Hi devs, Since 1.9 is nearly released, I think we could get back to FLIP-27. I believe it should be included in 1.10.
There are so many things mentioned in document of FLIP-27. [1] I think we'd better discuss them separately. However the wiki is not a good place to discuss. I wrote google doc about SplitReader API which misses some details in the document. [2] 1. https://cwiki.apache.org/confluence/display/FLINK/FLIP-27:+Refactor+Source+Interface 2. https://docs.google.com/document/d/1R1s_89T4S3CZwq7Tf31DciaMCqZwrLHGZFqPASu66oE/edit?usp=sharing CC Stephan, Aljoscha, Piotrek, Becket On Thu, Mar 28, 2019 at 4:38 PM Biao Liu <mmyy1...@gmail.com> wrote: > Hi Steven, > Thank you for the feedback. Please take a look at the document FLIP-27 > <https://cwiki.apache.org/confluence/display/FLINK/FLIP-27%3A+Refactor+Source+Interface> > which > is updated recently. A lot of details of enumerator were added in this > document. I think it would help. > > Steven Wu <stevenz...@gmail.com> 于2019年3月28日周四 下午12:52写道： > >> This proposal mentioned that SplitEnumerator might run on the JobManager >> or >> in a single task on a TaskManager. >> >> if enumerator is a single task on a taskmanager, then the job DAG can >> never >> been embarrassingly parallel anymore. That will nullify the leverage of >> fine-grained recovery for embarrassingly parallel jobs. >> >> It's not clear to me what's the implication of running enumerator on the >> jobmanager. So I will leave that out for now. >> >> On Mon, Jan 28, 2019 at 3:05 AM Biao Liu <mmyy1...@gmail.com> wrote: >> >> > Hi Stephan & Piotrek, >> > >> > Thank you for feedback. >> > >> > It seems that there are a lot of things to do in community. I am just >> > afraid that this discussion may be forgotten since there so many >> proposals >> > recently. >> > Anyway, wish to see the split topics soon :) >> > >> > Piotr Nowojski <pi...@da-platform.com> 于2019年1月24日周四 下午8:21写道： >> > >> > > Hi Biao! >> > > >> > > This discussion was stalled because of preparations for the open >> sourcing >> > > & merging Blink. I think before creating the tickets we should split >> this >> > > discussion into topics/areas outlined by Stephan and create Flips for >> > that. >> > > >> > > I think there is no chance for this to be completed in couple of >> > remaining >> > > weeks/1 month before 1.8 feature freeze, however it would be good to >> aim >> > > with those changes for 1.9. >> > > >> > > Piotrek >> > > >> > > > On 20 Jan 2019, at 16:08, Biao Liu <mmyy1...@gmail.com> wrote: >> > > > >> > > > Hi community, >> > > > The summary of Stephan makes a lot sense to me. It is much clearer >> > indeed >> > > > after splitting the complex topic into small ones. >> > > > I was wondering is there any detail plan for next step? If not, I >> would >> > > > like to push this thing forward by creating some JIRA issues. >> > > > Another question is that should version 1.8 include these features? >> > > > >> > > > Stephan Ewen <se...@apache.org> 于2018年12月1日周六 上午4:20写道： >> > > > >> > > >> Thanks everyone for the lively discussion. Let me try to summarize >> > > where I >> > > >> see convergence in the discussion and open issues. >> > > >> I'll try to group this by design aspect of the source. Please let >> me >> > > know >> > > >> if I got things wrong or missed something crucial here. >> > > >> >> > > >> For issues 1-3, if the below reflects the state of the discussion, >> I >> > > would >> > > >> try and update the FLIP in the next days. >> > > >> For the remaining ones we need more discussion. >> > > >> >> > > >> I would suggest to fork each of these aspects into a separate mail >> > > thread, >> > > >> or will loose sight of the individual aspects. >> > > >> >> > > >> *(1) Separation of Split Enumerator and Split Reader* >> > > >> >> > > >> - All seem to agree this is a good thing >> > > >> - Split Enumerator could in the end live on JobManager (and assign >> > > splits >> > > >> via RPC) or in a task (and assign splits via data streams) >> > > >> - this discussion is orthogonal and should come later, when the >> > > interface >> > > >> is agreed upon. >> > > >> >> > > >> *(2) Split Readers for one or more splits* >> > > >> >> > > >> - Discussion seems to agree that we need to support one reader >> that >> > > >> possibly handles multiple splits concurrently. >> > > >> - The requirement comes from sources where one poll()-style call >> > > fetches >> > > >> data from different splits / partitions >> > > >> --> example sources that require that would be for example >> Kafka, >> > > >> Pravega, Pulsar >> > > >> >> > > >> - Could have one split reader per source, or multiple split >> readers >> > > that >> > > >> share the "poll()" function >> > > >> - To not make it too complicated, we can start with thinking about >> > one >> > > >> split reader for all splits initially and see if that covers all >> > > >> requirements >> > > >> >> > > >> *(3) Threading model of the Split Reader* >> > > >> >> > > >> - Most active part of the discussion ;-) >> > > >> >> > > >> - A non-blocking way for Flink's task code to interact with the >> > source >> > > is >> > > >> needed in order to a task runtime code based on a >> > > >> single-threaded/actor-style task design >> > > >> --> I