Re: Parallelism for auto-scaling, memory for auto-tuning - Flink operator

2024-04-17 Thread Zhanghao Chen
If you have some experience before, I'd recommend setting a good parallelism 
and TM resource spec first, to give the autotuner a good starting point. 
Usually, the autoscaler can tune your jobs well within a few attempts (<=3). As 
for  `pekko.ask.timeout`, the default value should be sufficient in most cases.

Best,
Zhanghao Chen

From: Maxim Senin via user 
Sent: Thursday, April 18, 2024 5:56
To: user@flink.apache.org 
Subject: Parallelism for auto-scaling, memory for auto-tuning - Flink operator


Hi.

Does it make sense to specify `parallelism` for task managers or the `job`, 
and, similarly, to specify memory amount for the task managers, or it’s better 
to leave it to autoscaler and autotuner to pick the best values? How many times 
would the autoscaler need to restart task managers before it picks the right 
values? Does `pekko.ask.timeout` need to be sufficient for task managers to get 
into running state with all the restarts?



Cheers,

Maxim



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Parallelism for auto-scaling, memory for auto-tuning - Flink operator

2024-04-17 Thread Maxim Senin via user
Hi.

Does it make sense to specify `parallelism` for task managers or the `job`, 
and, similarly, to specify memory amount for the task managers, or it’s better 
to leave it to autoscaler and autotuner to pick the best values? How many times 
would the autoscaler need to restart task managers before it picks the right 
values? Does `pekko.ask.timeout` need to be sufficient for task managers to get 
into running state with all the restarts?

Cheers,
Maxim



COGILITY SOFTWARE CORPORATION LEGAL DISCLAIMER: The information in this email 
is confidential and is intended solely for the addressee. Access to this email 
by anyone else is unauthorized. If you are not the intended recipient, any 
disclosure, copying, distribution or any action taken or omitted to be taken in 
reliance on it, is prohibited and may be unlawful.