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From Arti Pande <pande.a...@gmail.com>
Subject Re: Flink Async IO operator tuning / micro-benchmarks
Date Fri, 12 Jun 2020 08:59:04 GMT
Hi Arvid,

Thanks for quick reply and totally agree with you on the differences
between microbenchmarks and a full benchmark with specific use-case. Thanks
for sending the microbenchmark screenshot.

For our use-case, the streaming pipeline has five main transformations that
have business logic, of which Async IO to external API endpoint is one
operator. To create benchmarks for operators, I run the real pipeline with
full load on a single machine and note the Throughput and latency. Then add
each operator one by one; always keeping the other basic operators like
source, watermark generator, deserializer, sink etc turned on. The
intention is to build a sort of incremental realistic benchmark for each
operator for given use-case. Adding the AsyncIO operator (with parallelism
7 and async buffer capacity 100) with AsyncHTTPClient library brings
throughput down from 102 K to a very low number i.e. 10K or 12 K
records/sec.

As you suggested, we tested the library being used (AsyncHTTPClient)
independently of Flink, in a similar way to what AsyncIO does. A
simple java program that invokes millions of API calls in a loop, with
hard-coded POST request values, and limited (configurable) number of
concurrent (maxInFlight) requests.  AsyncHTTPClient library by default uses
nCores * 2 (= 16) IO threads, plus a fixed set of threads (say 2 or 4) for
the ExecutorService to be passed to the Listener of result Future. So with
this library the code requires at least 18 or 20 threads. Varying the
maxInFlightRequests from 100 to 3000 the throughput varied from 17 K to 34
K records/sec. Ofcourse this was with hard-coded POST request values and
discarding the response body on reading (no further processing on it).

When we tried to vary the async buffer capacity of AsyncIO (equivalent of
maxInFlightRequests above) beyond 100, our throughput dropped further by
20% to 30%. Whereas in the test program above we would get better
performance as we increased maxInFlightRequests from 100 to 3000.

To reduce backpressure on upstream operators we had to increase the AsyncIO
operator parallelism upto 7. But that means at least 20*7 = 140 threads per
single pipeline plus the threads of other operators in the pipeline.

The question therefore is, given the pipeline is highly multithreaded can
8-core machines suit this? Also is Flink the right framework for such
multi-threaded streaming pipelines that have external API calls and
high-speed ingestion?

Thanks & regards
Arti


On Thu, Jun 11, 2020 at 1:13 PM Arvid Heise <arvid@ververica.com> wrote:

