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

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

Kafka consumer reprocessing involves safely replaying events to correct system states.

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

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

Kafka consumers can pause and resume message processing to handle backpressure effectively.

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

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

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

Kafka maintains message order within partitions using keys for routing.

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

Kafka uses partitions to allow parallel processing while ensuring message order with keys.

Kafka's commitSync waits for confirmation, while commitAsync continues immediately.

Kafka uses heartbeats and timeouts to check if consumers are alive.

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

Kafka Streams uses local state for quick data access and changelogs for recovery.

Kafka consumer offsets help track message processing to avoid loss and duplication.

Kafka consumer lag shows how far behind consumers are from new messages.