Deploy a Kubernetes Microservices Application

Architecture Overview

This architecture deploys a multi-service application on GKE where each component runs as a separate Kubernetes Deployment with its own scaling configuration. A frontend handles user requests, a backend API processes business logic, and a caching layer (such as Redis) reduces database load and improves response times. Each component communicates through Kubernetes Services, which provide stable DNS-based discovery regardless of how many pods are running.

The distinction from a single-service Kubernetes deployment is the networking layer. In a multi-service architecture, internal traffic flows between services over the cluster network using ClusterIP Services, while external traffic enters through an Ingress controller that terminates TLS and routes requests to the frontend. This separation means backend services are never directly exposed to the internet.

Horizontal pod autoscaling adjusts the number of backend pods based on CPU or memory utilization. When request volume increases, Kubernetes spins up additional pods to handle the load and scales them back down when traffic subsides. The frontend and caching layers can have their own autoscaling policies tuned to different metrics.

Application Services

Each service runs as a Kubernetes Deployment with a corresponding Service resource. The Deployment manages pod replicas, rolling updates, and health checks. The Service provides a stable internal DNS name (like backend.default.svc.cluster.local) that other services use to communicate, abstracting away individual pod IP addresses.

Ingress and TLS

An Ingress resource defines how external traffic reaches the application. It maps hostnames and URL paths to internal services, terminates TLS using certificates (typically from Let’s Encrypt via cert-manager), and handles load balancing across frontend pods. This gives you a single entry point with HTTPS without configuring each service individually.

Horizontal Pod Autoscaling

The HorizontalPodAutoscaler monitors resource utilization for a Deployment and adjusts the replica count within configured boundaries. For a backend API, scaling on CPU utilization is common: when average CPU exceeds a target threshold, Kubernetes adds pods. This reactive scaling ensures the application handles traffic bursts without over-provisioning during quiet periods.

Common Customizations

• Add a database tier: Extend the prompt to include a managed database like Cloud SQL, connected via the Cloud SQL Auth Proxy running as a sidecar container.
• Add custom health checks: Request readiness and liveness probes on each Deployment so Kubernetes can detect and restart unresponsive containers.
• Configure resource limits: Ask for CPU and memory requests and limits on each container to ensure fair scheduling and prevent a single service from consuming all cluster resources.
• Add network policies: Request Kubernetes NetworkPolicies that restrict which services can communicate with each other, enforcing the principle of least privilege at the network level.