Programming Tutorials

  In distributed systems, the guarantee of "exactly-once" processing is a myth when side effects are involved. While Kafka Streams offers exactly-once semantics for internal state, this guarantee evaporates the moment your service needs to make an external HTTP call (e.g., charging a credit card via Stripe) or write to a database that isn't part of the transaction log. The default delivery semantic for Kafka is  At-Least-Once . This means your system is architecturally destined to process duplicate messages. Without an idempotency strategy, you risk data corruption and financial loss. The Root Cause: Why Duplicates Occur To solve duplicate processing, we must understand where the duplicates originate. They primarily stem from two failures in the commitment protocol: Producer Retries (Network Jitter):  A producer sends a message to the broker. The broker writes it successfully, but the network acknowledgement (ACK) fails to reach the producer. The producer, assuming failur...

  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...