caching-patterns

Implement in-memory, Redis, and HTTP caching patterns for web applications.

1|Updated Jan 8, 2026

Search Tags:#performance #caching #web development #redis #optimization #data management

Ready to run

npx skills add https://github.com/JaveedIshaq/ai-workflow-orchestrator --skill caching-patterns-javeedishaq

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: caching-patterns
Source: https://github.com/JaveedIshaq/ai-workflow-orchestrator/tree/main/templates/skills/caching-patterns
Command: npx skills add https://github.com/JaveedIshaq/ai-workflow-orchestrator --skill caching-patterns-javeedishaq

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires lru-cache, ioredis, bullmq, next/cache, @upstash/redis, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the performance bottlenecks in web applications by implementing various caching strategies to reduce latency and database load.

Core Features & Use Cases

In-Memory Caching: Utilizes LRU and simple Map-based caches for fast, local data retrieval.
Distributed Caching: Integrates with Redis (including Upstash for serverless) for shared, persistent caching.
Cache Strategies: Implements Cache-Aside, Write-Through, Write-Behind, and Read-Through patterns.
Invalidation: Provides mechanisms for Tag-Based and Event-Based cache invalidation.
HTTP Caching: Demonstrates Next.js cache headers and unstable_cache for server-side caching.
Rate Limiting: Includes examples of rate limiting using Redis.
Monitoring: Offers patterns for instrumenting cache performance.

Quick Start

Implement a cache-aside pattern to fetch user data, falling back to the database and storing the result in Redis.

Frequently Asked Questions about caching-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I implement Redis caching in a web application to reduce database load?▼

Redis caching reduces database load by storing frequently accessed data in memory. This Skill implements distributed caching using ioredis and @upstash/redis, applying patterns like cache-aside and write-through to optimize web application performance and data retrieval latency.

What is the difference between cache-aside and write-through caching strategies?▼

Cache-aside and write-through are distinct caching strategies. Cache-aside loads data into the cache on demand, while write-through writes data to the cache and database simultaneously. This Skill implements both patterns to help manage data freshness and system scalability based on your specific requirements.

How do I handle cache invalidation in Next.js?▼

Cache invalidation in Next.js is handled through tag-based and event-based mechanisms. This Skill provides implementations for cache invalidation alongside Next.js caching utilities like unstable_cache and cache headers, ensuring data freshness without compromising server-side caching performance.

Can I use in-memory caching for serverless web applications?▼

In-memory caching is limited in serverless environments due to stateless function execution. This Skill addresses serverless constraints by integrating @upstash/redis for distributed caching, while still offering local LRU and Map-based caches for fast, non-persistent data retrieval when applicable.

What is the best way to monitor cache performance and hit rates?▼

Monitoring cache performance requires instrumenting your caching layer to track hit rates and latency. This Skill provides patterns for instrumenting cache performance, allowing you to evaluate the effectiveness of your caching strategies and optimize data management accordingly.

How do I implement rate limiting using Redis?▼

Rate limiting using Redis controls API request frequency by tracking user requests in shared memory. This Skill includes examples of Redis rate limiting, leveraging its distributed nature to enforce limits consistently across multiple application instances.