> Hi Arti,
>
> you are now comparing a microbenchmark to a full benchmark. The job
> contains multiple transformations (each dividing the power of the core) and
> most importantly, it contains network traffic (shuffle) to perform a join.
> Unless you do heavy transformation, your throughput will always be
> bottlenecked with network traffic. At the very least, that contains
> additional serialization costs. And it contains heavy states through the
> window aggregation, which also results in serialization and I/O.
>
> I attached the screenshot from the microbenchmark. In general,
> microbenchmarks are not really useful for end-users, but you explicitly
> asked for them. The bottleneck usually arises from the IO part, which is
> NOT benchmarked. I'm assuming you are actually more interested in whether
> and how your use case can be solved in Flink rather than technical details.
> First of all, it always helps to have more information about the intended
> query than going directly into technical details. I gathered that you have
> a third-part microservice that you need to query and you want to do
> additional transformations. It would also be interesting how you performed
> your benchmarks and measured the performance drop.
>
> First of all, even though I discouraged the use of microservices in a
> stream processor, it doesn't mean that it's not possible. You just lose
> some of the nice properties that are possible. 1) Your latency will
> typically be much higher as if it was a data source. 2) You lose
> exactly-once behavior in regard to the HTTP endpoint. On recovery, requests
> since the last checkpoint will be repeated. You need to check if that makes
> sense if the microservices has side-effects that don't allow that. But the
> same is true for most other stream processors and can only be avoided by
> using per-record commits (and then even then, this in-progress record may
> result in duplicate HTTP queries on recovery). 3) If that external endpoint
> is down, there is no way to do meaningful processing. So you add the
> downtime of your Flink cluster and your external microservice. That becomes
> especially important if you have a wide-range of microservices.
>
> So as I wrote in last mail, I'd first establish a boundary independent of
> Flink, by running some Java program with your used async library and tune
> the settings to reach saturation on one machine. That boundary becomes your
> gold standard - there is no way Flink or any other stream processor can do
> it better. If you publish this number, we can jointly find good async IO
> settings. You can of course also see if other libraries are more suitable
> for your needs, ideally implementations that use fewer threads and more
> light-weight constructs to achieve the same construct. Then I'd also
> measure a histogram of response times. Do you have stragglers or are
> response times rather uniform? For uniformity, configuration of async I/O
> is usually quite straight-forward, just use a queue length that fully
> satures your async library (so maxConnections+X, where X is a small
> constant safety buffer). It also helps to determine if you need to scale
> out or not.
>
>
> On Thu, Jun 11, 2020 at 9:03 AM Arti Pande <pande.arti@gmail.com> wrote:
>
>> Hi Arvid,
>>
>> Thanks for a quick reply.
>>
>> The second reference link (
>> http://codespeed.dak8s.net:8000/timeline/?ben=asyncWait.ORDERED&env=2)
>> from your answer is not accessible though. Could you share some more
>> numbers from it? Are these benchmarks published somewhere?
>>
>> Without actual IO call, Async IO operator benchmark of 1.6 K records/ms
>> per core translates to *1.6 million records/sec per core*. So an 8 core
>> machine should give roughly *12.8 million records/sec* ? Is this the
>> correct number? How do we compare it with this benchmark
>> <https://www.ververica.com/blog/curious-case-broken-benchmark-revisiting-apache-flink-vs-databricks-runtime>
>> article that talks about total throughput of 4 million records/sec (without
>> Async IO operator) in a cluster of about 10 machines with 16-core each?
>>
>> Ordered wait is indispensable for our use-case because we need to call
>> the external (partner organisation) system's API endpoint for each incoming
>> record. Depending on the response from that API we need to decide how to
>> process this record and order needs to be preserved. This may not have been
>> a problem if data ingestion rates were low. Real challenge is because of
>> the high-speed stream (millions of events per second) of input.
>>
>> Is higher core machines an answer or is Flink not suitable for use-cases
>> like this?
>>
>>
>>
>> On Thu, Jun 11, 2020 at 2:44 AM Arvid Heise <arvid@ververica.com> wrote:
>>
>>> Hi Arti,
>>>
>>> microbenchmarks for AsyncIO are available [1] and the results shown in
>>> [2]. So you can roughly expect 1.6k records/ms per core to be the upper
>>> limit without any actual I/O. That range should hold for Flink 1.10 and
>>> coming Flink 1.11. I cannot say much about older versions and you didn't
>>> specify which you use. But it shouldn't be an order of magnitude different.
