Architecture Overview
The most fundamental decision in system architecture: how to structure your application.
Every software system exists on a spectrum between two architectural extremes: monolithic (single unified codebase) and microservices (distributed collection of independent services). Neither is universally "better"—the right choice depends on team size, scaling requirements, organizational maturity, and business goals.
📦 Monolithic Architecture
A single, unified application where all components (UI, business logic, data access) run in the same process.
Best for: Startups, MVPs, small teams, simple domains.
🔗 Microservices Architecture
Multiple small, independent services that communicate over a network, each owning its own data and deployment.
Best for: Large teams, complex domains, independent scaling needs.
Monolithic Architecture
Structure & Organization
In a monolith, all code lives in a single codebase and runs as a single process. A typical structure might be:
- Presentation Layer: UI, API controllers, views
- Business Logic Layer: Services, domain models, workflows
- Data Access Layer: Database queries, ORM, repositories
All layers share the same database and memory space.
Benefits of Monoliths
- Simplicity: Single codebase, one deployment, simpler debugging and testing.
- Development Speed: No network overhead, easy refactoring across modules, shared code.
- ACID Transactions: Database transactions work natively across all operations.
- Lower Operational Overhead: One service to deploy, monitor, and scale.
- Performance: In-memory function calls are faster than network requests.
Challenges of Monoliths
- Scaling Limitations: Must scale the entire app, even if only one module is under load.
- Deployment Coupling: A bug in one module can break the entire deployment.
- Team Bottlenecks: Large teams often step on each other's toes with merge conflicts.
- Technology Lock-in: Harder to adopt new languages or frameworks for specific modules.
- Long Build Times: As the codebase grows, builds and tests can become slow.
💡 Modular Monolith: A well-architected monolith with clear module boundaries can provide
90% of the benefits of microservices without the operational complexity. Start here before considering
microservices.
Microservices Architecture
Structure & Organization
Microservices decompose the application into small, independent services:
- Each service handles a specific business capability (e.g., User Service, Order Service, Payment Service)
- Services communicate via APIs (REST, gRPC, message queues)
- Each service has its own database (Database per Service pattern)
- Services can be deployed independently
Benefits of Microservices
- Independent Scaling: Scale only the services that need it (e.g., scale Payment Service during Black Friday).
- Team Autonomy: Small teams own services end-to-end, reducing coordination overhead.
- Technology Diversity: Use the best tool for each job (Python for ML, Go for high throughput).
- Fault Isolation: A bug in one service doesn't crash the entire system.
- Independent Deployment: Deploy services separately, enabling faster release cycles.
Challenges of Microservices
- Operational Complexity: Need for service discovery, load balancing, distributed tracing, monitoring.
- Network Latency: Inter-service communication is slower than function calls.
- Distributed Transactions: No ACID guarantees across services. Must use eventual consistency or Sagas.
- Data Duplication: Each service owns its data, leading to potential inconsistencies.
- Testing Complexity: Integration tests require orchestrating multiple services.
- DevOps Investment: Requires mature CI/CD, containerization (Docker), orchestration (Kubernetes).
⚠️ Warning: Microservices add significant complexity. Don't adopt them unless you
have: (1) Multiple teams, (2) Independent scaling needs, (3) Mature DevOps practices, and (4) A complex
domain.
Detailed Comparison
A comprehensive comparison across key dimensions to guide your architectural decision.
| Dimension | Monolithic | Microservices |
|---|---|---|
| Development Complexity | Low. Single codebase, easy to navigate and refactor. | High. Distributed system patterns, API contracts, versioning. |
| Deployment | Simple. Single deployment artifact (JAR, container, binary). | Complex. Orchestrate multiple services, version compatibility. |
| Scaling | Vertical (add more CPU/RAM) or horizontal (replicate entire app). | Horizontal per service. Scale only what's needed. |
| Performance | Fast in-process function calls. No network overhead. | Network latency between services. Can be mitigated with caching, async messaging. |
| Fault Tolerance | Single point of failure. One bug can crash everything. | Isolated failures. Circuit breakers and retries prevent cascading failures. |
| Data Management | Single database. ACID transactions across all operations. | Database per service. Eventual consistency, distributed transactions (Sagas). |
| Team Organization | Works well for small teams (1-10 developers). Coordination required for larger teams. | Enables autonomous teams. Each team owns one or more services. |
| Technology Flexibility | Standardized stack. Entire app uses one language/framework. | Polyglot architecture. Use different languages per service. |
| Testing | Easier integration testing. All code in one process. | Complex. Requires contract testing, mocking, service virtualization. |
| Observability | Single log file, simple monitoring. | Distributed tracing (Jaeger, Zipkin), centralized logging (ELK), service mesh (Istio). |
Service Boundaries & Domain-Driven Design
The hardest part of microservices: deciding where to split. Poor boundaries lead to high coupling and distributed monoliths.
