Welcome to Distributed Systems Design with Spring Boot.

You've joined a course built for engineers who are done reading theoretical blog posts and ready to build systems that actually work at scale.

What You'll Master

Over 6 chapters and 18 lessons, you'll go from understanding distributed systems fundamentals to deploying resilient, observable microservices on Kubernetes:

• Foundations — The real constraints of distributed computing

• Service Decomposition — DDD, REST, gRPC, Kafka, and RabbitMQ

• Data Management — Database-per-service, Saga pattern, Event Sourcing, CQRS

• Resilience — Circuit breakers, bulkheads, retries with Resilience4j

• Observability — Distributed tracing, centralized logging, metrics & alerting

• Deployment — Docker, Kubernetes, service mesh, CI/CD pipelines

How to Get the Most Out of This

1. Go sequential. Each lesson builds on the previous one. Don't skip ahead.

2. Type the code. Don't copy-paste. Muscle memory matters.

3. Break things. The resilience chapter teaches you to handle failure — test it by causing failures.

4. Ask questions. Use the community chat. No question is too basic when you're building production systems.

Complete all 18 lessons and earn your certificate of completion. Let's build something that scales.

Most monolith-to-microservices migrations fail. Not because the team is bad — because they skip 3 critical patterns.

I've been building distributed systems with Spring Boot for years, and I see the same mistakes every time a team tries to decompose a monolith. Here's what actually matters:

1. Database-per-Service (Not Optional)

If your microservices share a database, you don't have microservices. You have a distributed monolith with network latency.

Every service owns its data. Period. Yes, this means you need to solve data consistency differently. That's what the Saga pattern is for.

// The Saga Orchestrator coordinates multi-service transactions
// without distributed locks or two-phase commits
@Service
public class OrderSagaOrchestrator {
    public OrderResult execute(OrderRequest request) {
        var inventory = inventoryService.reserve(request);
        try {
            var payment = paymentService.charge(request);
            return confirmOrder(request, inventory, payment);
        } catch (PaymentFailedException e) {
            inventoryService.release(inventory); // Compensating action
            throw e;
        }
    }
}

2. Circuit Breakers (Your System's Immune System)

In a monolith, a failing module slows down one thread. In microservices, a failing service can cascade across your entire architecture.

Circuit breakers with Resilience4j stop cascading failures before they start:

@CircuitBreaker(name = "inventoryService", fallbackMethod = "fallback")
public InventoryResponse check(String productId) {
    return restClient.get()
        .uri("/api/inventory/{id}", productId)
        .retrieve()
        .body(InventoryResponse.class);
}

50% failure rate? The breaker opens. All requests get a fast fallback response instead of timing out and consuming threads.

3. Distributed Tracing (You Can't Debug What You Can't See)

A single user request might touch 10 services. When the response is slow, which service is the bottleneck?

Without distributed tracing, you're guessing. With Spring Boot 3 and Micrometer, it's nearly zero-config:

management:
  tracing:
    sampling:
      probability: 1.0

Every request gets a trace ID that follows it across every service. One search in Zipkin shows you the entire journey.

---

These aren't advanced topics. They're prerequisites. Skip any one of them and your microservices architecture will collapse under real traffic.

I built a full course covering all of this — 6 chapters, 18 hands-on lessons, from foundations to Kubernetes deployment. Every pattern uses Spring Boot 3 with production-grade code.

If you're serious about building systems that scale, check it out.