2: Can the combination of "Checkpoint Barrier" and > > > "Watermark" > > > > >> > support the complete consistency semantics based on "Timestamp" > in > > > the > > > > >> > current Flink implementation? > > > > >> > My answer is "No", we need a new "Timestamp Barrier" mechanism > to > > do > > > > >> that > > > > >> > which may be upgraded from current "Watermark" or a new > mechanism, > > > we > > > > >> can > > > > >> > do it in the next second or third stage. > > > > >> > > > > > >> > > Point3: Are the "Checkpoint" and the new "Timestamp Barrier" > > > > >> completely > > > > >> > independent? The "Checkpoint" whatever "Aligned" or "Unaligned" > or > > > "Task > > > > >> > Local" supports the "Exactly-Once" between ETLs, and the > > "Timestamp > > > > >> > Barrier" mechanism guarantees data consistency between tables > > > according > > > > >> to > > > > >> > timestamp for queries. > > > > >> > My answer is "Yes", I totally agree with you. Let "Checkpoint" > be > > > > >> > responsible for fault tolerance and "Timestamp Barrier" for > > > consistency > > > > >> > independently. > > > > >> > > > > > >> > @Piotr, What do you think? If I am missing or misunderstanding > > > anything, > > > > >> > please correct me, thanks > > > > >> > > > > > >> > Best, > > > > >> > Shammon > > > > >> > > > > > >> > On Fri, Dec 16, 2022 at 4:17 PM Piotr Nowojski < > > > pnowoj...@apache.org> > > > > >> > wrote: > > > > >> > > > > > >> > > Hi Shammon, > > > > >> > > > > > > >> > > > I don't think we can combine watermarks and checkpoint > > barriers > > > > >> > together > > > > >> > > to > > > > >> > > > guarantee data consistency. There will be a "Timestamp > > Barrier" > > > in > > > > >> our > > > > >> > > > system to "commit data", "single etl failover", "low latency > > > between > > > > >> > > ETLs" > > > > >> > > > and "strong data consistency with completed semantics" in > the > > > end. > > > > >> > > > > > > >> > > Why do you think so? I've described to you above an > alternative > > > where > > > > >> we > > > > >> > > could be using watermarks for data consistency, regardless of > > what > > > > >> > > checkpointing/fault tolerance mechanism Flink would be using. > > Can > > > you > > > > >> > > explain what's wrong with that approach? Let me rephrase it: > > > > >> > > > > > > >> > > 1. There is an independent mechanism that provides > exactly-once > > > > >> > guarantees, > > > > >> > > committing records/watermarks/events and taking care of the > > > failover. > > > > >> It > > > > >> > > might be aligned, unaligned or task local checkpointing - this > > > doesn't > > > > >> > > matter. Let's just assume we have such a mechanism. > > > > >> > > 2. There is a watermarking mechanism (it can be some kind of > > > system > > > > >> > > versioning re-using watermarks code path if a user didn't > > > configure > > > > >> > > watermarks), that takes care of the data consistency. > > > > >> > > > > > > >> > > Because watermarks from 2. are also subject to the > exactly-once > > > > >> > guarantees > > > > >> > > from the 1., once they are committed downstream systems (Flink > > > jobs or > > > > >> > > other 3rd party systems) could just easily work with the > > committed > > > > >> > > watermarks to provide consistent view/snapshot of the tables. > > Any > > > > >> > > downstream system could always check what are the committed > > > > >> watermarks, > > > > >> > > select the watermark value (for example min across all used > > > tables), > > > > >> and > > > > >> > > ask every table: please give me all of the data up until the > > > selected > > > > >> > > watermark. Or give me all tables in the version for the > selected > > > > >> > watermark. > > > > >> > > > > > > >> > > Am I missing something? To me it seems like this way we can > > fully > > > > >> > decouple > > > > >> > > the fault tolerance mechanism from the subject of the data > > > > >> consistency. > > > > >> > > > > > > >> > > Best, > > > > >> > > Piotrek > > > > >> > > > > > > >> > > czw., 15 gru 2022 o 13:01 Shammon FY <zjur...@gmail.com> > > > napisał(a): > > > > >> > > > > > > >> > > > Hi Piotr, > > > > >> > > > > > > > >> > > > It's kind of amazing about the image, it's a simple example > > and > > > I > > > > >> have > > > > >> > to > > > > >> > > > put it in a document > > > > >> > > > > > > > >> > > > > > > > >> > > > > > > >> > > > > > >> > > > > > > https://bytedance.feishu.cn/docx/FC6zdq0eqoWxHXxli71cOxe9nEe?from=from_copylink > > > > >> > > > :) > > > > >> > > > > > > > >> > > > > Does it have to be combining watermarks and checkpoint > > > barriers > > > > >> > > together? > > > > >> > > > > > > > >> > > > It's an interesting question. As we discussed above, what we > > > need > > > > >> from > > > > >> > > > "Checkpoint" is the "Align Data Ability", and from > "Watermark" > > > is > > > > >> the > > > > >> > > > "Consistency Semantics", > > > > >> > > > > > > > >> > > > 1) Only "Align Data" can reach data consistency when > > performing > > > > >> queries > > > > >> > > on > > > > >> > > > upstream and downstream tables. I gave an example of "Global > > > Count > > > > >> > > Tables" > > > > >> > > > in our previous discussion. We need a "Align Event" in the > > > streaming > > > > >> > > > processing, it's the most basic. > > > > >> > > > > > > > >> > > > 2) Only "Timestamp" can provide complete consistency > > semantics. > > > You > > > > >> > gave > > > > >> > > > some good examples about "Window" and ect operators. > > > > >> > > > > > > > >> > > > I don't think we can combine watermarks and checkpoint > > barriers > > > > >> > together > > > > >> > > to > > > > >> > > > guarantee data consistency. There will be a "Timestamp > > Barrier" > > > in > > > > >> our > > > > >> > > > system to "commit data", "single etl failover", "low latency > > > between > > > > >> > > ETLs" > > > > >> > > > and "strong data consistency with completed semantics" in > the > > > end. > > > > >> > > > > > > > >> > > > At the beginning I think we can do the simplest thing first: > > > > >> guarantee > > > > >> > > the > > > > >> > > > basic data consistency with a "Barrier Mechanism". In the > > > current > > > > >> Flink > > > > >> > > > there's "Aligned Checkpoint" only, that's why we choose > > > > >> "Checkpoint" in > > > > >> > > our > > > > >> > > > FLIP. > > > > >> > > > > > > > >> > > > > I don't see an actual connection in the the implementation > > > steps > > > > >> > > between > > > > >> > > > the checkpoint barriers approach and the watermark-like > > approach > > > > >> > > > > > > > >> > > > As I mentioned above, we choose "Checkpoint" to guarantee > the > > > basic > > > > >> > data > > > > >> > > > consistency. But as we discussed, the most ideal solution is > > > > >> "Timestamp > > > > >> > > > Barrier". After the first stage is completed based on the > > > > >> "Checkpoint", > > > > >> > > we > > > > >> > > > need to evolve it to our ideal solution "Timestamp Barrier" > > > > >> > > (watermark-like > > > > >> > > > approach) in the next second or third stage. This does not > > mean > > > > >> > upgrading > > > > >> > > > "Checkpoint Mechanism" in Flink. It means that after we > > > implement a > > > > >> new > > > > >> > > > "Timestamp Barrier" or upgrade "Watermark" to support it, we > > can > > > > >> use it > > > > >> > > > instead of the current "Checkpoint Mechanism" directly in > our > > > > >> > > "MetaService" > > > > >> > > > and "Table Store". > > > > >> > > > > > > > >> > > > In the discussion between @David and me, I summarized the > work > > > of > > > > >> > > upgrading > > > > >> > > > "Watermark" to support "Timestamp Barrier". It looks like a > > big > > > job > > > > >> and > > > > >> > > you > > > > >> > > > can find the details in our discussion. I think we don't > need > > > to do > > > > >> > that > > > > >> > > in > > > > >> > > > our first stage. > > > > >> > > > > > > > >> > > > Also in that discussion (my reply to @David) too, I briefly > > > > >> summarized > > > > >> > > the > > > > >> > > > work that needs to be done to use the new mechanism > (Timestamp > > > > >> Barrier) > > > > >> > > > after we implement the basic function on "Checkpoint". It > > seems > > > that > > > > >> > the > > > > >> > > > work is not too big on my side, and it is feasible on the > > whole. > > > > >> > > > > > > > >> > > > Based on the above points, I think we can support basic data > > > > >> > consistency > > > > >> > > on > > > > >> > > > "Checkpoint" in the first stage which is described in FLIP, > > and > > > > >> > continue > > > > >> > > to > > > > >> > > > evolve it to "Timestamp Barrier" to support low latency > > between > > > ETLs > > > > >> > and > > > > >> > > > completed semantics in the second or third stage later. > What > > > do you > > > > >> > > think? > > > > >> > > > > > > > >> > > > Best, > > > > >> > > > Shammon > > > > >> > > > > > > > >> > > > > > > > >> > > > On Thu, Dec 15, 2022 at 4:21 PM Piotr Nowojski < > > > > >> pnowoj...@apache.org> > > > > >> > > > wrote: > > > > >> > > > > > > > >> > > > > Hi Shammon, > > > > >> > > > > > > > > >> > > > > > The following is a simple example. Data is transferred > > > between > > > > >> > ETL1, > > > > >> > > > ETL2 > > > > >> > > > > and ETL3 in Intermediate Table by Timestamp. > > > > >> > > > > > [image: simple_example.jpg] > > > > >> > > > > > > > > >> > > > > This time it's your image that doesn't want to load :) > > > > >> > > > > > > > > >> > > > > > Timestamp Barrier > > > > >> > > > > > > > > >> > > > > Does it have to be combining watermarks and checkpoint > > > barriers > > > > >> > > together? > > > > >> > > > > Can we not achieve the same result with two independent > > > processes > > > > >> > > > > checkpointing (regardless if this is a global > > > aligned/unaligned > > > > >> > > > checkpoint, > > > > >> > > > > or a task local checkpoint) plus watermarking? > Checkpointing > > > would > > > > >> > > > provide > > > > >> > > > > exactly-once guarantees, and actually committing the > > results, > > > and > > > > >> it > > > > >> > > > would > > > > >> > > > > be actually committing the last emitted watermark? From > the > > > > >> > perspective > > > > >> > > > of > > > > >> > > > > the sink/table, it shouldn't really matter how the > > > exactly-once is > > > > >> > > > > achieved, and whether the job has performed an unaligned > > > > >> checkpoint > > > > >> > or > > > > >> > > > > something completely different. It seems to me that the > > > sink/table > > > > >> > > > > could/should be able to understand/work with only the > basic > > > > >> > > information: > > > > >> > > > > here are records and watermarks (with at that point of > time > > > > >> already > > > > >> > > fixed > > > > >> > > > > order), they are committed and will never change. > > > > >> > > > > > > > > >> > > > > > However, from the perspective of implementation > > complexity, > > > I > > > > >> > > > personally > > > > >> > > > > think using Checkpoint in the first phase makes sense, > what > > > do you > > > > >> > > think? > > > > >> > > > > > > > > >> > > > > Maybe I'm missing something, but I don't see an actual > > > connection > > > > >> in > > > > >> > > the > > > > >> > > > > implementation steps between the checkpoint barriers > > approach > > > and > > > > >> the > > > > >> > > > > watermark-like approach. They seem to me (from the > > > perspective of > > > > >> > Flink > > > > >> > > > > runtime at least) like two completely different > mechanisms. > > > Not > > > > >> one > > > > >> > > > leading > > > > >> > > > > to the other. > > > > >> > > > > > > > > >> > > > > Best, > > > > >> > > > > Piotrek > > > > >> > > > > > > > > >> > > > > > > > > >> > > > > śr., 14 gru 2022 o 15:19 Shammon FY <zjur...