The document discusses distributed consensus algorithms in MongoDB. It explains that MongoDB uses a leader-based replicated state machine approach, where servers elect a primary node and replicate the primary's log of state transitions. Elections are triggered if a node does not receive heartbeats from the primary within a timeout period. The upcoming MongoDB 3.2 release aims to improve consensus by taking inspiration from the Raft algorithm, including using term IDs to prevent double voting, monitoring liveness via data replication rather than separate heartbeats, and varying election timeouts randomly to reduce tied votes and speed up failover.