Update: opened an issue and a PR. https://issues.apache.org/jira/browse/FLINK-22700 https://github.com/apache/flink/pull/15950
On Tue, May 18, 2021 at 10:03 AM Eron Wright <ewri...@streamnative.io> wrote: > Thanks Arvid and David for sharing your ideas on this subject. I'm glad > to hear that you're seeing use cases for watermark propagation via an > enhanced sink interface. > > As you've guessed, my interest is in Pulsar and am exploring some options > for brokering watermarks across stream processing pipelines. I think Arvid > is speaking to a high-fidelity solution where the difference between intra- > and inter-pipeline flow is eliminated. My goal is more limited; I want to > write the watermark that arrives at the sink to Pulsar. Simply imagine > that Pulsar has native support for watermarking in its producer/consumer > API, and we'll leave the details to another forum. > > David, I like your invariant. I see lateness as stemming from the problem > domain and from system dynamics (e.g. scheduling, batching, lag). When one > depends on order-of-observation to generate watermarks, the app may become > unduly sensitive to dynamics which bear on order-of-observation. My goal > is to factor out the system dynamics from lateness determination. > > Arvid, to be most valuable (at least for my purposes) the enhancement is > needed on SinkFunction. This will allow us to easily evolve the existing > Pulsar connector. > > Next step, I will open a PR to advance the conversation. > > Eron > > On Tue, May 18, 2021 at 5:06 AM David Morávek <david.mora...@gmail.com> > wrote: > >> Hi Eron, >> >> Thanks for starting this discussion. I've been thinking about this >> recently >> as we've run into "watermark related" issues, when chaining multiple >> pipelines together. My to cents to the discussion: >> >> How I like to think about the problem, is that there should an invariant >> that holds for any stream processing pipeline: "NON_LATE element entering >> the system, should never become LATE" >> >> Unfortunately this is exactly what happens in downstream pipelines, >> because >> the upstream one can: >> - break ordering (especially with higher parallelism) >> - emit elements that are ahead of output watermark >> >> There is not enough information to re-construct upstream watermark in >> latter stages (it's always just an estimate based on previous pipeline's >> output). >> >> It would be great, if we could have a general abstraction, that is >> reusable >> for various sources / sinks (not just Kafka / Pulsar, thought this would >> probably cover most of the use-cases) and systems. >> >> Is there any other use-case then sharing watermark between pipelines, that >> you're trying to solve? >> >> Arvid: >> >> 1. Watermarks are closely coupled to the used system (=Flink). I have a >> > hard time imagining that it's useful to use a different stream processor >> > downstream. So for now, I'm assuming that both upstream and downstream >> are >> > Flink applications. In that case, we probably define both parts of the >> > pipeline in the same Flink job similar to KafkaStream's #through. >> > >> >> I'd slightly disagree here. For example we're "materializing" change-logs >> produced by Flink pipeline into serving layer (random access db / in >> memory >> view / ..) and we need to know, whether responses we serve meet the >> "freshness" requirements (eg. you may want to respond differently, when >> watermark is lagging way too much behind processing time). Also not every >> stream processor in the pipeline needs to be Flink. It can as well be a >> simple element-wise transformation that reads from Kafka and writes back >> into separate topic (that's what we do for example with ML models, that >> have special hardware requirements). >> >> Best, >> D. >> >> >> On Tue, May 18, 2021 at 8:30 AM Arvid Heise <ar...@apache.org> wrote: >> >> > Hi Eron, >> > >> > I think this is a useful addition for storage systems that act as >> > pass-through for Flink to reduce recovery time. It is only useful if you >> > combine it with regional fail-over as only a small part of the pipeline >> is >> > restarted. >> > >> > A couple of thoughts on the implications: >> > 1. Watermarks are closely coupled to the used system (=Flink). I have a >> > hard time imagining that it's useful to use a different stream processor >> > downstream. So for now, I'm assuming that both upstream and downstream >> are >> > Flink applications. In that case, we probably define both parts of the >> > pipeline in the same Flink job similar to KafkaStream's #through. >> > 2. The schema of the respective intermediate stream/topic would need to >> be >> > managed by Flink to encode both records and watermarks. This reduces the >> > usability quite a bit and needs to be carefully crafted. >> > 3. It's not clear to me if constructs like SchemaRegistry can be >> properly >> > supported (and also if they should be supported) in terms of schema >> > evolution. >> > 4. Potentially, StreamStatus and LatencyMarker would also need to be >> > encoded. >> > 5. It's important to have some way to transport backpressure from the >> > downstream to the upstream. Or else you would have the same issue as >> > KafkaStreams where two separate pipelines can drift so far away that you >> > experience data loss if the data retention period is smaller than the >> > drift. >> > 6. It's clear that you trade a huge chunk of throughput for lower >> overall >> > latency in case of failure. So it's an interesting feature for use cases >> > with SLAs. >> > >> > Since we are phasing out SinkFunction, I'd prefer to only support >> > SinkWriter. Having a no-op default sounds good to me. >> > >> > We have some experimental feature for Kafka [1], which pretty much >> reflects >> > your idea. Here we have an ugly workaround to be able to process the >> > watermark by using a custom StreamSink task. We could also try to >> create a >> > FLIP that abstracts the actual system away and then we could use the >> > approach for both Pulsar and Kafka. >> > >> > [1] >> > >> > >> https://github.com/apache/flink/blob/master/flink-connectors/flink-connector-kafka/src/main/java/org/apache/flink/streaming/connectors/kafka/shuffle/FlinkKafkaShuffle.java#L103 >> > >> > >> > On Mon, May 17, 2021 at 10:44 PM Eron Wright >> > <ewri...@streamnative.io.invalid> wrote: >> > >> > > I would like to propose an enhancement to the Sink API, the ability to >> > > receive upstream watermarks. I'm aware that the sink context >> provides >> > the >> > > current watermark for a given record. I'd like to be able to write a >> > sink >> > > function that is invoked whenever the watermark changes. Out of scope >> > > would be event-time timers (since sinks aren't keyed). >> > > >> > > For context, imagine that a stream storage system had the ability to >> > > persist watermarks in addition to ordinary elements, e.g. to serve as >> > > source watermarks in a downstream processor. Ideally one could >> compose a >> > > multi-stage, event-driven application, with watermarks flowing >> end-to-end >> > > without need for a heuristics-based watermark at each stage. >> > > >> > > The specific proposal would be a new method on `SinkFunction` and/or >> on >> > > `SinkWriter`, called 'processWatermark' or 'writeWatermark', with a >> > default >> > > implementation that does nothing. >> > > >> > > Thoughts? >> > > >> > > Thanks! >> > > Eron Wright >> > > StreamNative >> > > >> > >> > > > -- > > Eron Wright Cloud Engineering Lead > > p: +1 425 922 8617 <18163542939> > > streamnative.io | Meet with me > <https://calendly.com/eronwright/regular-1-hour> > > <https://github.com/streamnative> > <https://www.linkedin.com/company/streamnative/> > <https://twitter.com/streamnativeio/> > -- Eron Wright Cloud Engineering Lead p: +1 425 922 8617 <18163542939> streamnative.io | Meet with me <https://calendly.com/eronwright/regular-1-hour> <https://github.com/streamnative> <https://www.linkedin.com/company/streamnative/> <https://twitter.com/streamnativeio/>
