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raft: better doc

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Xiang Li 2016-07-29 10:55:11 -07:00
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@ -5,6 +5,14 @@ The state machine is kept in sync through the use of a replicated log.
For more details on Raft, see "In Search of an Understandable Consensus Algorithm"
(https://ramcloud.stanford.edu/raft.pdf) by Diego Ongaro and John Ousterhout.
This Raft library is stable and feature complete. As of 2016, it is **the most widely used** Raft library in production, serving tens of thousands clusters each day. It powers distributed systems such as etcd, Kubernetes, Docker Swarm, Cloud Foundry Diego, CockroachDB, TiDB, Project Calico, Flannel, and more.
Most Raft implementations have a monolithic design, including storage handling, messaging serialization, and network transport. This library instead follows a minimalistic design philosophy by only implementing the core raft algorithm. This minimalism buys flexibility, determinism, and performance.
To keep the codebase small as well as provide flexibility, the library only implements the Raft algorithm; both network and disk IO are left to the user. Library users must implement their own transportation layer for message passing between Raft peers over the wire. Similarly, users must implement their own storage layer to persist the Raft log and state.
In order to easily test the Raft library, its behavior should be deterministic. To achieve this determinism, the library models Raft as a state machine. The state machine takes a `Message` as input. A message can either be a local timer update or a network message sent from a remote peer. The state machine's output is a 3-tuple `{[]Messages, []LogEntries, NextState}` consisting of an array of `Messages`, `log entries`, and `Raft state changes`. For state machines with the same state, the same state machine input should always generate the same state machine output.
A simple example application, _raftexample_, is also available to help illustrate
how to use this package in practice:
https://github.com/coreos/etcd/tree/master/contrib/raftexample