JMeybohm added a comment.

  In T301147#7821813 <https://phabricator.wikimedia.org/T301147#7821813>, 
@dcausse wrote:
  
  > The additional PODs won't be used as a flink job does not automatically 
scale so it would be a pure waste of resources (2.5G of reserved mem per 
additional POD). It would help I guess to improve redundancy in this scenario 
only if k8s assigns every POD to a distinct machine, in which case even with a 
single machine misbehaving flink would have enough redundancy to allocate the 
job to the spare POD. If k8s does do allocation randomly or that there are not 
enough k8s worker nodes (1 spare POD in our case would mean spreading the PODs 
over 8 different machines) then it's probably not worth the waste of resources.
  
  K8s will try to schedule replicas of one Deployment onto different Nodes by 
default and we can also force it to do so. But tbh I would not so that in this 
case as in most of the cases it should be just fine. I expect this situation to 
be a rare exception (and I probably jinxed that now) as we have not seen it 
before or happen again. So as long as it's not super critical, I would refrain 
from trying to optimize the workload for this type of failure. Ultimately this 
should be taken care of by k8s so we should invest there - especially if should 
happen again.

TASK DETAIL
  https://phabricator.wikimedia.org/T301147

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To: JMeybohm
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