I think Seed is correct that we don't properly report backpressure from an AsyncWaitOperator. The problem is that not the Task's main execution thread but the Emitter thread will emit the elements and, thus, be stuck in the `requestBufferBuilderBlocking` method. This, however, does not mean that the AsyncWaitOperator does not generate backpressure. In fact, if your `StreamElementQueue` is full, then it should generate backpressure. The upstream task should eventually report backpressure which should be visible in the web UI.
If you want, then you could open a JIRA issue for this problem and try to solve the problem. Cheers, Till On Thu, Mar 21, 2019 at 4:41 PM Ken Krugler <kkrugler_li...@transpac.com> wrote: > > On Mar 20, 2019, at 6:49 PM, Seed Zeng <seed.z...@klaviyo.com> wrote: > > Hey Andrey and Ken, > Sorry about the late reply. I might not have been clear in my question > The performance of writing to Cassandra is the same in both cases, only > that the source rate was higher in the case of the async function is > present. > > > OK, I was confused by what you’d originally written... > > Job 1 is backpressured because Cassandra cannot handle all the writes and >> eventually slows down the source rate to 6.5k/s. >> Job 2 is slightly backpressured but was able to run at 14k/s. >> >> > If the source rate is _temporarily_ higher, then that maybe makes sense, > as the async function will be able to buffer up to the configured capacity. > > E.g. in the documentation example > <https://ci.apache.org/projects/flink/flink-docs-stable/dev/stream/operators/asyncio.html#async-io-api> > > > AsyncDataStream.unorderedWait(stream, new AsyncDatabaseRequest(), 1000, > TimeUnit.MILLISECONDS, 100); > > The capacity is 100 (which is also the default, if you don’t specify it) > > Something is "buffering" and not propagating backpressure to slow down the > source speed from Kafka. > > In our use case, we prefer the backpressure to slow down the source so > that the write to Cassandra is not delayed while the source is consuming > fast. > > > You can use a smaller capacity to reduce the impact, but that could > obviously impact the performance whatever your using the async function to > parallelize. > > Regards, > > — Ken > > On Wed, Mar 20, 2019 at 9:38 AM Andrey Zagrebin <and...@ververica.com> > wrote: > >> Hi Seed, >> >> Sorry for confusion, I see now it is separate. Back pressure should still >> be created because internal async queue has capacity >> but not sure about reporting problem, Ken and Till probably have better >> idea. >> >> As for consumption speed up, async operator creates another thread to >> collect the result and Cassandra sink probably uses that thread to write >> data. >> This might parallelize and pipeline previous steps like Kafka fetching >> and Cassandra IO but I am also not sure about this explanation. >> >> Best, >> Andrey >> >> >> On Tue, Mar 19, 2019 at 8:05 PM Ken Krugler <kkrugler_li...@transpac.com> >> wrote: >> >>> Hi Seed, >>> >>> I was assuming the Cassandra sink was separate from and after your async >>> function. >>> >>> I was trying to come up for an explanation as to why adding the async >>> function would improve your performance. >>> >>> The only very unlikely reason I thought of was that the async function >>> somehow caused data arriving at the sink to be more “batchy”, which (if the >>> Cassandra sink had an “every x seconds do a write” batch mode) could >>> improve performance. >>> >>> — Ken >>> >>> On Mar 19, 2019, at 11:35 AM, Seed Zeng <seed.z...@klaviyo.com> wrote: >>> >>> Hi Ken and Andrey, >>> >>> Thanks for the response. I think there is a confusion that the writes to >>> Cassandra are happening within the Async function. >>> In my test, the async function is just a pass-through without doing any >>> work. >>> >>> So any Cassandra related batching or buffering should not be the cause >>> for this. >>> >>> Thanks, >>> >>> Seed >>> >>> On Tue, Mar 19, 2019 at 12:35 PM Ken Krugler < >>> kkrugler_li...@transpac.com> wrote: >>> >>>> Hi Seed, >>>> >>>> It’s a known issue that Flink doesn’t report back pressure properly for >>>> AsyncFunctions, due to how it monitors the output collector to gather back >>>> pressure statistics. >>>> >>>> But that wouldn’t explain how you get a faster processing with the >>>> AsyncFunction inserted into your workflow. >>>> >>>> I haven’t looked at how the Cassandra sink handles batching, but if the >>>> AsyncFunction somehow caused fewer, bigger Cassandra writes to happen then >>>> that’s one (serious hand waving) explanation. >>>> >>>> — Ken >>>> >>>> On Mar 18, 2019, at 7:48 PM, Seed Zeng <seed.z...@klaviyo.com> wrote: >>>> >>>> Flink Version - 1.6.1 >>>> >>>> In our application, we consume from Kafka and sink to Cassandra in the >>>> end. We are trying to introduce a custom async function in front of the >>>> Sink to carry out some customized operations. In our testing, it appears >>>> that the Async function is not generating backpressure to slow down our >>>> Kafka Source when Cassandra becomes unhappy. Essentially compared to an >>>> almost identical job where the only difference is the lack of the Async >>>> function, Kafka source consumption speed is much higher under the same >>>> settings and identical Cassandra cluster. The experiment is like this. >>>> >>>> Job 1 - without async function in front of Cassandra >>>> Job 2 - with async function in front of Cassandra >>>> >>>> Job 1 is backpressured because Cassandra cannot handle all the writes >>>> and eventually slows down the source rate to 6.5k/s. >>>> Job 2 is slightly backpressured but was able to run at 14k/s. >>>> >>>> Is the AsyncFunction somehow not reporting the backpressure correctly? >>>> >>>> Thanks, >>>> Seed >>>> >>>> >>>> -------------------------- >>>> Ken Krugler >>>> +1 530-210-6378 >>>> http://www.scaleunlimited.com >>>> Custom big data solutions & training >>>> Flink, Solr, Hadoop, Cascading & Cassandra >>>> >>>> >>> -------------------------- >>> Ken Krugler >>> +1 530-210-6378 >>> http://www.scaleunlimited.com >>> Custom big data solutions & training >>> Flink, Solr, Hadoop, Cascading & Cassandra >>> >>> > -------------------------- > Ken Krugler > +1 530-210-6378 > http://www.scaleunlimited.com > Custom big data solutions & training > Flink, Solr, Hadoop, Cascading & Cassandra > >
