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

The CAP Theorem explains the trade-offs in distributed systems during network failures.

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

Leader election helps distributed systems select one coordinator from many nodes.

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

Kafka uses different strategies to assign partitions to consumers in a group efficiently.

Vector clocks help track the order and concurrency of events in distributed systems.

Quorum Reads ensure data consistency by querying multiple replicas in distributed systems.

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

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

Choosing the right distributed lock depends on your system's needs and existing tools.