Also, the metrics is on a per-task granularity and allows us to identify
>>>bottleneck tasks.
>>>3. Autoscaler feature currently only works for K8s opeartor + native
>>>K8s mode.
>>>
>>>
>>> Best,
>>> Zhanghao Chen
>>&
;Also, the metrics is on a per-task granularity and allows us to identify
>>bottleneck tasks.
>>3. Autoscaler feature currently only works for K8s opeartor + native
>>K8s mode.
>>
>>
>> Best,
>> Zhanghao Chen
>> -----
k tasks.
>3. Autoscaler feature currently only works for K8s opeartor + native
>K8s mode.
>
>
> Best,
> Zhanghao Chen
> --
> *发件人:* Dennis Jung
> *发送时间:* 2023年9月2日 12:58
> *收件人:* Gyula Fóra
> *抄送:* user@flink.apache.org
> *主题:*
us to identify bottleneck
tasks.
3. Autoscaler feature currently only works for K8s opeartor + native K8s
mode.
Best,
Zhanghao Chen
发件人: Dennis Jung
发送时间: 2023年9月2日 12:58
收件人: Gyula Fóra
抄送: user@flink.apache.org
主题: Re: [Question] How to scale application
Hello,
Thanks for your notice.
1. In "Flink 1.18 + non-reactive", is parallelism being changed by the
number of TM?
2. In the document(
https://nightlies.apache.org/flink/flink-kubernetes-operator-docs-release-1.6/docs/custom-resource/autoscaler/),
it said "we are not using any container memory /
Pretty much, except that with Flink 1.18 autoscaler can scale the job in
place without restarting the JM (even without reactive mode )
So actually best option is autoscaler with Flink 1.18 native mode (no
reactive)
Gyula
On Fri, 1 Sep 2023 at 13:54, Dennis Jung wrote:
> Thanks for feedback.
>
Thanks for feedback.
Could you check whether I understand correctly?
*Only using 'reactive' mode:*
By manually adding TaskManager(TM) (such as using './bin/taskmanager.sh
start'), parallelism will be increased. For example, when job parallelism
is 1 and TM is 1, and if adding 1 new TM, JobManager
I would look at reactive scaling as a way to increase / decrease
parallelism.
It’s not a way to automatically decide when to actually do it as you need
to create new TMs .
The autoscaler could use reactive mode to change the parallelism but you
need the autoscaler itself to decide when new resour
For now, the thing I've found about 'reactive' mode is that it
automatically adjusts 'job parallelism' when TaskManager is
increased/decreased.
https://www.slideshare.net/FlinkForward/autoscaling-flink-with-reactive-mode
Is there some other feature that only 'reactive' mode offers for scaling?
T
Hello,
Thank you for your response. I have few more questions in following:
https://nightlies.apache.org/flink/flink-docs-release-1.15/docs/deployment/elastic_scaling/
*Reactive Mode configures a job so that it always uses all resources
available in the cluster. Adding a TaskManager will scale up
The reactive mode reacts to available resources. The autoscaler reacts to
changing load and processing capacity and adjusts resources.
Completely different concepts and applicability.
Most people want the autoscaler , but this is a recent feature and is
specific to the k8s operator at the moment.
Hello,
Thanks for your notice.
Than what is the purpose of using 'reactive', if this doesn't do anything
itself?
What is the difference if I use auto-scaler without 'reactive' mode?
Regards,
Jung
2023년 8월 18일 (금) 오후 7:51, Gyula Fóra 님이 작성:
> Hi!
>
> I think what you need is probably not the r
Hi!
I think what you need is probably not the reactive mode but a proper
autoscaler. The reactive mode as you say doesn't do anything in itself, you
need to build a lot of logic around it.
Check this instead:
https://nightlies.apache.org/flink/flink-kubernetes-operator-docs-main/docs/custom-resou
Hello,
Sorry for frequent questions. This is a question about 'reactive' mode.
1. As far as I understand, though I've setup `scheduler-mode: reactive`, it
will not change parallelism automatically by itself, by CPU usage or Kafka
consumer rate. It needs additional resource monitor features (such a
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