This document outlines the design and architecture of a distributed key-value store system implemented in Go. The system is designed to be horizontally scalable, strongly consistent, fault-tolerant, and support concurrent operations.

The distributed key-value store is built using a peer-to-peer architecture where each node can serve client requests and participate in cluster operations. The system uses the Raft consensus algorithm for ensuring strong consistency and fault tolerance.

The system consists of the following key components:

1. Server Nodes: Individual instances of the service running on separate machines
2. Raft Consensus Module: Implements the Raft algorithm for leader election and log replication
3. Storage Engine: Responsible for storing and retrieving data
4. Partitioning Module: Distributes data across nodes using consistent hashing
5. Network Communication: Manages inter-node communication using gRPC

1. Client connects to any node in the cluster
2. The receiving node determines if it owns the requested key or forwards the request
3. For write operations, the request is processed through the Raft consensus module
4. Operations are executed on the local storage engine
5. Changes are replicated to other nodes as needed

The system is designed to scale horizontally by adding more nodes to the cluster:

• Dynamic Cluster Membership: Nodes can join and leave the cluster at runtime
• Consistent Hashing: Enables efficient data distribution with minimal redistribution when nodes change
• Load Balancing: Evenly distributes data and requests across nodes

Implementation details:

• The consistent hash ring uses virtual nodes to ensure even distribution
• When a new node joins, it only receives a portion of the data from existing nodes
• Client requests are automatically routed to the appropriate node

The system provides strong consistency guarantees through:

• Raft Consensus Algorithm: Ensures all nodes agree on the sequence of operations
• Leader-Based Writes: All write operations go through the leader node
• Log Replication: Changes are replicated to a majority of nodes before acknowledging

Implementation details:

• The Raft module handles leader election and log replication
• Write operations are committed only after replication to a quorum of nodes
• Read operations can be served from any node, with optional leader verification for strict linearizability

The system is resilient to various failure scenarios:

• Node Failures: The cluster continues to operate as long as a majority of nodes are available

• Data Replication: Each piece of data is stored on multiple nodes (configurable replication factor)

Implementation details:

• Automatic leader re-election when the leader fails
• Data redistribution when nodes join or leave

Implementation details:

• Read-write locks protect in-memory data structures
• The consensus log provides a total order for all write operations
• Optimistic concurrency control for client operations

Implementation details:

• A configurable number of virtual nodes per physical node
• Keys are mapped to positions on a hash ring
• Primary responsibility and replica locations are determined by walking the ring

The server component is the main runtime of the system:

• Handles client requests via gRPC
• Integrates with the storage engine, Raft module, and partitioning module
• Manages node lifecycle and cluster membership

The storage engine manages the actual data:

• Provides key-value storage and retrieval
• Supports both in-memory and file-based backends
• Handles data persistence and recovery

The Raft module ensures consistent state across the cluster:

• Leader election mechanism
• Log replication between nodes
• Snapshot and log compaction

The partitioning module distributes data:

• Implements consistent hashing
• Manages virtual node placement
• Determines key ownership and routing

The client API provides an interface for applications:

• Simple Put/Get/Delete operations

This distributed key-value store provides a robust, scalable, and consistent storage system implemented in Go. By leveraging the Raft consensus algorithm and consistent hashing, it achieves a good balance between consistency, availability, and partition tolerance according to the CAP theorem.