Demystifying Raft: Achieving Consensus in Distributed Systems

Table of Contents

1. Introduction
2. The Problem of Storing Data
3. Protecting Data
4. Introducing Raft
5. The Log
6. The Cluster
7. Selecting a Leader
8. Managing the Log
9. Syncing the Log
10. Consensus Process for the Log
11. Maintaining Consistency
12. Applications of Raft
13. Raft as a Finite State Machine
14. Conclusion

Introduction {#introduction}

In this article, we will delve into the world of distributed Consensus algorithms, specifically focusing on Raft. While the topic may seem complex, don't worry! We will break it down into simple terms to help you grasp the concept easily.

The Problem of Storing Data {#problem-of-storing-data}

Let's start by addressing the problem of storing data. Imagine you want to store certain information, such as the price of potatoes. Initially, you might opt for a simple solution and store the data in a single file on a single server. However, this approach is prone to risks. If a disaster strikes and the server goes down, you would lose all your data. So, how can you protect yourself in such situations?

Protecting Data {#protecting-data}

To protect your data, you would need to invest in additional servers. By distributing the data across multiple servers, even if one server fails, the others can continue to serve the data. However, this introduces complexities, such as inconsistencies in data across servers. To address this, consensus among the servers is crucial.

Introducing Raft {#introducing-raft}

This is where Raft comes into play. Raft is a distributed consensus algorithm that helps achieve consensus among nodes or servers in a cluster. At the core of Raft is the log, which serves as the source of truth.

The Log {#the-log}

The log is stored on each node and records all the operations performed on the data. Whether it's updating, adding, or removing data in a database, each operation is recorded in the log. By getting all nodes to agree on the log, consensus is achieved on the state of the data.

The Cluster {#the-cluster}

A Raft cluster consists of multiple nodes. To interact with the cluster, there is a leader node that delegates information to be added to the log. But how is the leader selected? Let's find out.

Selecting a Leader {#selecting-a-leader}

Within the cluster, there is one leader node and the rest are follower nodes. The leader node periodically sends heartbeats to all other nodes, indicating its presence. In case the leader fails, a new leader must be chosen. An election is held among the follower nodes, and the one with the majority of votes becomes the new leader.

Managing the Log {#managing-the-log}

As we Mentioned earlier, the log stores all the operations performed on the data. To ensure consistency, the leader manages the log information. It receives requests to change or add data and creates corresponding log entries.

Syncing the Log {#syncing-the-log}

To maintain consistency across nodes, the log must be Synced. When the leader receives a request to change the data, it generates a log entry with a proper index and term number. This entry is sent to other nodes, which then append it to their logs. However, the entry is not immediately committed.

Consensus Process for the Log {#consensus-process-for-the-log}

The leader waits for responses from the followers. If more than half of the nodes acknowledge receiving and appending the log entry, the leader commits it. The followers are then instructed to commit the entry as well, completing the consensus process for the log.

Maintaining Consistency {#maintaining-consistency}

By achieving consensus on the log, the operations performed on the data become consistent. This is why Raft is widely used in distributed consistent databases like YugaByte or CockroachDB.

Applications of Raft {#applications-of-raft}

While the discussion primarily focused on using Raft with databases, the theoretical applications are broader. Raft can be used with any finite state machine. If all the nodes have the same finite state machine, given an operation in the log, they will end up in the same state.

Raft as a Finite State Machine {#raft-as-a-finite-state-machine}

In a nutshell, think of Raft as a mechanism to achieve consistency across a cluster of nodes. Whether it's maintaining a database or a finite state machine, Raft ensures that all nodes reach the same state, making their data consistent.

Conclusion {#conclusion}

Raft provides a reliable solution for distributed consensus. Through the log and the consensus process, Raft enables consistent data across nodes, ensuring the integrity and reliability of distributed systems.

Highlights

• Raft is a distributed consensus algorithm that ensures consistency in a cluster of nodes.
• The log serves as the source of truth and records all operations performed on the data.
• Nodes in a Raft cluster elect a leader through an election process.
• Consensus is achieved by syncing and committing log entries across the cluster.
• Raft is commonly used in distributed consistent databases.

FAQ

Q: How does Raft handle node failures? A: When a node fails, an election is triggered to select a new leader among the remaining nodes.

Q: Can Raft be used with different types of databases? A: Yes, Raft can be applied to various distributed databases to maintain consistency and reliability.

Q: What is the role of the leader in Raft? A: The leader node delegates information to be added to the log and manages the consensus process.

Q: How does Raft handle inconsistencies in data across nodes? A: By achieving consensus on the log, Raft ensures that data operations are consistent across all nodes in the cluster.

Q: Are there any alternative consensus algorithms to Raft? A: Yes, other consensus algorithms like Paxos also exist, but Raft is known for its simplicity and understandability.