@gmail.com> > > > > >> napisał(a): > > > > >> > > > > > > > > >> > > > > > Hi Piotr, > > > > >> > > > > > > > > > >> > > > > > Thanks for your valuable input which makes me consider > the > > > core > > > > >> > point > > > > >> > > > of > > > > >> > > > > > data consistency in deep. I'd like to define the data > > > > >> consistency > > > > >> > on > > > > >> > > > the > > > > >> > > > > > whole streaming & batch processing as follows and I hope > > > that we > > > > >> > can > > > > >> > > > have > > > > >> > > > > > an agreement on it: > > > > >> > > > > > > > > > >> > > > > > BOutput = Fn(BInput), BInput is a bounded input which is > > > > >> splitted > > > > >> > > from > > > > >> > > > > > unbounded streaming, Fn is the computation of a node or > > ETL, > > > > >> > BOutput > > > > >> > > is > > > > >> > > > > the > > > > >> > > > > > bounded output of BInput. All the data in BInput and > > > BOutput are > > > > >> > > > > unordered, > > > > >> > > > > > and BInput and BOutput are data consistent. > > > > >> > > > > > > > > > >> > > > > > The key points above include 1) the segment semantics of > > > > >> BInput; 2) > > > > >> > > the > > > > >> > > > > > computation semantics of Fn > > > > >> > > > > > > > > > >> > > > > > 1. The segment semantics of BInput > > > > >> > > > > > a) Transactionality of data. It is necessary to ensure > the > > > > >> semantic > > > > >> > > > > > transaction of the bounded data set when it is splitted > > > from the > > > > >> > > > > unbounded > > > > >> > > > > > streaming. For example, we cannot split multiple records > > in > > > one > > > > >> > > > > transaction > > > > >> > > > > > to different bounded data sets. > > > > >> > > > > > b) Timeliness of data. Some data is related with time, > > such > > > as > > > > >> > > boundary > > > > >> > > > > > data for a window. It is necessary to consider whether > the > > > > >> bounded > > > > >> > > data > > > > >> > > > > set > > > > >> > > > > > needs to include a watermark which can trigger the > window > > > > >> result. > > > > >> > > > > > c) Constraints of data. The Timestamp Barrier should > > perform > > > > >> some > > > > >> > > > > specific > > > > >> > > > > > operations after computation in operators, for example, > > > force > > > > >> flush > > > > >> > > > data. > > > > >> > > > > > > > > > >> > > > > > Checkpoint Barrier misses all the semantics above, and > we > > > should > > > > >> > > > support > > > > >> > > > > > user to define Timestamp for data on Event Time or > System > > > Time > > > > >> > > > according > > > > >> > > > > to > > > > >> > > > > > the job and computation later. > > > > >> > > > > > > > > > >> > > > > > 2. The computation semantics of Fn > > > > >> > > > > > a) Deterministic computation > > > > >> > > > > > Most computations are deterministic such as map, filter, > > > count, > > > > >> sum > > > > >> > > and > > > > >> > > > > > ect. They generate the same unordered result from the > same > > > > >> > unordered > > > > >> > > > > input > > > > >> > > > > > every time, and we can easily define data consistency on > > the > > > > >> input > > > > >> > > and > > > > >> > > > > > output for them. > > > > >> > > > > > > > > > >> > > > > > b) Non-deterministic computation > > > > >> > > > > > Some computations are non-deterministic. They will > produce > > > > >> > different > > > > >> > > > > > results from the same input every time. I try to divide > > them > > > > >> into > > > > >> > the > > > > >> > > > > > following types: > > > > >> > > > > > 1) Non-deterministic computation semantics, such as rank > > > > >> operator. > > > > >> > > When > > > > >> > > > > it > > > > >> > > > > > computes multiple times (for example, failover), the > first > > > or > > > > >> last > > > > >> > > > output > > > > >> > > > > > results can both be the final result which will cause > > > different > > > > >> > > > failover > > > > >> > > > > > handlers for downstream jobs. I will expand it later. > > > > >> > > > > > 2) Non-deterministic computation optimization, such as > > async > > > > >> io. It > > > > >> > > is > > > > >> > > > > > necessary to sync these operations when the barrier of > > input > > > > >> > arrives. > > > > >> > > > > > 3) Deviation caused by data segmentat and computation > > > semantics, > > > > >> > such > > > > >> > > > as > > > > >> > > > > > Window. This requires that the users should customize > the > > > data > > > > >> > > > > segmentation > > > > >> > > > > > according to their needs correctly. > > > > >> > > > > > > > > > >> > > > > > Checkpoint Barrier matches a) and Timestamp Barrier can > > > match > > > > >> all > > > > >> > a) > > > > >> > > > and > > > > >> > > > > > b). > > > > >> > > > > > > > > > >> > > > > > We define data consistency of BInput and BOutput based > all > > > > >> above. > > > > >> > The > > > > >> > > > > > BOutput of upstream ETL will be the BInput of the next > > ETL, > > > and > > > > >> > > > multiple > > > > >> > > > > > ETL jobs form a complex "ETL Topology". > > > > >> > > > > > > > > > >> > > > > > Based on the above definitions, I'd like to give a > general > > > > >> proposal > > > > >> > > > with > > > > >> > > > > > "Timetamp Barrier" in my mind, it's not very detailed > and > > > please > > > > >> > help > > > > >> > > > to > > > > >> > > > > > review it and feel free to comment @David, @Piotr > > > > >> > > > > > > > > > >> > > > > > 1. Data segment with Timestamp > > > > >> > > > > > a) Users can define the Timestamp Barrier with System > > Time, > > > > >> Event > > > > >> > > Time. > > > > >> > > > > > b) Source nodes generate the same Timestamp Barrier > after > > > > >> reading > > > > >> > > data > > > > >> > > > > > from RootTable > > > > >> > > > > > c) There is a same Timetamp data in each record > according > > to > > > > >> > > Timestamp > > > > >> > > > > > Barrier, such as (a, T), (b, T), (c, T), (T, barrier) > > > > >> > > > > > > > > > >> > > > > > 2. Computation with Timestamp > > > > >> > > > > > a) Records are unordered with the same Timestamp. > > Stateless > > > > >> > operators > > > > >> > > > > such > > > > >> > > > > > as map/flatmap/filter can process data without aligning > > > > >> Timestamp > > > > >> > > > > Barrier, > > > > >> > > > > > which is different from Checkpoint Barrier. > > > > >> > > > > > b) Records between Timestamp are ordered. Stateful > > operators > > > > >> must > > > > >> > > align > > > > >> > > > > > data and compute by each Timestamp, then compute by > > Timetamp > > > > >> > > sequence. > > > > >> > > > > > c) Stateful operators will output results of specific > > > Timestamp > > > > >> > after > > > > >> > > > > > computation. > > > > >> > > > > > d) Sink operator "commit records" with specific > Timestamp > > > and > > > > >> > report > > > > >> > > > the > > > > >> > > > > > status to JobManager > > > > >> > > > > > > > > > >> > > > > > 3. Read data with Timestamp > > > > >> > > > > > a) Downstream ETL reads data according to Timestamp > after > > > > >> upstream > > > > >> > > ETL > > > > >> > > > > > "commit" it. > > > > >> > > > > > b) Stateful operators interact with state when computing > > > data of > > > > >> > > > > > Timestamp, but they won't trigger checkpoint for every > > > > >> Timestamp. > > > > >> > > > > Therefore > > > > >> > > > > > source ETL job can generate Timestamp every few seconds > or > > > even > > > > >> > > > hundreds > > > > >> > > > > of > > > > >> > > > > > milliseconds > > > > >> > > > > > c) Based on Timestamp the delay between ETL jobs will be > > > very > > > > >> > small, > > > > >> > > > and > > > > >> > > > > > in the best case the E2E latency maybe only tens of > > seconds. > > > > >> > > > > > > > > > >> > > > > > 4. Failover and Recovery > > > > >> > > > > > ETL jobs are cascaded through the Intermediate Table. > > After > > > a > > > > >> > single > > > > >> > > > ETL > > > > >> > > > > > job fails, it needs to replay the input data and > recompute > > > the > > > > >> > > results. > > > > >> > > > > As > > > > >> > > > > > you mentioned, whether the cascaded ETL jobs are > restarted > > > > >> depends > > > > >> > on > > > > >> > > > the > > > > >> > > > > > determinacy of the intermediate data between them. > > > > >> > > > > > a) An ETL job will rollback and reread data from > upstream > > > ETL by > > > > >> > > > specific > > > > >> > > > > > Timestamp according to the Checkpoint. > > > > >> > > > > > b) According to the management of Checkpoint and > > Timestamp, > > > ETL > > > > >> can > > > > >> > > > > replay > > > > >> > > > > > all Timestamp and data after failover, which means > BInput > > > is the > > > > >> > same > > > > >> > > > > > before and after failover. > > > > >> > > > > > > > > > >> > > > > > c) For deterministic Fn, it generates the same BOutput > > from > > > the > > > > >> > same > > > > >> > > > > BInput > > > > >> > > > > > 1) If there's no data of the specific Timestamp in the > > sink > > > > >> table, > > > > >> > > ETL > > > > >> > > > > > just "commit" it as normal. > > > > >> > > > > > 2) If the Timestamp data exists in the sink table, ETL > can > > > just > > > > >> > > discard > > > > >> > > > > > the new