Microservices architecture structures applications as collections of loosely coupled services, each handling a specific business capability. Instead of building one large application where all code shares the same database and deployment cycle, you build multiple smaller services that communicate through APIs.
The architecture gained traction because it solves a fundamental scaling problem: monolithic applications force you to deploy everything together, scale everything together, and coordinate changes across teams working on the same codebase. Netflix discovered this in 2009 when a database corruption took down their entire streaming service for three days. Their response was to break their monolith into hundreds of microservices, each independently deployable and scalable.
What is Microservices Architecture
Microservices architecture decomposes applications into services organized around business capabilities. Each service runs in its own process, manages its own data store, and communicates via lightweight protocols like HTTP/REST, gRPC, or message queues. Services deploy independently and are owned by small teams.
A typical e-commerce platform separates into services like product catalog, shopping cart, payment processing, inventory management, and order fulfillment. Each service handles one business domain and exposes APIs for other services to consume.
Microservices vs Monolithic
Microservices architecture differs from monolithic applications in deployment boundaries and data management. A monolithic application deploys as a single unit where all code shares one database. Any change requires testing and deploying the entire application, and scaling means replicating everything.
Microservices split the application into separate deployment units. Each service manages its own database and deploys independently. When you update the payment service, you don't redeploy the product catalog. When traffic spikes on the search feature, you scale only that service.
Monolithic architecture works better for small teams under 10 developers working on straightforward business domains. If you deploy monthly and handle moderate traffic, a well-built monolith serves you fine without the operational overhead.
Microservices make sense when you have multiple teams working on different business capabilities that need independent deployment cycles. When your payment processing needs different scaling than your recommendation engine, when you need high availability where one service failing doesn't take down the entire application—that's when microservices solve real problems.
Core Microservices Patterns
Service Decomposition
Services should align with business capabilities, not technical layers. Instead of having "database service" and "UI service," create services like "user management" and "payment processing."
Domain-Driven Design (DDD) provides the framework for this decomposition. Bounded contexts define service boundaries. Each service owns its domain logic and data. Services communicate through well-defined contracts.
A banking application might decompose into Account Management (deposits, withdrawals, balance), Loan Processing (applications, approvals, payments), Transaction History (records, statements, exports), and Authentication & Authorization (login, permissions, tokens).
Communication Patterns
Microservices communicate through two primary models. Synchronous communication uses HTTP/REST APIs for simple request-response, gRPC for high-performance internal communication, or GraphQL for flexible client-driven queries. Use this when you need immediate responses and can tolerate temporary service unavailability.
Asynchronous communication uses message queues like RabbitMQ or Amazon SQS, event streams like Apache Kafka, or pub/sub patterns like Redis Pub/Sub. Use this for eventual consistency, high throughput requirements, and decoupling services that don't need immediate responses.
Data Management
The database-per-service pattern gives each microservice exclusive access to its data store. No other service directly queries its database. This enables independent schema evolution, technology choice per service (PostgreSQL for transactions, MongoDB for product catalogs), and team autonomy in data modeling decisions.
Handling distributed data requires the Saga pattern for distributed transactions across services, Event sourcing to maintain consistency through event logs, and CQRS (Command Query Responsibility Segregation) to separate read and write operations.
Observability
Splitting a monolith into microservices means a single user request now traverses 10+ services. When something breaks, you need to trace that request through the entire system. Observability provides this visibility through three types of data.
Metrics track quantitative health indicators—request rates, error rates, latency percentiles, resource consumption. They tell you something is wrong. A spike in error rates points to a problem but doesn't explain why.
Logs capture discrete events with context—errors, warnings, business events. When a payment fails, logs show the error message, user ID, transaction amount, and stack trace. They answer what happened.
Traces follow requests across service boundaries. Each request carries a unique trace ID through every service it touches. When debugging a slow checkout, traces reveal which services were involved, how long each took, and where the bottleneck occurred.
OpenTelemetry standardized this instrumentation. It provides vendor-neutral APIs for collecting metrics, logs, and traces regardless of language. Uptrace as a Free APM correlates this data into a unified view where you can jump from a latency spike directly to the trace and logs that caused it.
API Gateway Pattern
An API gateway sits between clients and microservices, providing a single entry point for all client requests. It handles:
| Responsibility | Implementation |
|---|---|
| Request routing | Direct requests to appropriate services |
| Authentication | Verify client identity before routing |
| Rate limiting | Prevent abuse and ensure fair usage |
| Request aggregation | Combine multiple service calls into one client response |
| Protocol translation | Convert between client and service protocols |
Popular API gateways include Kong, Amazon API Gateway, and Spring Cloud Gateway.
