Microservices Design Patterns: Part 1 - Breaking the Monolith

• Amrit Adhikari • 📖 9 min read
microservicesarchitecturedesign-patternsmonolithdecomposition

The Problem We’re All Facing

You know the pain. I know you do.

It starts innocently enough. A small, clean codebase. Everything makes sense. Features get added quickly. Life is good.

Then, months (or years) later, you’re staring at a monster. A codebase so large and interconnected that:

  • Every change feels like defusing a bomb
  • Deploying means coordinating multiple teams and crossing your fingers
  • Scaling one feature means scaling the entire application
  • You can’t update the checkout flow without redeploying everything

Sound familiar? Welcome to monolith hell.

The promise of microservices is simple: split your system into small, independent services. Each service handles one piece of functionality and can be deployed separately.

Sounds great, right?

Here’s the catch: Just chopping a monolith into pieces doesn’t automatically give you benefits. Done wrong, you get a “distributed monolith” - all the pain of a monolith plus network latency and debugging nightmares.

The real challenge is: How do you split a system into the RIGHT services?

What Makes Good Service Boundaries?

Before we dive into strategies, let’s clarify what we’re aiming for:

High Cohesion

Each service handles one well-defined area. Everything inside it belongs together.

Low Coupling

Services minimize dependencies on each other. Most changes impact only one service.

Independent Deployability

You should be able to update Service A without touching Services B, C, or D.

Think of it like this: if adding a new feature requires changing three services, your boundaries might be wrong.

Four Proven Decomposition Strategies

1. Decompose by Business Capability

What it is: Split services based on what your business does to generate value.

For an e-commerce site, business capabilities might be:

  • Product Catalog Management
  • Inventory Management
  • Order Processing
  • Shipping
  • Payments

Each becomes its own service. The Catalog Service handles everything about products - listings, details, search. The Order Service handles everything about orders - creation, status, history.

Why it works: Business capabilities are stable boundaries. They align with how companies organize teams. Changes in shipping logic rarely affect inventory management. Plus, business stakeholders understand these boundaries naturally.

Real Example: Amazon used this approach. Small “two-pizza teams” each owned a service aligned with a business function - the cart, payments, catalog, reviews. Each team could innovate independently with clear goals.

The challenge: You need to deeply understand your business domain. Sometimes capabilities aren’t obvious. You might need to iterate - start broad, then refine.

2. Decompose by Subdomain (Domain-Driven Design)

What it is: Use Domain-Driven Design to break your business into subdomains, each with its own model and rules.

In DDD, you identify bounded contexts - areas where a specific model applies. An “Order” might mean something different in:

  • Order Management (focused on fulfillment status)
  • Inventory (focused on stock reservation)

Why it works: Like business capabilities, subdomains are stable. They often align with business areas but emphasize modeling consistency. Each service owns its model without confusion from other parts of the system.

Real Example: Atlassian decomposed Jira by identifying major domains - projects, issues, users. Each had clear boundaries and made sense to isolate.

The challenge: Requires collaboration with domain experts and iterative modeling. Finding the right bounded contexts isn’t always obvious upfront.

Note: Business capability and DDD subdomain approaches often lead to similar boundaries - they’re just different lenses on the same problem.

3. Decompose by Transaction Boundary

What it is: Group functionalities that need to happen together atomically into the same service.

The problem it solves: In a monolith, you can wrap multiple operations in one database transaction. In microservices, distributed transactions are complex and slow. If creating a Claim always requires updating a Customer record, doing this across two services means orchestrating a distributed transaction.

Why it works: Keeping tightly-coupled transactions in one service gives you:

  • Faster response times (no network calls)
  • Simpler data consistency (local transactions)
  • Fewer failure points

The caution: Don’t overuse this. You might end up creating mini-monoliths. Use it selectively when latency or consistency absolutely requires it.

Example: If your Order Service constantly calls Customer Service and Inventory Service in a tight dance for every checkout, consider whether Order Service should own the bits of data it needs, or redesign the boundaries.

4. Decompose by Data Ownership

What it is: Each service owns its data and manages its own database. No shared databases between services.

In a monolith, there’s typically one big database. In microservices, you split the data:

  • Customer Service has its customer database
  • Order Service has its order database
  • They don’t share tables - they interact through APIs

Why it works:

  • Autonomy: Services can scale or fail independently
  • Flexibility: Each service can choose the best database technology for its needs
  • Encapsulation: One service’s schema changes don’t break others

Real Example: Netflix moved to “polyglot persistence” - each microservice chose its own database type (SQL, NoSQL, etc.). Amazon enforces that no team directly queries another team’s database - everything goes through APIs.

The trade-off: You can’t do cross-table joins anymore. If you need combined data, you call APIs or use event-driven approaches. This is a known complexity, but most teams find the benefits outweigh the drawbacks.

Key principle: If two services conceptually need to update the same data, that’s a sign they might actually be one service, or you need a different boundary.

