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

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

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

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

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

Kafka maintains message order within partitions using keys for routing.

Paxos is a protocol that helps distributed systems agree on one value despite failures.

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

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

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

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

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

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

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

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

RPC allows one service to call a function in another service over the network.

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