Comments (1)
Hey @boryssemeniuk thanks for the flag. Yes you are right that as long as the resource reservation object exists it will be considered for scheduling decisions. They have an owner reference to the driver pod object so we expect the pods to be deleted, which in turn will delete the resource reservation objects and free up space.
One possible solution I can think of would be to filter resource reservations that point to driver pods that are either Failed
or Completed
, so we only accumulate usages of resource reservations that point to running driver pods here: https://github.com/palantir/k8s-spark-scheduler/blob/master/internal/extender/resource.go#L442
from k8s-spark-scheduler.
Related Issues (19)
- Autoscaling HOT 1
- configuration options from the install.yml does not seem to work HOT 8
- warn when an invalid binpacking algorithm is set in config
- [Feature] Extending the scheduler-extender to support non-Spark workload HOT 2
- Consider pod limits when scheduling pods
- Pods scheduled stuck in Pending state HOT 2
- executor pod schedule stucked with enough resource HOT 2
- Work with Cluster Autoscaler? HOT 1
- Separate out failure-fits from failure-fifo
- The spark job is always in pending HOT 1
- Exclude completed pods from overhead calculation HOT 1
- binpacking log line should include all binpacking parameters
- Compatibility with K8S 1.25
- pods go in Pending state intermittently, scheduler restart solves the issue HOT 1
- Spark scheduler should keep the pods and resource reservation mapping uptodate HOT 2
- Goland project generated by `./godelw goland` does not compile HOT 2
- Example in examples/extender.yml bug HOT 2
- [METRICS] - No information on the README.md about a basic metric client setup HOT 4
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from k8s-spark-scheduler.