Debugging CrashLoopBackOff in ETL Kubernetes Pod

Context

DataCorp, a data analytics company, utilizes a Kubernetes-based architecture to orchestrate ETL jobs that process large datasets from various sources (e.g., MySQL, MongoDB, and S3). Recently, one of the ETL pods has been experiencing a CrashLoopBackOff state, causing delays in data processing and impacting downstream analytics.

Scale Requirements

• Pods: 10 ETL pods running concurrently, each processing 100GB of data per hour.
• Throughput: Each pod must handle 500 records/second.
• Data Size: Average record size is 2KB, leading to 1GB of data processed per pod per hour.
• Latency: Jobs should complete within 1 hour.

Requirements

1. Identify the root cause of the CrashLoopBackOff state for the affected pod.
2. Review logs and metrics to determine the failure points and patterns.
3. Implement health checks and readiness probes to prevent future occurrences.
4. Ensure that the pod can recover gracefully from transient errors without affecting overall ETL pipeline performance.
5. Document the debugging process and solutions implemented for future reference.

Constraints

• Infrastructure: Limited to existing Kubernetes cluster resources (e.g., 32 CPU cores, 64GB RAM).
• Budget: Minimal additional costs allowed for troubleshooting tools.
• Compliance: Must adhere to data governance policies regarding data handling and processing failures.