Raft-KV-Example: A Storage System Based on ETCD Raft

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Introduction

This project is a simplified implementation of a key-value (KV) storage system built on top of the ETCD Raft consensus algorithm. It serves as an educational tool to understand the interaction between the Raft consensus model and the upper-layer KV storage system.

If you are not familiar with raft, here is my understanding on how raft works. (Basically, it’s just a Chinese translation of the Raft paper.)

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Architecture Overview

The system architecture is divided into several key components, as illustrated in the provided diagram:

1. KV Transport:

   • The KV transport layer handles communication between clients and the Raft model. It is responsible for proposing new entries (key-value pairs) and managing configuration changes in the cluster.

2. Raft Model:

   • Unstable Storage: This is where entries and snapshots are temporarily stored during the Raft process. Entries are commands proposed by clients, and snapshots are compacted state representations.
   • Ready: This component is responsible for processing entries and snapshots, preparing them for commit or communication with peers.
   • Memory Storage: This represents the in-memory state of the persisted entries and snapshots. It mirrors the data persisted in the underlying file system, ensuring consistency between the in-memory state and the disk.

3. Peer Transport:

   • This layer handles communication between different nodes in the Raft cluster, ensuring consistency and coordination across all participating nodes.

4. Disk Storage:

   • WAL (Write-Ahead Log) File: The WAL file persists all entries sequentially to ensure data integrity in case of a crash.
   • Snapshot File: The snapshot file stores compacted data from the memory storage, providing a way to recover the state of the system without replaying all WAL entries.
   • Database (.db): Optionally, a database (such as BoltDB in ETCD's implementation) may be used for additional disk-based storage. This provides persistent storage of the state machine.

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Running the System

To run the server, the following steps will guide you through the setup and execution:

Three Nodes Raft Cluster

To start all three server nodes simultaneously, use:

make run-all

This will start:

• Node 1 with PeerPort=2380 and ClientPort=2379
• Node 2 with PeerPort=12380 and ClientPort=12379
• Node 3 with PeerPort=22380 and ClientPort=22379

Stop a Specific Node in Cluster

To stop a specific node, for instance, Node 2, use:

make stop-node2

Restart a Specific Node in Cluster

To restart a specific node, for instance, Node 2, use:

make start-node2

Single Node Cluster

To start a specific node, for example, Node 1, use:

make run-single

Send KV Requests

Send a PUT Request to a Node

make put-node PORT=2379 DATA="key1=val1"

Send a GET Request to a Node

make get-node PORT=2379 DATA="key1"

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Conclusion

This project provides a hands-on experience with the Raft consensus algorithm and its integration into a KV storage system. By studying this implementation, you will gain a deeper understanding of distributed systems, consensus algorithms, and the challenges of building reliable storage systems.

Feel free to explore the code, modify it, and experiment with different configurations to enhance your understanding of the underlying concepts. Happy learning!