Paxos vs. Raft: The Secret Sauce for Making Computers Agree
Discover how Paxos and Raft consensus algorithms keep your favourite apps and websites in sync, even when things go wrong.
Imagine you and your friends are playing a game where everyone has to agree on the rules. What if not everyone is in the same room? Maybe some of you are sitting together, but others are far away. What happens if one person misses the decision or forgets the rule? How do you make sure that everyone knows the same thing, even if some players aren't paying attention?
This problem is exactly what many web applications face when they're running on multiple computers at once. Web apps need a way for all the computers (or "servers") to agree on the same thing — like what data to show, which transaction to keep, or which user is logged in. The challenge is that some servers might crash, lose connection, or go offline while others keep working. In those situations, how can we ensure all the servers still agree on the data they are using?
To solve this problem, we use something called consensus algorithms. These are special rules that help servers agree on things even when there are failures. Two well-known consensus algorithms are Paxos and Raft. In this article, we’ll break down what these two algorithms are, how they work, and why they matter for web applications.
What Is Consensus, and Why Does It Matter?
In simple terms, consensus means that everyone in a group agrees on something. For computers, consensus is about agreeing on data — making sure all servers in a system have the same information at the same time. Imagine a website that shows the availability of a product. If one server thinks the product is in stock and another thinks it’s sold out, the users will get confused. We need all servers to agree on the same data.
To achieve consensus in a distributed system (a system with many computers), we need algorithms that help servers agree on data, even if some of them fail or disconnect. This is where Paxos and Raft come in. They are two different ways of ensuring that all servers agree, even when things go wrong.
Paxos: The Classic Consensus Algorithm
Paxos is one of the oldest and most famous consensus algorithms. It was created by a computer scientist named Leslie Lamport in 1989. Paxos is powerful and very reliable, but it can be complicated to understand and implement. Let’s break it down.
How Paxos Works:
Paxos works by involving three types of roles:
Proposers: These computers suggest a decision. Imagine one computer saying, “Let’s make Bob the new game leader.”
Acceptors: These computers vote on whether they agree with the proposal. They decide whether they’ll accept the proposal or not.
Learners: These computers learn what decision has been made once the system reaches an agreement.
Here’s how Paxos works in a simple way:
First, a proposer sends a request to the acceptors to propose a decision, like making Bob the leader.
The acceptors can either accept or reject the proposal. If enough acceptors agree, the decision is made.
Once the decision is made, the learners are informed.
Paxos ensures that no two decisions are made at the same time. Even if some computers crash, the remaining ones can still agree on the same decision.
Why Is Paxos Hard to Understand?
The problem with Paxos is that it can be difficult to implement and understand. It involves multiple steps, which can confuse developers. Imagine trying to follow a recipe with many different instructions, some of which depend on previous steps. If you make a mistake in one step, the entire process can fail.
Because of its complexity, Paxos isn’t always the best choice when you need something simple and easy to use.
Raft: The Simpler Consensus Algorithm
Now, let’s talk about Raft. Raft was created in 2014 to be a simpler alternative to Paxos. The goal of Raft was to keep the benefits of Paxos (making sure all computers agree) while making it easier to understand and implement.
How Raft Works:
Raft is simpler because it uses a leader-based system. In Raft, one computer is chosen as the leader, and the leader is responsible for making decisions and making sure that the other computers follow. The other computers are called followers.
Here’s how Raft works:
Leader Election: When Raft starts, the computers hold an election to choose one leader.
Log Replication: The leader makes decisions (like adding data to a database) and sends these decisions to the followers.
Commitment: Once the majority of followers agree with the leader’s decision, it’s considered final, and all followers update their data.
This system is simpler because there’s always one clear leader, and the leader makes all the important decisions. If the leader crashes, the system will hold another election to choose a new leader.
Why Is Raft Simpler?
Raft is easier to understand because there’s a single leader that takes care of most of the work. It doesn’t require multiple rounds of voting like Paxos, making it faster and simpler to follow. It’s kind of like having one person who makes all the rules in the game — if they’re gone, another person can take over.
Paxos vs. Raft: Which One Should You Choose?
So, which algorithm is better — Paxos or Raft? The answer depends on your needs.
When Should You Use Paxos?
When you need the highest level of reliability. Paxos is great for systems where nothing can go wrong. If you’re building something very critical, like a database for a bank, Paxos might be the way to go.
When you’re okay with complexity. Paxos can be tricky to implement, so it’s not the best choice if you’re new to distributed systems or need something fast.
When Should You Use Raft?
When you want simplicity and ease of use. Raft is much easier to understand and implement compared to Paxos, so if you need something that works well and is easy to manage, Raft is a great choice.
When your system can handle a leader. If your system doesn’t need too many complex decisions at the same time and can work with a leader taking charge, Raft is a perfect fit.
In practice, Raft is the algorithm most web applications use. It’s simple, reliable, and works well in many situations. Many popular services, like etcd (used in Kubernetes for managing clusters) and HashiCorp Consul, use Raft because it’s easy to manage.
Real-Life Examples of Paxos and Raft
Here’s how Paxos and Raft are used in the real world:
Paxos in Action:
Google’s Chubby: Chubby is a system that helps other systems agree on the data they’re using, and it’s based on Paxos.
Zookeeper: This system, often used in large distributed applications, is based on Paxos-like algorithms.
Raft in Action:
etcd: A key-value store used to manage configurations in systems like Kubernetes. It uses Raft to keep data consistent.
Consul: A system for managing services and networking uses Raft to keep everything running smoothly.
Conclusion: Choosing Between Paxos and Raft
Both Paxos and Raft solve the same problem — making sure multiple computers agree on data. Paxos is very powerful and reliable but complex, while Raft is simpler and easier to use. For most modern applications, Raft is the go-to choice because it’s easier to understand and implement, and it’s good enough for most situations.
However, if you’re building something that absolutely cannot fail, like a financial system, Paxos may still be the best choice. Understanding the strengths and weaknesses of both algorithms will help you decide which one is best for your project.
In the end, whether you choose Paxos or Raft, these algorithms are like the rules of the game — making sure that everyone agrees and plays fair, even when things go wrong.