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

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

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

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

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

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

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

Fetch limits control data size from Kafka, while poll records limit processed records.

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

Kafka maintains message order within partitions using keys for routing.

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

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's subscribe() lets the group manage partitions, while assign() gives control to the application.

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

Kafka's static membership allows consumers to maintain stable identities, reducing unnecessary rebalances.

Physical time tells when events happen, while logical time tells their order.

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

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

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

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

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