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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.

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

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

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

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

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

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

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

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

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

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

2-Phase Commit helps maintain data consistency in transactions across multiple databases.