Building scalable, maintainable, and resilient systems requires an awareness of important design patterns, which are becoming increasingly prevalent with the microservices architecture. Microservices, as opposed to monolithic architectures, divide big systems into more manageable, independent services that connect with one another via a network. However, this distributed nature introduces complexity in areas such as communication, data management, and service coordination.

Adopting well-known microservices design patterns can help to mitigate these issues and significantly improve the reliability and effectiveness of your system. The top 7 microservices design patterns that every software developer should be aware of are covered in this article.

1. API Gateway Pattern

The API Gateway acts as a single entry point for all client requests to the microservices. Instead of having clients directly interact with multiple services, the API Gateway consolidates these requests, routes them to the appropriate microservices, and aggregates responses. It simplifies client-server communication and provides a way to manage cross-cutting concerns such as authentication, logging, and rate-limiting.

Benefits:

✓ Centralized control over request/response handling.

✓ Simplifies client-side interactions by abstracting internal microservice complexity.

✓ Enables easier implementation of security, caching, and throttling.

Example:

Using Express.js in Node.js to build a basic API Gateway:

import express from 'express';
import proxy from 'express-http-proxy';

const app = express();

// Forward requests to microservice A
app.use('/serviceA', proxy('http://serviceA-url'));

// Forward requests to microservice B
app.use('/serviceB', proxy('http://serviceB-url'));

app.listen(3000, () => {
  console.log('API Gateway running on port 3000');
});

2. Circuit Breaker Pattern

In a microservices architecture, service failures are inevitable. The Circuit Breaker Pattern helps prevent cascading failures by monitoring service calls and stopping further calls to a failing service when a certain failure threshold is reached. Once the service recovers, the circuit breaker allows calls again. This improves system resilience and prevents unnecessary load on already struggling services.

Benefits:

✓ Protects against system-wide failure.

✓ Provides fallback or alternative responses during failures.

✓ Enhances the robustness of the microservice architecture.

Example:

Using the opossum library in Node.js for a circuit breaker:

import CircuitBreaker from 'opossum';
import axios from 'axios';

const options = {
  timeout: 5000,
  errorThresholdPercentage: 50,
  resetTimeout: 30000,
};

const circuitBreaker = new CircuitBreaker(() => axios.get('http://serviceB-url'), options);

circuitBreaker.fire()
  .then(response => console.log(response.data))
  .catch(err => console.log('Service B is down. Circuit is open.'));

3. Database per Service Pattern

Each microservice should have its own dedicated database, allowing teams to work independently and reducing tight coupling between services. This design pattern ensures that microservices can evolve independently without being affected by changes to a shared database schema.

Benefits:

✓ Reduces cross-service dependency and contention.

✓ Facilitates independent scaling and schema evolution.

✓ Isolates data ownership and responsibility.

4. Saga Pattern

In a distributed architecture, handling transactions that span across multiple services can be challenging. The Saga Pattern manages distributed transactions using a series of local transactions that are coordinated across multiple services. Each service executes its transaction and triggers the next one, with compensation mechanisms to undo operations if something goes wrong.

Benefits:

✓ Allows for consistent distributed transactions without a centralized transaction manager.

✓ Supports eventual consistency across microservices.

✓ Enables rollback of incomplete operations when necessary.

Example:

In an e-commerce system, the order service might create an order, the payment service processes the payment, and the inventory service updates stock levels. If the payment fails, the order and stock updates need to be rolled back, which is handled through compensating transactions.

5. Event Sourcing Pattern

The Event Sourcing Pattern stores the state of a system as a sequence of events. Instead of saving the current state in a database, microservices store events that represent state changes. By replaying these events, the current state can always be reconstructed, which provides a complete audit trail and enables sophisticated recovery mechanisms.

Benefits:

✓ Provides a clear audit trail of all changes.

✓ Enables historical analysis by replaying past events.

✓ Facilitates rebuilding state if necessary.

Example:

In an accounting system, events like "transaction created", "transaction approved", and "transaction completed" are stored as events. The current balance can be recalculated by replaying all transaction events.

6. CQRS (Command Query Responsibility Segregation) Pattern

The CQRS Pattern separates the read and write operations into different models. Write operations are handled by the command model, and read operations are handled by the query model. This pattern is particularly useful for high-performance applications where reads are much more frequent than writes.

Benefits:

✓ Optimizes performance by segregating read/write concerns.

✓ Supports different scalability strategies for reads and writes.

✓ Allows for flexible models tailored to specific tasks.

7. Strangler Fig Pattern

The Strangler Fig Pattern is a gradual migration strategy that allows you to refactor or replace parts of a monolith with microservices. As new functionality is added, it’s built as a microservice. Over time, the monolith is replaced, service by service, without disrupting the entire system at once.

Benefits:

✓ Provides a non-disruptive path to migrate from monoliths to microservices.

✓ Reduces the risk of complete system rewrites.

✓ Enables incremental improvement and refactoring.

Example:

You might begin by extracting the user authentication component of a monolithic application into a standalone microservice while keeping other parts of the system intact. Over time, more components are moved to microservices until the entire system is modularized.

Conclusion

There are many different ways to address the problems that arise in distributed systems when using microservices design principles. Building a dependable, scalable microservices architecture requires a grasp of these patterns, regardless of your goals—managing data across services, enhancing communication between services, or handling errors gracefully. By addressing particular requirements and trade-offs, each of these patterns contributes to the resilience and performance of your microservices.