Raft Consensus

Created: 2018-09-11
Updated: 2018-09-11

A shorter version of the official Raft whitepaper.


Crash Fault Tolerant (CFT) algorithm for replicated state machines.

Raft primary goal is understandability.

Novel features:

  • Strong leadership: log entries only flow from the leader to other nodes.
  • Leader election: randomized timers to elect leaders.
  • Membership changes: joint consensus approach to change the cluster nodes set.

Randomized approaches introduce nondeterminism, but also reduces the algorithm possible state spaces.

Replicated state machines

Nodes in a cluster compute identical copies of the same state and can continue operating even if some of the nodes are down (crash fault).

Replicated state machines are typically implemented using a replicated log. Each node stores a log containing a series of commands, which its state machine executes in order, thus producing the same result.

Since the state machines are deterministic, each computes the same state and the same sequence of outputs. As a result the nodes appear to form a single reliable state machine.

Keeping the replicated log consistent is the job of the consensus algorithm.


  • Safety: under all non-Byzantine conditions including network delays, partitions, packet loss, duplication and reordering.
  • Availability: as long as any majority of the nodes are operational. Nodes can fail and recover from state on stable stora to rejoin the cluster.
  • Time-free: delays and clock failures can, at worst, cause availability problems.
  • A command can be executed as soon as the majority has confirmed the operation. A minority of slow nodes do not impact the overall system performances.

Consensus Algorithm

First a leader is elected. The leader has complete responsibility for managing the replicated log.

Leader accepts new log entries from the clients, replicates them on other nodes and tells nodes when is safe to apply log entries to their state machines.

If a leader fails, a new leader shall be elected.

Separation of key elements of consensus:

  • Leader election: a new leader is chosen when an existing leader fails.
  • Log replication: leader shall accept new log entries from the clients and manage their replication across the cluster.
  • Safety: if a node has applied a log entry to its state machine, then no other node may apply a different command for the same log index.

Raft guarantees that each of these properties is true at all times:

  • Election Safety: at most one leader can be elected in a given term (because of the majority rule).
  • Leader Append-Only: a leader never overwrites or deletes entries in its log.
  • *Log Matching: if two logs contain an entry with the same index and term, then the logs are identical in all entries up through the given index.
  • Leader Completeness: if a log entry is commited in a given term, then that entry will be present in the logs of the leaders for all higher-numbered terms.
  • State Machine Safety: if a node has applied a log entry at a given index, no other node will ever apply a different entry for the same index.

Raft Basics

At any given time each node is in one of three states:

  • Candidate
  • Leader
  • Follower

In normal operation there is exactly one leader and all the other nodes are followers.

Followers are passive, they simply respond to requests from leaders and candidates.

The leader handles all client requests. If a client contacts a follower it will redirect the request to the leader.

Time is divided into terms of arbitrary length and numbered with consecutive integers. Each term begins with an election, in which one or more candidates attempt to become leader. If a candidate wins the election if will become the leader for the rest of the term. In case of “split-vote” the term will have no leader and a new term will begin shortly.

Different nodes may observe the transitions between terms at different times, and in some situations a node may not observe an election or even entire terms.

Terms act as logical clock as they allow nodes to detect obsolete information. Each node stores a current term number which increases monotonically over time. Current terms are exchanged whenever nodes communicate; if a node finds that its term is smaller than the other, it updates its value. If a candidate or a leader discovers its term is out of date, it reverts to follower. If a node receies a request with an outdated term, it just drops it.

Raft requires only two RPC types:

  • RequestVote: sent by candidates during elections;
  • AppendEntries: sent by leaders to replicate log entries and as “heartbeat”.

Leader Election

When a node starts up it begins as a follower. It remains in follower state as long as it receives valid RPCs from a leader or candidate. Leaders send periodc heartbeats (empty AppendEntries messages) to maintain their authority.

If a follower doesn’t receive any message over a period of time (election timeout) then it assumes there is no leader and begins a new election. The election is started by incrementing its term value and transitioning to candidate state. It sends a RequestVote message and votes for itself. Possible outcomes:

  • it wins the election,
  • another node wins the election,
  • a period of time goes by with no winner.

Candidate wins if it receives votes from the majority of the nodes for the same term. The majority rule ensures that at most one candidate can win the election for one term (Election Safety property).

While waiting for votes, a candidate may receive an AppendEntries RPC from another node claiming to be leader. It the leader term is greater than or equal the candidate term then the candidate becomes a follower. If the term is smaller it just drops the message.

In the eventuality of a vote split that prevents to reach a majority then the candidate will time out and starts a new election by incrementing its term and sending a new RequestVote message.

To prevent the vote split issue from happening again a randomized election timeout is used (between 150ms and 300ms). This probabilistic approach ensures that splits votes are rare and resolved quickly.

Log Replication

Each log entry contains a command to be executed by the replicated state machines. The leader appends the command to its log as a new entry, then broadcasts an AppendEntries message. When the entry has been safely replicated the leader applies the entry to its state machine.

If followers crash or run slowly the leaders retries AppendEntries indefinitely until all followers eventually store all log entries. (TODO: the majority is not sufficient?)

Each log entry stores a state machine command along with the term number when the entry was received by the leader. The term number in log entries is used to detect inconsistencies between logs.

