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Deployment Guide

The API Reliability Suite is designed to be cloud-native and highly portable, utilizing Docker for containerization and Kubernetes for orchestration.

🐳 Containerization

Dockerfile

The project uses a multi-stage Docker build to minimize the final image size and reduce the attack surface.

  • Stage 1 (Builder): Installs Poetry and downloads all runtime dependencies.
  • Stage 2 (Runtime): Copies the installed packages, the src/ application code, and the runtime helper scripts into a pinned Python 3.12 slim image.
  • Runtime User: The container drops root and runs as the dedicated app user (UID/GID 10001).

Building the Image

make docker-build

Note

The local docker-compose.yml stack includes Postgres for user persistence, Redis for rate limiting and breaker fallback caching, Prometheus, Alertmanager, Jaeger, and Grafana. Production deployments should configure equivalent shared services through environment variables and secrets.

☸️ Kubernetes (Infrastructure as Code)

Example manifests are located in infra/k8s/ to show a baseline deployment layout and scaling strategy.

1. Deployment (deployment.yaml)

The deployment ensures at least 2 replicas are always running.

  • Probes: livenessProbe uses /health, while readinessProbe uses /ready so traffic is only sent to pods whose database and shared dependencies are reachable.
  • Resources: Sets explicit CPU and Memory requests/limits to prevent resource contention.
  • Metrics: Annotated for automatic Prometheus scraping.
  • Security Context: Runs as a non-root user, drops Linux capabilities, and uses the runtime-default seccomp profile.
  • Rollout Strategy: Uses a rolling update strategy with revisionHistoryLimit so rollbacks stay available.

2. Auto-scaling (hpa.yaml)

A HorizontalPodAutoscaler is configured to scale the API based on CPU utilization.

  • Min/Max: Scales between 2 and 10 pods.
  • Target: Triggers scaling when average CPU utilization hits 70%.
  • Stability: Includes a stabilizationWindowSeconds of 300 to prevent "flapping" (rapid scaling up and down during minor load fluctuations).

Applying to Cluster

kubectl apply -f infra/k8s/

Note

The templates expect secrets to be injected through Kubernetes Secrets (see infra/k8s/deployment.yaml), and shared environments should provide DATABASE_URL, RATE_LIMIT_STORAGE_URI, and CIRCUIT_BREAKER_CACHE_URL.

Release and Rollback Discipline

For shared environments, avoid mutable image tags such as latest.

Recommended release flow:

  1. Build and push a versioned image tag such as ghcr.io/daretechie/api-reliability-suite:1.2.0.
  2. Update infra/k8s/deployment.yaml to that immutable release tag.
  3. Apply the manifest and watch rollout health with kubectl rollout status deployment/reliability-api.
  4. If the rollout is unhealthy, use kubectl rollout undo deployment/reliability-api.

Backups and Restore Notes

Once Postgres is your primary identity store, backup and restore need to be part of the deploy plan.

  • Capture logical backups with pg_dump -Fc.
  • Test restores with pg_restore into a fresh database before calling a backup policy complete.
  • Run poetry run alembic upgrade head during deployment so schema state stays aligned with application code.

📊 Observability Stack

When deploying to a cluster, ensure the following services are available (or use the provided docker-compose.yml for local testing):

  • Prometheus: Scrapes metrics from the /metrics endpoint.
  • Alertmanager: Receives Prometheus alerts and keeps alert routing close to the metrics stack.
  • Jaeger: Receives distributed traces via the OTLP exporter (if OTLP_ENDPOINT is configured).
  • Grafana: Visualizes the "Golden Signals" (Latency, Errors, Traffic, Saturation).

🚀 CI/CD Integration

The provided Docker and K8s assets are designed to integrate seamlessly with CI/CD runners (GitHub Actions, GitLab CI).

  1. Build: Create the image using the multi-stage Dockerfile.
  2. Push: Push the image to your private registry (ECR, GCR, ACR).
  3. Deploy: Update the image field in infra/k8s/deployment.yaml and apply.