How does this skill prevent a cache stampede?

It uses a Stampede Protection class with distributed locks, ensuring that if multiple requests hit a missed cache simultaneously, only one performs the expensive database fetch while others wait.

Is this compatible with FastAPI?

Absolutely. The code patterns utilize redis.asyncio and are designed to work seamlessly within asynchronous frameworks like FastAPI and Sanic.

Does this skill handle cache consistency?

Yes, it provides Write-Through and Write-Behind patterns specifically designed to maintain consistency between your cache layer and primary database during write operations.

Which caching pattern is best for general-purpose use?

The Cache-Aside (Lazy Loading) pattern is generally recommended for most use cases as it balances simplicity with efficiency, fetching data from the database only on a cache miss.

Caching Strategies & Redis Patterns

Name: Caching Strategies & Redis Patterns
Author: yonatangross

byyonatangross

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API Development

Implements high-performance Redis caching patterns and invalidation strategies for scalable backend architectures.

About

This skill provides production-ready implementations for essential Redis caching strategies including Cache-Aside, Write-Through, and Write-Behind patterns. It enables developers to optimize backend performance, manage data consistency, and solve complex challenges like cache stampedes and memory leaks. By incorporating standardized logic for TTL management, event-based invalidation, and distributed locks, this skill ensures Claude can build resilient, high-throughput applications that minimize database load and maximize response speeds.

Key Features

Standardized serialization and deserialization patterns for complex data types
Async-first Redis patterns optimized for FastAPI and modern Python environments
Production-grade implementations of Cache-Aside, Write-Through, and Write-Behind patterns
29 GitHub stars
Advanced invalidation techniques using TTL jitter, tagging, and version-based namespaces
Cache stampede protection through distributed locking mechanisms

Use Cases

Managing data consistency between volatile caches and primary SQL/NoSQL databases
Reducing database latency in high-traffic web applications and APIs
Scaling expensive data computations by implementing efficient 'get or set' patterns

About

Key Features

Standardized serialization and deserialization patterns for complex data types
Async-first Redis patterns optimized for FastAPI and modern Python environments
Production-grade implementations of Cache-Aside, Write-Through, and Write-Behind patterns
29 GitHub stars
Advanced invalidation techniques using TTL jitter, tagging, and version-based namespaces
Cache stampede protection through distributed locking mechanisms

Use Cases

Managing data consistency between volatile caches and primary SQL/NoSQL databases
Reducing database latency in high-traffic web applications and APIs
Scaling expensive data computations by implementing efficient 'get or set' patterns