This article explains the Raft consensus algorithm, which helps multiple servers maintain consistency in distributed systems. It covers how Raft elects leaders and replicates logs to ensure that all servers produce the same outputs, even in the event of failures.

The article uses a sauna scenario to explain how distributed systems can manage time without clocks by relying on causal relationships. The author describes a method of exiting the sauna based on the arrival and departure of others to ensure a safe duration. This analogy helps illustrate concepts like happened-before relationships in distributed systems.

This article introduces a new framework for understanding consensus algorithms, focusing on distributed durability and high availability. It critiques traditional methods like Paxos and Raft, proposing flexible durability policies and goal-oriented rules that adapt to modern cloud environments.