raft [![Build Status](https://travis-ci.org/hashicorp/raft.png)](https://travis-ci.org/hashicorp/raft)
raft is a [Go](http://www.golang.org) library that manages a replicated
log and can be used with an FSM to manage replicated state machines. It
is library for providing [consensus](http://en.wikipedia.org/wiki/Consensus_(computer_science)).
The use cases for such a library are far-reaching as replicated state
machines are a key component of many distributed systems. They enable
building Consistent, Partition Tolerant (CP) systems, with limited
fault tolerance as well.
If you wish to build raft you'll need Go version 1.2+ installed.
Please check your installation with:
For complete documentation, see the associated [Godoc](http://godoc.org/github.com/hashicorp/raft).
To prevent complications with cgo, the primary backend `MDBStore` is in a separate repositoy,
called [raft-mdb](http://github.com/hashicorp/raft-mdb). That is the recommended implementation
for the `LogStore` and `StableStore`.
A pure Go backend using [BoltDB](https://github.com/boltdb/bolt) is also available called
[raft-boltdb](https://github.com/hashicorp/raft-boltdb). It can also be used as a `LogStore`
raft is based on ["Raft: In Search of an Understandable Consensus Algorithm"](https://ramcloud.stanford.edu/wiki/download/attachments/11370504/raft.pdf)
A high level overview of the Raft protocol is described below, but for details please read the full
followed by the raft source. Any questions about the raft protocol should be sent to the
[raft-dev mailing list](https://groups.google.com/forum/#!forum/raft-dev).
### Protocol Description
Raft nodes are always in one of three states: follower, candidate or leader. All
nodes initially start out as a follower. In this state, nodes can accept log entries
from a leader and cast votes. If no entries are received for some time, nodes
self-promote to the candidate state. In the candidate state nodes request votes from
their peers. If a candidate receives a quorum of votes, then it is promoted to a leader.
The leader must accept new log entries and replicate to all the other followers.
In addition, if stale reads are not acceptable, all queries must also be performed on
Once a cluster has a leader, it is able to accept new log entries. A client can
request that a leader append a new log entry, which is an opaque binary blob to
Raft. The leader then writes the entry to durable storage and attempts to replicate
to a quorum of followers. Once the log entry is considered *committed*, it can be
*applied* to a finite state machine. The finite state machine is application specific,
and is implemented using an interface.
An obvious question relates to the unbounded nature of a replicated log. Raft provides
a mechanism by which the current state is snapshotted, and the log is compacted. Because
of the FSM abstraction, restoring the state of the FSM must result in the same state
as a replay of old logs. This allows Raft to capture the FSM state at a point in time,
and then remove all the logs that were used to reach that state. This is performed automatically
without user intervention, and prevents unbounded disk usage as well as minimizing
time spent replaying logs.
Lastly, there is the issue of updating the peer set when new servers are joining
or existing servers are leaving. As long as a quorum of nodes is available, this
is not an issue as Raft provides mechanisms to dynamically update the peer set.
If a quorum of nodes is unavailable, then this becomes a very challenging issue.
For example, suppose there are only 2 peers, A and B. The quorum size is also
2, meaning both nodes must agree to commit a log entry. If either A or B fails,
it is now impossible to reach quorum. This means the cluster is unable to add,
or remove a node, or commit any additional log entries. This results in *unavailability*.
At this point, manual intervention would be required to remove either A or B,
and to restart the remaining node in bootstrap mode.
A Raft cluster of 3 nodes can tolerate a single node failure, while a cluster
of 5 can tolerate 2 node failures. The recommended configuration is to either
run 3 or 5 raft servers. This maximizes availability without
greatly sacrificing performance.
In terms of performance, Raft is comparable to Paxos. Assuming stable leadership,
committing a log entry requires a single round trip to half of the cluster.
Thus performance is bound by disk I/O and network latency.