🤝 Consensus & Coordination in Distributed Systems

Welcome back to The Code Hut Distributed Systems series! In this post, we explore how distributed nodes agree on shared state and coordinate their actions — a core challenge in distributed systems.

Why Consensus Matters

In a distributed system, multiple nodes must agree on decisions such as:

• Which node is the leader?
• Which transaction should commit?
• How to maintain consistent data across nodes?

Without proper coordination, you risk data inconsistency, split-brain scenarios, or lost updates.

Leader Election

Leader election is a common pattern where one node acts as the coordinator for a group of nodes. Popular approaches include:

• Using Zookeeper to manage ephemeral nodes.
• Using distributed algorithms like Raft or Paxos.

Raft Algorithm (Overview)

Raft ensures consensus in distributed systems by:

• Electing a leader
• Replicating logs consistently across followers
• Handling leader failures gracefully

In Java, you can use libraries like Atomix or Copycat to implement Raft-based coordination.

Paxos Algorithm (Overview)

Paxos is another consensus protocol that ensures agreement even if some nodes fail. It is more mathematically rigorous but harder to implement than Raft.

Practical Coordination in Java

Distributed locks are a simple way to coordinate actions between nodes. For example, using Redisson with Redis:

RLock lock = redisson.getLock("resourceLock");
lock.lock();
try {
    // critical section
} finally {
    lock.unlock();
}

This prevents multiple nodes from updating the same resource concurrently.

Next in the Series

In the next post, we’ll explore Transactions & Sagas and how to manage distributed transactions safely in Java.

Label for this post: Distributed Systems