26-Jan-2025

A fundamental problem in distributed systems is achieving consensus among multiple nodes. This means getting all nodes to agree on a single value, even in the presence of failures. Distributed consensus is crucial for tasks like:

• Leader election: Choosing a single node to act as the leader in a cluster.
• Distributed transactions: Ensuring that a transaction either commits on all nodes or none at all.
• State machine replication: Replicating state across multiple nodes to maintain availability and fault tolerance.

Several algorithms address the distributed consensus problem. One prominent example is Paxos, and another is Raft, known for its comparative simplicity. Let’s briefly illustrate a simplified concept using a single-decree Paxos-like approach in JavaScript:

// Simplified representation of a node
class Node {
  constructor(id) {
    this.id = id;
    this.proposedValue = null;
    this.acceptedValue = null;
  }

  propose(value) {
    this.proposedValue = value;
    // Broadcast proposal to other nodes (simplified)
    nodes.forEach(node => {
      if (node !== this) {
        node.receiveProposal(this.id, value);
      }
    });
  }

  receiveProposal(proposerId, value) {
    if (this.acceptedValue === null) {
      this.acceptedValue = value;
      // Broadcast acceptance (simplified)
      nodes.forEach(node => {
        if (node !== this) {
          node.receiveAcceptance(this.id, value);
        }
      });
    }
  }

  receiveAcceptance(acceptorId, value) {
    // Check if majority have accepted (simplified)
    let acceptanceCount = 0;
    nodes.forEach(node => {
      if (node.acceptedValue === value) {
        acceptanceCount++;
      }
    });

    if (acceptanceCount > nodes.length / 2) {
      console.log(`Consensus reached: Value ${value} accepted by a majority.`);
    }
  }
}


// Simulate a network of nodes
const nodes = [new Node(1), new Node(2), new Node(3)];

// Node 1 proposes a value
nodes[0].propose("Hello");

This simplified example demonstrates the basic idea of proposing and accepting values. A real-world implementation would require handling failures, network partitions, and more complex scenarios. The key takeaway is that distributed consensus algorithms aim to guarantee agreement on a single value despite the challenges inherent in distributed systems. Understanding these challenges and the algorithms designed to overcome them is essential for building robust and reliable distributed applications.