One of your containers suddenly died. You check the logs:

$ docker logs <container_id>
$ docker ps -a

Output:
Exited (137)

Exit code 137 is not a random number. It tells you exactly how the container was terminated. Code 137 = 128 + 9, where signal 9 is SIGKILL — meaning the container was forcefully killed, most commonly by the Linux OOM (Out Of Memory) Killer.

Docker is one of the most widely used containerization platforms in modern DevOps. Whether you're deploying microservices, running CI/CD pipelines, or managing production workloads, Docker issues are inevitable.

• Common Docker production issues
• Root causes
• Troubleshooting commands
• Real-world scenarios
• Interview questions

• Application downtime
• Failed deployments
• Container crashes
• Image pull failures
• Network connectivity problems
• Storage exhaustion
• CI/CD pipeline failures

A production-ready DevOps Engineer should be able to identify the root cause quickly and restore services with minimal downtime.

Learn how to build smaller, faster, and more secure Docker images using industry best practices.

A Docker image contains everything your application needs to run.

• ❌ Slow image builds
• ❌ Slow deployments
• ❌ Longer download/upload time
• ❌ Higher storage costs
• ❌ More bandwidth usage
• ❌ Larger attack surface
• ❌ Slower CI/CD pipelines

• ✅ Faster builds
• ✅ Faster deployments
• ✅ Better security
• ✅ Lower storage costs
• ✅ Better performance

Use Docker AI Assistance and Hardened Images

Modern Docker tooling can help identify optimization and security improvements automatically.

• Smaller base images
• Layer optimization opportunities
• Security fixes
• Dockerfile best practices

Use Docker Hardened Images

Docker Hardened Images are curated, security-focused base images with a reduced attack surface and regular security updates.

• Fewer vulnerabilities
• Easier compliance
• Improved supply chain security
• Production-ready defaults

Combining Docker AI recommendations with hardened base images helps you build containers that are both efficient and secure.

One of the most common Kubernetes production incidents is when the application appears healthy, all pods are running, deployments look fine, but users continuously receive 503 Service Unavailable errors.

Initial verification shows:

• Pods are Running
• Deployment is Healthy
• No CrashLoopBackOff
• No OOMKilled Events
• Application Containers Started Successfully

Yet users cannot access the application.

Ingress is the first entry point for external traffic.

$ kubectl get ingress
$ kubectl describe ingress <ingress-name>

• Hostname configuration
• Path rules
• Backend service mapping
• TLS configuration
• Ingress Controller status

DNS is one of the most critical components in a Kubernetes cluster. When DNS fails, applications may be healthy, Pods may be running, and Services may exist, but applications suddenly lose the ability to communicate with each other.

Users report:

• Application Timeout
• Connection Refused
• Service Unreachable
• Pods are healthy
• Services exist
• No application crashes

$ kubectl get pods -n kube-system

Expected:
coredns-6d4b75cb6d-abcde    Running
coredns-6d4b75cb6d-fghij    Running

During peak business hours, traffic suddenly increases by 10x. Users report slow responses, request timeouts, and API failures — but the Horizontal Pod Autoscaler (HPA) does not create additional Pods.

Monitoring shows:

• Existing Pods Running
• Nodes Healthy
• Cluster Healthy
• HPA Created

First verify whether HPA is receiving metrics.

$ kubectl get hpa
$ kubectl describe hpa payment-api

Example Output:
NAME          REFERENCE                 TARGETS     MINPODS   MAXPODS   REPLICAS
payment-api   Deployment/payment-api    20%/70%     2         10        2

Disk space issues are among the most common production incidents that every Linux Administrator

A disk reaching **95% utilization** can cause:

• Application downtime
• Database failures
• Inability to write logs
• Container failures
• Kubernetes Pod evictions
• CI/CD pipeline failures

In this guide, you'll learn how to troubleshoot high disk utilization using a structured production approach.

$ df -h

Example Output:
Filesystem      Size  Used Avail Use%
/dev/xvda1       50G   48G    2G  95%

Amazon RDS is a managed database service, but that doesn't mean it's immune to performance issues. One of the most common production incidents is a sudden increase in database latency, causing applications to become slow or unresponsive.

Problem Statement

• Slow page loading
• API response delays
• Database timeout errors
• Transaction failures

CloudWatch alarms indicate increased database latency.

How would you troubleshoot this issue?