Thanks @Fabian for your confirmation about the better performance when scaling
happened at same TM machine. But it is so funny that it give impression "the
more I scale the less I get" when the performance drop with more TM in play.
@Ovidiu question is interesting to know too. @Till do you mind to share your
thoughts?
Thank you guys!
________________________________
From: Ovidiu-Cristian MARCU <ovidiu-cristian.ma...@inria.fr>
Sent: Monday, June 18, 2018 6:28 PM
To: Fabian Hueske
Cc: Siew Wai Yow; Jörn Franke; user@flink.apache.org
Subject: Re: Flink application does not scale as expected, please help!
Hi all,
Allow me to add some comments/questions on this issue that is very interesting.
According to documentation [1] the pipeline example assumes the source is
running with the same parallelism as successive map operator and the workflow
optimizes to collocate source and map tasks if possible.
For an application configuring the source with different parallelism, assuming
N task managers each with m slots, if I configure
the source operator with parallelism m, then all of the source's tasks could be
scheduled on the first task manager?
I think the same story holds for sinks tasks.
So, in general is there any control over scheduling of source and sink tasks?
Would it be possible to enforce scheduling of source tasks to be balanced
across task managers? Not sure if this is the default.
If the source creates a non-keyed stream, can we enforce the source to push
records to local map tasks?
For Siew’s example, after source#map a keyBy complicates further things since
each key can be possibly processed on another task manager.
At least the keyBy operator should run with the same parallelism as source and
map and be pipelined on same slot (maybe shared slot configuration could
enforce that).
DataStream<Record> AggregatedRecordWithAuditStream = sourceStringStream
.map(new JsonToRecordTranslator(markerFactory.getMarker(),
inputlink)).name("JsonRecTranslator").setParallelism(pJ2R)
.keyBy(new KeySelector<Record, String>() {
private static final long serialVersionUID = 1L;
@Override
public String getKey(Record r) throws Exception {
return r.getUNIQUE_KEY();
}
})
.process(new ProcessAggregation(aggrDuration, markerFactory.getMarker(),
markerFactory.getMarker())).setParallelism(pAggr)
.name("AggregationDuration: " + aggrDuration +"ms");
Thanks,
Ovidiu
[1]
https://ci.apache.org/projects/flink/flink-docs-release-1.5/internals/job_scheduling.html
[https://ci.apache.org/projects/flink/flink-docs-release-1.5/fig/slots.svg]<https://ci.apache.org/projects/flink/flink-docs-release-1.5/internals/job_scheduling.html>
Apache Flink 1.5 Documentation: Jobs and
Scheduling<https://ci.apache.org/projects/flink/flink-docs-release-1.5/internals/job_scheduling.html>
ci.apache.org
Execution resources in Flink are defined through Task Slots. Each TaskManager
will have one or more task slots, each of which can run one pipeline of
parallel tasks. A pipeline consists of multiple successive tasks, such as the
n-th parallel instance of a MapFunction together with the n-th parallel ...
On 18 Jun 2018, at 10:05, Fabian Hueske
<fhue...@gmail.com<mailto:fhue...@gmail.com>> wrote:
Not sure if TM local assignment is explicitly designed in 1.5.0, but it might
be an artifact of how slots are registered in the resource manager.
Till (in CC) should know how that works.
Anyway, tasks that run in the same TM exchange data via in-memory channels
which is of course much faster than going over the network.
So yes, a performance drop when tasks are scheduled to different TMs is not
unexpected IMO.
You can check that by starting multiple TMs with a single slot each and running
you job on that setup.
Best, Fabian
2018-06-18 9:57 GMT+02:00 Siew Wai Yow
<wai_...@hotmail.com<mailto:wai_...@hotmail.com>>:
Hi Fabian,
We are using Flink 1.5.0. Any different in scheduler in Flink 1.5.0?
"Hence, applications might scale better until tasks are scheduled to different
machines."
This seems the case. We have 32 vCPU 16 slots in one TM machine. So the scaling
work perfectly 1-2-4-8-16 because all happens in same TM. When scale to 32 the
performance drop, not even in par with case of parallelism 16. Is this
something expected? Thank you.
Regards,
Yow
________________________________
From: Fabian Hueske <fhue...@gmail.com<mailto:fhue...@gmail.com>>
Sent: Monday, June 18, 2018 3:47 PM
To: Siew Wai Yow
Cc: Jörn Franke; user@flink.apache.org<mailto:user@flink.apache.org>
Subject: Re: Flink application does not scale as expected, please help!
Hi,
Which Flink version are you using?
Did you try to analyze the bottleneck of the application, i.e., is it CPU, disk
IO, or network bound?
Regarding the task scheduling. AFAIK, before version 1.5.0, Flink tried to
schedule tasks on the same machine to reduce the amount of network transfer.
Hence, applications might scale better until tasks are scheduled to different
machines.
Fabian
2018-06-16 12:20 GMT+02:00 Siew Wai Yow
<wai_...@hotmail.com<mailto:wai_...@hotmail.com>>:
Hi Jorn, Please find the source @https://github.com/swyow/flink_sample_git
Thank you!
________________________________
From: Jörn Franke <jornfra...@gmail.com<mailto:jornfra...@gmail.com>>
Sent: Saturday, June 16, 2018 6:03 PM
To: Siew Wai Yow
Cc: user@flink.apache.org<mailto:user@flink.apache.org>
Subject: Re: Flink application does not scale as expected, please help!
