Hi Dawid,
Thanks for the comments. This actually brings another relevant question
about what does a "bounded source" imply. I actually had the same
impression when I look at the Source API. Here is what I understand after
some discussion with Stephan. The bounded source has the following impacts.
1. API validity.
- A bounded source generates a bounded stream so some operations that only
works for bounded records would be performed, e.g. sort.
- To expose these bounded stream only APIs, there are two options:
a. Add them to the DataStream API and throw exception if a method is
called on an unbounded stream.
b. Create a BoundedDataStream class which is returned from
env.boundedSource(), while DataStream is returned from env.continousSource().
Note that this cannot be done by having single env.source(theSource) even
the Source has a getBoundedness() method.
2. Scheduling
- A bounded source could be computed stage by stage without bringing up all
the tasks at the same time.
3. Operator behaviors
- A bounded source indicates the records are finite so some operators can
wait until it receives all the records before it starts the processing.
In the above impact, only 1 is relevant to the API design. And the current
proposal in FLIP-27 is following 1.b.
// boundedness depends of source property, imo this should always be
> preferred
>
DataStream<MyType> stream = env.source(theSource);
In your proposal, does DataStream have bounded stream only methods? It
looks it should have, otherwise passing a bounded Source to env.source()
would be confusing. In that case, we will essentially do 1.a if an
unbounded Source is created from env.source(unboundedSource).
If we have the methods only supported for bounded streams in DataStream, it
seems a little weird to have a separate BoundedDataStream interface.
Am I understand it correctly?
Thanks,
Jiangjie (Becket) Qin
On Mon, Dec 9, 2019 at 6:40 PM Dawid Wysakowicz <dwysakow...@apache.org>
wrote:
> Hi all,
>
> Really well written proposal and very important one. I must admit I have
> not understood all the intricacies of it yet.
>
> One question I have though is about where does the information about
> boundedness come from. I think in most cases it is a property of the
> source. As you described it might be e.g. end offset, a flag should it
> monitor new splits etc. I think it would be a really nice use case to be
> able to say:
>
> new KafkaSource().readUntil(long timestamp),
>
> which could work as an "end offset". Moreover I think all Bounded sources
> support continuous mode, but no intrinsically continuous source support the
> Bounded mode. If I understood the proposal correctly it suggest the
> boundedness sort of "comes" from the outside of the source, from the
> invokation of either boundedStream or continousSource.
>
> I am wondering if it would make sense to actually change the method
>
> boolean Source#supportsBoundedness(Boundedness)
>
> to
>
> Boundedness Source#getBoundedness().
>
> As for the methods #boundedSource, #continousSource, assuming the
> boundedness is property of the source they do not affect how the enumerator
> works, but mostly how the dag is scheduled, right? I am not against those
> methods, but I think it is a very specific use case to actually override
> the property of the source. In general I would expect users to only call
> env.source(theSource), where the source tells if it is bounded or not. I
> would suggest considering following set of methods:
>
> // boundedness depends of source property, imo this should always be preferred
>
> DataStream<MyType> stream = env.source(theSource);
>
>
> // always continous execution, whether bounded or unbounded source
>
> DataStream<MyType> boundedStream = env.continousSource(theSource);
>
> // imo this would make sense if the BoundedDataStream provides additional
> features unavailable for continous mode
> BoundedDataStream<MyType> batch = env.boundedSource(theSource);
>
>
> Best,
>
> Dawid
>
>
> On 04/12/2019 11:25, Stephan Ewen wrote:
>
> Thanks, Becket, for updating this.
>
> I agree with moving the aspects you mentioned into separate FLIPs - this
> one way becoming unwieldy in size.
>
> +1 to the FLIP in its current state. Its a very detailed write-up, nicely
> done!
>
> On Wed, Dec 4, 2019 at 7:38 AM Becket Qin <becket....@gmail.com>
> <becket....@gmail.com> wrote:
>
>
> Hi all,
>
> Sorry for the long belated update. I have updated FLIP-27 wiki page with
> the latest proposals. Some noticeable changes include:
> 1. A new generic communication mechanism between SplitEnumerator and
> SourceReader.
> 2. Some detail API method signature changes.
>
> We left a few things out of this FLIP and will address them in separate
> FLIPs. Including:
> 1. Per split event time.
> 2. Event time alignment.
> 3. Fine grained failover for SplitEnumerator failure.
>
> Please let us know if you have any question.