personally am a big proponent of that, it will help with >> > > >> well-behaved checkpoints, efficiency, and simpler yet more robust >> > > runtime >> > > >> code >> > > >> >> > > >> - Users care about simple abstraction, so as a subclass of >> > SplitReader >> > > >> (non-blocking / async) we need to have a BlockingSplitReader which >> > will >> > > >> form the basis of most source implementations. BlockingSplitReader >> > lets >> > > >> users do blocking simple poll() calls. >> > > >> - The BlockingSplitReader would spawn a thread (or more) and the >> > > >> thread(s) can make blocking calls and hand over data buffers via a >> > > blocking >> > > >> queue >> > > >> - This should allow us to cover both, a fully async runtime, and a >> > > simple >> > > >> blocking interface for users. >> > > >> - This is actually very similar to how the Kafka connectors work. >> > Kafka >> > > >> 9+ with one thread, Kafka 8 with multiple threads >> > > >> >> > > >> - On the base SplitReader (the async one), the non-blocking method >> > that >> > > >> gets the next chunk of data would signal data availability via a >> > > >> CompletableFuture, because that gives the best flexibility (can >> await >> > > >> completion or register notification handlers). >> > > >> - The source task would register a "thenHandle()" (or similar) on >> the >> > > >> future to put a "take next data" task into the actor-style mailbox >> > > >> >> > > >> *(4) Split Enumeration and Assignment* >> > > >> >> > > >> - Splits may be generated lazily, both in cases where there is a >> > > limited >> > > >> number of splits (but very many), or splits are discovered over >> time >> > > >> - Assignment should also be lazy, to get better load balancing >> > > >> - Assignment needs support locality preferences >> > > >> >> > > >> - Possible design based on discussion so far: >> > > >> >> > > >> --> SplitReader has a method "addSplits(SplitT...)" to add one >> or >> > > more >> > > >> splits. Some split readers might assume they have only one split >> ever, >> > > >> concurrently, others assume multiple splits. (Note: idea behind >> being >> > > able >> > > >> to add multiple splits at the same time is to ease startup where >> > > multiple >> > > >> splits may be assigned instantly.) >> > > >> --> SplitReader has a context object on which it can call >> indicate >> > > when >> > > >> splits are completed. The enumerator gets that notification and can >> > use >> > > to >> > > >> decide when to assign new splits. This should help both in cases of >> > > sources >> > > >> that take splits lazily (file readers) and in case the source >> needs to >> > > >> preserve a partial order between splits (Kinesis, Pravega, Pulsar >> may >> > > need >> > > >> that). >> > > >> --> SplitEnumerator gets notification when SplitReaders start >> and >> > > when >> > > >> they finish splits. They can decide at that moment to push more >> splits >> > > to >> > > >> that reader >> > > >> --> The SplitEnumerator should probably be aware of the source >> > > >> parallelism, to build its initial distribution. >> > > >> >> > > >> - Open question: Should the source expose something like "host >> > > >> preferences", so that yarn/mesos/k8s can take this into account >> when >> > > >> selecting a node to start a TM on? >> > > >> >> > > >> *(5) Watermarks and event time alignment* >> > > >> >> > > >> - Watermark generation, as well as idleness, needs to be per split >> > > (like >> > > >> currently in the Kafka Source, per partition) >> > > >> - It is desirable to support optional event-time-alignment, >> meaning >> > > that >> > > >> splits that are ahead are back-pressured or temporarily >> unsubscribed >> > > >> >> > > >> - I think i would be desirable to encapsulate watermark generation >> > > logic >> > > >> in watermark generators, for a separation of concerns. The >> watermark >> > > >> generators should run per split. >> > > >> - Using watermark generators would also help with another problem >> of >> > > the >> > > >> suggested interface, namely supporting non-periodic watermarks >> > > efficiently. >> > > >> >> > > >> - Need a way to "dispatch" next record to different watermark >> > > generators >> > > >> - Need a way to tell SplitReader to "suspend" a split until a >> certain >> > > >> watermark is reached (event time backpressure) >> > > >> - This would in fact be not needed (and thus simpler) if we had a >> > > >> SplitReader per split and may be a reason to re-open that >> discussion >> > > >> >> > > >> *(6) Watermarks across splits and in the Split Enumerator* >> > > >> >> > > >> - The split enumerator may need some watermark awareness, which >> > should >> > > be >> > > >> purely based on split metadata (like create timestamp of file >> splits) >> > > >> - If there are still more splits with overlapping event time range >> > for >> > > a >> > > >> split reader, then that split reader should not advance the >> watermark >> > > >> within the split beyond the overlap boundary. Otherwise future >> splits >> > > will >> > > >> produce late data. >> > > >> >> > > >> - One way to approach this could be that the split enumerator may >> > send >> > > >> watermarks to the readers, and the readers cannot emit watermarks >> > beyond >> > > >> that received watermark. >> > > >> - Many split enumerators would simply immediately send Long.MAX >> out >> > and >> > > >> leave the progress purely to the split readers. >> > > >> >> > > >> - For event-time alignment / split back pressure, this begs the >> > > question >> > > >> how we can avoid deadlocks that may arise when splits are suspended >> > for >> > > >> event time back pressure, >> > > >> >> > > >> *(7) Batch and streaming Unification* >> > > >> >> > > >> - Functionality wise, the above design should support both >> > > >> - Batch often (mostly) does not care about reading "in order" and >> > > >> generating watermarks >> > > >> --> Might use different enumerator logic that is more locality >> > aware >> > > >> and ignores event time order >> > > >> --> Does not generate watermarks >> > > >> - Would be great if bounded sources could be identified at compile >> > > time, >> > > >> so that "env.addBoundedSource(...)" is type safe and can return a >> > > >> "BoundedDataStream". >> > > >> - Possible to defer this discussion until later >> > > >> >> > > >> *Miscellaneous Comments* >> > > >> >> > > >> - Should the source have a TypeInformation for the produced type, >> > > instead >> > > >> of a serializer? We need a type information in the stream anyways, >> and >> > > can >> > > >> derive the serializer from that. Plus, creating the serializer >> should >> > > >> respect the ExecutionConfig. >> > > >> >> > > >> - The TypeSerializer interface is very powerful but also not easy >> to >> > > >> implement. Its purpose is to handle data super efficiently, support >> > > >> flexible ways of evolution, etc. >> > > >> For metadata I would suggest to look at the >> SimpleVersionedSerializer >> > > >> instead, which is used for example for checkpoint master hooks, or >> for >> > > the >> > > >> streaming file sink. I think that is is a good match for cases >> where >> > we >> > > do >> > > >> not need more than ser/deser (no copy, etc.) and don't need to push >> > > >> versioning out of the serialization paths for best performance (as >> in >> > > the >> > > >> TypeSerializer) >> > > >> >> > > >> >> > > >> On Tue, Nov 27, 2018 at 11:45 AM Kostas Kloudas < >> > > >> k.klou...@data-artisans.com> >> > > >> wrote: >> > > >> >> > > >>> Hi Biao, >> > > >>> >> > > >>> Thanks for the answer! >> > > >>> >> > > >>> So given the multi-threaded readers, now we have as open >> questions: >> > > >>> >> > > >>> 1) How do we let the checkpoints pass through our multi-threaded >> > reader >> > > >>> operator? >> > > >>> >> > > >>> 2) Do we have separate reader and source operators or not? In the >> > > >> strategy >> > > >>> that has a separate source, the source operator has a parallelism >> of >> > 1 >> > > >> and >> > > >>> is responsible for split recovery only. >> > > >>> >> > > >>> For the first one, given also the constraints (blocking, finite >> > queues, >> > > >>> etc), I do not have an answer yet. >> > > >>> >> > > >>> For the 2nd, I think that we should go with separate operators for >> > the >> > > >>> source and the readers, for the following reasons: >> > > >>> >> > > >>> 1) This is more aligned with a potential future improvement where >> the >> > > >> split >> > > >>> discovery becomes a responsibility of the JobManager and readers >> are >> > > >>> pooling more work from the JM. >> > > >>> >> > > >>> 2) The source is going to be the "single point of truth". It will >> > know >> > > >> what >> > > >>> has been processed and what not. If the source and the readers >> are a >> > > >> single >> > > >>> operator with parallelism > 1, or in general, if the split >> discovery >> > is >> > > >>> done by each task individually, then: >> > > >>> i) we have to have a deterministic scheme for each reader to >> assign >> > > >>> splits to itself (e.g. mod subtaskId). This is not necessarily >> > trivial >> > > >> for >> > > >>> all sources. >> > > >>> ii) each reader would have to keep a copy of all its processed >> > slpits >> > > >>> iii) the state has to be a union state with a non-trivial >> merging >> > > >> logic >> > > >>> in order to support rescaling. >> > > >>> >> > > >>> Two additional points that you raised above: >> > > >>> >> > > >>> i) The point that you raised that we need to keep all splits >> > (processed >> > > >> and >> > > >>> not-processed) I think is a bit of a strong requirement. This >> would >> > > imply >> > > >>> that for infinite sources the state will grow indefinitely. This >> is >> > > >> problem >> > > >>> is even more pronounced if we do not have a single source that >> > assigns >> > > >>> splits to readers, as each reader will have its own copy of the >> > state. >> > > >>> >> > > >>> ii) it is true that for finite sources we need to somehow not >> close >> > the >> > > >>> readers when the source/split discoverer finishes. The >> > > >>> ContinuousFileReaderOperator has a work-around for that. It is not >> > > >> elegant, >> > > >>> and checkpoints are not emitted after closing the source, but >> this, I >> > > >>> believe, is a bigger problem which requires more changes than just >> > > >>> refactoring the source interface. >> > > >>> >> > > >>> Cheers, >> > > >>> Kostas >> > > >>> >> > > >> >> > > >> > > >> > >> >