Can you share the app source on gitlab, github or bitbucket etc?
On 16. Jun 2018, at 11:46, Siew Wai Yow
<wai_...@hotmail.com<mailto:wai_...@hotmail.com>> wrote:
Hi, There is an interesting finding, the reason of low parallelism work much
better is because all task being run in same TM, once we scale more, the task
is distributed to different TM and the performance worse than the low
parallelism case. Is this something expected? The more I scale the less I get?
________________________________
From: Siew Wai Yow <wai_...@hotmail.com<mailto:wai_...@hotmail.com>>
Sent: Saturday, June 16, 2018 5:09 PM
To: Jörn Franke
Cc: user@flink.apache.org<mailto:user@flink.apache.org>
Subject: Re: Flink application does not scale as expected, please help!
Hi Jorn, the input data is 1kb per record, in production it will have 10
billions of record per day and it will be increased so scalability is quite
important to us to handle more data. Unfortunately this is not work as expected
even with only 10 millions of testing data. The test application is just a
simple jackson map + an empty process. CPU and memory is not an issue as we
have 32 vCPU + 100 GB RAM per TM. Network should be fine as well as total TX+RX
peak is around 800Mbps while we have 1000Mbps. Do you mind to share your
thought? Or mind to test the attach application in your lab?
To run the program, sample parameters,
"aggrinterval=6000000 loop=7500000 statsd=1 psrc=4 pJ2R=32 pAggr=72
URL=do36.mycompany.com:8127<http://do36.mycompany.com:8127/>"
* aggrinterval: time in ms for timer to trigger
* loop: how many row of data to feed
* statsd: to send result to statsd
* psrc: source parallelism
* pJ2R: parallelism of map operator(JsonRecTranslator)
* pAggr: parallelism of process+timer operator(AggregationDuration)
Thank you!
Yow
________________________________
From: Jörn Franke <jornfra...@gmail.com<mailto:jornfra...@gmail.com>>
Sent: Saturday, June 16, 2018 4:46 PM
To: Siew Wai Yow
Cc: user@flink.apache.org<mailto:user@flink.apache.org>
Subject: Re: Flink application does not scale as expected, please help!
How large is the input data? If the input data is very small then it does not
make sense to scale it even more. The larger the data is the more parallelism
you will have. You can modify this behavior of course by changing the partition
on the Dataset.
On 16. Jun 2018, at 10:41, Siew Wai Yow
<wai_...@hotmail.com<mailto:wai_...@hotmail.com>> wrote:
Hi,
We found that our Flink application with simple logic, which using process
function is not scale-able when scale from 8 parallelism onward even though
with sufficient resources. Below it the result which is capped at ~250k TPS. No
matter how we tune the parallelism of the operators it just not scale, same to
increase source parallelism.
Please refer to "scaleNotWork.png",
1. fixed source parallelism 4, other operators parallelism 8
2. fixed source parallelism 4, other operators parallelism 16
3. fixed source parallelism 4, other operators parallelism 32
4. fixed source parallelism 6, other operators parallelism 8
5. fixed source parallelism 6, other operators parallelism 16
6. fixed source parallelism 6, other operators parallelism 32
7. fixed source parallelism 6, other operators parallelism 64 performance worse
than parallelism 32.
Sample source code attached(flink_app_parser_git.zip). It is a simple program,
parsing json record into object, and pass it to a empty logic Flink's process
function. Rocksdb is in used, and the source is generated by the program
itself. This could be reproduce easily.
We choose Flink because of it scalability, but this is not the case now,
appreciated if anyone could help as this is impacting our projects! thank you.
To run the program, sample parameters,
"aggrinterval=6000000 loop=7500000 statsd=1 psrc=4 pJ2R=32 pAggr=72
URL=do36.mycompany.com:8127<http://do36.mycompany.com:8127/>"
* aggrinterval: time in ms for timer to trigger
* loop: how many row of data to feed
* statsd: to send result to statsd
* psrc: source parallelism
* pJ2R: parallelism of map operator(JsonRecTranslator)
* pAggr: parallelism of process+timer operator(AggregationDuration)
We are running in VMWare, 5 Task Managers and each has 32 slots.
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 32
On-line CPU(s) list: 0-31
Thread(s) per core: 1
Core(s) per socket: 1
Socket(s): 32
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
Model: 63
Model name: Intel(R) Xeon(R) CPU E5-2640 v3 @ 2.60GHz
Stepping: 2
CPU MHz: 2593.993
BogoMIPS: 5187.98
Hypervisor vendor: VMware
Virtualization type: full
L1d cache: 32K
L1i cache: 32K
L2 cache: 256K
L3 cache: 20480K
NUMA node0 CPU(s): 0-31
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36
clflush dts mmx fxsr sse sse2 ss syscall nx pdpe1gb rdtscp lm constant_tsc
arch_perfmon pebs bts nopl xtopology tsc_reliable nonstop_tsc aperfmperf pni
pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx
f16c rdrand hypervisor lahf_lm epb fsgsbase smep dtherm ida arat pln pts
total used free shared buff/cache available
Mem: 98 24 72 0 1 72
Swap: 3 0 3
Please refer TM.png and JM.png for further details.
The test without any checkpoint enable.
Thanks.
Regards,
Yow
<flink_app_parser_git.zip>
<JM.png>
<sample.png>
<scaleNotWork.png>
<TM.png>
