computer science

Raft (algorithm)

Raft (algorithm) is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Raft (algorithm) rather than just read about it. In short: Raft is a consensus algorithm designed as an alternative to the Paxos family of algorithms. 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.

Key takeaways

Raft (algorithm) belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Raft (algorithm) to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Raft (algorithm) from memory before moving on to harder problems.

Reference excerpt

Raft is a consensus algorithm designed as an alternative to the Paxos family of algorithms. 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. Raft offers a generic way to distribute 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, JavaScript, and Scala. It is named after Reliable, Replicated, Redundant, And Fault-Tolerant. Raft is not Byzantine fault tolerant in the base form; the nodes trust the elected leader, and the algorithm assumes all participants are trustworthy, yielding only crash fault tolerance. However there exist variants that use cryptographic signatures to achieve Byzantine fault tolerance.

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.

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 surviving servers elect a new leader. The consensus problem is decomposed in Raft into two relatively independent subproblems listed down below.

Leader election When the existing leader fails or when the algorithm initializes, 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 for 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 larger than the candidate's current term, then the candidate's election is defeated 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. Raft uses a randomized election timeout to ensure that split vote problems 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 time out, win the election, then become leader and send heartbeat messages to other servers before any of the followers can become candidates.

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, until the log entry is eventually stored by all of the followers. Once the leader receives confirmation from half or more 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. 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 a 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.

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.

… excerpt ends here. Continue reading the full article.

Illustrations

Worked examples

Example 1 — a first encounter with Raft (algorithm)

Start with the simplest possible case. Write down what Raft (algorithm) claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Raft (algorithm) before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Raft (algorithm) ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Raft (algorithm)

In research: Raft (algorithm) appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Raft (algorithm) in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Raft (algorithm) is common in secondary-school and first-year university syllabi. It links to neighbouring topics Distributed algorithms, Fault-tolerant computer systems, so understanding it makes those chapters shorter.
In everyday life: Look for Raft (algorithm) outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

Ask Teacher Smith questions about this articleOpens your AI tutor with a question about “Raft (algorithm)” →

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Raft (algorithm) in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Raft (algorithm) means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Raft (algorithm) out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Raft (algorithm) in simple terms?

Why does Raft (algorithm) matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Raft (algorithm)?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Raft (algorithm).