Lecture 7: Fault Tolerance: Raft (2)

The podcast features a detailed explanation of the Raft consensus algorithm, focusing on log replication, leader election, and persistence. It covers how a leader replicates log entries to followers, the mechanisms for handling inconsistencies, and the rules for electing a new leader based on log completeness. The discussion also addresses the importance of persisting critical state information (log, current term, votedFor) to disk to ensure fault tolerance and crash recovery. Furthermore, the podcast delves into log compaction and snapshotting as methods to manage log size and improve performance, including the install snapshot RPC to bring lagging followers up to date. Finally, the podcast introduces the concept of linearizability as a criterion for evaluating the correctness of replicated systems, providing examples of linearizable and non-linearizable execution histories.