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

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

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

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

Kafka uses partitions to allow multiple consumers to process messages in parallel.

Kafka maintains message order within partitions using keys for routing.

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

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

Kafka commit strategies determine how offsets are managed during message processing.

Classifying failures and using controlled retries in Kafka prevents outages.

Backpressure helps manage message flow in Kafka during traffic spikes.

Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.

Kafka consumers can use multiple threads to speed up message processing, but it complicates ordering and offset management.

Kafka uses state stores to remember previous events in stateful stream processing.

Older offsets can overwrite newer ones in Kafka if not handled properly.

Kafka's parallel processing depends on partitions, not just consumers or threads.

You can replay Kafka events safely by separating live and replay processes.

Backpressure prevents system overload by managing message processing rates in Kafka.

Kafka Streams offers higher-level abstractions, while Kafka Consumer provides direct control.

Kafka consumers have three important states: Position, Committed Offset, and Application State.

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

Kafka's `seek()` lets you replay only specific messages, saving time.