Senior/Staff System Design Interviews: Raft vs. Paxos

Whether a senior/staff system design interview ends up meriting an offer often depends on how well one understands scalability and distributed systems. In the realm of distributed systems, achieving consensus among nodes is a fundamental challenge. Two popular algorithms that address this challenge are Raft and Paxos. These consensus protocols ensure that distributed systems can operate correctly even in the presence of failures and network partitions. In this technical blog post, we will embark on a comprehensive journey to explore the complexities and intricacies that set Raft and Paxos apart, shedding light on their underlying principles, leader election, log replication, safety properties, and fault tolerance mechanisms.


At the heart of both Raft and Paxos lies the goal of achieving distributed consensus, ensuring that a group of nodes agrees on a particular value despite failures and communication delays. Both algorithms achieve this by maintaining a replicated log that records commands or transactions, which is critical for recovering from failures and ensuring consistency across the distributed system.

Leader Election
Raft adopts a leader-based approach, where one node is elected as the leader responsible for coordinating the consensus process. The leader accepts client requests, replicates them to other nodes, and ensures that they are committed to the majority of the cluster before responding to the clients.

Paxos, on the other hand, operates without a permanent leader. Nodes can take turns being the leader for specific phases of the consensus protocol. A node can only propose values to be decided upon when it is acting as the leader, and other nodes must accept the proposal before it can be committed.

Log Replication
Raft’s log replication is straightforward. The leader receives client commands, appends them to its log, and then replicates them to the followers in the order they were received. Once a majority of followers acknowledge the replication, the leader considers the command committed and informs the clients.

In Paxos, the leader proposes a value to be decided upon, and if a majority of nodes accept the proposal, it becomes committed. However, Paxos is more intricate than Raft in its handling of competing proposals, which can lead to multiple rounds of proposal and acceptance.

Safety Properties
Raft focuses on simplicity and understandability, making it easier to reason about and implement. Its safety properties are well-defined, and it avoids certain complex scenarios present in Paxos.

Paxos, while more challenging to comprehend, offers stronger guarantees. It can tolerate the failure of a minority of nodes (N/2 – 1) and still make progress. However, its safety properties can be difficult to prove formally.

Fault Tolerance Mechanisms
Raft simplifies fault tolerance by electing a single leader, reducing the complexity of handling concurrent proposals.

Paxos copes with a broader range of failure scenarios, as it can progress even in the absence of a permanent leader.

In the vast landscape of distributed systems, Raft and Paxos stand as seminal algorithms, both aiming to achieve consensus across distributed nodes. The choice between Raft and Paxos depends on specific requirements, design trade-offs, and the need for simplicity versus stronger guarantees. As we’ve delved deep into their technical complexities, we recognize that mastering these algorithms empowers software engineers to build robust and fault-tolerant distributed systems, paving the way for more reliable and scalable applications in our interconnected world.

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