Real-World Lessons from the Trenches

Amazon: Hundreds of Services, Massive Scale

Amazon went from a two-tier monolith to hundreds of services. Bezos mandated that all teams expose functionality only through service interfaces.

Their approach: Business capabilities + team ownership. Small teams owned complete services - Shopping Cart, Payments, Catalog. Each service had its own database and APIs.

The result: Went from a few deployments per year to many per day. Teams could innovate independently without coordination hell.

Key lesson: Align architecture with organization structure. Each team’s responsibility was crystal clear and encapsulated.

Netflix: Scaling to Millions

Netflix started with a DVD rental monolith. When they pivoted to streaming globally, they split into microservices for user recommendations, video encoding, playback tracking, user profiles, etc.

Their approach: Functional decomposition + infrastructure concerns. They could scale the Streaming Service for evening traffic without scaling the entire user account system.

The result: Massive scale and resilience. If Recommendations Service fails, users see generic popular titles instead of personalized picks - degraded but not broken.

Key lesson: Microservices enabled hyper-scale, but required heavy investment in DevOps, monitoring, and automation (Eureka, Hystrix, Chaos Engineering).

Shopify: The Modular Monolith

Shopify has one of the world’s largest Rails codebases (2.8M+ lines). Instead of going full microservices, they took a “modular monolith” approach.

Their approach: Decomposed the monolith into components internally - Billing, Shop Management, Orders - still in one Rails app but with enforced boundaries. They prevented components from reaching into each other’s internals.

The result: Teams could work more independently, tests ran in isolation, and they avoided the operational overhead of hundreds of services.

Key lesson: You don’t have to go all-in on microservices. Apply the principles (clear boundaries, ownership, decoupling) even within a monolith. Decompose selectively where it makes sense.

Common Pitfalls (And How to Avoid Them)

The Distributed Monolith 💀

Services so tightly coupled they must deploy together. You get none of the benefits, plus network latency. Often happens when you split by technical layers (UI Service → Logic Service → DB Service) instead of by domain.

Over-Coupling 🕸️

Service A calls B, C, and D for every operation. Any failure cascades. Changes require coordinating all four services. This defeats the purpose.

Too Fine-Grained 🔬

Every tiny function becomes a service. You end up with hundreds of services where a dozen would do. Each service has overhead - infrastructure, CI/CD, monitoring. Engineers get overwhelmed.

Data Consistency Nightmares 😵

Multiple services maintain their own “truth” for the same data. User profiles scattered everywhere. Changes get out of sync. Solution: Clear data ownership - one service is the authority.

Shared Database Anti-Pattern 🚫

Services sharing a database for “convenience.” Schema changes now require coordination. You’ve undone the isolation. Avoid this at all costs.

Practical Guidelines for Success

Start with the Domain

Understand your business workflows. The nouns (customers, orders, products) hint at services. The verbs hint at interactions.

High Cohesion, Low Coupling

Ask “Will most changes to this area involve only this service?” If yes, good boundary.

Don’t Split by Technical Layers

Think vertical slices. Each service handles its domain from API to database.

Align with Teams

Use Conway’s Law to your advantage. Structure services so teams can own them end-to-end.

One Service, One Database

Draw clear lines around data ownership. Only the owning service modifies its data.

Consider a Modular Monolith First

Test boundaries within the monolith before splitting. It’s easier to adjust and reduces risk.

Monitor and Iterate

Your first cut won’t be perfect. Observe what works in reality and adjust. Architecture can evolve.

The Bottom Line

Breaking a monolith is a journey, not a destination. The goal isn’t microservices for their own sake - it’s enabling teams to work independently, systems to scale efficiently, and deployments to happen safely and frequently.

Find the right seams in your system by cutting along business capabilities or subdomains, respecting data ownership, and ensuring each service is a strong, autonomous unit.

Start small. Maybe just extract one or two high-value services first. See how it goes. Adjust.

Remember: Microservices trade one complexity (managing a monolith) for another (managing a distributed system). Make sure the trade-off is worth it for your situation.

A well-structured monolith can take you far. But when you’re ready to decompose, do it thoughtfully along these proven patterns, and you’ll unlock real benefits.

What’s Next?

In this first part, we’ve covered the foundational patterns for breaking down monoliths. In Part 2 of this series, we’ll dive into:

  • Communication Patterns between microservices
  • Synchronous vs Asynchronous messaging
  • Event-Driven Architecture patterns
  • API Gateway and service mesh patterns

The decomposition is just the beginning. How services talk to each other is where the real magic (and complexity) happens.

Key Takeaways

  • Business capabilities and subdomains are the most stable boundaries
  • Data ownership prevents coupling through shared databases
  • Transaction boundaries help maintain consistency where needed
  • Real companies like Amazon, Netflix, and Shopify prove these patterns work at scale
  • Start small and iterate - you don’t have to go all-in immediately
  • A modular monolith might be the right first step

Ready to explore microservices communication patterns? See you in Part 2! 🚀

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