Raft is a consensus algorithm that enables a cluster of servers to agree on a sequence of values (log entries) even when some servers fail. Designed by Diego Ongaro and John Ousterhout in 2014 as an understandable alternative to Paxos, Raft has become the de facto standard for replication in distributed databases, key-value stores, and coordination services.
Consensus is the foundation of fault-tolerant distributed systems. Without it, a server crash or network partition could leave replicas in conflicting states—one replica believes a document was deleted while another still serves it. Raft ensures that as long as a majority of nodes are alive and can communicate, the cluster makes progress and all replicas converge to the same state.
For AI infrastructure, Raft is what makes your vector database, document store, and metadata service survive hardware failures without data loss. Systems like etcd (which backs Kubernetes), CockroachDB, TiKV (which backs TiDB), and Consul all use Raft. Multi-Raft extends the algorithm to handle millions of independent data ranges, making it practical for databases storing billions of embeddings across hundreds of nodes.
Raft decomposes consensus into three relatively independent subproblems:
Raft implements a replicated state machine: every node maintains an identical log of commands. By applying the same commands in the same order, every node arrives at the same state. The log is the source of truth; the state machine is derived from it.
Every Raft node is in one of three states at any given time:
Time in Raft is divided into terms, which are monotonically increasing integers. Each term has at most one leader. Terms act as a logical clock: if a node receives a message with a higher term number, it knows its information is stale and steps down to follower state.
When a follower’s election timeout expires (randomized between 150-300ms to prevent split votes), it increments its term, transitions to candidate, votes for itself, and sends RequestVote RPCs to all other nodes.
// Leader election process (simplified)
func (n *Node) startElection() {
n.currentTerm++
n.state = Candidate
n.votedFor = n.id
votesReceived := 1 // vote for self
for _, peer := range n.peers {
reply := peer.RequestVote(RequestVoteArgs{
Term: n.currentTerm,
CandidateID: n.id,
LastLogIndex: n.log.lastIndex(),
LastLogTerm: n.log.lastTerm(),
})
if reply.VoteGranted {
votesReceived++
}
}
if votesReceived > len(n.peers)/2 {
n.state = Leader
n.sendHeartbeats() // establish authority
}
}A candidate wins the election if it receives votes from a majority (N/2 + 1) of nodes. Each node votes for at most one candidate per term, and only if the candidate’s log is at least as up-to-date as the voter’s log. This “election restriction” ensures that the elected leader always has all committed entries.
Each node picks a random election timeout (e.g., 150-300ms). This randomization prevents split votes where multiple candidates start elections simultaneously and no one wins a majority. In practice, one node almost always times out first and wins the election before others even start.
Once elected, the leader accepts client requests and appends them as new entries to its log. It then sends AppendEntries RPCs to all followers in parallel:
AppendEntries RPCs containing the new entry (and possibly missing earlier entries) to each follower.AppendEntries RPCs and apply them to their own state machines.// Log replication flow
Leader Log: [1:SET x=1] [2:SET y=2] [3:DEL z] [4:SET x=5]
^ commitIndex=3
Follower A: [1:SET x=1] [2:SET y=2] [3:DEL z] (up to date)
Follower B: [1:SET x=1] [2:SET y=2] (behind, will catch up)
Follower C: [offline] (will catch up on rejoin)The leader sends empty AppendEntries RPCs as heartbeats at a regular interval (typically every 50-150ms). These serve two purposes: they prevent followers from starting unnecessary elections, and they carry the leader’s commitIndex so followers know which entries are safe to apply. If a follower doesn’t receive a heartbeat within its election timeout, it assumes the leader has failed and starts an election.
Raft provides several critical safety properties:
A single Raft group has a fundamental scalability limit: all writes go through one leader. For a database with terabytes of data, a single group becomes a bottleneck. Multi-Raft solves this by running many independent Raft groups on the same cluster, each managing a subset of the data.
In Multi-Raft, the data is partitioned into ranges (or regions), and each range has its own independent Raft group with its own leader. Different ranges can have leaders on different nodes, distributing write load across the entire cluster. A single physical node typically participates in thousands or millions of Raft groups simultaneously.
TiKV (TiDB’s storage layer): Data is split into Regions of ~96 MB each. Each Region is replicated across 3 nodes using its own Raft group. When a Region grows beyond the size limit, it splits into two new Regions, each with its own Raft group. TiKV’s Placement Driver (PD) balances Region leaders across nodes to spread load evenly.
CockroachDB: Data is split into Ranges of ~512 MB. Each Range is a Raft group replicated across typically 3 or 5 nodes. CockroachDB optimizes Multi-Raft with coalesced heartbeats—instead of each of the millions of Raft groups sending independent heartbeats, a single node-to-node heartbeat message covers all Raft groups between those two nodes. This reduces network overhead from O(groups) to O(nodes).
// Multi-Raft: each data range has its own Raft group
Cluster with 5 nodes, 3 replicas per range:
Range [a-f]: Leader=Node1, Followers=Node2,Node4
Range [f-m]: Leader=Node3, Followers=Node1,Node5
Range [m-s]: Leader=Node2, Followers=Node3,Node4
Range [s-z]: Leader=Node5, Followers=Node1,Node3
// Write load is distributed: each node leads different ranges
// If Node1 fails, Ranges [a-f] elect new leader from Node2 or Node4When a vector database shard fails, its Raft followers can elect a new leader within milliseconds (typically 1-3 election timeout periods, so 300-900ms). Search queries continue being served from the surviving replicas with no data loss, because Raft guarantees all committed writes are on a majority of replicas before acknowledging them to the client.
AI pipeline orchestration requires a reliable metadata store (e.g., etcd) to coordinate which workers are processing which documents, track embedding job state, and manage configuration. Raft ensures this metadata store survives node failures. Kubernetes itself depends on etcd’s Raft implementation for cluster state management.
A large-scale embedding pipeline ingesting millions of documents must track which documents have been processed, which chunks have been embedded, and which vectors have been successfully indexed. This state must be durable and consistent across the pipeline’s coordinator nodes. Raft-replicated state machines provide exactly this: crash-tolerant, linearizable state that all coordinator nodes agree on.