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

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

Crash failures stop a service, while network failures disrupt communication.

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

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

Network partitions occur when servers are operational but can't communicate, affecting system performance.

Raft's safety rules guarantee that committed entries are preserved even after leader failures.

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

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

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

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

Raft consensus helps distributed systems elect leaders and replicate logs reliably.

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

Kafka rebalances consumer groups to manage partition assignments effectively.

Log replication ensures data consistency by requiring majority acknowledgment before committing changes.

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.

Elasticsearch uses master election and quorum to prevent split brain scenarios.

Raft handles leader failure by allowing followers to elect a new leader through voting.

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.