Any concept.
clear in 60 seconds.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Kafka maintains message order within partitions using keys for routing.

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

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

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

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

Kafka lag recovery time is how fast consumers can process backlogged messages.