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wikipedia Raft (computer science)

Raft is a consensus algorithm designed as an alternative to Paxos. It was meant to be more understandable than Paxos by means of separation of logic, but it is also formally proven safe and offers some additional features.[1] Raft offers a generic way to distribute(分散) a state machine across a cluster of computing systems, ensuring that each node in the cluster agrees upon the same series of state transitions. It has a number of open-source reference implementations, with full-specification implementations in Go, C++, Java, and Scala.[2] It is named after Reliable, Replicated, Redundant, And Fault-Tolerant.[3]

Raft is not a Byzantine fault tolerant algorithm: the nodes trust the elected leader.[1]

NOTE : 可以结合redis来理解Raft；

NOTE : raft算法的设计考虑到了consensus和consistency

Basics

Raft achieves consensus via an elected leader. A server in a raft cluster is either a leader or a follower, and can be a candidate in the precise case of an election (leader unavailable). The leader is responsible for log replication to the followers. It regularly informs the followers of its existence by sending a heartbeat message. Each follower has a timeout (typically between 150 and 300 ms) in which it expects the heartbeat from the leader. The timeout is reset on receiving the heartbeat. If no heartbeat is received the follower changes its status to candidate and starts a leader election.[1][4]

Approach of the consensus problem in Raft

Raft implements consensus by a leader approach. The cluster has one and only one elected leader which is fully responsible for managing log replication on the other servers of the cluster. It means that the leader can decide on new entries placement and establishment of data flow between it and the other servers without consulting other servers. A leader leads until it fails or disconnects, in which case a new leader is elected.

The consensus problem is decomposed in Raft into two relatively independent subproblems listed down below.

Leader Election

When the existing leader fails or when you start your algorithm, a new leader needs to be elected.

In this case, a new term starts in the cluster. A term is an arbitrary period of time on the server during which a new leader needs to be elected. Each term starts with a leader election. If the election is completed successfully (i.e. a single leader is elected) the term keeps going with normal operations orchestrated by the new leader. If the election is a failure, a new term starts, with a new election.

A leader election is started by a candidate server. A server becomes a candidate if it receives no communication by the leader over a period called the election timeout, so it assumes there is no acting leader anymore. It starts the election by increasing the term counter, voting for itself as new leader, and sending a message to all other servers requesting their vote. A server will vote only once per term, on a first-come-first-served basis. If a candidate receives a message from another server with a term number at least as large as the candidate's current term, then the candidate's election is defeated（被打败了） and the candidate changes into a follower and recognizes the leader as legitimate. If a candidate receives a majority of votes, then it becomes the new leader. If neither happens, e.g., because of a split vote, then a new term starts, and a new election begins.[1]

Raft uses randomized election timeout to ensure that split votes problem are resolved quickly. This should reduce the chance of a split vote because servers won't become candidates at the same time: a single server will timeout, win the election, then become leader and sends heartbeat messages to other servers before any of the followers can become candidates.[1]

NOTE : redis中，选举是由slave发起的；redis中的currentEpoch和configEpoch相当于raft中的term

Log Replication

The leader is responsible for the log replication. It accepts client requests. Each client request consists of a command to be executed by the replicated state machines in the cluster. After being appended to the leader's log as a new entry, each of the requests is forwarded to the followers as AppendEntries messages. In case of unavailability of the followers, the leader retries AppendEntries messages indefinitely（不定的）, until the log entry is eventually stored by all of the followers.

Once the leader receives confirmation from the majority of its followers that the entry has been replicated, the leader applies the entry to its local state machine, and the request is considered committed.[1][4] This event also commits all previous entries in the leader's log. Once a follower learns that a log entry is committed, it applies the entry to its local state machine. This ensures consistency of the logs between all the servers through the cluster, ensuring that the safety rule of Log Matching is respected.

In the case of leader crash, the logs can be left inconsistent, with some logs from the old leader not being fully replicated through the cluster. The new leader will then handle inconsistency by forcing the followers to duplicate its own log. To do so, for each of its followers, the leader will compare its log with the log from the follower, find the last entry where they agree, then delete all the entries coming after this critical entry in the follower log and replace it with its own log entries. This mechanism will restore log consistency in a cluster subject to failures.

NOTE : 只有字leader crash的情况下，才会出现inconsistent，fellow的crash并不会导致inconsistent；

NOTE : 需要梳理log和state machine之间的关系：log表示的是需要被执行/发生的state transition，它只有commit到了node的local state machine中才能够生效；

NOTE : 需要注意的是，raft算法也实现了State machine replication。redis中也实现了State machine replication。

Safety

Safety rules in Raft

Raft guarantees each of these safety properties :

  • Election safety: at most one leader can be elected in a given term.
  • Leader Append-Only: a leader can only append new entries to its logs (it can neither overwrite nor delete entries).
  • 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 committed in a given term then it will be present in the logs of the leaders since this term
  • State Machine Safety: if a server has applied a particular log entry to its state machine, then no other server may apply a different command for the same log.

The first four rules are guaranteed by the details of the algorithm described in the previous section. The State Machine Safety is guaranteed by a restriction on the election process.