3.3.3.5. Analyzing Incidents Regarding Failed Processes (Auto Scaling, Amazon ECS, Amazon EKS)

First Principle: Rapidly identifying root causes, minimizing impact, and implementing preventative measures continuously improves system reliability.

In dynamic, scalable AWS environments, failed processes can quickly escalate into significant operational disruptions. Analyzing these incidents is crucial, applying the principle of operational excellence. This systematic approach enables rapid problem diagnosis and system optimization.

• For Auto Scaling incidents (e.g., instances failing to launch/register), review CloudWatch metrics (e.g., GroupDesiredCapacity, HealthyHostCount) and ASG activity history. Inspect EC2 system/application logs for startup errors.
• When troubleshooting Amazon ECS unhealthy tasks/service instability, focus on service events in the ECS console. Dive into task logs (CloudWatch Logs) for application errors. Verify container health checks in task definitions.
• For Amazon EKS issues (e.g., pod scheduling failures, application crashes), use kubectl. Use kubectl describe pod <pod-name> for status/events. Access container logs with kubectl logs <pod-name>. Review Kubernetes events (kubectl get events) for cluster-wide issues.

Key Tools for Analyzing Process Failures:

• Auto Scaling: CloudWatch metrics, ASG activity, EC2 logs.
• Amazon ECS: ECS service events, task logs (CloudWatch Logs), container health checks.
• Amazon EKS: kubectl commands (describe pod, logs, get events), Kubernetes events.

Scenario: A DevOps team manages an Amazon ECS cluster where a critical microservice occasionally fails to launch new tasks, leading to under-provisioned capacity. Separately, their EC2 Auto Scaling Group sometimes fails to replace unhealthy instances.

Reflection Question: How would you analyze incidents regarding failed processes in both Amazon ECS (e.g., task logs, service events) and EC2 Auto Scaling Groups (e.g., ASG activity history, EC2 logs) to rapidly identify root causes and implement preventative measures for these scaling and availability issues?

💡 Tip: For microservices architectures, consider how distributed logging and tracing solutions (e.g., AWS X-Ray, OpenTelemetry) provide end-to-end visibility, essential for diagnosing issues spanning multiple services.