Raft Consensus Algorithm

Raft uses a leader-based consensus model where a single, elected leader is responsible for managing log replication to all follower nodes. This simplifies the management of the replicated state machine.

• Election Terms: Time is divided into terms, numbered with consecutive integers. Each term begins with an election.
• Candidate States: If a follower receives no communication from a leader during its election timeout, it increments its current term and transitions to candidate state to start a new election.
• Majority Rule: A candidate wins an election if it receives votes from a majority of servers in the cluster for the same term, becoming the leader.
• Safety Guarantee: At most one leader can be elected per term, preventing split-brain scenarios.

All changes to the system state are managed through a replicated log. The leader appends new commands to its log, then replicates them to follower logs.

• Log Entries: Each entry contains a command for the state machine, the term number when it was created, and an integer index.
• Commitment: An entry is committed (safe to apply to the state machine) once the leader has replicated it to a majority of servers and has also replicated an entry from its current term.
• Log Matching Property: Raft guarantees that if two logs contain an entry with the same index and term, then the logs are identical in all preceding entries. This ensures strong consistency.
• Client Interaction: Clients only interact with the leader, which ensures all operations are linearizable.

Raft is a Crash Fault Tolerant (CFT) algorithm, guaranteeing safety (nothing bad happens) and liveness (something good eventually happens) despite node failures.

• Election Safety: At most one leader can be elected for any given term.
• Leader Append-Only: A leader never overwrites or deletes entries in its log; it only appends new entries.
• Log Completeness: If a log entry is committed in a given term, it will be present in the logs of leaders for all higher-numbered terms.
• State Machine Safety: If a server has applied a log entry at a given index to its state machine, no other server will ever apply a different log entry for the same index.
• Fault Model: Raft can tolerate the failure of f nodes in a cluster of 2f + 1 nodes, maintaining availability with a majority (quorum).

Raft includes a mechanism for changing the set of servers in the cluster (e.g., adding or removing a node) without compromising safety during the transition.

• Joint Consensus: The standard approach uses a two-phase transition to a new configuration to ensure a quorum is always available. The cluster first transitions to a joint consensus configuration (Cold,new), which combines both the old and new configurations, before committing to the new configuration (Cnew).
• Safety: This prevents situations where two disjoint majorities could form, each believing it is the legitimate leader.
• Leader-Based: Configuration changes are treated as special entries in the replicated log, managed by the leader, ensuring all servers switch configurations at the same point in the log.

A primary design goal of Raft was to be more understandable than Paxos. It achieves this through decomposition and state reduction.

• Separated Concerns: The algorithm is decomposed into three relatively independent sub-problems: Leader Election, Log Replication, and Safety.
• Reduced State: Server states are simplified to Leader, Follower, or Candidate. The rules governing state transitions are explicit and deterministic.
• Strong Leadership: The leader-based model centralizes complex decision-making (log management, commitment) into a single node, simplifying the logic required on followers.
• Randomized Timeouts: The use of randomized election timeouts reduces the likelihood of split votes and makes the system's behavior easier to reason about.

To prevent the log from growing unbounded, Raft incorporates a mechanism for log compaction via snapshots.

• Snapshot Creation: Each server takes snapshots of its current state machine state independently. This includes all applied log entries up to a specific index.
• Metadata: A snapshot replaces all log entries up to that index and includes metadata: the last included index and the last included term from the log.
• Leader Synchronization: A follower that falls far behind can have its log rebuilt efficiently by the leader sending a snapshot. This is done via a dedicated InstallSnapshot RPC.
• Determinism: Because state machines are deterministic, creating a snapshot is a local operation that does not require cluster coordination, preserving the algorithm's simplicity.

Raft ensures metadata consistency and coordination in distributed storage systems.

