Programming Tutorials

  You have implemented the Saga pattern (likely Choreography) to manage distributed transactions across your Order, Inventory, and Payment microservices. The "Happy Path" works flawlessly. The "Forward Failure" path (Payment fails, triggering an Inventory release) works in your integration tests. But in production, you are seeing "Zombie" records: Orders that are marked as  FAILED , but the Inventory is still  RESERVED . This happens because you assumed that the  Compensating Transaction  (the undo action) would always succeed. It doesn't. Network partitions, database deadlocks, and deployment race conditions affect compensations just as often as they affect the initial commit. When a compensation fails and you simply log the error or push to a generic Dead Letter Queue (DLQ), you have implicitly accepted data inconsistency. Here is the root cause analysis and a deterministic, code-first solution to guarantee eventual consistency without manual interve...

  In distributed payment and inventory systems, exact-once processing is a myth. We operate in a world of   at-least-once delivery . If your Kafka broker doesn't receive an acknowledgment (ACK) due to a network partition or a consumer crash, it   will   redeliver that message. If your consumer logic isn't idempotent, you just charged a customer twice or decremented inventory for an item that doesn't exist. Worse, if messages arrive out of order (e.g., "Order Cancelled" arrives before "Order Created" due to partition rebalancing), your system state becomes corrupt. This post details a rigorous implementation of the  Idempotency Key Pattern  using Redis (for atomic locking) and PostgreSQL (for consistent state), ensuring data integrity regardless of duplicate deliveries or race conditions. The Root Cause: Why "At-Least-Once" Breaks Data The core issue lies in the gap between  Side Effect Execution  and  Broker Acknowledgment . Consider this sta...