Service Discovery
In dynamic environments where services scale up and down, hardcoded IP addresses break immediately. Service discovery maintains a registry where services register themselves and clients find available instances.
Client-side discovery puts the logic in the client. The client queries the service registry, gets a list of healthy instances, and chooses which one to call. The client controls load balancing but every client needs discovery logic. Server-side discovery moves this to infrastructure—clients call a load balancer, which handles the registry lookup and routing. Clients stay simple but you need load balancer infrastructure.
Kubernetes provides built-in service discovery through DNS. When you create a service, Kubernetes assigns it a DNS name that resolves to healthy pods.
Deployment Strategies
Containerization
Containers package services with their dependencies, ensuring consistency across environments. Docker has become the standard, with Kubernetes orchestrating container deployment, scaling, and management.
Container benefits for microservices include identical runtime environments from development to production, efficient resource utilization through shared OS kernels, fast startup times for rapid scaling, and built-in service discovery and load balancing in Kubernetes.
CI/CD for Microservices
Continuous integration and deployment become essential with microservices. Each service needs automated testing (unit, integration, contract tests), independent deployment pipelines, blue-green or canary deployments for zero-downtime releases, and automated rollback capabilities.
Tools like Jenkins, GitLab CI, GitHub Actions, and ArgoCD handle these requirements.
Security
Distributed systems expose more attack surface than monoliths. Security needs to be architectural, not added later.
Service-to-service communication requires authentication and encryption. Mutual TLS (mTLS) provides both—each service proves its identity with a certificate, and all traffic encrypts automatically. Service meshes like Istio and Linkerd handle this without changing application code. JWT tokens work for stateless authentication where services validate tokens without calling back to an auth service.
API security starts with proper authorization. OAuth 2.0 handles delegation—letting one service act on behalf of a user without seeing their credentials. API keys identify calling services. Rate limiting prevents abuse by capping requests per client.
Zero Trust assumes breach. Never trust, always verify. Every request authenticates, even between internal services. Every service gets minimum necessary permissions. A compromised payment service shouldn't access user profiles.
When NOT to Use Microservices
Microservices add complexity. Don't use them if your team is small (under 10 developers can coordinate effectively on a monolith), your domain is simple (straightforward business logic without distinct bounded contexts), you lack operational maturity (microservices require strong DevOps practices, automated testing, monitoring, and deployment pipelines), your traffic is low (hundreds of requests per second, not thousands), or you need ACID transactions everywhere (eventual consistency creates unnecessary complexity).
Getting Started
Start with observability from day one. Before splitting your monolith, instrument your existing application with metrics, logs, and traces. Identify service boundaries using domain-driven design. Extract one service at a time, starting with the least coupled component. Establish monitoring for the new service before production. Automate deployment to handle multiple services efficiently.
Spring Boot microservices provide a solid foundation for Java developers, with built-in support for metrics, health checks, and distributed tracing. For event-driven architectures, understanding Kafka microservices patterns helps you design asynchronous communication effectively.
Essential Tools
Kubernetes handles production container orchestration with automatic health checks, rolling updates, and self-healing. Docker Compose manages local development environments. Istio and Linkerd provide service mesh capabilities—traffic management, circuit breaking, and automatic mTLS between services.
OpenTelemetry instruments your code to emit metrics, logs, and traces. Uptrace aggregates this into a unified interface. Prometheus collects time-series metrics. Kong routes external traffic to internal services while handling authentication and rate limiting. Apache Kafka enables event streaming where multiple services consume the same events for different purposes.
Why Observability Matters
The shift from monolith to microservices fundamentally changes how you debug production issues. In a monolith, you set a breakpoint and step through code. In microservices, that request might touch 15 services across 3 data centers.
Without proper observability, you're debugging distributed systems blind. You see the symptom (slow checkout) but not the cause (a dependency 5 services deep hitting its connection pool limit). OpenTelemetry standardizes how you collect this data. Uptrace provides the analysis platform to make sense of it—correlating metrics spikes with error logs, connecting slow traces to their root causes, and surfacing patterns across thousands of services.
Ready to build observable microservices? Start with Uptrace for complete visibility into your distributed systems.
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