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From Ken Krugler <kkrugler_li...@transpac.com>
Subject Re: Async Function Not Generating Backpressure
Date Thu, 21 Mar 2019 15:41:40 GMT

> On Mar 20, 2019, at 6:49 PM, Seed Zeng <seed.zeng@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 <andrey@ververica.com <mailto:andrey@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_lists@transpac.com <mailto:kkrugler_lists@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.zeng@klaviyo.com <mailto:seed.zeng@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_lists@transpac.com <mailto:kkrugler_lists@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.zeng@klaviyo.com <mailto:seed.zeng@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 <http://www.scaleunlimited.com/>
>> Custom big data solutions & training
>> Flink, Solr, Hadoop, Cascading & Cassandra
>> 
> 
> --------------------------
> Ken Krugler
> +1 530-210-6378
> http://www.scaleunlimited.com <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


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