A Beginner’s Guide to Building High‑Availability Microservices on Kubernetes

With the rapid development of the Internet, microservices have become the preferred architecture for backend services, and Kubernetes is now the de‑facto standard for container orchestration. The author shares a step‑by‑step guide that combines microservice design with Kubernetes deployment, aiming to help readers understand how the two technologies work together.

Chapter 1: Design of the Microservice Project

1.1 Microservice Design Philosophy

The article revisits Martin Fowler’s definition of microservices and illustrates the core idea with diagrams that show functional decomposition, high cohesion, and low coupling, as well as the splitting of databases into independent stores.

1.2 Practical Design and Improvements

A simple front‑end‑back‑end separation scenario is introduced: a user accesses www.demo.com , which forwards the request to a.demo.com , then to b.demo.com , and finally to c.demo.com . The response travels back to the front‑end, demonstrating a full microservice call chain.

1.3 Project Improvements

1.3.1 Adding Multiple Instances and a Service Registry

To avoid single‑point failures, each service is run with multiple instances and a registry (Eureka) is introduced for service discovery.

1.3.2 Monitoring System (Metrics)

Prometheus and Grafana are selected as the monitoring stack. Each microservice instance exports metrics (CPU, memory, error counts, JVM stats) which Prometheus scrapes automatically.

1.3.3 Logging System

Logs are collected via Kafka to avoid writing to local disks, reducing I/O pressure on the host.

1.3.4 Tracing System

Zipkin is used for distributed tracing, allowing each request to be uniquely tagged and followed across services.

1.3.5 Traffic Control

Sentinel is employed for rate limiting, circuit breaking, and degradation, ensuring the system remains stable under heavy load.

Chapter 2: Concrete Implementation of the Microservice Project

2.1 Front‑End Site

The front‑end displays a page with a button that triggers an AJAX request to the back‑end via Nginx, which forwards the request through the service chain (a → b → c) and returns the aggregated result.

2.2 Service Registry

A minimal Eureka server is configured with a simple declaration, and three instances are deployed in the Kubernetes cluster for high availability.

2.3 Base Library

The base library packages common dependencies and utilities (e.g., response wrappers, logging configuration) so that each microservice can focus on business logic.

2.4 Service Implementations (a, b, c)

Each service implements a health‑check endpoint ( /hs ) required by Kubernetes for readiness probes, and simply forwards calls to the next service in the chain.

Chapter 3: Deploying Kubernetes

The guide recommends the one‑click installer K8seasy for setting up a production‑grade Kubernetes cluster, including built‑in images, support for multiple versions, and HA configuration.

3.1 Installation Process

Download the installer and run two commands:

sudo ./installer --genkey -hostlist=192.168.2.1

to generate keys, and

sudo ./installer -kubernetestarfile kubernetes-server-linux-amd64v1.18.2.tar.gz -masterip 192.168.2.50

to create the cluster. After a short wait the cluster, Prometheus, Grafana, and Alertmanager are up and running.

3.2 Multi‑Cluster Management

Export the generated lens.kubeconfig file and import it into Lens to manage multiple clusters from a single UI.

Chapter 4: High‑Availability Deployment and Validation

Compile the Java projects into JARs, build Docker images, and apply the provided YAML manifests. The services start automatically, and the Kubernetes dashboard shows the three Eureka instances.

4.1 Service Verification

Access www.demo.com , click the request button, and observe that responses are served by different instances, confirming load balancing and HA.

4.2 Tracing Verification

Open zipkin.demo.com to view detailed request traces.

4.3 Rate‑Limiting and Circuit‑Breaking Verification

Use the Sentinel console (default credentials) to monitor traffic, apply limits, and observe automatic protection mechanisms.

4.4 Monitoring Verification

Grafana dashboards display JVM metrics, memory usage, error rates, and other health indicators for each service.

Overall, the article provides a practical, end‑to‑end example of building a resilient microservice architecture on Kubernetes, covering design, implementation, deployment, monitoring, tracing, and traffic control.