Can I use Cloudflare KV for real-time transactional data?

No, because KV is eventually consistent. For applications requiring strong consistency or high-frequency writes, Cloudflare Durable Objects is the recommended alternative.

How do I handle large datasets in KV with this skill?

The skill provides optimized pagination helpers and bulk operation guides to efficiently manage thousands of keys without exceeding the 1,000 operations per invocation limit.

What is Cloudflare KV best used for?

It is ideal for read-heavy workloads (100:1 ratio) where data changes infrequently, such as configuration settings, feature flags, and global caching.

How does this skill handle the 1 write per second limit?

The skill includes implementation patterns for exponential backoff and retry logic to gracefully manage 429 rate-limit errors when writing to the same key.

Cloudflare KV for Workers

Name: Cloudflare KV for Workers
Author: jezweb

byjezweb

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云基础设施

Manages globally distributed key-value storage at the edge using Cloudflare Workers KV.

关于

This skill provides specialized guidance for implementing and managing Cloudflare Workers KV storage within your serverless applications. It enables developers to efficiently store and retrieve global data with low latency, handling complex tasks like TTL expiration, metadata optimization, bulk operations, and pagination. By incorporating production-ready patterns for eventual consistency and rate-limiting, it ensures robust data management for read-heavy workloads such as configuration, caching, and user preferences.

主要功能

Automated exponential backoff patterns for handling 429 rate limits
Metadata-driven performance optimization for efficient list operations
91 GitHub stars
Full CRUD operations with global edge distribution and <5ms p99 latency
Advanced caching patterns using cacheTtl for read-heavy workloads
Standardized pagination and bulk operation helpers for large datasets

使用场景

Storing global application configuration and feature flags
Implementing high-performance caching layers for external API responses
Managing user session data and preferences with automated TTL expiration

关于

主要功能

Automated exponential backoff patterns for handling 429 rate limits
Metadata-driven performance optimization for efficient list operations
91 GitHub stars
Full CRUD operations with global edge distribution and <5ms p99 latency
Advanced caching patterns using cacheTtl for read-heavy workloads
Standardized pagination and bulk operation helpers for large datasets

使用场景

Storing global application configuration and feature flags
Implementing high-performance caching layers for external API responses
Managing user session data and preferences with automated TTL expiration