Walk the stages in order for Raft Uses Terms and Quorums to Choose a Leader: start from the contract, trace the representation through the operation, then confirm the boundary. Worked case: Each server tracks a current term. For a worked explanation, continue to the topic article and execution trace.
Stage-by-stage execution walkthrough
This request flow decomposes Raft Uses Terms and Quorums to Choose a Leader into 5 sequential stages so you can trace where latency accumulates, which state transitions are irreversible, and where production failure modes occur.
Stage 1: Contract
Consensus lets a group of processes agree on an ordered sequence of decisions despite specified failures. Raft separates leader election, log replication, and safety rules into understandable mechanisms. It assumes crash failures and a communication network that can delay, drop, or reorder messages. During this transition, the runtime enforces strict state invariants before handing control to the next stage: inputs are validated at the boundary, state mutations are ordered deterministically, and latency/resource costs are bounded so downstream consumers never observe a torn or partially initialized state.
Stage 2: Representation
Each server tracks a current term. A candidate requests votes; a server grants at most one vote per term under the protocol’s log-freshness rule. A leader appends client commands to its log and considers an entry committed once the required majority has replicated it, subject to Raft’s commit rules. During this transition, the runtime enforces strict state invariants before handing control to the next stage: inputs are validated at the boundary, state mutations are ordered deterministically, and latency/resource costs are bounded so downstream consumers never observe a torn or partially initialized state.
Stage 3: Operation
In a five-server group, how many servers form a majority? What happens to write availability if three servers are mutually isolated from the remaining two? During this transition, the runtime enforces strict state invariants before handing control to the next stage: inputs are validated at the boundary, state mutations are ordered deterministically, and latency/resource costs are bounded so downstream consumers never observe a torn or partially initialized state.
Stage 4: Boundary
A majority quorum tolerates some unavailable servers but cannot make progress if a majority cannot communicate. Network partitions may create temporary competing leaders in different terms, while safety rules prevent committed history from being overwritten. Consensus does not solve application-level deduplication or external side effects. During this transition, the runtime enforces strict state invariants before handing control to the next stage: inputs are validated at the boundary, state mutations are ordered deterministically, and latency/resource costs are bounded so downstream consumers never observe a torn or partially initialized state.
Stage 5: Result
In a five-server group, how many servers form a majority? What happens to write availability if three servers are mutually isolated from the remaining two? During this transition, the runtime enforces strict state invariants before handing control to the next stage: inputs are validated at the boundary, state mutations are ordered deterministically, and latency/resource costs are bounded so downstream consumers never observe a torn or partially initialized state.
Production invariants and failure modes
Done when the worked check holds: In a five-server group, how many servers form a majority? When debugging or capacity-planning this flow in production, instrument each stage boundary separately (measuring both p50 median and p99.9 tail latency), verify what happens when an intermediate stage times out or drops packets, and ensure retry or fallback paths preserve end-to-end idempotency.