data. > > > > >> > > > > > > > > > >> > > > > > d) For non-deterministic Fn, it generates different > > BOutput > > > from > > > > >> > the > > > > >> > > > same > > > > >> > > > > > BInput before and after failover. For example, BOutput1 > > > before > > > > >> > > failover > > > > >> > > > > and > > > > >> > > > > > BOutput2 after failover. The state in ETL is consistent > > with > > > > >> > > BOutput2. > > > > >> > > > > > There are two cases according to users' requirements > > > > >> > > > > > 1) Users can accept BOutput1 as the final output and > > > downstream > > > > >> > ETLs > > > > >> > > > > don't > > > > >> > > > > > need to restart. Sink in ETL can discard BOutput2 > directly > > > if > > > > >> the > > > > >> > > > > Timestamp > > > > >> > > > > > exists in the sink table. > > > > >> > > > > > 2) Users only accept BOutput2 as the final output, then > > all > > > the > > > > >> > > > > downstream > > > > >> > > > > > ETLs and Intermediate Table should rollback to specific > > > > >> Timestamp, > > > > >> > > the > > > > >> > > > > > downstream ETLs should be restarted too. > > > > >> > > > > > > > > > >> > > > > > The following is a simple example. Data is transferred > > > between > > > > >> > ETL1, > > > > >> > > > ETL2 > > > > >> > > > > > and ETL3 in Intermediate Table by Timestamp. > > > > >> > > > > > [image: simple_example.jpg] > > > > >> > > > > > > > > > >> > > > > > Besides Timestamp, there's a big challenge in > Intermediate > > > > >> Table. > > > > >> > It > > > > >> > > > > > should support a highly implemented "commit Timestamp > > > snapshot" > > > > >> > with > > > > >> > > > high > > > > >> > > > > > throughput, which requires the Table Store to enhance > > > streaming > > > > >> > > > > > capabilities like pulsar or kafka. > > > > >> > > > > > > > > > >> > > > > > In this FLIP, we plan to implement the proposal with > > > Checkpoint, > > > > >> > the > > > > >> > > > > above > > > > >> > > > > > Timestamp can be replaced by Checkpoint. Of course, > > > Checkpoint > > > > >> has > > > > >> > > some > > > > >> > > > > > problems. I think we have reached some consensus in the > > > > >> discussion > > > > >> > > > about > > > > >> > > > > > the Checkpoint problems, including data segment > semantics, > > > flush > > > > >> > data > > > > >> > > > of > > > > >> > > > > > some operators, and the increase of E2E delay. However, > > > from the > > > > >> > > > > > perspective of implementation complexity, I personally > > think > > > > >> using > > > > >> > > > > > Checkpoint in the first phase makes sense, what do you > > > think? > > > > >> > > > > > > > > > >> > > > > > Finally, I think I misunderstood the "Rolling > Checkpoint" > > > and > > > > >> "All > > > > >> > at > > > > >> > > > > once > > > > >> > > > > > Checkpoint" in my last explanation which you and @David > > > > >> mentioned. > > > > >> > I > > > > >> > > > > > thought their differences were mainly to select > different > > > table > > > > >> > > > versions > > > > >> > > > > > for queries. According to your reply, I think it is > > whether > > > > >> there > > > > >> > are > > > > >> > > > > > multiple "rolling checkpoints" in each ETL job, right? > If > > I > > > > >> > > understand > > > > >> > > > > > correctly, the "Rolling Checkpoint" is a good idea, and > we > > > can > > > > >> > > > guarantee > > > > >> > > > > > "Strong Data Consistency" between multiple tables in > > > MetaService > > > > >> > for > > > > >> > > > > > queries. Thanks. > > > > >> > > > > > > > > > >> > > > > > Best, > > > > >> > > > > > Shammon > > > > >> > > > > > > > > > >> > > > > > > > > > >> > > > > > On Tue, Dec 13, 2022 at 9:36 PM Piotr Nowojski < > > > > >> > pnowoj...@apache.org > > > > >> > > > > > > > >> > > > > > wrote: > > > > >> > > > > > > > > > >> > > > > >> Hi Shammon, > > > > >> > > > > >> > > > > >> > > > > >> Thanks for the explanations, I think I understand the > > > problem > > > > >> > better > > > > >> > > > > now. > > > > >> > > > > >> I have a couple of follow up questions, but first: > > > > >> > > > > >> > > > > >> > > > > >> >> 3. I'm pretty sure there are counter examples, where > > > your > > > > >> > > proposed > > > > >> > > > > >> mechanism of using checkpoints (even aligned!) will > > produce > > > > >> > > > > >> inconsistent data from the perspective of the event > time. > > > > >> > > > > >> >> a) For example what if one of your "ETL" jobs, has > > the > > > > >> > following > > > > >> > > > > DAG: > > > > >> > > > > >> >> > > > > >> > > > > >> >> Even if you use aligned checkpoints for committing > > the > > > > >> data to > > > > >> > > the > > > > >> > > > > >> sink table, the watermarks of "Window1" and "Window2" > are > > > > >> > completely > > > > >> > > > > >> independent. The sink table might easily have data from > > the > > > > >> > > > Src1/Window1 > > > > >> > > > > >> from the event time T1 and Src2/Window2 from later > event > > > time > > > > >> T2. > > > > >> > > > > >> >> b) I think the same applies if you have two > > completely > > > > >> > > > > >> independent ETL jobs writing either to the same sink > > > table, or > > > > >> two > > > > >> > > to > > > > >> > > > > >> different sink tables (that are both later used in the > > same > > > > >> > > downstream > > > > >> > > > > job). > > > > >> > > > > >> > > > > > >> > > > > >> > Thank you for your feedback. I cannot see the DAG in > > 3.a > > > in > > > > >> your > > > > >> > > > > reply, > > > > >> > > > > >> > > > > >> > > > > >> I've attached the image directly. I hope you can see it > > > now. > > > > >> > > > > >> > > > > >> > > > > >> Basically what I meant is that if you have a topology > > like > > > > >> (from > > > > >> > the > > > > >> > > > > >> attached image): > > > > >> > > > > >> > > > > >> > > > > >> window1 = src1.keyBy(...).window(...) > > > > >> > > > > >> window2 = src2.keyBy(...).window(...) > > > > >> > > > > >> window1.join(window2, ...).addSink(sink) > > > > >> > > > > >> > > > > >> > > > > >> or with even simpler (note no keyBy between `src` and > > > > >> `process`): > > > > >> > > > > >> > > > > >> > > > > >> > > src.process(some_function_that_buffers_data)..addSink(sink) > > > > >> > > > > >> > > > > >> > > > > >> you will have the same problem. Generally speaking if > > > there is > > > > >> an > > > > >> > > > > >> operator buffering some data, and if the data are not > > > flushed > > > > >> on > > > > >> > > every > > > > >> > > > > >> checkpoint (any windowed or temporal operator, > > > > >> AsyncWaitOperator, > > > > >> > > CEP, > > > > >> > > > > >> ...), you can design a graph that will produce > > > "inconsistent" > > > > >> data > > > > >> > > as > > > > >> > > > > part > > > > >> > > > > >> of a checkpoint. > > > > >> > > > > >> > > > > >> > > > > >> Apart from that a couple of other questions/issues. > > > > >> > > > > >> > > > > >> > > > > >> > 1) Global Checkpoint Commit: a) "rolling fashion" or > b) > > > > >> > altogether > > > > >> > > > > >> > > > > >> > > > > >> Do we need to support the "altogether" one? Rolling > > > > >> checkpoint, as > > > > >> > > > it's > > > > >> > > > > >> more independent, I could see it scale much better, and > > > avoid a > > > > >> > lot > > > > >> > > of > > > > >> > > > > >> problems that I mentioned before. > > > > >> > > > > >> > > > > >> > > > > >> > 1) Checkpoint VS Watermark > > > > >> > > > > >> > > > > > >> > > > > >> > 1. Stateful Computation is aligned according to > > Timestamp > > > > >> > Barrier > > > > >> > > > > >> > > > > >> > > > > >> Indeed the biggest obstacle I see here, is that we > would > > > indeed > > > > >> > most > > > > >> > > > > >> likely have: > > > > >> > > > > >> > > > > >> > > > > >> > b) Similar to the window operator, align data in > memory > > > > >> > according > > > > >> > > to > > > > >> > > > > >> Timestamp. > > > > >> > > > > >> > > > > >> > > > > >> for every operator. > > > > >> > > > > >> > > > > >> > > > > >> > 4. Failover supports Timestamp fine-grained data > > recovery > > > > >> > > > > >> > > > > > >> > > > > >> > As we mentioned in the FLIP, each ETL is a complex > > single > > > > >> node. > > > > >> > A > > > > >> > > > > single > > > > >> > > > > >> > ETL job failover should not cause the failure of the > > > entire > > > > >> "ETL > > > > >> > > > > >> Topology". > > > > >> > > > > >> > > > > >> > > > > >> I don't understand this point. Regardless if we are > using > > > > >> > > > > >> rolling checkpoints, all at once checkpoints or > > > watermarks, I > > > > >> see > > > > >> > > the > > > > >> > > > > same > > > > >> > > > > >> problems with non determinism, if we want to preserve > the > > > > >> > > requirement > > > > >> > > > to > > > > >> > > > > >> not fail over the whole topology at once. > > > > >> > > > > >> > > > > >> > > > > >> Both Watermarks and "rolling checkpoint" I think have > the > > > same > > > > >> > > issue, > > > > >> > > > > >> that either require deterministic logic, or global > > > failover, or > > > > >> > > > > downstream > > > > >> > > > > >> jobs can only work on the already committed by the > > upstream > > > > >> > records. > > > > >> > > > But > > > > >> > > > > >> working with only "committed records" would either > brake > > > > >> > consistency > > > > >> > > > > >> between different jobs, or would cause huge delay in > > > > >> checkpointing > > > > >> > > and > > > > >> > > > > e2e > > > > >> > > > > >> latency, as: > > > > >> > > > > >> 1. upstream job has to produce some data, downstream > can > > > not > > > > >> > process > > > > >> > > > it, > > > > >> > > > > >> downstream can not process this data yet > > > > >> > > > > >> 2. checkpoint 42 is triggered on the upstream job > > > > >> > > > > >> 3. checkpoint 42 is completed on the upstream job, data > > > > >> processed > > > > >> > > > since > > > > >> > > > > >> last checkpoint has been committed > > > > >> > > > > >> 4. upstream job can continue producing more data > > > > >> > > > > >> 5. only now downstream can start processing the data > > > produced > > > > >> in > > > > >> > 1., > > > > >> > > > but > > > > >> > > > > >> it can not read the not-yet-committed data from 4. > > > > >> > > > > >> 6. once downstream finishes processing data from 1., it > > can > > > > >> > trigger > > > > >> > > > > >> checkpoint 42 > > > > >> > > > > >> > > > > >> > > > > >> The "all at once checkpoint", I can see only working > with > > > > >> global > > > > >> > > > > failover > > > > >> > > > > >> of everything. > > > > >> > > > > >> > > > > >> > > > > >> This is assuming exactly-once mode. at-least-once would > > be > > > much > > > > >> > > > easier. > > > > >> > > > > >> > > > > >> > > > > >> Best, > > > > >> > > > > >> Piotrek > > > > >> > > > > >> > > > > >> > > > > >> wt., 13 gru 2022 o 08:57 Shammon FY <zjur...