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

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

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

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

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

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

Kafka uses brokers for data handling and KRaft controllers for metadata management.

gRPC allows efficient communication between services using a structured request-response model.

ZooKeeper watches notify clients of changes, avoiding constant polling and reducing server load.

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

Kafka maintains message order within partitions using keys for routing.

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

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

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

Kafka uses retention to keep historical data and compaction to keep the latest state.

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

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