Pau Dang

Posted on Mar 19

Master Caching Patterns: A Clean Architecture Guide with AI-Native Tooling

#webdev #node #redis #microservices

In high-performance systems, caching is the ultimate "lifesaver" for reducing database load and optimizing response times. However, choosing the right pattern (Cache-Aside, Write-Through, etc.) depends heavily on your specific use case. In this article, I’ll walk you through a hands-on demo project that showcases 5 essential caching patterns, built with Clean Architecture and the power of an AI-Native tool: nodejs-quickstart-structure.

🚀 Why Caching?

When scaling applications, the database often becomes the bottleneck. Caching strategically places frequently accessed data in memory (like Redis) to bypass slow disk I/O. But not all caching is equal. To understand the nuances, I built a showcase service from scratch.

To skip the boilerplate and focus on the patterns, I used nodejs-quickstart-structure, a CLI tool designed for AI-ready, structured development.

📂 5 Caching Patterns You Must Know

Here is a breakdown of the 5 patterns implemented in this demo:

1. Cache-Aside (Lazy Loading)

Purpose: The application manages the cache. Data is only loaded into the cache when specifically requested.
When to use: Best for systems with Read >> Write ratios (e.g., User Profiles).
Pros: Memory efficiency; cache only contains data that is actually needed.

2. Read-Through

Purpose: Offloads data-fetching logic to the Cache Provider. The app simply calls "Get" from the cache.
When to use: To keep the business logic (Use Case) clean and decouple from the database.
Pros: Extremely clean business code.

3. Write-Through

Purpose: Data is written to both the Cache and the Database simultaneously.
When to use: When Strong Consistency is required.
Pros: Minimal data inconsistency risk.

4. Write-Around

Purpose: Data is written directly to the DB, and the corresponding cache entry is invalidated (deleted).
When to use: When the written data might not be read again immediately.
Pros: Prevents "polluting" the cache with data that won't be reused soon.

5. Write-Back (Write-Behind)

Purpose: Data is written to the Cache first; the DB update happens asynchronously in the background.
When to use: For exceptionally high write performance (e.g., Logging, Real-time Metrics).
Pros: Near-instant write response.

🛠️ Technical Implementation (Step-by-Step)

Follow this roadmap to see how I build a production-ready demo using Clean Architecture:

1. Project Initialization

Bootstrap the project using the AI-Native CLI:

npx nodejs-quickstart-structure@latest init

Selection Guide:

Project name: nodejs-service-caching-pattern
Architecture: Clean Architecture
Database: MySQL
Caching: Redis
Communication: REST APIs

2. Defining Domain Interfaces

Abstract the caching and repository logic to ensure true decoupling.

ICacheService.ts

export interface ICacheService {
  get<T>(key: string): Promise<T | null>;
  set(key: string, value: unknown, ttl?: number): Promise<void>;
  del(key: string): Promise<void>;
  getOrSet<T>(key: string, fetcher: () => Promise<T>, ttl?: number): Promise<T>;
}

IUserRepository.ts

3. Infrastructure Layer

Implementation for Redis and Sequelize (MySQL).

4. Implementing the Use Cases

This is where the pattern logic lives.

Read-Through Example: getUserInfoReadThrough.ts

// The cache provider handles DB fetching upon miss
return await this.cacheService.getOrSet<User | null>(cacheKey, async () => {
    return await this.userRepository.findById(id);
}, 3600);

Write-Back Example: updateUserWriteBack.ts

// Update cache immediately, DB updates in background
await this.cacheService.set(cacheKey, updatedUser, 3600);
this.asyncDatabaseUpdate(updatedUser);

5. API & Documentation

🔍 How to Test & Verify

Once deployed, you can trigger these endpoints to verify the internal logic:

1. Test Cache-Aside / Read-Through (Read Path)

First Call (Cold Cache): curl -X GET http://localhost:3000/api/demo/cache-aside/1
- Internal: App checks Redis (Miss) -> Queries MySQL -> Updates Redis -> Returns.
Subsequent Call (Warm Cache): Repeat the command.
- Internal: App checks Redis (Hit) -> Returns immediately with zero DB overhead.

2. Test Write-Through (Sync Write)

Trigger: curl -X POST http://localhost:3000/api/demo/write-through -H "Content-Type: application/json" -d '{"name": "Performance", "email": "perf@test.com"}'
- Internal: Service writes to MySQL AND Redis simultaneously.

3. Test Write-Around (Clean Write)

Trigger: curl -X POST http://localhost:3000/api/demo/write-around -H "Content-Type: application/json" -d '{"name": "Guest", "email": "guest@test.com"}'
- Internal: Writes to MySQL, then calls DEL to invalidate the Redis key. The next read will be a Miss to load the fresh data.

4. Test Write-Back (Async Write)

Trigger: curl -X PUT http://localhost:3000/api/demo/write-back/1 -H "Content-Type: application/json" -d '{"name": "Fast Update"}'
- Internal: Updates Redis immediately (super fast response). The DB update happens after a short delay asynchronously.

💡 Final Thoughts on AI-Native Tooling

What makes nodejs-quickstart-structure special is not just the speed of initialization, but its AI-Native nature. With .cursorrules and pre-built prompts, it ensures that your project maintains Clean Architecture and consistent coding standards automatically from development to production.

Full Source Code: GitHub Repository

I hope this guide helps you choose the right caching pattern for your next project! Happy coding!

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