Backend & DevOps Series

Distributed Consensus

Understanding how distributed systems agree on truth even when parts of the system fail. Visualizing Leader Election and Quorum logic.

Consensus & Quorum

STATUS: ELECTION

Rule: To operate, the cluster needs 1 nodes (Majority) alive.
Currently Alive: 0 / 0.

Click Node to Kill/Restore

Event Log

Waiting for events...
Heartbeat
Vote Request

Quick Guide: Consensus Algorithms

Understanding the basics in 30 seconds

How It Works

  • Leader sends heartbeats to followers
  • Timeout → Follower starts election
  • Candidate requests votes from others
  • Majority votes → Become new leader
  • Quorum ensures no split-brain

Key Benefits

  • Fault tolerance (N/2 failures)
  • Strong consistency across nodes
  • Automatic leader election
  • Self-healing on failures
  • No single point of failure

Real-World Uses

  • etcd, Consul: Config management
  • ZooKeeper: Coordination service
  • CockroachDB: Distributed SQL
  • Kafka: Partition leadership
  • Kubernetes: Control plane

The "2N + 1" Rule (Quorum)

Why N/2 + 1?

In a distributed system, to ensure data consistency and prevent "Split Brain" (two leaders existing at once), any decision must be approved by a **Majority** of nodes.

Majority = Floor(N / 2) + 1

  • For 3 nodes: Needs 2 to work (Tolerates 1 failure)
  • For 5 nodes: Needs 3 to work (Tolerates 2 failures)
  • For 7 nodes: Needs 4 to work (Tolerates 3 failures)

Leader Election (Raft)

When a follower stops receiving "Heartbeats" (signals "I am alive!") from the Leader, it assumes the Leader is dead. It then starts an election, votes for itself, and asks others for votes. If it gets a majority, it becomes the new Leader. This ensures the system self-heals automatically.

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