Programming Tutorials

  It’s the distributed system nightmare every backend engineer encounters eventually. Your application successfully commits a record to the database (e.g., a new user sign-up). Immediately after, it attempts to publish an event to Apache Kafka to trigger downstream services (e.g., send a welcome email). But the network blips. The message broker is unreachable. The application throws an error, but the database transaction is already committed. You now have a  dual-write inconsistency . The user exists in your primary database, but the rest of your microservices architecture has no idea. The system is out of sync, and manual reconciliation is painful. This guide details how to solve this strictly using the  Transactional Outbox Pattern  with PostgreSQL, Kafka, and Debezium. The Anatomy of the Dual-Write Problem Before fixing it, we must understand why the naive approach fails. In a monolithic architecture, you might rely on a single ACID transaction. In microservices, ...

  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...