@gmail.com > > > > > > >> > napisał(a): > > > > >> > > > > >> > > > > >> > > > > >>> Hi David, > > > > >> > > > > >>> > > > > >> > > > > >>> Thanks for the comments from you and @Piotr. I'd like > to > > > > >> explain > > > > >> > > the > > > > >> > > > > >>> details about the FLIP first. > > > > >> > > > > >>> > > > > >> > > > > >>> 1) Global Checkpoint Commit: a) "rolling fashion" or > b) > > > > >> > altogether > > > > >> > > > > >>> > > > > >> > > > > >>> This mainly depends on the needs of users. Users can > > > decide > > > > >> the > > > > >> > > data > > > > >> > > > > >>> version of tables in their queries according to > > different > > > > >> > > > requirements > > > > >> > > > > >>> for > > > > >> > > > > >>> data consistency and freshness. Since we manage > multiple > > > > >> versions > > > > >> > > for > > > > >> > > > > >>> each > > > > >> > > > > >>> table, this will not bring too much complexity to the > > > system. > > > > >> We > > > > >> > > only > > > > >> > > > > >>> need > > > > >> > > > > >>> to support different strategies when calculating table > > > > >> versions > > > > >> > for > > > > >> > > > > >>> query. > > > > >> > > > > >>> So we give this decision to users, who can use > > > > >> "consistency.type" > > > > >> > > to > > > > >> > > > > set > > > > >> > > > > >>> different consistency in "Catalog". We can continue to > > > refine > > > > >> > this > > > > >> > > > > later. > > > > >> > > > > >>> For example, dynamic parameters support different > > > consistency > > > > >> > > > > >>> requirements > > > > >> > > > > >>> for each query > > > > >> > > > > >>> > > > > >> > > > > >>> 2) MetaService module > > > > >> > > > > >>> > > > > >> > > > > >>> Many Flink streaming jobs use application mode, and > they > > > are > > > > >> > > > > independent > > > > >> > > > > >>> of > > > > >> > > > > >>> each other. So we currently assume that MetaService is > > an > > > > >> > > independent > > > > >> > > > > >>> node. > > > > >> > > > > >>> In the first phase, it will be started in standalone, > > and > > > HA > > > > >> will > > > > >> > > be > > > > >> > > > > >>> supported later. This node will reuse many Flink > > modules, > > > > >> > including > > > > >> > > > > REST, > > > > >> > > > > >>> Gateway-RpcServer, etc. We hope that the core > functions > > of > > > > >> > > > MetaService > > > > >> > > > > >>> can > > > > >> > > > > >>> be developed as a component. When Flink subsequently > > uses > > > a > > > > >> large > > > > >> > > > > session > > > > >> > > > > >>> cluster to support various computations, it can be > > > integrated > > > > >> > into > > > > >> > > > the > > > > >> > > > > >>> "ResourceManager" as a plug-in component. > > > > >> > > > > >>> > > > > >> > > > > >>> Besides above, I'd like to describe the Checkpoint and > > > > >> Watermark > > > > >> > > > > >>> mechanisms > > > > >> > > > > >>> in detail as follows. > > > > >> > > > > >>> > > > > >> > > > > >>> 1) Checkpoint VS Watermark > > > > >> > > > > >>> > > > > >> > > > > >>> As you mentioned, I think it's very correct that what > we > > > want > > > > >> in > > > > >> > > the > > > > >> > > > > >>> Checkpoint is to align streaming computation and data > > > > >> according > > > > >> > to > > > > >> > > > > >>> certain > > > > >> > > > > >>> semantics. Timestamp is a very ideal solution. To > > achieve > > > this > > > > >> > > goal, > > > > >> > > > we > > > > >> > > > > >>> can > > > > >> > > > > >>> think of the following functions that need to be > > > supported in > > > > >> the > > > > >> > > > > >>> Watermark > > > > >> > > > > >>> mechanism: > > > > >> > > > > >>> > > > > >> > > > > >>> 1. Stateful Computation is aligned according to > > Timestamp > > > > >> Barrier > > > > >> > > > > >>> > > > > >> > > > > >>> As the "three tables example" we discussed above, we > > need > > > to > > > > >> > align > > > > >> > > > the > > > > >> > > > > >>> stateful operator computation according to the barrier > > to > > > > >> ensure > > > > >> > > the > > > > >> > > > > >>> consistency of the result data. In order to align the > > > > >> > computation, > > > > >> > > > > there > > > > >> > > > > >>> are two ways in my mind > > > > >> > > > > >>> > > > > >> > > > > >>> a) Similar to the Aligned Checkpoint Barrier. > Timestamp > > > > >> Barrier > > > > >> > > > aligns > > > > >> > > > > >>> data > > > > >> > > > > >>> according to the channel, which will lead to > > backpressure > > > just > > > > >> > like > > > > >> > > > the > > > > >> > > > > >>> aligned checkpoint. It seems not a good idea. > > > > >> > > > > >>> > > > > >> > > > > >>> b) Similar to the window operator, align data in > memory > > > > >> according > > > > >> > > to > > > > >> > > > > >>> Timestamp. Two steps need to be supported here: first, > > > data is > > > > >> > > > aligned > > > > >> > > > > by > > > > >> > > > > >>> timestamp for state operators; secondly, Timestamp is > > > strictly > > > > >> > > > > >>> sequential, > > > > >> > > > > >>> global aggregation operators need to perform > aggregation > > > in > > > > >> > > timestamp > > > > >> > > > > >>> order > > > > >> > > > > >>> and output the final results. > > > > >> > > > > >>> > > > > >> > > > > >>> 2. Coordinate multiple source nodes to assign unified > > > > >> Timestamp > > > > >> > > > > Barriers > > > > >> > > > > >>> > > > > >> > > > > >>> Since the stateful operator needs to be aligned > > according > > > to > > > > >> the > > > > >> > > > > >>> Timestamp > > > > >> > > > > >>> Barrier, source subtasks of multiple jobs should > > generate > > > the > > > > >> > same > > > > >> > > > > >>> Timestamp Barrier. ETL jobs consuming RootTable should > > > > >> interact > > > > >> > > with > > > > >> > > > > >>> "MetaService" to generate the same Timestamp T1, T2, > T3 > > > ... > > > > >> and > > > > >> > so > > > > >> > > > on. > > > > >> > > > > >>> > > > > >> > > > > >>> 3. JobManager needs to manage the completed Timestamp > > > Barrier > > > > >> > > > > >>> > > > > >> > > > > >>> When the Timestamp Barrier of the ETL job has been > > > completed, > > > > >> it > > > > >> > > > means > > > > >> > > > > >>> that > > > > >> > > > > >>> the data of the specified Timestamp can be queried by > > > users. > > > > >> > > > JobManager > > > > >> > > > > >>> needs to summarize its Timestamp processing and report > > the > > > > >> > > completed > > > > >> > > > > >>> Timestamp and data snapshots to the MetaServer. > > > > >> > > > > >>> > > > > >> > > > > >>> 4. Failover supports Timestamp fine-grained data > > recovery > > > > >> > > > > >>> > > > > >> > > > > >>> As we mentioned in the FLIP, each ETL is a complex > > single > > > > >> node. A > > > > >> > > > > single > > > > >> > > > > >>> ETL job failover should not cause the failure of the > > > entire > > > > >> "ETL > > > > >> > > > > >>> Topology". > > > > >> > > > > >>> This requires that the result data of Timestamp > > generated > > > by > > > > >> > > upstream > > > > >> > > > > ETL > > > > >> > > > > >>> should be deterministic. > > > > >> > > > > >>> > > > > >> > > > > >>> a) The determinacy of Timestamp, that is, before and > > > after ETL > > > > >> > job > > > > >> > > > > >>> failover, the same Timestamp sequence must be > generated. > > > Each > > > > >> > > > > Checkpoint > > > > >> > > > > >>> needs to record the included Timestamp list, > especially > > > the > > > > >> > source > > > > >> > > > node > > > > >> > > > > >>> of > > > > >> > > > > >>> the RootTable. After Failover, it needs to regenerate > > > > >> Timestamp > > > > >> > > > > according > > > > >> > > > > >>> to the Timestamp list. > > > > >> > > > > >>> > > > > >> > > > > >>> b) The determinacy of Timestamp data, that is, the > same > > > > >> Timestamp > > > > >> > > > needs > > > > >> > > > > >>> to > > > > >> > > > > >>> replay the same data before and after Failover, and > > > generate > > > > >> the > > > > >> > > same > > > > >> > > > > >>> results in Sink Table. Each Timestamp must save start > > and > > > end > > > > >> > > offsets > > > > >> > > > > (or > > > > >> > > > > >>> snapshot id) of RootTable. After failover, the source > > > nodes > > > > >> need > > > > >> > to > > > > >> > > > > >>> replay > > > > >> > > > > >>> the data according to the offset to ensure that the > data > > > of > > > > >> each > > > > >> > > > > >>> Timestamp > > > > >> > > > > >>> is consistent before and after Failover. > > > > >> > > > > >>> > > > > >> > > > > >>> For the specific requirements and complexity, please > > help > > > to > > > > >> > review > > > > >> > > > > when > > > > >> > > > > >>> you are free @David @Piotr, thanks :) > > > > >> > > > > >>> > > > > >> > > > > >>> 2) Evolution from Checkpoint to Timestamp Mechanism > > > > >> > > > > >>> > > > > >> > > > > >>> You give a very important question in your reply > which I > > > > >> missed > > > > >> > > > before: > > > > >> > > > > >>> if > > > > >> > > > > >>> Aligned Checkpoint is used in the first stage, how > > > complex is > > > > >> the > > > > >> > > > > >>> evolution > > > > >> > > > > >>> from Checkpoint to Timestamp later? I made a general > > > > >> comparison > > > > >> > > here, > > > > >> > > > > >>> which > > > > >> > > > > >>> may not be very detailed. There are three roles in the > > > whole > > > > >> > > system: > > > > >> > > > > >>> MetaService, Flink ETL Job and Table Store. > > > > >> > > > > >>> > > > > >> > > > > >>> a) MetaService > > > > >> > > > > >>> > > > > >> > > > > >>> It manages the data consistency among multiple ETL > jobs, > > > > >> > including > > > > >> > > > > >>> coordinating the Barrier for the Source ETL nodes, > > > setting the > > > > >> > > > starting > > > > >> > > > > >>> Barrier for ETL job startup, and calculating the Table > > > version > > > > >> > for > > > > >> > > > > >>> queries > > > > >> > > > > >>> according to different strategies. It has little to do > > > with > > > > >> > > > Checkpoint > > > > >> > > > > in > > > > >> > > > > >>> fact, we can pay attention to it when designing the > API > > > and > > > > >> > > > > implementing > > > > >> > > > > >>> the functions. > > > > >> > > > > >>> > > > > >> > > > > >>> b) Flink ETL