distributed-systems

Reliable event publication in microservices is typically handled using the outbox pattern, which saves domain changes and event records in the same database transaction to ensure messages are durably delivered without data inconsistency.

Data consistency across asynchronous messaging workflows is best managed using the saga pattern, which coordinates multi-step transactions across services with compensating actions to maintain consistency if individual operations fail.

Duplicate processing in distributed event-driven systems is prevented by implementing idempotent consumers that deduplicate incoming messages using correlation IDs, ensuring the same event is not applied multiple times.

You need CQRS and event sourcing for read models when your system requires durable event trails, traceable workflows, and projection-based read models with versioning to handle high-volume reads independently from complex domain writes.

Failed asynchronous messages are handled using dead-letter queues to capture unprocessable events, paired with replay tooling and retry policies to reprocess messages once transient issues or downstream failures resolve.

Yes, common anti-patterns to avoid when building event-driven distributed systems include synchronous coupling between microservices, skipping event outbox publication, and lacking dead-letter handling, which can disrupt reliable workflow coordination.