Microservices Patterns: Scenario-Based Questions

Event-driven architecture (EDA) is a design paradigm where services communicate via asynchronous events instead of direct API calls. It promotes loose coupling, improved scalability, and system resilience.

📡 Key Concepts

• Producer: Service that emits an event (e.g., "order.created").
• Consumer: Service that listens and reacts to events.
• Event Bus: Middleware (Kafka, RabbitMQ, SNS/SQS, NATS) that delivers events.
• Event: A message representing a fact (immutable), like a domain state change.

📦 Benefits of Event-Driven Systems

• Loose Coupling: Services don’t need to know each other directly.
• Scalability: Consumers scale independently based on demand.
• Flexibility: New consumers can be added without modifying producers.
• Auditability: Event logs provide a source of truth and replayability.

🧱 Common Patterns

• Event Notification: Fire-and-forget with no response needed.
• Event-Carried State Transfer: Event includes all necessary payload data.
• Event Sourcing: Persist events instead of current state — rebuild state from events.

✅ Best Practices

• Use idempotency keys to prevent duplicate processing.
• Design clear event schemas and version them over time.
• Partition topics/queues to ensure ordering where needed.
• Monitor lag, throughput, and DLQs (dead-letter queues).

🚫 Common Pitfalls

• Tight coupling via shared event schemas or strong dependencies.
• Unreliable or non-durable event delivery setups.
• Complex debugging due to asynchrony and lack of tracing.

📌 Real-World Insight

EDA enables systems like Netflix, Uber, and Shopify to handle millions of decoupled operations daily. It requires rigor in schema design, observability, and failure handling — but unlocks major scaling advantages.