Kubernetes Pod Resource Limits

# Kubernetes Pod Resource Limits

Today I learned about the critical importance of properly configuring resource requests and limits for Kubernetes pods, and how it affects cluster stability and efficiency.

## The Resource Configuration Problem

In our production Kubernetes cluster, we were experiencing two major issues:
1. Some pods were being terminated unexpectedly (OOMKilled)
2. Cluster nodes were underutilized despite the autoscaler not adding more pods

After investigation, I discovered these issues stemmed from improper resource configuration.

## Understanding Kubernetes Resource Controls

Kubernetes uses two key concepts for resource management:

- **Requests**: The minimum guaranteed resources a pod needs (used for scheduling)
- **Limits**: The maximum resources a pod can consume (enforced by the container runtime)

## Best Practices I Implemented

After analyzing our workloads, I implemented these guidelines:

1. **Set realistic requests based on actual usage**: Used metrics from Prometheus to determine baseline needs
2. **Configure memory limits conservatively**: Set to ~30% above the request to prevent OOMKilled events
3. **Consider CPU limits carefully**: Sometimes removed CPU limits entirely as they can cause throttling
4. **Use different configurations for different workloads**: Created standard profiles for stateless vs. stateful services
5. **Implement Quality of Service (QoS) classes**: Structured our pods to fall into appropriate QoS tiers

## Results

After implementing these changes:
- Pod terminations due to OOM decreased by 94%
- Cluster utilization improved by 35%
- Cost savings of approximately 22% through better resource efficiency
- More predictable application performance

This experience taught me that resource configuration is not just a technical detail but a critical aspect of Kubernetes operations that directly impacts reliability and cost.