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

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

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

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

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

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

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

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

Causality in distributed systems helps understand how events influence each other, beyond just timestamps.

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

Fail-stop means a system stops and stays down, while fail-recover means it can come back but needs to be ready.

Kafka consumers resume processing from the last committed offset after a crash.

Raft and Paxos are protocols that help computers agree on values in distributed systems.

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

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

ZooKeeper uses ephemeral and sequential nodes for managing distributed services effectively.