etcd is a replicated log, and Raft is the algorithm that keeps the replicas identical. One member is leader for a term and is the only one allowed to propose entries; an entry is committed once a majority has it. Leader election decides who may write, and log replication decides what is durable — separate mechanisms with separate failure modes. ...
Majority quorum is what forbids a split brain. When the cluster is partitioned, the side with fewer than half the nodes cannot form a quorum or stay authoritative. One leader, many votes A replicated system that must not diverge elects a single leader for a term. A candidate asks peers for a vote, and to become leader it needs a quorum, more than half of the voting members in a fault-tolerant scheme, to say yes within a term. That majority is what lets the winner act with confidence that no split-off minority can out-vote it, so the group can make decisions with one authority at a time. Exactly half is not enough: quorums are defined as strictly more than half, which is what makes two halves never both be authoritative. ...
Peers keep exchanging and forwarding state with random partners. The system ends up consistent and tolerates messages being dropped along the way. Everyone tells someone Each round a node picks a random peer and exchanges state, so a single fact spreads by repetition rather than by a single authoritative hop. Because the partner changes each round, information fans out geometrically: each round roughly doubles the nodes that know, which is why a new membership entry can reach a large cluster in a handful of rounds. The exchange is usually symmetrical, both sides learning from each other in one message pair, which keeps the network cheap and symmetric. It trades the guarantee of a single message delivered in order for a high, self-healing probability that the state eventually arrives. ...
Consistent hashing limits placement changes when membership changes; it does not itself move data, make replicas agree, or distribute a hot key’s requests. In the classic clockwise ring, a key belongs to the first node point at or after its hash position, wrapping to the smallest point when necessary. ...
Each node keeps a counter per participant. Comparing vectors reveals a causal edge or a true concurrent write, which decides whether to merge or to ask. One timestamp is not enough A single timestamp cannot order concurrent events, because a clock is not a cause, so two nodes that each wrote something at the same label may still disagree about which came first. Vector clocks solve that by giving every participant its own counter: a process bumps its own slot whenever it does something, and merges the sender’s vector into its own whenever it receives a message. The result is a vector that grows as events happen, and comparing two vectors tells you whether one event provably preceded the other, or whether neither did. ...
An idempotency key identifies one logical action only within a service’s defined scope and retention window. Reusing the same text is not a universal exactly-once guarantee. The service must associate that identity with the right principal and operation, coordinate competing attempts, and retain enough authoritative information to recognize the action later. ...