Microservices architecture patterns

- [Presenter] Understanding microservices architecture patterns is crucial for building robust scalable systems. These patterns work together to address the fundamental challenges of distributed systems: data consistency, communication complexity, failure handling, and system evolution. Understanding when and how to apply each pattern is crucial for a successful microservices implementation. Let's start with the API gateway pattern. The API gateway sits at your systems edge as the single entry point for all client requests. It functions as an ingress controller that shields the actual APIs inside your service mesh. The gateway routes client requests, aggregates responses from multiple services, and centralizes cross-cutting concerns such as authentication, rate limiting, and protocol translation. This pattern simplifies client interactions because clients only need to know about one endpoint and it offloads common functionality from individual services. The gateway also enables API versioning and request composition where you can combine data from multiple microservices into a single response. Next, database per service pattern. This pattern ensures each microservice owns its own data store, eliminating the shared database anti-pattern that creates tight coupling. With independent databases, you can choose the optimal database technology for each service's specific needs, what we call polyglot persistence. You might use a graph database for your recommendation service, a document store for your catalog service, and a relational database for your billing service altogether. However, this pattern demands sophisticated, distributed consistency strategies like sagas or event-driven architectures since you can no longer rely on a database transaction across services. Rate limiting controls the volume of requests a client or service can make within a given time period, protecting your backend resources from overload and preventing abuse. This can be enforced at the API gateway level for external clients or within individual services for internal rate limiting. This pattern supports various strategies like quotas, throttling, and burst control, significantly improving system stability during traffic spikes. When migrating from a monolith to microservices, the strangler fig pattern provides a safe, incremental approach. Named after the strangler fig plant that gradually replaces its host tree, This pattern incrementally replaces monolithic functionality by routing specific endpoints to new microservices while keeping the monolith operational. The API gateway plays a crucial role here, intelligently routing requests based on rules you define. This allows for phase migration with minimal risk and provides easy rollback capabilities if issues arise. The circuit breaker pattern is essential for preventing cascading failures in distributed systems. It monitors failure rates and response times of downstream services, and when thresholds are exceeded, it opens the circuit to stop forwarding requests to for the failing service. The pattern has three states: closed for normal operation, open for failure protection, and half-open for testing recovery. This prevents system-wide failures and enables graceful integration with fallback strategies like cache responses or default values. Event sourcing persists every state change as an immutable event log, providing a complete audit trail of all system changes. CQRS, that is command query responsibility segregation, separates write and read models, allowing you to optimize each for the specific use cases. Together, these patterns enable complex workflows, temporal queries, and highly scalable read operations to de-normalized read models.