Job > > > > >> > > > > >>> > > > > >> > > > > >>> At present, the workload is relatively small and we > need > > > to > > > > >> > trigger > > > > >> > > > > >>> checkpoints in CheckpointCoordinator manually by > > > > >> SplitEnumerator. > > > > >> > > > > >>> > > > > >> > > > > >>> c) Table Store > > > > >> > > > > >>> > > > > >> > > > > >>> Table Store mainly provides the ability to write and > > read > > > > >> data. > > > > >> > > > > >>> > > > > >> > > > > >>> c.1) Write data. At present, Table Store generates > > > snapshots > > > > >> > > > according > > > > >> > > > > to > > > > >> > > > > >>> two phases in Flink. When using Checkpoint as > > consistency > > > > >> > > management, > > > > >> > > > > we > > > > >> > > > > >>> need to write checkpoint information to snapshots. > After > > > using > > > > >> > > > > Timestamp > > > > >> > > > > >>> Barrier, the snapshot in Table Store may be > disassembled > > > more > > > > >> > > finely, > > > > >> > > > > and > > > > >> > > > > >>> we need to write Timestamp information to the data > > file. A > > > > >> > > > > "checkpointed > > > > >> > > > > >>> snapshot" may contain multiple "Timestamp snapshots". > > > > >> > > > > >>> > > > > >> > > > > >>> c.2) Read data. The SplitEnumerator that reads data > from > > > the > > > > >> > Table > > > > >> > > > > Store > > > > >> > > > > >>> will manage multiple splits according to the version > > > number. > > > > >> > After > > > > >> > > > the > > > > >> > > > > >>> specified splits are completed, it sends a Barrier > > > command to > > > > >> > > > trigger a > > > > >> > > > > >>> checkpoint in the ETL job. The source node will > > broadcast > > > the > > > > >> > > > > checkpoint > > > > >> > > > > >>> barrier downstream after receiving it. When using > > > Timestamp > > > > >> > > Barrier, > > > > >> > > > > the > > > > >> > > > > >>> overall process is similar, but the SplitEnumerator > does > > > not > > > > >> need > > > > >> > > to > > > > >> > > > > >>> trigger a checkpoint to the Flink ETL, and the Source > > node > > > > >> needs > > > > >> > to > > > > >> > > > > >>> support > > > > >> > > > > >>> broadcasting Timestamp Barrier to the downstream at > that > > > time. > > > > >> > > > > >>> > > > > >> > > > > >>> From the above overall, the evolution complexity from > > > > >> Checkpoint > > > > >> > to > > > > >> > > > > >>> Timestamp seems controllable, but the specific > > > implementation > > > > >> > needs > > > > >> > > > > >>> careful > > > > >> > > > > >>> design, and the concept and features of Checkpoint > > should > > > not > > > > >> be > > > > >> > > > > >>> introduced > > > > >> > > > > >>> too much into relevant interfaces and functions. > > > > >> > > > > >>> > > > > >> > > > > >>> What do you think of it? Looking forward to your > > feedback, > > > > >> thanks > > > > >> > > > > >>> > > > > >> > > > > >>> Best, > > > > >> > > > > >>> Shammon > > > > >> > > > > >>> > > > > >> > > > > >>> > > > > >> > > > > >>> > > > > >> > > > > >>> On Mon, Dec 12, 2022 at 11:46 PM David Morávek < > > > > >> d...@apache.org> > > > > >> > > > > wrote: > > > > >> > > > > >>> > > > > >> > > > > >>> > Hi Shammon, > > > > >> > > > > >>> > > > > > >> > > > > >>> > I'm starting to see what you're trying to achieve, > and > > > it's > > > > >> > > really > > > > >> > > > > >>> > exciting. I share Piotr's concerns about e2e latency > > and > > > > >> > > disability > > > > >> > > > > to > > > > >> > > > > >>> use > > > > >> > > > > >>> > unaligned checkpoints. > > > > >> > > > > >>> > > > > > >> > > > > >>> > I have a couple of questions that are not clear to > me > > > from > > > > >> > going > > > > >> > > > over > > > > >> > > > > >>> the > > > > >> > > > > >>> > FLIP: > > > > >> > > > > >>> > > > > > >> > > > > >>> > 1) Global Checkpoint Commit > > > > >> > > > > >>> > > > > > >> > > > > >>> > Are you planning on committing the checkpoints in > a) a > > > > >> "rolling > > > > >> > > > > >>> fashion" - > > > > >> > > > > >>> > one pipeline after another, or b) altogether - once > > the > > > data > > > > >> > have > > > > >> > > > > been > > > > >> > > > > >>> > processed by all pipelines? > > > > >> > > > > >>> > > > > > >> > > > > >>> > Option a) would be eventually consistent (for batch > > > queries, > > > > >> > > you'd > > > > >> > > > > >>> need to > > > > >> > > > > >>> > use the last checkpoint produced by the most > > downstream > > > > >> table), > > > > >> > > > > >>> whereas b) > > > > >> > > > > >>> > would be strongly consistent at the cost of > increasing > > > the > > > > >> e2e > > > > >> > > > > latency > > > > >> > > > > >>> even > > > > >> > > > > >>> > more. > > > > >> > > > > >>> > > > > > >> > > > > >>> > I feel that option a) is what this should be headed > > for. > > > > >> > > > > >>> > > > > > >> > > > > >>> > 2) MetaService > > > > >> > > > > >>> > > > > > >> > > > > >>> > Should this be a new general Flink component or one > > > > >> specific to > > > > >> > > the > > > > >> > > > > >>> Flink > > > > >> > > > > >>> > Table Store? > > > > >> > > > > >>> > > > > > >> > > > > >>> > 3) Follow-ups > > > > >> > > > > >>> > > > > > >> > > > > >>> > From the above discussion, there is a consensus > that, > > > in the > > > > >> > > ideal > > > > >> > > > > >>> case, > > > > >> > > > > >>> > watermarks would be a way to go, but there is some > > > > >> underlying > > > > >> > > > > mechanism > > > > >> > > > > >>> > missing. It would be great to discuss this option in > > > more > > > > >> > detail > > > > >> > > to > > > > >> > > > > >>> compare > > > > >> > > > > >>> > the solutions in terms of implementation cost, maybe > > it > > > > >> could > > > > >> > not > > > > >> > > > be > > > > >> > > > > as > > > > >> > > > > >>> > complex. > > > > >> > > > > >>> > > > > > >> > > > > >>> > > > > > >> > > > > >>> > All in all, I don't feel that checkpoints are > suitable > > > for > > > > >> > > > providing > > > > >> > > > > >>> > consistent table versioning between multiple > > pipelines. > > > The > > > > >> > main > > > > >> > > > > >>> reason is > > > > >> > > > > >>> > that they are designed to be a fault tolerance > > > mechanism. > > > > >> > > Somewhere > > > > >> > > > > >>> between > > > > >> > > > > >>> > the lines, you've already noted that the primitive > > > you're > > > > >> > looking > > > > >> > > > for > > > > >> > > > > >>> is > > > > >> > > > > >>> > cross-pipeline barrier alignment, which is the > > > mechanism a > > > > >> > subset > > > > >> > > > of > > > > >> > > > > >>> > currently supported checkpointing implementations > > > happen to > > > > >> be > > > > >> > > > using. > > > > >> > > > > >>> Is > > > > >> > > > > >>> > that correct? > > > > >> > > > > >>> > > > > > >> > > > > >>> > My biggest concern is that tying this with a > > > "side-effect" > > > > >> of > > > > >> > the > > > > >> > > > > >>> > checkpointing mechanism could block us from evolving > > it > > > > >> > further. > > > > >> > > > > >>> > > > > > >> > > > > >>> > Best, > > > > >> > > > > >>> > D. > > > > >> > > > > >>> > > > > > >> > > > > >>> > On Mon, Dec 12, 2022 at 6:11 AM Shammon FY < > > > > >> zjur...@gmail.com> > > > > >> > > > > wrote: > > > > >> > > > > >>> > > > > > >> > > > > >>> > > Hi Piotr, > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > Thank you for your feedback. I cannot see the DAG > in > > > 3.a > > > > >> in > > > > >> > > your > > > > >> > > > > >>> reply, > > > > >> > > > > >>> > but > > > > >> > > > > >>> > > I'd like to answer some questions first. > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > Your understanding is very correct. We want to > align > > > the > > > > >> data > > > > >> > > > > >>> versions of > > > > >> > > > > >>> > > all intermediate tables through checkpoint > mechanism > > > in > > > > >> > Flink. > > > > >> > > > I'm > > > > >> > > > > >>> sorry > > > > >> > > > > >>> > > that I have omitted some default constraints in > > FLIP, > > > > >> > including > > > > >> > > > > only > > > > >> > > > > >>> > > supporting aligned checkpoints; one table can only > > be > > > > >> written > > > > >> > > by > > > > >> > > > > one > > > > >> > > > > >>> ETL > > > > >> > > > > >>> > > job. I will add these later. > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > Why can't the watermark mechanism achieve the data > > > > >> > consistency > > > > >> > > we > > > > >> > > > > >>> wanted? > > > > >> > > > > >>> > > For example, there are 3 tables, Table1 is word > > table, > > > > >> Table2 > > > > >> > > is > > > > >> > > > > >>> > word->cnt > > > > >> > > > > >>> > > table and Table3 is cnt1->cnt2 table. > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > 1. ETL1 from Table1 to Table2: INSERT INTO Table2 > > > SELECT > > > > >> > word, > > > > >> > > > > >>> count(*) > > > > >> > > > > >>> > > FROM Table1 GROUP BY word > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > 2. ETL2 from Table2 to Table3: INSERT INTO Table3 > > > SELECT > > > > >> cnt, > > > > >> > > > > >>> count(*) > > > > >> > > > > >>> > FROM > > > > >> > > > > >>> > > Table2 GROUP BY cnt > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > ETL1 has 2 subtasks to read multiple buckets from > > > Table1, > > > > >> > where > > > > >> > > > > >>> subtask1 > > > > >> > > > > >>> > > reads streaming data as [a, b, c, a, d, a, b, c, d > > > ...] > > > > >> and > > > > >> > > > > subtask2 > > > > >> > > > > >>> > reads > > > > >> > > > > >>> > > streaming data as [a, c, d, q, a, v, c, d ...]. > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > 1. Unbounded streaming data is divided into > multiple > > > sets > > > > >> > > > according > > > > >> > > > > >>> to > > > > >> > > > > >>> > some > > > > >> > > > > >>> > > semantic requirements. The most extreme may be one > > > set for > > > > >> > each > > > > >> > > > > data. > > > > >> > > > > >>> > > Assume that the sets of subtask1 and subtask2 > > > separated by > > > > >> > the > > > > >> > > > same > > > > >> > > > > >>> > > semantics are [a, b, c, a, d] and [a, c, d, q], > > > > >> respectively. > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > 2. After the above two sets are computed by ETL1, > > the > > > > >> result > > > > >> > > data > > > > >> > > > > >>> > generated > > > > >> > > > > >>> > > in Table 2 is [(a, 3), (b, 1), (c, 1), (d, 2), (q, > > > 1)]. > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > 3. The result data generated in Table 3 after the > > > data in > > > > >> > > Table 2 > > > > >> > > > > is > > > > >> > > > > >>> > > computed by ETL2 is [(1, 3), (2, 1), (3, 1)] > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > We want to align the data of Table1, Table2 and > > > Table3 and > > > > >> > > manage > > > > >> > > > > the > > > > >> > > > > >>> > data > > > > >> > > > > >>> > > versions. When users execute OLAP/Batch queries > join > > > on > > > > >> these > > > > >> > > > > >>> tables, the > > > > >> > > > > >>> > > following consistency data can be found > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > 1. Table1: [a, b, c, a, d] and [a, c, d, q] > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > 2. Table2: [a, 3], [b, 1], [c, 1], [d, 2], [q, 1] > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > 3. Table3: [1, 3], [2, 1], [3, 1] > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > Users can perform query: SELECT t1.word, t2.cnt, > > > t3.cnt2 > > > > >> from > > > > >> > > > > Table1 > > > > >> > > > > >>> t1 > > > > >> > > > > >>> > > JOIN Table2 t2 JOIN Table3 t3 on t1.word=t2.word > and > > > > >> > > > > t2.cnt=t3.cnt1; > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > In the view of users, the data is consistent on a > > > unified > > > > >> > > > "version" > > > > >> > > > > >>> > between > > > > >> > > > > >>> > > Table1, Table2 and Table3. > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > In the current Flink implementation, the aligned > > > > >> checkpoint > > > > >> > can > > > > >> > > > > >>> achieve > > > > >> > > > > >>> > the > > > > >> > > > > >>> > > above capabilities (let's ignore the segmentation > > > > >> semantics > > > > >> > of > > > > >> > > > > >>> checkpoint > > > > >> > > > > >>> > > first). Because the Checkpoint Barrier will align > > the > > > data > > > > >> > when > > > > >> > > > > >>> > performing > > > > >> > > > > >>> > > the global Count aggregation, we can associate the > > > > >> snapshot > > > > >> > > with > > > > >> > > > > the > > > > >> > > > > >>> > > checkpoint in the Table Store, query the specified > > > > >> snapshot > > > > >> > of > > > > >> > > > > >>> > > Table1/Table2/Table3 through the checkpoint, and > > > achieve > > > > >> the > > > > >> > > > > >>> consistency > > > > >> > > > > >>> > > requirements of the above unified "version". > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > Current watermark mechanism in Flink cannot > achieve > > > the > > > > >> above > > > > >> > > > > >>> > consistency. > > > > >> > > > > >>> > > For example, we use watermark to divide data into > > > multiple > > > > >> > sets > > > > >> > > > in > > > > >> > > > > >>> > subtask1 > > > > >> > > > > >>> > > and subtask2 as followed > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > 1. subtask1：[(a, T1), (b, T1), (c, T1), (a, T1), > (d, > > > T1)], > > > > >> > T1, > > > > >> > > > [(a, > > > > >> > > > > >>> T2), > > > > >> > > > > >>> > > (b, T2), (c, T2), (d, T2)], T2 > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > 2. subtask2: [(a, T1), (c, T1), (d, T1), (q, T1)], > > T1, > > > > >> .... > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > As Flink watermark does not have barriers and > cannot > > > align > > > > >> > > data, > > > > >> > > > > ETL1 > > > > >> > > > > >>> > Count > > > > >> > > > > >>> > > operator may compute the data of subtask1 first: > > [(a, > > > T1), > > > > >> > (b, > > > > >> > > > T1), > > > > >> > > > > >>> (c, > > > > >> > > > > >>> > > T1), (a, T1), (d, T1)], T1, [(a, T2), (b, T2)], > then > > > > >> compute > > > > >> > > the > > > > >> > > > > >>> data of > > > > >> > > > > >>> > > subtask2: [(a, T1), (c, T1), (d, T1), (q, T1)], > T1, > > > which > > > > >> is > > > > >> > > not > > > > >> > > > > >>> possible > > > > >> > > > > >>> > > in aligned checkpoint. > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > In this order, the result output to Table2 after > the > > > Count > > > > >> > > > > >>> aggregation > > > > >> > > > > >>> > will > > > > >> > > > > >>> > > be: (a, 1, T1), (b, 1, T1), (c, 1, T1), (a, 2, > T1), > > > (a, 3, > > > > >> > T2), > > > > >> > > > (b, > > > > >> > > > > >>> 2, > > > > >> > > > > >>> > T2), > > > > >> > > > > >>> > > (a, 4, T1), (c, 2, T1), (d, 1, T1), (q, 1, T1), > > which > > > can > > > > >> be > > > > >> > > > > >>> simplified > > > > >> > > > > >>> > as: > > > > >> > > > > >>> > > [(b, 1, T1), (a, 3, T2), (b, 2, T2), (a, 4, T1), > (c, > > > 2, > > > > >> T1), > > > > >> > > (d, > > > > >> > > > 1, > > > > >> > > > > >>> T1), > > > > >> > > > > >>> > > (q, 1, T1)] > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > There's no (a, 3, T1), we have been unable to > query > > > > >> > consistent > > > > >> > > > data > > > > >> > > > > >>> > results > > > > >> > > > > >>> > > on Table1 and Table2 according to T1. Table 3 has > > the > > > same > > > > >> > > > problem. > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > In addition to using Checkpoint Barrier, the other > > > > >> > > implementation > > > > >> > > > > >>> > > supporting watermark above is to convert Count > > > aggregation > > > > >> > into > > > > >> > > > > >>> Window > > > > >> > > > > >>> > > Count. After the global Count is converted into > > window > > > > >> > > operator, > > > > >> > > > it > > > > >> > > > > >>> needs > > > > >> > > > > >>> > > to support cross window data computation. Similar > to > > > the > > > > >> data > > > > >> > > > > >>> > relationship > > > > >> > > > > >>> > > between the previous and the current Checkpoint, > it > > is > > > > >> > > equivalent > > > > >> > > > > to > > > > >> > > > > >>> > > introducing the Watermark Barrier, which requires > > > > >> adjustments > > > > >> > > to > > > > >> > > > > the > > > > >> > > > > >>> > > current Flink Watermark mechanism. > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > Besides the above global aggregation, there are > > window > > > > >> > > operators > > > > >> > > > in > > > > >> > > > > >>> > Flink. > > > > >> > > > > >>> > > I don't know if my understanding is correct(I > cannot > > > see > > > > >> the > > > > >> > > DAG > > > > >> > > > in > > > > >> > > > > >>> your > > > > >> > > > > >>> > > example), please correct me if it's wrong. I think > > you > > > > >> raise > > > > >> > a > > > > >> > > > very > > > > >> > > > > >>> > > important and interesting question: how to define > > data > > > > >> > > > consistency > > > > >> > > > > in > > > > >> > > > > >>> > > different window computations which will generate > > > > >> different > > > > >> > > > > >>> timestamps of > > > > >> > > > > >>> > > the same data. This situation also occurs when > using > > > event > > > > >> > time > > > > >> > > > to > > > > >> > > > > >>> align > > > > >> > > > > >>> > > data. At present, what I can think of is to store > > > these > > > > >> > > > information > > > > >> > > > > >>> in > > > > >> > > > > >>> > > Table Store, users can perform filter or join on > > data > > > with > > > > >> > > them. > > > > >> > > > > This > > > > >> > > > > >>> > FLIP > > > > >> > > > > >>> > > is our first phase, and the specific > implementation > > of > > > > >> this > > > > >> > > will > > > > >> > > > be > > > > >> > > > > >>> > > designed and considered in the next phase and > FLIP. > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > Although the Checkpoint Barrier can achieve the > most > > > basic > > > > >> > > > > >>> consistency, > > > > >> > > > > >>> > as > > > > >> > > > > >>> > > you mentioned, using the Checkpoint mechanism will > > > cause > > > > >> many > > > > >> > > > > >>> problems, > > > > >> > > > > >>> > > including the increase of checkpoint time for > > multiple > > > > >> > cascade > > > > >> > > > > jobs, > > > > >> > > > > >>> the > > > > >> > > > > >>> > > increase of E2E data freshness time (several > minutes > > > or > > > > >> even > > > > >> > > > dozens > > > > >> > > > > >>> of > > > > >> > > > > >>> > > minutes), and the increase of the overall system > > > > >> complexity. > > > > >> > At > > > > >> > > > the > > > > >> > > > > >>> same > > > > >> > > > > >>> > > time, the semantics of Checkpoint data > segmentation > > is > > > > >> > unclear. > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > The current FLIP is the first phase of our whole > > > proposal, > > > > >> > and > > > > >> > > > you > > > > >> > > > > >>> can > > > > >> > > > > >>> > find > > > > >> > > > > >>> > > the follow-up plan in our future worker. In the > > first > > > > >> stage, > > > > >> > we > > > > >> > > > do > > > > >> > > > > >>> not > > > > >> > > > > >>> > want > > > > >> > > > > >>> > > to modify the Flink mechanism. We'd like to > realize > > > basic > > > > >> > > system > > > > >> > > > > >>> > functions > > > > >> > > > > >>> > > based on existing mechanisms in Flink, including > the > > > > >> > > relationship > > > > >> > > > > >>> > > management of ETL and tables, and the basic data > > > > >> consistency, > > > > >> > > so > > > > >> > > > we > > > > >> > > > > >>> > choose > > > > >> > > > > >>> > > Global Checkpoint in our FLIP. > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > We agree with you very much that event time is > more > > > > >> suitable > > > > >> > > for > > > > >> > > > > data > > > > >> > > > > >>> > > consistency management. We'd like consider this > > > matter in > > > > >> the > > > > >> > > > > second > > > > >> > > > > >>> or > > > > >> > > > > >>> > > third stage after the current FLIP. We hope to > > > improve the > > > > >> > > > > watermark > > > > >> > > > > >>> > > mechanism in Flink to support barriers. As you > > > mentioned > > > > >> in > > > > >> > > your > > > > >> > > > > >>> reply, > > > > >> > > > > >>> > we > > > > >> > > > > >>> > > can achieve data consistency based on timestamp, > > while > > > > >> > > > maintaining > > > > >> > > > > >>> E2E > > > > >> > > > > >>> > data > > > > >> > > > > >>> > > freshness of seconds or even milliseconds for 10+ > > > cascaded > > > > >> > > jobs. > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > What do you think? Thanks > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > Best, > > > > >> > > > > >>> > > Shammon > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > On Fri, Dec 9, 2022 at 6:13 PM Piotr Nowojski < > > > > >> > > > > pnowoj...