• Dragonfly: A modern P2P-based image and file distribution system. Its supernode cluster uses Raft for configuration management and leader election to coordinate peer networks.
• Longhorn: A cloud-native distributed block storage system for Kubernetes. It uses Raft to manage the replication of volume data across multiple nodes, ensuring data durability.
• Chubby (Google): While not open-source, Google's Chubby lock service, which inspired systems like ZooKeeper, uses a Paxos-like protocol. Raft is often described as a more understandable equivalent to such systems used for coarse-grained synchronization and configuration storage.

Raft's primary innovation is not raw performance but understandability. It was explicitly designed to be easier to teach, implement, and debug than Paxos.

• Decomposition: Raft separates key elements: leader election, log replication, and safety.
• Strong Leadership: A key simplification is its use of a strong leader. All client requests go through the leader, which simplifies log replication and management.
• Impact: This focus on clarity is a major reason for its widespread adoption. Engineers can read the whitepaper and implement a correct version, reducing the risk of subtle bugs common in Paxos implementations. This makes Raft an excellent choice for Crash Fault Tolerant (CFT) systems where operational simplicity and correctness are paramount.

A distributed algorithm that enables a group of processes or machines to agree on a single data value or system state, even in the presence of failures. Raft is a specific, understandable implementation of a consensus protocol designed to be equivalent to Paxos in fault-tolerance and performance. Core properties include:

• Safety: Never returning an incorrect result.
• Liveness: The system eventually makes progress.
• Fault Tolerance: Ability to withstand node failures (typically up to (N-1)/2 for a cluster of N nodes).

A distributed algorithm by which nodes in a cluster select a single node to act as the coordinator or leader. Raft uses a timeout-based election mechanism where:

• Each server starts as a follower.
• If a follower receives no communication from a leader or candidate, it becomes a candidate and starts an election.
• The candidate requests votes from other nodes; if it receives votes from a majority of the cluster, it becomes the leader.
• The leader then manages all client requests and log replication, ensuring a single point of coordination for consistency.

A method for implementing a fault-tolerant service by replicating a deterministic state machine across multiple servers. Raft provides the underlying consensus to ensure all replicas process the same sequence of commands in the same order. The process is:

1. Client commands are appended to the leader's replicated log.
2. The leader replicates the log entry to follower nodes.
3. Once the entry is safely replicated to a majority, the leader applies it to its state machine.
4. The leader notifies followers to apply the entry. This ensures all servers execute identical command sequences, making the cluster appear as a single, highly reliable state machine.

The ability of a distributed system to maintain correct operation despite the failure of some components, assuming those components fail by stopping (crashing) and do not behave maliciously. Raft is a Crash Fault Tolerant (CFT) consensus algorithm. Key aspects:

• It is designed to handle fail-stop failures where nodes become unresponsive.
• It can tolerate the failure of up to F nodes in a cluster of 2F + 1 nodes (e.g., 1 failure in 3 nodes, 2 failures in 5 nodes).
• This contrasts with Byzantine Fault Tolerance (BFT), which defends against arbitrary, potentially malicious failures. CFT protocols like Raft are simpler and more performant for trusted environments like internal datacenter clusters.

Distributed systems that require a majority or specific subset of nodes (a quorum) to agree before an operation is considered successful. Raft uses quorums for both leader election and log replication to ensure consistency despite failures.

• For a cluster with N nodes, a quorum is typically a majority (N/2 + 1).
• A leader must contact a quorum to win an election.
• A log entry is considered committed once it is stored on a quorum of nodes.
• This mechanism ensures progress can be made as long as a majority of nodes are alive and connected, and prevents split-brain scenarios in network partitions.

A property of a system or function where, given the same initial state and sequence of inputs, it will always produce the exact same outputs and state transitions. This is essential for the state machine replication that Raft enables.

• The state machine being replicated (e.g., a key-value store) must be deterministic.
• If all replicas start from the same state and apply the same log of commands in the same order, they will reach identical final states.
• Non-determinism (e.g., using random numbers or local timestamps) would cause replicas to diverge, breaking consistency. Raft ensures the order of commands is agreed upon; the application must ensure the execution is deterministic.