Building a Remote Storage MCP Server in Go: From Zero to One Practice and Reflection

"Writing services in Go is as simple as stacking blocks." — A passionate Gopher

Preface: Why Choose Go to Build Remote Storage Services?

In daily development, remote storage services are almost a standard for every medium and large system. Whether it's log archiving, configuration centers, or distributed caching, efficient and reliable remote storage is essential. Go, with its concurrency, performance, and ecosystem advantages, has become a popular choice for building such services.

Recently, I implemented a remote storage MCP (Mock Cloud Platform) server in Go. This article combines practical cases to discuss technology selection, architecture design, pitfalls, and best practices, aiming to inspire you to feel "I can do it too" after reading!

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Table of Contents

1. Requirements Analysis and Technology Selection
2. Core Architecture Design
3. Key Implementation and Code Examples
4. Technical Challenges and Solutions
5. Practical Advice and Best Practices
6. Summary and Outlook

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Requirements Analysis and Technology Selection

1. Requirements Breakdown

• Support file upload, download, and deletion
• Support multi-client concurrent access
• Data persistence, no loss on power failure
• Simple and easy to use, convenient for secondary development

2. Why Use Go?

Referencing the views in "Go Concurrency Patterns Practice Guide" and "Golang Productivity Tools.md", Go's concurrency model (goroutine + channel) makes developing remote storage services under high concurrency very easy. At the same time, Go's cross-platform compilation and rich third-party libraries (such as gin, gorm, zap) greatly improve development efficiency.

"Writing network services in Go, you get both performance and development efficiency." — From "Go Concurrency Patterns Practice Guide"

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Core Architecture Design

Architecture Diagram

graph TD
A[Client] -->|HTTP/REST| B(MCP Server)
B --> C[Local Storage]
B --> D[Remote Object Storage (optional)]
B --> E[Metadata Database]

Main Modules

• API Layer: Handles client requests and responses (RESTful style)
• Storage Engine: Local file system or cloud storage (e.g., S3, OSS)
• Metadata Management: Records file info, permissions, etc.
• Concurrency Control: goroutine + channel for high-concurrency processing

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Key Implementation and Code Examples

1. API Layer (Gin Framework)

import "github.com/gin-gonic/gin"

func main() {
    r := gin.Default()
    r.POST("/upload", uploadHandler)
    r.GET("/download/:filename", downloadHandler)
    r.DELETE("/delete/:filename", deleteHandler)
    r.Run(":8080")
}

2. File Upload Handling

func uploadHandler(c *gin.Context) {
    file, _ := c.FormFile("file")
    dst := "./data/" + file.Filename
    if err := c.SaveUploadedFile(file, dst); err != nil {
        c.JSON(500, gin.H{"error": err.Error()})
        return
    }
    // Record metadata to database
    c.JSON(200, gin.H{"message": "Upload successful"})
}

3. Concurrency Handling and Data Safety

Referencing "Golang Implementing Goroutine Pool.md", you can use a worker pool to control concurrency and avoid resource exhaustion:

var uploadPool = make(chan struct{}, 10) // Max 10 concurrent uploads

func safeUploadHandler(c *gin.Context) {
    uploadPool <- struct{}{}
    defer func() { <-uploadPool }()
    // ...upload logic...
}

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Technical Challenges and Solutions

1. Concurrency Safety

• Challenge: Filename conflicts and data consistency issues under high concurrency
• Solution: Use unique IDs for filenames, lock metadata operations, or use database transactions

2. Large File Uploads

• Challenge: High memory usage, slow uploads
• Solution: Use chunked uploads, write to disk while receiving, refer to streaming techniques in "Go Terminal Tools.md"

3. Persistence and Disaster Recovery

• Challenge: Local storage is prone to loss; how to ensure data safety?
• Solution: Regularly sync to cloud storage, or use RAID/NAS solutions, combined with backup advice in "Golang Web Application Security Guide"

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Practical Advice and Best Practices

1. Keep API design simple: RESTful style, easy for frontend-backend collaboration
2. Logging and monitoring are essential: Use zap, prometheus to record and monitor service status
3. Error handling should be meticulous: Every step should have a fallback, avoid panic
4. Comprehensive testing: Unit + integration tests to ensure core functions are stable
5. Documentation should be complete: API docs, deployment docs, and maintenance manuals are all necessary

"No matter how good the tool, if no one uses it, it's wasted." — From "Productivity-Boosting Golang Tools.md"

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Summary and Outlook

Building a remote storage MCP server in Go not only lets you enjoy high performance and concurrency, but also lets you experience the "joy of engineering"—solving complex problems with clean and elegant code. Along the way, you'll inevitably encounter various challenges, but as long as you make good use of Go's features and community resources, every problem can be solved.

In the future, consider:

• Supporting multiple storage backends (e.g., S3, OSS, MinIO)
• Introducing distributed consistency protocols (e.g., Raft) to improve reliability
• Opening up plugin mechanisms for easier secondary development

I hope this article provides some inspiration and practical advice for your exploration in the Go field. If you have similar experiences, feel free to comment and exchange!

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"Beyond code, there is also scenery." — Wishing you happy Go coding and stable services!