When to Split a Service: A Distributed Systems Perspective

I saw Travis McCracken's post about Rust and Go for backend development, and while the language choice matters, the bigger question is: when do you actually need to split that monolith into separate services?

Every distributed systems engineer has seen teams split services too early or too late. Both are expensive mistakes. The real art is recognizing the signals that tell you it's time to create new boundaries.

The Monolith That Works

First, let's be honest: a well-built monolith is often the right answer. Single codebase, single deployment, single database. Simple transactions, easy debugging, straightforward refactoring. If you can move fast and sleep well, why split?

The problem is scale happens in multiple dimensions:

• Development scale: 50 engineers trying to touch the same codebase
• Traffic scale: Millions of requests hitting your API
• Data scale: Database tables growing to hundreds of millions of rows
• Complexity scale: Feature interactions creating subtle bugs

Different scaling pressures require different solutions.

Signal 1: Deployment Coupling Breaks You

You know the feeling. You're fixing a bug in the billing system, but to deploy it, you need to test and coordinate with changes to the recommendation engine, user notifications, and inventory management. One team's typo can break three other services.

This is the classic sign you need service boundaries. When the risk of any single change grows because it touches unrelated code, you need to split.

The key insight: split based on change frequency, not just functionality. If your billing logic changes monthly but your recommendation algorithm changes daily, they should be separate services even if they're conceptually related.

Signal 2: Resource Requirements Diverge

This is where Rust and Go actually matter. Your image processing service needs 16GB RAM and 8 cores. Your API gateway needs 2GB and minimal CPU. Your ML inference service needs GPUs.

Running these as separate services lets you:

• Scale independently based on actual load patterns
• Deploy to appropriate hardware
• Optimize costs
• Choose the right language per service

Rust shines for compute-heavy components where memory safety and performance are critical. Go excels for API orchestration where concurrency and development speed matter. But the real win is independent scaling, not language choice.

Signal 3: Data Consistency Boundaries

Here's where distributed systems get interesting. When different parts of your system need different consistency guarantees, you need separate databases.

Your order management needs strong consistency and transactions. Your product catalog can tolerate eventual consistency. Your analytics system needs read-optimized queries.

Splitting services lets you:

• Use the right database per use case (PostgreSQL for orders, Redis for cache, Elasticsearch for search)
• Accept different consistency models (ACID vs. eventual)
• Scale databases independently

But this introduces the hardest problem: distributed transactions. When an order creation needs to update inventory, you're now dealing with sagas, two-phase commit, or compensating transactions. Each approach has trade-offs:

Saga pattern: Eventual consistency with compensating actions. Complex to implement, but scales well.

Two-phase commit: Strong consistency, but blocks under failures and doesn't scale.

Event sourcing: Append-only event log, replay for state. Great for audit trails, but complex to query.

There's no free lunch. Splitting for data reasons means accepting distributed systems complexity.

Signal 4: Failure Isolation

When your recommendation service goes down, should it take down the entire platform?

Service boundaries create failure domains. A bug in one service stays contained. But you need to design for it:

Circuit breakers prevent cascading failures. When the inventory service is slow, the ordering service should fail fast, not hang threads.

Timeouts everywhere. Every network call needs a timeout. Every database query needs a timeout. Every service-to-service call needs a timeout.

Fallbacks and graceful degradation. Can you show cached recommendations when the service is down? Can you accept orders without real-time fraud checks?

This is where the "seen failures" part of distributed systems engineering matters. You learn that network calls fail, databases lock, disks fill up, and clocks drift. Service boundaries amplify these failures unless you design for them.

Signal 5: Team Autonomy

This is the organizational signal. If you have three teams that need to coordinate every deployment, you have three services.

The Conway's Law reality: your architecture mirrors your communication structure. If teams can't work independently, splitting the code won't help. But if you have genuinely independent teams, separate services let them move at their own pace.

The trade-off: independent teams mean independent decisions. One team uses Rust, another uses Go. One deploys daily, another weekly. One uses PostgreSQL, another uses MongoDB. This creates operational complexity, but it might be worth it for development velocity.

The Splitting Decision Framework

Before splitting, ask:

1. What problem am I solving? If it's just "microservices sound cool," don't split.

2. Can I simulate the boundary first? Use modules, internal libraries, or separate packages in a monorepo. If you can't enforce boundaries in code, you won't enforce them across services.

3. What's the operational cost? New services need monitoring, logging, deployment pipelines, error tracking, documentation. Each service is a tax on your ops team.

4. Do you have the tooling? Without good observability (tracing, metrics, structured logging), distributed debugging is a nightmare.

5. Can you handle the consistency trade-offs? If you need ACID transactions across services, you're not ready to split.

Implementation Patterns

When you do split, start with these patterns:

Strangler fig pattern: Build new services around the edges of the monolith, gradually pulling out functionality. Don't rewrite everything at once.

API gateway: Single entry point that routes to services. Handles authentication, rate limiting, request/response transformation. Essential for hiding internal complexity.

Service mesh: For complex deployments, tools like Istio or Linkerd handle service discovery, retries, circuit breaking, and mTLS. But it's operational complexity—don't add it until you need it.

Event-driven architecture: Services communicate via events (Kafka, RabbitMQ) instead of direct calls. Decouples services in time, but adds eventual consistency.

The Real Answer

The right time to split is when the cost of coordination exceeds the cost of distribution.

That's it. Everything else is implementation detail.

When you're spending more time in meetings coordinating deployments than writing code, split. When your database is locking under load and you can shard logically by service, split. When different parts of your system need different reliability or performance characteristics, split.

But until then, that monolith is probably fine. Build features, ship code, and watch for the signals.

The languages matter less than the boundaries. Rust and Go are excellent choices for services, but the best service is the one you don't need to build yet.

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For more on distributed systems patterns, see the Microservices.io pattern catalog and Google's SRE book.

Featured image: Systems architecture thinking

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This article was inspired by Travis McCracken's discussion of Rust and Go for backend development. While language choice matters, service boundaries are the real architectural decision.