🤝 Consensus & Coordination in Distributed Systems

Welcome back to The Code Hut Distributed Systems series! In this post, we’ll 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 key decisions, such as:

• 👑 Which node becomes 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. Popular approaches include:

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

📝 Raft Algorithm (Overview)

Raft ensures consensus by:

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

In Java, libraries like Atomix or Copycat help implement Raft-based coordination.

🔹 Paxos Algorithm (Overview)

Paxos is another consensus protocol that guarantees agreement even if some nodes fail. It’s mathematically rigorous but more complex 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