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Message delivery semantics define how messages are sent in distributed systems and handle failures.

Kafka producers use ACKs, retries, and idempotence to ensure reliable message delivery.

Message queues allow services to communicate asynchronously, improving scalability and reliability.

Kafka can help ensure messages are processed once, but requires careful design.

Kafka uses Dead Letter Topics to handle messages that repeatedly fail processing.

Kafka maintains message order within partitions using keys for routing.

Poison messages in Kafka can cause infinite retries, risking system stability.

Kafka uses retries and Dead Letter Queues to manage message processing failures.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

Queues distribute tasks to one consumer, while Pub/Sub broadcasts events to multiple subscribers.

Kafka uses retry topics and delayed retries to manage message failures efficiently.

Availability is about access; reliability is about correct performance.

Fallbacks allow systems to handle failures safely and maintain user experience.

The CAP Theorem explains the trade-offs in distributed systems during network failures.