Today we’re gonna talk about something that might sound like a mouthful: Non-Byzantine consensus algorithms. But don’t worry, we won’t bore you with technical jargon or complicated math equations instead, let’s break it down in simple terms and have some fun while doing so!

Before anything else, what is consensus? In the context of distributed systems (like blockchain), consensus refers to a process where all nodes agree on the same state. This might seem like an easy task, but when you have multiple parties involved with different interests and agendas, it can be quite challenging. That’s why we need algorithms that help us reach agreement in a fair and efficient way enter non-Byzantine consensus!

Non-Byzantine fault tolerance is the ability of a system to continue functioning correctly even when some nodes are malicious or fail due to other reasons (like hardware failure). In contrast, Byzantine faults refer to situations where some nodes intentionally try to disrupt the system. Non-Byzantine consensus algorithms goal to handle non-malicious errors while still providing security against malicious attacks.

Now that we’ve got that out of the way, a popular example: Paxos! Paxos is a consensus algorithm designed by Leslie Lamport in 1986 (yes, it’s been around for quite some time). It works by having nodes propose values and then agreeing on which value to accept. The key idea behind Paxos is that each node has a leader who proposes new values, but the final decision is made based on majority vote among all nodes.

Here’s an example of how Paxos might work in practice: let’s say we have three nodes (A, B, and C) trying to agree on which value to accept. Node A proposes a value, and then sends it to node B and C for their approval. If both B and C approve the proposal within some time limit, then the value is accepted by all nodes. However, if one of the nodes (let’s say C) fails or becomes malicious, then the other two nodes can still agree on a different value without waiting for C to respond.

Another popular non-Byzantine consensus algorithm is Raft, which was introduced in 2014 by Diego Ongaro and John Ousterhout. Unlike Paxos, Raft uses a leader-based approach where one node (the leader) proposes values and the other nodes follow suit. The key idea behind Raft is that if the leader fails or becomes malicious, then another node can take over as the new leader without disrupting the system.

Here’s an example of how Raft might work in practice: let’s say we have five nodes (A, B, C, D, and E) trying to agree on which value to accept. Node A is elected as the leader and proposes a value. The other four nodes follow suit by sending their votes to node A. If node A fails or becomes malicious, then one of the other nodes can take over as the new leader without disrupting the system.

While these examples might seem simple, they’re actually quite powerful and widely used in distributed systems like blockchain. By using these algorithms, we can ensure that all nodes agree on the same state even when some of them are malicious or fail due to other reasons.