Leader decides when it is safe to commit a new log entry. A log entry is commited once the leader that created it has replicated it on a majority of nodes. This also commits all preceding entries in the leader’s log, including entries created by previous leaders. (TODO: approfondire)

The leader keeps track of the highest index it knows to be committed, and it includes that index in future AppendEntries RPCs. Once a follower learns that a log entry is committed, it applies the entry to its local state machine (in log order).

Log Matching property: if two entries in different logs have the same index and term, then:

  • they store the same command,
  • their logs are identical in all preceding entries.

The first property follows the fact that each term can have only one leader and a leader creates at most one entry with a given log index.

The second property is guaranteed by a consistency check performed by AppendEntries. The leader includes the index and term of the entry in its log that immediately precedes the new entries. If the follower doesn’t find an entry with same index and term then it refuses the new entries.

As a result, id AppendEntries returns successfully the leaders knows that the follower’s log is identical to its own log up through the new entries.

During normal operation, the logs of the leader and followers stay consistent. However, leader crashes can leave the logs inconsistent.

When a new leader comes to power it may find the following situations:

  • a follower log is missing some entries the leader have,
  • a follower may have extra uncommited entries the leader doesn’t have,
  • both the cases in a single follower’s log.

The leader handles inconsistencies by forcing the followers logs to duplicate its own. This means that conflicting entries in follower logs will be overwritten with entries from leader’s log.

The leader must first find the latest log entry where the the two logs agree.

The leader maintains a next-index for each follower, which is the index of the next log entry the leader will send to that follower. When a leader comes to power it initialized all next-index values to the index just after the last one in its log.

If a follower log is inconsistent with the leaders’s, the next AppendEntries message consistency check will fail and the leader decrements its next-index and retries. Eventually next-index will reach a point where the leader and follower log matches, when this happens the follower removes any conflicting entries and appends entries from the leader’s log.

Optimization: when the follower rejects an AppendEntries request, it can include the term of the conflicting entry and the first index it stores for that term. This allows the leader to skip some messages.

A leader never overwrites or deletes entries in its own log (Leader Append-Only property).


The mechanisms described so far are not sufficient to ensure that each state machine executes exactly the same commands in the same order. For example, a follower may not be available when a leader commits a log entry, then it becomes leader and overwrites this entry with a new one (because of leader append-only property).

A constraint shall be added to restrict which nodes that may be elected as leaders. The restriction ensures that the leader for any given term contains all the entries committed in the previous terms (Leader Completeness property).

Election Restriction

The leader must eventually store all of the committed log entries.

Raft guarantees that all the committed entries from previous terms are present on each leader from the moment of its election, without the need to transfer those entries to the new leader.

This means that log entries only flow in one direction, from leaders to followers, and leaders never overwrite existing entries in their logs.

The algorithm prevents a candidate from winning an election unless its log contains all committed entries.

A candidate must contact a majority of the cluster in order to be elected, which means that every committed entry must be present in at least one of those nodes (entries are committed only when are present on at least the majority of nodes).

If the candidate’s log is at least as up-to-date as any other log in that majority then it will hold all the committed entries.

In short, a voter denies its vote if its own log is more up-to-date than the candidate’s one.

Which log is more up-to-date is determined by comparing the index and term of the last entries in the logs. If the logs have last entries with different terms, then the log with the later term is more up-to-date. If the logs end with the same term, then whichever log is longer is more up-to-date.

Committing entries from previous terms

A leader knows that an entry from its current term is committed once that entry is stored on a majority of the servers.

If a leder crashes before committing an entry, future leaders will attempt to finish replicating the entry. However a leader cannot immediately conclude that an entry from a previous term is committed once it is stored on a majority of servers (example on fig.8 of the whitepaper).

To overcome the issue nodes never commits log entries from previous terms by counting replicas. Only entries from leader’s current term are committed by counting replicas. Once an entry from the current term has been committed in this way then all prior entries are committed indirectly because of the Log Matching property.

Safety argument

By contradiction, we assume that the Leader Completeness property doesn’t hold.

Suppose leader for term T (leaderT) commits an entry for its term, but that log entry is not stored by the leader of some future term.

Consider the smallest term U > T whose leaderU does not store the entry.

… refer to 5.4.3 of the official Raft whitepaper.

Follower and candidate crashes

In case of follower or candidate crashes the future RequestVote and AppendEntries RPC sent will fail.

Raft handles these failures by retrying indefinitely.

Timing and availability

Safety must not depend on timing. Availability inevitably depends on timing.

Without a steady leader Raft cannot make progress.

E.g. If messages exchange takes longer than longer than the typical time between server crashes, candidates will not stay up long enough to win an election.

Raft is able to maintain a steady leader as far as the following condition is satisfied:

    broadcast_RTT_time << election_timeout << MTBF
  • broadcast_RTT_time: average time it takes a server to send RPCs in parallel and receive their responses (RTT: round trip time). An order of magnitude less than the election timeout
  • election timeout: range used by followers to start an election. The values in the range must be few orders of magnitude less than the MTBF.
  • MTBF: mean time between failures for a single server.

Broadcast time and MTBF are properties of the underlying system while election timeout is something we can configure.

E.g. If broadcast time is ~0.5/20ms then the election timeout should be somewhere between 100ms and 500ms. Typical node MTBF are several months or more.