Can I use this for web scraping?

Yes, it includes specific patterns for concurrent requests and rate limiting using semaphores, which are essential for building robust web scrapers.

What is the main benefit of using this skill?

It helps you implement non-blocking code correctly, avoiding common pitfalls in Python's asyncio to build highly scalable and responsive applications.

Is this skill compatible with FastAPI?

Yes, the patterns provided are foundational for FastAPI, Sanic, and other modern async Python frameworks that rely on the asyncio event loop.

Does it cover error handling in async code?

Absolutely. It provides patterns for safe execution and exception management within concurrent tasks, specifically using features like asyncio.gather with exception returns.

Async Python Patterns

Name: Async Python Patterns
Author: prof-ramos

byprof-ramos

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API Development

Implements high-performance asynchronous Python patterns using asyncio and concurrent programming techniques.

About

Async Python Patterns is a specialized skill designed to help developers build scalable, non-blocking applications using Python's asyncio library. It provides a comprehensive set of implementation patterns for managing the event loop, coroutines, and tasks, alongside advanced techniques like the producer-consumer model and semaphore-based rate limiting. Whether you are developing high-concurrency web APIs with FastAPI, building efficient web scrapers, or managing real-time data streams, this skill offers the structural guidance needed to optimize I/O-bound workloads and ensure robust error handling in asynchronous environments.

Key Features

1 GitHub stars
Robust error handling and timeout patterns for resilient asynchronous systems
Standardized async/await implementation for non-blocking I/O operations
Advanced synchronization patterns including semaphores for rate limiting and locks
Concurrent task management using asyncio.gather and task scheduling
Async context managers and iterators for efficient resource lifecycle management

Use Cases

Developing efficient web scrapers with concurrent requests and rate limiting
Implementing real-time background processing using the producer-consumer pattern
Building high-concurrency web APIs with frameworks like FastAPI or aiohttp

About

Key Features

1 GitHub stars
Robust error handling and timeout patterns for resilient asynchronous systems
Standardized async/await implementation for non-blocking I/O operations
Advanced synchronization patterns including semaphores for rate limiting and locks
Concurrent task management using asyncio.gather and task scheduling
Async context managers and iterators for efficient resource lifecycle management

Use Cases

Developing efficient web scrapers with concurrent requests and rate limiting
Implementing real-time background processing using the producer-consumer pattern
Building high-concurrency web APIs with frameworks like FastAPI or aiohttp