Diego Ongaro
A distributed systems researcher and one of the proposers of the Raft consensus algorithm, which is known for its understandability and is widely used for consistency implementations in distributed systems.
Timeline (last 90 days)
The Raft consensus algorithm is a distributed consistency algorithm used to manage replicated logs, proposed by Diego Ongaro and John Ousterhout in 2014, with the design goal of being easier to understand and implement than Paxos.
Raft was proposed by Diego Ongaro in 2014 with understandability as a design goal, decomposing the consensus process into three sub-problems: leader election, log replication, and safety.
Leslie Lamport's Paxos algorithm was proposed in 1989 and published in 1998, and Diego Ongaro's Raft algorithm was proposed in 2014, providing solutions to the distributed consensus problem.
Diego Ongaro proposed Raft in his 2014 doctoral dissertation, with user studies demonstrating Raft is significantly easier to understand and teach than Paxos
Raft's authors Diego Ongaro and John Ousterhout stated that algorithm understandability should be the primary design goal
All Facts (5)
Diego Ongaro proposed Raft in his 2014 doctoral dissertation, with user studies demonstrating Raft is significantly easier to understand and teach than Paxos
90%UnverifiedRaft's authors Diego Ongaro and John Ousterhout stated that algorithm understandability should be the primary design goal
90%UnverifiedThe Raft consensus algorithm is a distributed consistency algorithm used to manage replicated logs, proposed by Diego Ongaro and John Ousterhout in 2014, with the design goal of being easier to understand and implement than Paxos.
50%UnverifiedRaft was proposed by Diego Ongaro in 2014 with understandability as a design goal, decomposing the consensus process into three sub-problems: leader election, log replication, and safety.
50%UnverifiedLeslie Lamport's Paxos algorithm was proposed in 1989 and published in 1998, and Diego Ongaro's Raft algorithm was proposed in 2014, providing solutions to the distributed consensus problem.
50%