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 > > >
