Hi Roman and Till,
Thank you very much for your responses.
With regards on the workload variation across the jobs, let me put it like this
1,. We have some jobs which are CPU intensive (and only operator state being
persisted) and there are other jobs which are not so CPU intensive, but have
Hi Sushruth,
if your jobs need significantly different configurations, then I would
suggest to think about dedicated clusters per job. That way you can
configure the cluster to work best for the respective job. Of course,
running multiple clusters instead of a single one comes at the cost of more
Hi,
Do I understand correctly that:
1. The workload varies across the jobs but stays the same for the same job
2. With a small number of slots per TM you are concerned about uneven
resource utilization when running low- and high-intensive jobs on the
same cluster simultaneously?
If so, wouldn't
Hi,
We have multiple jobs that need to be deployed to a Flink cluster. Parallelism
for jobs vary and dependent on the type of work being done and so are the
memory requirements. All jobs currently use the same state backend. Since the
workloads handled by each job is different, the scaling
