Any concept.
clear in 60 seconds.

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

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

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

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

Kafka uses retries and Dead Letter Queues to manage message processing failures.

Classifying failures and using controlled retries in Kafka prevents outages.

You can replay Kafka events safely by separating live and replay processes.

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

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

Kafka uses retry topics and delayed retries to manage message failures efficiently.

Kafka consumers have three important states: Position, Committed Offset, and Application State.

Graceful shutdown in Kafka ensures no data loss during consumer termination.

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

Kafka rebalance can lead to lost or duplicated work if not handled carefully.

Kafka rebalances consumer groups to manage partition assignments effectively.

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

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

Eager rebalancing revokes all assignments, while cooperative allows incremental changes.

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

Kafka uses replication and in-sync replicas to ensure data is always available.

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

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