Unicorn domain devops

DevOps Domain Skill

Docker

Quick Commands

docker build -t myapp:v1.0.0 .                          # Build image
docker build --target production -t myapp:prod .         # Multi-stage build
docker run --cpus=0.5 --memory=512m myapp:v1.0.0         # Run with limits
docker history myapp:v1.0.0                              # Inspect layers
docker image prune -f                                    # Remove dangling
docker logs -f --tail=100 container_id                   # Tail logs

Dockerfile Best Practices

• Use specific base image tags (never :latest)
• Multi-stage builds for minimal runtime images
• Copy dependency files first for layer caching
• Run as non-root user
• Use .dockerignore to exclude unnecessary files
• Minimize layers (combine RUN commands with &&)
• Use distroless or alpine for production
• Set health checks
• Label images with metadata

See: references/docker-complete.md for optimization techniques and Compose configurations.

CI/CD Pipelines

Pipeline Stages

1. Lint - Code quality checks (parallel with tests)
2. Test - Unit/integration tests with coverage
3. Build - Container image build and push
4. Deploy - Environment-specific deployments
5. Verify - Smoke tests and health checks

Best Practices

• Cache dependencies between runs
• Matrix builds for multi-version testing
• Separate fast checks (lint) from slow (integration)
• Fail fast on quality gates
• Tag images with commit SHA and semantic versions
• Store secrets in CI secret store, never in code
• Use environments for staging/production approvals

See: references/github-actions.md for complete workflows, matrix builds, caching, and deployment automation.

Kubernetes

Essential Resources

Resource	Purpose
Deployment	Manages replica sets and rolling updates
Service	Stable networking endpoint for pods
Ingress	HTTP(S) routing to services
ConfigMap	Non-sensitive configuration
Secret	Sensitive data (credentials, tokens)
HPA	Horizontal Pod Autoscaler

Key kubectl Commands

kubectl apply -f deployment.yaml                         # Apply manifests
kubectl get pods,svc,ing -n production                   # Resource status
kubectl logs -f deployment/myapp -n production           # View logs
kubectl exec -it pod/myapp-xxx -- /bin/sh                # Shell into pod
kubectl port-forward svc/myapp 8080:80                   # Port forward
kubectl rollout status deployment/myapp                  # Rollout status
kubectl rollout undo deployment/myapp                    # Rollback
kubectl scale deployment/myapp --replicas=5              # Manual scale
kubectl top pods -n production                           # Resource usage

Deployment Checklist

• Resource requests and limits defined
• Liveness and readiness probes configured
• Running as non-root user
• Secrets externalized (not in manifests)
• Labels for monitoring and service discovery
• Multiple replicas for high availability
• Rolling update strategy configured
• HPA configured for auto-scaling

See: references/kubernetes-manifests.md for manifest examples, Helm charts, and security configurations.

Observability

1. Logging (What happened?)

• Structured JSON logs with context (request_id, user_id, service_name)
• Levels: DEBUG < INFO < WARNING < ERROR < CRITICAL
• Centralize with Loki, ElasticSearch, or CloudWatch
• Never log sensitive data

2. Metrics (How much/how many?)

• Counter: Monotonically increasing (requests_total)
• Gauge: Current value (active_connections)
• Histogram: Distribution (request_duration_seconds)
• Summary: Quantiles (p95, p99 latency)
• Track: request rate, error rate (4xx/5xx), latency percentiles, saturation (CPU/memory/disk)

3. Tracing (Where did time go?)

• Distributed tracing across services with OpenTelemetry
• Track request path, identify bottlenecks and slow queries

See: references/observability-stack.md for Prometheus, Grafana, Loki, Jaeger, and OpenTelemetry configurations.

Deployment Strategies

Strategy	How It Works	When to Use	Trade-off
Rolling	Gradually replace old pods	Standard deploys, backward-compatible changes	Slower rollout
Blue-Green	Two environments, instant switch	DB migrations, major version updates	2x infrastructure cost
Canary	Route small % to new version, increase if healthy	High-risk changes, need real-traffic validation	Complexity, needs metrics

All strategies: use readiness probes, have rollback plan, monitor error rate and latency during rollout.

See: references/deployment-strategies.md for rollback procedures and automated canary configurations.

Security Hardening

Container Security

• Scan images for vulnerabilities (Trivy, Snyk)
• Minimal base images (distroless, scratch)
• Non-root user, read-only root filesystem, drop all capabilities
• Regular image updates

Kubernetes Security

• Network policies (deny-all by default)
• Pod Security Standards (restricted mode)
• RBAC for least privilege access
• External secrets management (Vault, AWS Secrets Manager)
• Encrypt secrets at rest
• Admission controllers for policy enforcement

Secrets Management

• Never commit to Git
• Use external secret stores, rotate regularly
• Audit access, mount as files (not env vars when possible)
• Scope to namespaces

See: references/security-hardening.md for network policies, image scanning automation, and compliance configurations.

Infrastructure as Code

Principles

• Version control all manifests
• Declarative over imperative
• Separate environment configs (dev/staging/prod)
• Validate before apply (kubectl dry-run, helm lint)
• Use GitOps (ArgoCD, Flux) -- Git as single source of truth

Helm Best Practices

• Template for environment differences, override with env-specific values files
• Version charts semantically
• Include sane defaults, validate before release

Troubleshooting Checklist

Symptom	Commands	Common Causes
Pod not starting	kubectl describe pod <name>, kubectl logs <name>, kubectl get events --sort-by=.metadata.creationTimestamp	Image pull errors, resource limits, health check failures
Service unreachable	kubectl get svc,endpoints <name>, kubectl describe svc <name>	Label mismatch, port misconfiguration, network policies
High resource usage	kubectl top pods, kubectl describe node <name>	No resource limits, memory leaks, inefficient code
Deployment stuck	kubectl rollout status deployment/<name>, kubectl get events | grep <name>	Failing health checks, insufficient resources, image issues

Quick Reference Links

• references/docker-complete.md - Comprehensive Docker guide
• references/kubernetes-manifests.md - K8s manifests and Helm charts
• references/github-actions.md - Complete CI/CD workflows
• references/observability-stack.md - Monitoring and logging setup
• references/deployment-strategies.md - Deployment patterns and rollbacks
• references/security-hardening.md - Security best practices

Key Principles

1. Automate Everything - Manual processes are error-prone
2. Measure Everything - Can't improve what you don't measure
3. Fail Fast - Catch issues early in pipeline
4. Immutable Infrastructure - Replace, don't modify
5. Infrastructure as Code - Version control all config
6. Monitor Proactively - Alert before users notice
7. Practice Chaos - Test failure scenarios regularly
8. Document Runbooks - Incident response should be scripted

<!-- Last reviewed: 2026-03 -->