>
> Thanks,
>
> Jiangjie (Becket) Qin
>
> On Sat, Nov 16, 2019 at 6:10 AM Stephan Ewen <se...@apache.org>
> <se...@apache.org> wrote:
>
>
> Hi Łukasz!
>
> Becket and me are working hard on figuring out the last details and
> implementing the first PoC. We would update the FLIP hopefully next week.
>
> There is a fair chance that a first version of this will be in 1.10, but
>
> I
>
> think it will take another release to battle test it and migrate the
> connectors.
>
> Best,
> Stephan
>
>
>
>
> On Fri, Nov 15, 2019 at 11:14 AM Łukasz Jędrzejewski <l...@touk.pl>
> <l...@touk.pl>
>
> wrote:
>
> Hi,
>
> This proposal looks very promising for us. Do you have any plans in
>
> which
>
> Flink release it is going to be released? We are thinking on using a
>
> Data
>
> Set API for our future use cases but on the other hand Data Set API is
> going to be deprecated so using proposed bounded data streams solution
> could be more viable in the long term.
>
> Thanks,
> Łukasz
>
> On 2019/10/01 15:48:03, Thomas Weise <thomas.we...@gmail.com>
> <thomas.we...@gmail.com> wrote:
>
> Thanks for putting together this proposal!
>
> I see that the "Per Split Event Time" and "Event Time Alignment"
>
> sections
>
> are still TBD.
>
> It would probably be good to flesh those out a bit before proceeding
>
> too
>
> far
>
> as the event time alignment will probably influence the interaction
>
> with
>
> the split reader, specifically ReaderStatus emitNext(SourceOutput<E>
> output).
>
> We currently have only one implementation for event time alignment in
>
> the
>
> Kinesis consumer. The synchronization in that case takes place as the
>
> last
>
> step before records are emitted downstream (RecordEmitter). With the
> currently proposed interfaces, the equivalent can be implemented in
>
> the
>
> reader loop, although note that in the Kinesis consumer the per shard
> threads push records.
>
> Synchronization has not been implemented for the Kafka consumer yet.
> https://issues.apache.org/jira/browse/FLINK-12675
>
> When I looked at it, I realized that the implementation will look
>
> quite
>
> different
> from Kinesis because it needs to take place in the pull part, where
>
> records
>
> are taken from the Kafka client. Due to the multiplexing it cannot be
>
> done
>
> by blocking the split thread like it currently works for Kinesis.
>
> Reading
>
> from individual Kafka partitions needs to be controlled via
>
> pause/resume
>
> on the Kafka client.
>
> To take on that responsibility the split thread would need to be
>
> aware
>
> of
>
> the
> watermarks or at least whether it should or should not continue to
>
> consume
>
> a given split and this may require a different SourceReader or
>
> SourceOutput
>
> interface.
>
> Thanks,
> Thomas
>
>
> On Fri, Jul 26, 2019 at 1:39 AM Biao Liu <mmyy1...@gmail.com>
> <mmyy1...@gmail.com> wrote:
>
>
> Hi Stephan,
>
> Thank you for feedback!
> Will take a look at your branch before public discussing.
>
>
> On Fri, Jul 26, 2019 at 12:01 AM Stephan Ewen <se...@apache.org>
> <se...@apache.org>
>
> wrote:
>
> Hi Biao!
>
> Thanks for reviving this. I would like to join this discussion,
>
> but
>
> am
>
> quite occupied with the 1.9 release, so can we maybe pause this
>
> discussion
>
> for a week or so?
>
> In the meantime I can share some suggestion based on prior
>
> experiments:
>
> How to do watermarks / timestamp extractors in a simpler and more
>
> flexible
>
> way. I think that part is quite promising should be part of the
>
> new
>
> source
>
> interface.
>
>
>
> https://github.com/StephanEwen/flink/tree/source_interface/flink-core/src/main/java/org/apache/flink/api/common/eventtime
>
> https://github.com/StephanEwen/flink/blob/source_interface/flink-core/src/main/java/org/apache/flink/api/common/src/SourceOutput.java
>
> Some experiments on how to build the source reader and its
>
> library
>
> for
>
> common threading/split patterns:
>
>
>
> https://github.com/StephanEwen/flink/tree/source_interface/flink-core/src/main/java/org/apache/flink/api/common/src
>
> Best,
> Stephan
>
>
> On Thu, Jul 25, 2019 at 10:03 AM Biao Liu <mmyy1...@gmail.com>
> <mmyy1...@gmail.com>
>
> wrote:
>
> 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>
> <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> <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>
> <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> <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> <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> <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
>
>
>
