Saga

Answer-first: Dapr Workflows implement the Saga pattern in Go by coordinating distributed transactions through stateful, durable orchestration. If a step fails, the orchestrator executes compensating transactions in reverse order, ensuring eventual consistency without requiring complex manual state management or two-phase commit overhead. What You’ll Learn That AI Won’t Tell You Compensation handlers configuration in Dapr to guarantee atomic rollback. How to handle transient workflows when the orchestrator instance restarts mid-transaction. Most Go developers building microservices know the Choreography Saga pattern: service A emits an event, service B reacts, service C reacts to B, and so on. If step C fails, services emit “compensation” events in reverse order. The pattern works elegantly for simple flows, but breaks down as the number of steps grows: debugging a failed saga requires tracing events across five message broker topics, and implementing compensation logic requires every service to understand the full saga’s state. ...

Answer-first: Build resilient event-driven microservices by using Dapr’s Pub/Sub APIs to decouple message transport. Ensuring eventual consistency requires implementing the Transactional Outbox pattern on writes, utilizing dead-letter queues (DLQs) for failed runs, and designing idempotent message handlers. What You’ll Learn That AI Won’t Tell You How to configure dead-letter queues in Dapr to handle poison messages. Designing idempotent message handlers that process duplicate events safely. In my previous post, we explored how abandoning monolithic architecture in favor of strict Domain-Driven Design (DDD) bounded contexts allowed an e-commerce platform to scale beyond 10,000+ orders per day. However, splitting one big database into 20+ isolated Postgres databases introduces a terrifying new problem: How do we maintain data consistency across disconnected services? ...