Hi Shivang,

This sounds like classic Spark-on-Kubernetes behavior:
-

Executors do not necessarily shut down immediately after finishing their
tasks, unless:

- Dynamic resource allocation is enabled.

- And Spark knows it can safely scale down
-

The Driver pod manages the whole lifecycle. If you’re not using dynamic
allocation or spark.kubernetes.executor.deleteOnTermination, executors live
until the driver completes the job and exits.

Try,

spark.dynamicAllocation.enabled=true
spark.dynamicAllocation.shuffleTracking.enabled=true

This should address your first issue.

Best Regards
Soumasish Goswami
in: www.linkedin.com/in/soumasish
# (415) 530-0405

   -



On Fri, Apr 4, 2025 at 4:43 AM Shivang Modi <sm...@provenir.com.invalid>
wrote:

> Hi Team,
>
>
>
> We are using spark java 3.5.3 and we have a requirement to run a batch of
> millions of transactions. i.e. I am running batch of 1M with 640 executors
> and each executor is having 8 cores and 16 GB memory running under
> kubernetes cluster.
>
>
>
> But our conversation is that one executor is done with completed task, it
> is not going away. All executors are released at the end of job with driver
> pod.
>
>
>
> And also, any performance suggestion which can help in reducing the
> timing. Currently we have 10M running with 640 executors and taking 60 plus
> mins to be finished.
>
>
>
> Let me know if need more information.
>
>
>
> Thanks,
>
> Shivang.
>
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