@apache.org> > > > > >> > > > > >>> > > wrote: > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > > Hi Shammon, > > > > >> > > > > >>> > > > > > > > >> > > > > >>> > > > Do I understand it correctly, that you > effectively > > > want > > > > >> to > > > > >> > > > expand > > > > >> > > > > >>> the > > > > >> > > > > >>> > > > checkpoint alignment mechanism across many > > different > > > > >> jobs > > > > >> > and > > > > >> > > > > hand > > > > >> > > > > >>> over > > > > >> > > > > >>> > > > checkpoint barriers from upstream to downstream > > jobs > > > > >> using > > > > >> > > the > > > > >> > > > > >>> > > intermediate > > > > >> > > > > >>> > > > tables? > > > > >> > > > > >>> > > > > > > > >> > > > > >>> > > > Re the watermarks for the "Rejected > > Alternatives". I > > > > >> don't > > > > >> > > > > >>> understand > > > > >> > > > > >>> > why > > > > >> > > > > >>> > > > this has been rejected. Could you elaborate on > > this > > > > >> point? > > > > >> > > Here > > > > >> > > > > >>> are a > > > > >> > > > > >>> > > > couple of my thoughts on this matter, but please > > > > >> correct me > > > > >> > > if > > > > >> > > > > I'm > > > > >> > > > > >>> > wrong, > > > > >> > > > > >>> > > > as I haven't dived deeper into this topic. > > > > >> > > > > >>> > > > > > > > >> > > > > >>> > > > > As shown above, there are 2 watermarks T1 and > > T2, > > > T1 < > > > > >> > T2. > > > > >> > > > > >>> > > > > The StreamTask reads data in order: > > > > >> > > > > >>> > > > V11，V12，V21，T1(channel1)，V13，T1(channel2). > > > > >> > > > > >>> > > > > At this time, StreamTask will confirm that > > > watermark > > > > >> T1 > > > > >> > is > > > > >> > > > > >>> completed, > > > > >> > > > > >>> > > > but the data beyond > > > > >> > > > > >>> > > > > T1 has been processed(V13) and the results are > > > > >> written to > > > > >> > > the > > > > >> > > > > >>> sink > > > > >> > > > > >>> > > > table. > > > > >> > > > > >>> > > > > > > > >> > > > > >>> > > > 1. I see the same "problem" with unaligned > > > checkpoints > > > > >> in > > > > >> > > your > > > > >> > > > > >>> current > > > > >> > > > > >>> > > > proposal. > > > > >> > > > > >>> > > > 2. I don't understand why this is a problem? > Just > > > store > > > > >> in > > > > >> > > the > > > > >> > > > > >>> "sink > > > > >> > > > > >>> > > > table" what's the watermark (T1), and downstream > > > jobs > > > > >> > should > > > > >> > > > > >>> process > > > > >> > > > > >>> > the > > > > >> > > > > >>> > > > data with that "watermark" anyway. Record "V13" > > > should > > > > >> be > > > > >> > > > treated > > > > >> > > > > >>> as > > > > >> > > > > >>> > > > "early" data. Downstream jobs if: > > > > >> > > > > >>> > > > a) they are streaming jobs, for example they > > should > > > > >> > > aggregate > > > > >> > > > it > > > > >> > > > > >>> in > > > > >> > > > > >>> > > > windowed/temporal state, but they shouldn't > > produce > > > the > > > > >> > > result > > > > >> > > > > that > > > > >> > > > > >>> > > > contains it, as the watermark T2 was not yet > > > processed. > > > > >> Or > > > > >> > > they > > > > >> > > > > >>> would > > > > >> > > > > >>> > > just > > > > >> > > > > >>> > > > pass that record as "early" data. > > > > >> > > > > >>> > > > b) they are batch jobs, it looks to me like > batch > > > jobs > > > > >> > > > shouldn't > > > > >> > > > > >>> take > > > > >> > > > > >>> > > > "all available data", but only consider "all the > > > data > > > > >> until > > > > >> > > > some > > > > >> > > > > >>> > > > watermark", for example the latest available: T1 > > > > >> > > > > >>> > > > > > > > >> > > > > >>> > > > 3. I'm pretty sure there are counter examples, > > where > > > > >> your > > > > >> > > > > proposed > > > > >> > > > > >>> > > > mechanism of using checkpoints (even aligned!) > > will > > > > >> produce > > > > >> > > > > >>> > > > inconsistent data from the perspective of the > > event > > > > >> time. > > > > >> > > > > >>> > > > a) For example what if one of your "ETL" jobs, > > > has the > > > > >> > > > > following > > > > >> > > > > >>> DAG: > > > > >> > > > > >>> > > > [image: flip276.jpg] > > > > >> > > > > >>> > > > Even if you use aligned checkpoints for > > > committing the > > > > >> > data > > > > >> > > > to > > > > >> > > > > >>> the > > > > >> > > > > >>> > sink > > > > >> > > > > >>> > > > table, the watermarks of "Window1" and "Window2" > > are > > > > >> > > completely > > > > >> > > > > >>> > > > independent. The sink table might easily have > data > > > from > > > > >> the > > > > >> > > > > >>> > Src1/Window1 > > > > >> > > > > >>> > > > from the event time T1 and Src2/Window2 from > later > > > event > > > > >> > time > > > > >> > > > T2. > > > > >> > > > > >>> > > > b) I think the same applies if you have two > > > completely > > > > >> > > > > >>> independent > > > > >> > > > > >>> > ETL > > > > >> > > > > >>> > > > jobs writing either to the same sink table, or > two > > > to > > > > >> > > different > > > > >> > > > > >>> sink > > > > >> > > > > >>> > > tables > > > > >> > > > > >>> > > > (that are both later used in the same downstream > > > job). > > > > >> > > > > >>> > > > > > > > >> > > > > >>> > > > 4a) I'm not sure if I like the idea of > > centralising > > > the > > > > >> > whole > > > > >> > > > > >>> system in > > > > >> > > > > >>> > > > this way. If you have 10 jobs, the likelihood of > > the > > > > >> > > checkpoint > > > > >> > > > > >>> failure > > > > >> > > > > >>> > > > will be 10 times higher, and/or the duration of > > the > > > > >> > > checkpoint > > > > >> > > > > can > > > > >> > > > > >>> be > > > > >> > > > > >>> > > much > > > > >> > > > > >>> > > > much longer (especially under backpressure). And > > > this is > > > > >> > > > actually > > > > >> > > > > >>> > > already a > > > > >> > > > > >>> > > > limitation of Apache Flink (global checkpoints > are > > > more > > > > >> > prone > > > > >> > > > to > > > > >> > > > > >>> fail > > > > >> > > > > >>> > the > > > > >> > > > > >>> > > > larger the scale), so I would be anxious about > > > making it > > > > >> > > > > >>> potentially > > > > >> > > > > >>> > > even a > > > > >> > > > > >>> > > > larger issue. > > > > >> > > > > >>> > > > 4b) I'm also worried about increased complexity > of > > > the > > > > >> > system > > > > >> > > > > after > > > > >> > > > > >>> > > adding > > > > >> > > > > >>> > > > the global checkpoint, and additional (single?) > > > point of > > > > >> > > > failure. > > > > >> > > > > >>> > > > 5. Such a design would also not work if we ever > > > wanted > > > > >> to > > > > >> > > have > > > > >> > > > > task > > > > >> > > > > >>> > local > > > > >> > > > > >>> > > > checkpoints. > > > > >> > > > > >>> > > > > > > > >> > > > > >>> > > > All in all, it seems to me like actually the > > > watermarks > > > > >> and > > > > >> > > > even > > > > >> > > > > >>> time > > > > >> > > > > >>> > are > > > > >> > > > > >>> > > > the better concept in this context that should > > have > > > been > > > > >> > used > > > > >> > > > for > > > > >> > > > > >>> > > > synchronising and data consistency across the > > whole > > > > >> system. > > > > >> > > > > >>> > > > > > > > >> > > > > >>> > > > Best, > > > > >> > > > > >>> > > > Piotrek > > > > >> > > > > >>> > > > > > > > >> > > > > >>> > > > czw., 1 gru 2022 o 11:50 Shammon FY < > > > zjur...@gmail.com> > > > > >> > > > > >>> napisał(a): > > > > >> > > > > >>> > > > > > > > >> > > > > >>> > > >> Hi @Martijn > > > > >> > > > > >>> > > >> > > > > >> > > > > >>> > > >> Thanks for your comments, and I'd like to reply > > to > > > them > > > > >> > > > > >>> > > >> > > > > >> > > > > >>> > > >> 1. It sounds good to me, I'll update the > content > > > > >> structure > > > > >> > > in > > > > >> > > > > FLIP > > > > >> > > > > >>> > later > > > > >> > > > > >>> > > >> and give the problems first. > > > > >> > > > > >>> > > >> > > > > >> > > > > >>> > > >> 2. "Each ETL job creates snapshots with > > checkpoint > > > > >> info on > > > > >> > > > sink > > > > >> > > > > >>> tables > > > > >> > > > > >>> > > in > > > > >> > > > > >>> > > >> Table Store" -> That reads like you're > proposing > > > that > > > > >> > > > snapshots > > > > >> > > > > >>> need > > > > >> > > > > >>> > to > > > > >> > > > > >>> > > >> be > > > > >> > > > > >>> > > >> written to Table Store? > > > > >> > > > > >>> > > >> > > > > >> > > > > >>> > > >> Yes. To support the data consistency in the > FLIP, > > > we > > > > >> need > > > > >> > to > > > > >> > > > get > > > > >> > > > > >>> > through > > > > >> > > > > >>> > > >> checkpoints in Flink and snapshots in store, > this > > > > >> > requires a > > > > >> > > > > close > > > > >> > > > > >>> > > >> combination of Flink and store implementation. > In > > > the > > > > >> > first > > > > >> > > > > stage > > > > >> > > > > >>> we > > > > >> > > > > >>> > > plan > > > > >> > > > > >>> > > >> to implement it based on Flink and Table Store > > > only, > > > > >> > > snapshots > > > > >> > > > > >>> written > > > > >> > > > > >>> > > to > > > > >> > > > > >>> > > >> external storage don't support consistency. > > > > >> > > > > >>> > > >> > > > > >> > > > > >>> > > >> 3. If you introduce a MetaService, it becomes > the > > > > >> single > > > > >> > > point > > > > >> > > > > of > > > > >> > > > > >>> > > failure > > > > >> > > > > >>> > > >> because it coordinates everything. But I can't > > find > > > > >> > anything > > > > >> > > > in > > > > >> > > > > >>> the > > > > >> > > > > >>> > FLIP > > > > >> > > > > >>> > > >> on > > > > >> > > > > >>> > > >> making the MetaService high available or how to > > > deal > > > > >> with > > > > >> > > > > >>> failovers > > > > >> > > > > >>> > > there. > > > > >> > > > > >>> > > >> > > > > >> > > > > >>> > > >> I think you raise a very important problem and > I > > > > >> missed it > > > > >> > > in > > > > >> > > > > >>> FLIP. > > > > >> > > > > >>> > The > > > > >> > > > > >>> > > >> MetaService is a single point and should > support > > > > >> failover, > > > > >> > > we > > > > >> > > > > >>> will do > > > > >> > > > > >>> > it > > > > >> > > > > >>> > > >> in > > > > >> > > > > >>> > > >> future in the first stage we only support > > > standalone > > > > >> mode, > > > > >> > > THX > > > > >> > > > > >>> > > >> > > > > >> > > > > >>> > > >> 4. The FLIP states under Rejected Alternatives > > > > >> "Currently > > > > >> > > > > >>> watermark in > > > > >> > > > > >>> > > >> Flink cannot align data." which is not true, > > given > > > that > > > > >> > > there > > > > >> > > > is > > > > >> > > > > >>> > > FLIP-182 > > > > >> > > > > >>> > > >> > > > > >> > > > > >>> > > >> > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > > > > >> > > > > >>> > > > > >> > > > > > > > > >> > > > > > > > >> > > > > > > >> > > > > > >> > > > > > > https://cwiki.apache.org/confluence/display/FLINK/FLIP-182%3A+Support+watermark+alignment+of+FLIP-27+Sources > > > > >> > > > > >>> > > >> > > > > >> > > > > >>> > > >> Watermark alignment in FLIP-182 is different > from > > > > >> > > requirements > > > > >> > > > > >>> > > "watermark > > > > >> > > > > >>> > > >> align data" in our FLIP. FLIP-182 aims to fix > > > watermark > > > > >> > > > > >>> generation in > > > > >> > > > > >>> > > >> different sources for "slight imbalance or data > > > skew", > > > > >> > which > > > > >> > > > > >>> means in > > > > >> > > > > >>> > > some > > > > >> > > > > >>> > > >> cases the source must generate watermark even > if > > > they > > > > >> > should > > > > >> > > > > not. > > > > >> > > > > >>> When > > > > >> > > > > >>> > > the > > > > >> > > > > >>> > > >> operator collects watermarks, the data > processing > > > is as > > > > >> > > > > described > > > > >> > > > > >>> in > > > > >> > > > > >>> > our > > > > >> > > > > >>> > > >> FLIP, and the data cannot be aligned through > the > > > > >> barrier > > > > >> > > like > > > > >> > > > > >>> > > Checkpoint. > > > > >> > > > > >>> > > >> > > > > >> > > > > >>> > > >> 5. Given the MetaService role, it feels like > this > > > is > > > > >> > > > > introducing a > > > > >> > > > > >>> > tight > > > > >> > > > > >>> > > >> dependency between Flink and the Table Store. > How > > > > >> > pluggable > > > > >> > > is > > > > >> > > > > >>> this > > > > >> > > > > >>> > > >> solution, given the changes that need to be > made > > to > > > > >> Flink > > > > >> > in > > > > >> > > > > >>> order to > > > > >> > > > > >>> > > >> support this? > > > > >> > > > > >>> > > >> > > > > >> > > > > >>> > > >> This is a good question, and I will try to > expand > > > it. > > > > >> Most > > > > >> > > of > > > > >> > > > > the > > > > >> > > > > >>> work > > > > >> > > > > >>> > > >> will > > > > >> > > > > >>> > > >> be completed in the Table Store, such as the > new > > > > >> > > > SplitEnumerator > > > > >> > > > > >>> and > > > > >> > > > > >>> > > >> Source > > > > >> > > > > >>> > > >> implementation. The changes in Flink are as > > > followed: > > > > >> > > > > >>> > > >> 1) Flink job should put its job id in context > > when > > > > >> > creating > > > > >> > > > > >>> > source/sink > > > > >> > > > > >>> > > to > > > > >> > > > > >>> > > >> help MetaService to create relationship between > > > source > > > > >> and > > > > >> > > > sink > > > > >> > > > > >>> > tables, > > > > >> > > > > >>> > > >> it's tiny > > > > >> > > > > >>> > > >> 2) Notify a listener when job is terminated in > > > Flink, > > > > >> and > > > > >> > > the > > > > >> > > > > >>> listener > > > > >> > > > > >>> > > >> implementation in Table Store will send "delete > > > event" > > > > >> to > > > > >> > > > > >>> MetaService. > > > > >> > > > > >>> > > >> 3) The changes are related to Flink Checkpoint > > > includes > > > > >> > > > > >>> > > >> a) Support triggering checkpoint with > > checkpoint > > > id > > > > >> by > > > > >> > > > > >>> > SplitEnumerator > > > > >> > > > > >>> > > >> b) Create the SplitEnumerator in Table Store > > > with a > > > > >> > > strategy > > > > >> > > > > to > > > > >> > > > > >>> > > perform > > > > >> > > > > >>> > > >> the specific checkpoint when all > > > "SplitEnumerator"s in > > > > >> the > > > > >> > > job > > > > >> > > > > >>> manager > > > > >> > > > > >>> > > >> trigger it. > > > > >> > > > > >>> > > >> > > > > >> > > > > >>> > > >> > > > > >> > > > > >>> > > >> Best, > > > > >> > > > > >>> > > >> Shammon > > > > >> > > > > >>> > > >> > > > > >> > > > > >>> > > >> > > > > >> > > > > >>> > > >> On Thu, Dec 1, 2022 at 3:43 PM Martijn Visser < > > > > >> > > > > >>> > martijnvis...@apache.org > > > > >> > > > > >>> > > > > > > > >> > > > > >>> > > >> wrote: > > > > >> > > > > >>> > > >> > > > > >> > > > > >>> > > >> > Hi all, > > > > >> > > > > >>> > > >> > > > > > >> > > > > >>> > > >> > A couple of first comments on this: > > > > >> > > > > >>> > > >> > 1. I'm missing the problem statement in the > > > overall > > > > >> > > > > >>> introduction. It > > > > >> > > > > >>> > > >> > immediately goes into proposal mode, I would > > > like to > > > > >> > first > > > > >> > > > > read > > > > >> > > > > >>> what > > > > >> > > > > >>> > > is > > > > >> > > > > >>> > > >> the > > > > >> > > > > >>> > > >> > actual problem, before diving into solutions. > > > > >> > > > > >>> > > >> > 2. "Each ETL job creates snapshots with > > > checkpoint > > > > >> info > > > > >> > on > > > > >> > > > > sink > > > > >> > > > > >>> > tables > > > > >> > > > > >>> > > >> in > > > > >> > > > > >>> > > >> > Table Store" -> That reads like you're > > proposing > > > > >> that > > > > >> > > > > snapshots > > > > >> > > > > >>> > need > > > > >> > > > > >>> > > >> to be > > > > >> > > > > >>> > > >> > written to Table Store? > > > > >> > > > > >>> > > >> > 3. If you introduce a MetaService, it becomes > > the > > > > >> single > > > > >> > > > point > > > > >> > > > > >>> of > > > > >> > > > > >>> > > >> failure > > > > >> > > > > >>> > > >> > because it coordinates everything. But I > can't > > > find > > > > >> > > anything > > > > >> > > > > in > > > > >> > > > > >>> the > > > > >> > > > > >>> > > >> FLIP on > > > > >> > > > > >>> > > >> > making the MetaService high available or how > to > > > deal > > > > >> > with > > > > >> > > > > >>> failovers > > > > >> > > > > >>> > > >> there. > > > > >> > > > > >>> > > >> > 4. The FLIP states under Rejected > Alternatives > > > > >> > "Currently > > > > >> > > > > >>> watermark > > > > >> > > > > >>> > in > > > > >> > > > > >>> > > >> > Flink cannot align data." which is not true, > > > given > > > > >> that > > > > >> > > > there > > > > >> > > > > is > > > > >> > > > > >>> > > >> FLIP-182 > > > > >> > > > > >>> > > >> > > > > > >> > > > > >>> > > >> > > > > > >> > > > > >>> > > >> > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > > > > >> > > > > >>> > > > > >> > > > > > > > > >> > > > > > > > >> > > > > > > >> > > > > > >> > > > > > > https://cwiki.apache.org/confluence/display/FLINK/FLIP-182%3A+Support+watermark+alignment+of+FLIP-27+Sources > > > > >> > > > > >>> > > >> > > > > > >> > > > > >>> > > >> > 5. Given the MetaService role, it feels like > > > this is > > > > >> > > > > >>> introducing a > > > > >> > > > > >>> > > tight > > > > >> > > > > >>> > > >> > dependency between Flink and the Table Store. > > How > > > > >> > > pluggable > > > > >> > > > is > > > > >> > > > > >>> this > > > > >> > > > > >>> > > >> > solution, given the changes that need to be > > made > > > to > > > > >> > Flink > > > > >> > > in > > > > >> > > > > >>> order > > > > >> > > > > >>> > to > > > > >> > > > > >>> > > >> > support this? > > > > >> > > > > >>> > > >> > > > > > >> > > > > >>> > > >> > Best regards, > > > > >> > > > > >>> > > >> > > > > > >> > > > > >>> > > >> > Martijn > > > > >> > > > > >>> > > >> > > > > > >> > > > > >>> > > >> > > > > > >> > > > > >>> > > >> > On Thu, Dec 1, 2022 at 4:49 AM Shammon FY < > > > > >> > > > zjur...@gmail.com> > > > > >> > > > > >>> > wrote: > > > > >> > > > > >>> > > >> > > > > > >> > > > > >>> > > >> > > Hi devs: > > > > >> > > > > >>> > > >> > > > > > > >> > > > > >>> > > >> > > I'd like to start a discussion about > > FLIP-276: > > > Data > > > > >> > > > > >>> Consistency of > > > > >> > > > > >>> > > >> > > Streaming and Batch ETL in Flink and Table > > > > >> Store[1]. > > > > >> > In > > > > >> > > > the > > > > >> > > > > >>> whole > > > > >> > > > > >>> > > data > > > > >> > > > > >>> > > >> > > stream processing, there are consistency > > > problems > > > > >> such > > > > >> > > as > > > > >> > > > > how > > > > >> > > > > >>> to > > > > >> > > > > >>> > > >> manage > > > > >> > > > > >>> > > >> > the > > > > >> > > > > >>> > > >> > > dependencies of multiple jobs and tables, > how > > > to > > > > >> > define > > > > >> > > > and > > > > >> > > > > >>> handle > > > > >> > > > > >>> > > E2E > > > > >> > > > > >>> > > >> > > delays, and how to ensure the data > > consistency > > > of > > > > >> > > queries > > > > >> > > > on > > > > >> > > > > >>> > flowing > > > > >> > > > > >>> > > >> > data? > > > > >> > > > > >>> > > >> > > This FLIP aims to support data consistency > > and > > > > >> answer > > > > >> > > > these > > > > >> > > > > >>> > > questions. > > > > >> > > > > >>> > > >> > > > > > > >> > > > > >>> > > >> > > I'v discussed the details of this FLIP with > > > > >> @Jingsong > > > > >> > > Lee > > > > >> > > > > and > > > > >> > > > > >>> > > >> @libenchao > > > > >> > > > > >>> > > >> > > offline several times. We hope to support > > data > > > > >> > > consistency > > > > >> > > > > of > > > > >> > > > > >>> > > queries > > > > >> > > > > >>> > > >> on > > > > >> > > > > >>> > > >> > > tables, managing relationships between > Flink > > > jobs > > > > >> and > > > > >> > > > tables > > > > >> > > > > >>> and > > > > >> > > > > >>> > > >> revising > > > > >> > > > > >>> > > >> > > tables on streaming in Flink and Table > Store > > to > > > > >> > improve > > > > >> > > > the > > > > >> > > > > >>> whole > > > > >> > > > > >>> > > data > > > > >> > > > > >>> > > >> > > stream processing. > > > > >> > > > > >>> > > >> > > > > > > >> > > > > >>> > > >> > > Looking forward to your feedback. > > > > >> > > > > >>> > > >> > > > > > > >> > > > > >>> > > >> > > [1] > > > > >> > > > > >>> > > >> > > > > > > >> > > > > >>> > > >> > > > > > > >> > > > > >>> > > >> > > > > > >> > > > > >>> > > >> > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > > > > >> > > > > >>> > > > > >> > > > > > > > > >> > > > > > > > >> > > > > > > >> > > > > > >> > > > > > > https://cwiki.apache.org/confluence/display/FLINK/FLIP-276%3A+Data+Consistency+of+Streaming+and+Batch+ETL+in+Flink+and+Table+Store > > > > >> > > > > >>> > > >> > > > > > > >> > > > > >>> > > >> > > > > > > >> > > > > >>> > > >> > > Best, > > > > >> > > > > >>> > > >> > > Shammon > > > > >> > > > > >>> > > >> > > > > > > >> > > > > >>> > > >> > > > > > >> > > > > >>> > > >> > > > > >> > > > > >>> > > > > > > > >> > > > > >>> > > > > > > >> > > > > >>> > > > > > >> > > > > >>> > > > > >> > > > > >> > > > > >> > > > > > > > > >> > > > > > > > >> > > > > > > >> > > > > > >> > > > > > > > > > > > > > > >
