Raft Price

What is Raft?

Raft is a consensus algorithm designed as an alternative to the traditional leader-based approach. It was developed by Diego Ongaro and John Ousterhout at Stanford University in 2014. The goal of Raft is to provide a practical, understandable, and robust consensus protocol that can be used in distributed systems. Unlike other consensus algorithms, such as Paxos, Raft focuses on providing a simple and easy-to-understand interface for developers. This makes it easier to use and debug compared to other protocols.

Raft works by having a group of servers (known as "nodes") agree on the same set of commands or operations that need to be performed in order for the system to function correctly. To do this, each node maintains its own log of commands that have been executed so far. When a new command needs to be added, the nodes must first agree on which node will take responsibility for executing it. This process is known as "leader election". Once a leader has been elected, all other nodes must follow the leader's instructions.

Once the leader has received agreement from all other nodes about which command should be executed next, it sends out an "append entry" message containing the command details. All other nodes then execute this command and update their logs accordingly. If any node fails during this process, another node will take over leadership until all nodes are back online.

Raft provides several advantages over traditional consensus algorithms such as Paxos. First, it is much simpler and easier to understand than Paxos or similar protocols due to its straightforward design and intuitive interface. Second, it provides better performance since there is no need for complex coordination between multiple leaders or replicas. Finally, Raft also offers better fault tolerance since any failed node can easily be replaced without disrupting the entire system.

Overall, Raft is an excellent choice for distributed systems that require strong consistency guarantees while still offering good performance and scalability.

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