Getting Started with Distributed Systems

Why Distributed Systems?

Every backend engineer starts with a monolith. It works fine — until it doesn't. When your single process can't handle the load, or your team grows beyond what a single codebase can support, you start thinking about breaking things apart.

But distributed computing is fundamentally harder than local computation. Network partitions, partial failures, inconsistent clocks — these are problems that don't exist in a monolith.

The Hard Parts

1. Network Unreliability

In a monolith, a function call either succeeds or throws. In a distributed system, a service call can succeed, fail, timeout, or succeed but you never hear back. You need to design for all four.

// Always wrap external calls with a circuit breaker
const getUser = async (id: string): Promise<User> => {
  try {
    return await userService.getUser(id, { timeout: 3000 });
  } catch (error) {
    if (error.code === "BREAKER_OPEN") {
      return getCachedUser(id) ?? fallbackUser;
    }
    throw error;
  }
};

2. Data Consistency

When an order is placed, you need to update inventory, charge payment, and send confirmation. In a monolith, you wrap it in a transaction. In microservices, each of these is a different database.

The Saga pattern is the most practical answer: a sequence of local transactions, each with a compensating action.

OrderCreated → ReserveInventory → ChargePayment → SendConfirmation
                    ↓ (if payment fails)
               ReleaseInventory

3. Observability From Day One

A distributed system without tracing is a black box. Instrument everything with OpenTelemetry from the start — not after your first production incident at 2 AM.

What I've Learned Building These Systems

• Start with a modular monolith, not microservices. Extract services only when the module boundary proves itself.
• Idempotency keys are non-negotiable for any write operation across services. Stripe and Shopify use them — so should you.
• Database-per-service is the right default, but shared databases can be pragmatic for read-heavy reporting workloads.
• Correlation IDs on every request save more debugging hours than any other single practice.

Next Steps

Start small. Build a two-service system — one writes, one reads — with a message queue between them. Add correlation IDs. Add health checks. Run it for a week. Then add the third service.

Distributed systems aren't mastered in a blog post. They're learned through incidents, rollbacks, and the slow accumulation of scar tissue.