Senior LevelSoftware Engineering Principles
How does the Raft Consensus Algorithm achieve distributed state machine replication compared to Paxos?
❓ Interview Prompt: Explain Raft Consensus Algorithm, Leader Election, Log Replication, Safety invariants, Term numbers, and comparison to Multi-Paxos.
Distributed consensus algorithms ensure a cluster of server nodes agree on a shared state machine log despite network failures:
Raft Algorithm Sub-problems:
1
Leader Election: Server nodes exist in 3 states (Follower, Candidate, Leader). Randomized election timeouts select a single Leader node per Term.
2
Log Replication: Leader accepts client commands, appends them to its log, and replicates log entries to Follower nodes. Once entry is replicated to a majority quorum ($N/2 + 1$), the log entry is Committed!
•
Raft vs Paxos: Raft decomposes consensus into explicit, understandable sub-problems (Leader Election, Log Replication, Safety).
Code Implementation Example
typescript
// Conceptual Raft Quorum Check
export function isQuorumAchieved(ackCount: number, clusterSize: number): boolean {
const majorityQuorum = Math.floor(clusterSize / 2) + 1;
return ackCount >= majorityQuorum; // e.g. 3 out of 5 nodes!
}💡Verifying Raft consensus majority quorum.
🎯 Interview Key Takeaway
Raft achieves distributed consensus by electing a leader and replicating log entries across a majority quorum of cluster nodes.
#Raft#Paxos#Consensus#Distributed Systems#Senior
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