Distributed Systems
The Foundation Behind Kubernetes, etcd, and Kafka
In modern cloud-native infrastructure, virtually nothing runs on a single server anymore. To remain scalable and resilient in the event of hardware failures, critical components such as Kubernetes clusters, databases, and message brokers operate across multiple machines within distributed clusters.
This introduces one of the most challenging problems in computer science: the Consensus Problem. How can a cluster of independent servers reliably agree on the state of data, even when networks fail, servers restart, or communication is interrupted?
For decades, this challenge was addressed using the highly complex Paxos algorithm. Despite its effectiveness, Paxos gained a reputation for being difficult to understand and implement. In 2014, computer scientists introduced a more accessible alternative: Raft. Designed with understandability as its primary goal, Raft has become the consensus engine behind major infrastructure technologies such as etcd (the database used by Kubernetes), HashiCorp Consul, and CockroachDB.
The Three Roles in a Raft Cluster
A Raft cluster consists of multiple nodes, typically an odd number such as three, five, or seven to prevent split-brain scenarios. At any given moment, each node has exactly one of three possible roles:
- Leader: All incoming write requests from clients are handled by the Leader. The Leader is responsible for replicating changes to all Followers. Only one Leader can exist within a given term.
- Follower: Passive nodes that wait for instructions and heartbeat messages from the Leader. If the Leader becomes unavailable, Followers may become Candidates.
- Candidate: A temporary role assumed when a node determines that the Leader is no longer responding and initiates a leadership election.
Leader Election
Raft uses a carefully synchronized timing mechanism based on heartbeats and randomized election timeouts. Followers expect periodic heartbeat messages from the Leader. If a Follower does not receive a heartbeat within its configured election timeout, typically between 150 and 300 milliseconds, it assumes that the Leader has failed.
The node then promotes itself to Candidate, increments the current term number, and requests votes from the other nodes in the cluster. If it receives votes from a majority of the cluster members, it becomes the new Leader and immediately begins sending heartbeat messages to establish authority.
Log Replication and Safety
When a Leader receives a write request from a client, such as updating a value from 3 to 5, the request is first appended to the Leader’s local log. The Leader then distributes the log entry to all Followers through the AppendEntries Remote Procedure Call (RPC).
Only after a majority of nodes confirm that the log entry has been safely written does the Leader mark the transaction as committed. The change is applied to the Leader’s state machine, and a successful response is returned to the client.
During subsequent heartbeat exchanges, any lagging or temporarily unavailable nodes receive the missing committed entries and update their state accordingly. This process ensures that the entire cluster eventually converges on the same state.
One of Raft’s most important guarantees is that committed transactions are never lost, even when multiple nodes fail simultaneously. This reliability makes Raft a cornerstone technology for modern distributed databases, cloud platforms, and container orchestration systems.
Why Raft Matters
Understanding consensus algorithms such as Raft provides valuable insight into how cloud-native platforms maintain reliability and consistency at scale. Whether running Kubernetes, managing distributed databases, or operating large-scale messaging systems, consensus mechanisms are fundamental to keeping distributed infrastructure functioning correctly.
For additional insights into cloud architecture and distributed computing, visit: Computable.
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