event

To design an event-driven architecture with correct ordering and replayability, evaluate broker suitability and implement partitioning strategies alongside outbox or CDC patterns. Using event-store snapshots and projections further guarantees reliable data recovery during system audits or migrations.

An idempotent consumer pattern prevents duplicate processing when message brokers redeliver messages due to network failures or rebalancing. You need it in event-driven architectures to maintain reliable state convergence and prevent duplicate side effects during retry scenarios.

Configuring DLQ and retry backoff policies for Kafka or SQS requires defining exponential backoff strategies and routing unprocessable messages to a dead-letter queue. This isolates poison pills, maintains system throughput, and preserves debuggability during transient broker failures.

Migrating to CQRS and event sourcing requires a schema registry to enforce event schema versioning and compatibility. This prevents schema breakage between producers and consumers, ensuring reliable event envelope standards and seamless projection updates during architectural scaling.

Choosing between Kafka, RabbitMQ, SQS/SNS, and NATS requires evaluating throughput, message ordering, and replay requirements. Kafka suits high-throughput event replay, whereas NATS or SQS/SNS fit simpler routing, needing careful pattern guidance to match broker capabilities with architecture needs.

Duplicate processing in event-driven systems occurs when consumers crash or messages redeliver, causing repeated side effects. Implementing outbox or CDC patterns helps by atomically writing events to a relay table, ensuring reliable delivery semantics to downstream message brokers.