Hi all,
First time poster, so go easy on me :)
What is Flink's story for accommodating task workloads with vastly
disparate resource requirements: e.g. some require very little CPU and RAM,
while others require quite a lot.
Our current strategy is to bundle resource-intensive tasks and send them
Thanks for the great feedback.
Just as Xintong said, fine grained resource management has not been
> introduced to flink. And i think it is the elegant solution for
> your scenario. Task managers with different resource specification will be
> allocated and started by Yarn/k8s resource manager acc
Hi,
I'm still on my task management investigation, and I'm curious to know how
many task managers people are reliably using with Flink. We're currently
using AWS | Thinkbox Deadline, and we're able to easily utilize over 300
workers, and I've heard from other customers who use several thousand, so
o 于2019年8月11日周日 下午6:17写道：
>
>> Hi Chad,
>>
>> In our cases, 1~2k TMs with up to ~10k TM slots are used in one job. In
>> general, the CPU/memory of Job Manager should be increased with more TMs.
>>
>> Regards,
>> Qi
>>
>> > On Aug 11, 2019
Hi,
Very cool. I’m curious about the relationship between this feature and
Apache Beam. What parts of Beam are used and for what? Does this have any
impact on existing Beam users like myself who use the Beam python API on
top of Flink? Can someone give me a brief overview or point me at the
righ
%3A+Flink+Python+User-Defined+Stateless+Function+for+Table
> [2]
> https://enjoyment.cool/2020/02/19/Deep-dive-how-to-support-Python-UDF-in-Apache-Flink-1-10/
>
> Chad Dombrova 于2020年2月21日周五 上午12:16写道：
>
>> Hi,
>> Very cool. I’m curious about the relationship between thi