>>>
>>> The biggest performance improvement will probably be to switch to
>>> unordered - results are emitted as soon as they arrive. On ordered, the
>>> first element that came in, needs to be finished before any emission. If
>>> some elements take longer than others, these slow elements quickly become a
>>> bottleneck.
>>>
>>> If async I/O needs to be ordered, then you need to tweak what you
>>> already mentioned. Set DOP to the number of physical cores, there is no
>>> benefit in going higher. If you indeed use an async HTTP client, then the
>>> queue size should be a bit higher than the thread pool size. The thread
>>> pool size will effectively limit the parallelism per subtask and you want
>>> to saturate that from the Flink side. The thread pool size (together with
>>> maxConnections) will put the hard limit together with the request
>>> processing time on your application.
>>>
>>> I'd probably consider using more machines in your stead instead of more
>>> cores per machine (sounded like that is an option). So instead of using
>>> 10x12 cores, use 15x8 cores. You could measure how much max throughput to
>>> expect by using one machine and use a benchmarking tool that increases the
>>> requests per second on that machine until it hits the limit. Then you know
>>> how many machines you need at the very least.
>>>
>>> Finally, it might also be a good time to review your architecture.
>>> Microservices are not the best fit for a streaming application. For
>>> example, if this is a lookup service, it would scale and fit much better if
>>> all data could be ingested by Flink as an additional data source (e.g.
>>> Kafka topic). Existing microservices might be converted into such data
>>> sources with change-data-capture.
>>>
>>> [1]
>>> https://github.com/dataArtisans/flink-benchmarks/blob/master/src/main/java/org/apache/flink/benchmark/AsyncWaitOperatorBenchmark.java
>>> [2]
>>> http://codespeed.dak8s.net:8000/timeline/?ben=asyncWait.ORDERED&env=2
>>>
>>> On Wed, Jun 10, 2020 at 10:06 PM Arti Pande <pande.arti@gmail.com>
>>> wrote:
>>>
>>>> As Flink Async IO operator is designed for external API or DB calls,
>>>> are there any specific guidelines / tips for scaling up this operator?
>>>> Particularly for use-cases where incoming events are being ingested at a
>>>> very high-speed and the Async IO operator with orderedWait mode can
>>>> not keep up with that speed (although the target API endpoint it is calling
>>>> is load tested to provide much higher throughput with very minimal
>>>> latency). In our case adding Async IO operator to the pipeline *reduced
>>>> the throughput by 88% to 90%*. This is huge performance hit!
>>>>
>>>> We tried a couple of things:
>>>>
>>>>    1. Increasing the async buffer capacity parameter, there by
>>>>    increasing the number of concurrent requests at any given point in time
>>>>    that are waiting for response. This proved counter-productive beyond a
very
>>>>    small number like 50 or 100.
>>>>    2. Increasing the operator parallelism. This does not help much as
>>>>    the number of cores on our machines are limited (8 or 12)
>>>>    3. Tuning the AsyncHTTPClient configuration (keepAlive=true,
>>>>    maxConnections, maxConnectionsPerHost) and the size of
>>>>    FixedThreadPool used by the Listener of its Future. Again without much
>>>>    improvement.
>>>>
>>>> Our observation is that although Async IO operator works for one
>>>> stream element at a time, the operator and its underlying HTTP client are
>>>> multithreaded and need higher core machines for high-speed stream
>>>> processing. If the only machines available for this kind of applications
>>>> are 8 to 16 cores, we face challenges in meeting the required throughput
>>>> and latency SLAs.
>>>>
>>>> Are there any micro-benchmarks or tuning guidelines for using Async IO for
>>>> high-speed stream processing, so we know how much throughput to expect from
>>>> it?
>>>> Thanks & regards,
>>>> Arti
>>>>
>>>
>>>
>>> --
>>>
>>> Arvid Heise | Senior Java Developer
>>>
>>> <https://www.ververica.com/>
>>>
>>> Follow us @VervericaData
>>>
>>> --
>>>
>>> Join Flink Forward <https://flink-forward.org/> - The Apache Flink
>>> Conference
>>>
>>> Stream Processing | Event Driven | Real Time
>>>
>>> --
>>>
>>> Ververica GmbH | Invalidenstrasse 115, 10115 Berlin, Germany
>>>
>>> --
>>> Ververica GmbH
>>> Registered at Amtsgericht Charlottenburg: HRB 158244 B
>>> Managing Directors: Timothy Alexander Steinert, Yip Park Tung Jason, Ji
>>> (Toni) Cheng
>>>
>>
>
> --
>
> Arvid Heise | Senior Java Developer
>
> <https://www.ververica.com/>
>
> Follow us @VervericaData
>
> --
>
> Join Flink Forward <https://flink-forward.org/> - The Apache Flink
> Conference
>
> Stream Processing | Event Driven | Real Time
>
> --
>
> Ververica GmbH | Invalidenstrasse 115, 10115 Berlin, Germany
>
> --
> Ververica GmbH
> Registered at Amtsgericht Charlottenburg: HRB 158244 B
> Managing Directors: Timothy Alexander Steinert, Yip Park Tung Jason, Ji
> (Toni) Cheng
>

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