Domain-Driven Design (DDD) Principles
Use Bounded Contexts from DDD to identify service boundaries:
- Bounded Context: A logical boundary where a specific domain model applies (e.g., "Order Management", "Inventory", "Billing").
- Ubiquitous Language: Each context has its own language. "Customer" in Billing may differ from "Customer" in Support.
- Context Map: Document relationships between contexts (Shared Kernel, Customer-Supplier, Anti-Corruption Layer).
Identifying Service Boundaries
✅ Good Boundaries
- High cohesion within service
- Low coupling between services
- Aligned with business capabilities
- Clear ownership by a single team
- Independent data model
❌ Poor Boundaries
- Services that constantly call each other
- Shared database across services
- Boundaries based on tech layers (UI, Logic, Data)
- Too fine-grained (nano-services)
- Tight coupling via synchronous calls
Conway's Law: "Organizations design systems that mirror their communication structure."
Align service boundaries with team boundaries. If teams must coordinate heavily, merge their services.
Migration Strategies
Most companies start with a monolith and migrate to microservices. Here's how to do it incrementally.
1. Strangler Fig Pattern
Gradually replace parts of the monolith with microservices, routing traffic to the new service while the monolith still handles other requests.
Steps:
- Identify a module to extract (e.g., User Authentication)
- Build a new microservice with the same functionality
- Route requests to the new service via a proxy/API Gateway
- Once stable, remove the code from the monolith
- Repeat for other modules
Why it works: Zero downtime. You can test the new service with a small percentage of
traffic before full migration.
2. Database Decomposition
The hardest part of migration: splitting the monolith's database.
Strategies:
- Separate Schema: Move service tables to a new schema, still in the same database (low risk).
- Separate Database: Move to a completely separate database (higher isolation).
- Data Duplication: Allow some data to be duplicated across services (eventual consistency via events).
- Shared Database Anti-Pattern: Avoid services sharing the same database tables—it defeats the purpose of microservices.
3. Event-Driven Migration
Use events to decouple services during migration:
- The monolith publishes events (e.g., "OrderCreated") to a message broker (Kafka, RabbitMQ)
- New microservices subscribe to these events
- Services can be added/removed without modifying the monolith
Deployment & Operations
Microservices require significantly more infrastructure investment than monoliths.
Container Orchestration
Docker + Kubernetes: Standard for microservices deployment.
- Docker: Package each service as a container with all dependencies.
- Kubernetes: Orchestrates containers—handles scaling, rolling updates, health checks, service discovery.
CI/CD Pipeline Differences
| Stage | Monolith | Microservices |
|---|---|---|
| Build | Single build pipeline | Separate pipeline per service |
| Testing | Unit + integration tests in one suite | Unit tests per service + contract testing + E2E tests |
| Deployment | Deploy entire app at once | Deploy services independently (blue-green, canary) |
| Rollback | Rollback entire app | Rollback individual services (version compatibility required) |
Observability Requirements
Microservices require advanced monitoring:
- Distributed Tracing: Track requests across multiple services (Jaeger, Zipkin, AWS X-Ray).
- Centralized Logging: Aggregate logs from all services (ELK Stack, Splunk).
- Service Mesh: Manage service-to-service communication (Istio, Linkerd).
- Metrics & Alerting: Monitor latency, error rates, throughput per service (Prometheus, Grafana).
Decision Framework: When to Choose Each
✅ Start with a Monolith If:
- You're building an MVP or new product (speed > scalability)
- Your team is small (< 10 developers)
- The domain is simple or not yet well-understood
- You don't have mature DevOps practices
- You need ACID transactions across all operations
✅ Migrate to Microservices When:
- You have multiple teams that need to work independently
- Different parts of the app have different scaling needs
- You need to adopt new technologies for specific modules
- Deployment coupling is slowing down releases
- The domain is complex and well-understood (clear bounded contexts)
- You have mature CI/CD, monitoring, and DevOps culture
Real-World Examples
- Netflix: Migrated from monolith to microservices to scale globally (1000+ services).
- Amazon: "Two-pizza teams" each owning microservices to move faster at scale.
- Shopify: Started with a modular monolith, selectively extracted services for payment, shipping, etc.
- Etsy: Stayed with a monolith for years, invested in continuous deployment instead.
Anti-Pattern: Don't build microservices just because "it's what Google does." Most
companies aren't Google-scale. A well-designed monolith beats a poorly-designed microservices architecture.
Summary
- Monoliths are simpler, faster to develop, and sufficient for most teams. Start here.
- Microservices enable team autonomy and independent scaling but add significant operational complexity.
- Use Domain-Driven Design (Bounded Contexts) to identify service boundaries.
- Migrate incrementally using the Strangler Fig Pattern—don't do a big-bang rewrite.
- Invest in observability, CI/CD, and automation before adopting microservices.
- The best architecture is the one that matches your team size, domain complexity, and scaling needs.