Hi Pedro,
You can try to call either
.rebalance() or|.shuffle()|
||
|before the Async operator. Shuffle might give a better result if you
have fewer tasks than parallelism. Best regards, Kien |
On 4/18/2018 11:10 PM, PedroMrChaves wrote:
Hello,
I have a job that has one async operational node (i.e. implements
AsyncFunction). This Operational node will spawn multiple threads that
perform heavy tasks (cpu bound).
I have a Flink Standalone cluster deployed on two machines of 32 cores and
128 gb of RAM, each machine has one task manager and one Job Manager. When I
deploy the job, all of the subtasks from the async operational node end up
on the same machine, which causes it to have a much higher cpu load then the
other.
I've researched ways to overcome this issue, but I haven't found a solution
to my problem.
Ideally, the subtasks would be evenly split across both machines.
Can this problem be solved somehow?
Regards,
Pedro Chaves.
-----
Best Regards,
Pedro Chaves
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