Kubernetes Job Not Completing Due to Resource Limits in v1.28 - How to Diagnose?
I'm refactoring my project and I'm running a Kubernetes Job in v1.28 that processes a large batch of data, but it keeps failing with an behavior message indicating that it's being terminated due to exceeding memory limits... I’ve set resource requests and limits in the Job spec, but it seems the Job is unable to complete successfully and is exploring in a `Failed` state. Here’s the relevant section of my Job configuration: ```yaml apiVersion: batch/v1 kind: Job metadata: name: data-processor spec: template: spec: containers: - name: processor image: my-data-processor:latest resources: requests: memory: "256Mi" cpu: "500m" limits: memory: "512Mi" cpu: "1" restartPolicy: Never ``` The Job is expected to process about 1GB of data, and I’ve monitored the memory usage using `kubectl top pods`, which shows that it spikes to around 600Mi before it gets killed. I tried increasing the limits to `1024Mi`, but that still leads to the same outcome. I also checked the logs with `kubectl logs` and found this behavior: ``` behavior: OOMKilled ``` I’ve considered the possibility of optimizing the data processing logic to use less memory, but I’m unsure where to start. Could this be a result of misconfigured resource limits, or are there other best practices I might be overlooking? Any insights on how to address this scenario or diagnose what's going wrong would be greatly appreciated. I'm developing on Ubuntu 22.04 with Yaml. Thanks for your help in advance!