How does it handle errors in async tasks?

The skill covers robust error handling patterns, including managing exceptions within concurrent groups using asyncio.gather and implementing graceful degradation.

What is the main benefit of using this skill?

It helps you implement efficient asynchronous patterns in Python to handle I/O-bound tasks without blocking the main execution thread, significantly increasing application throughput.

Does this skill support frameworks like FastAPI?

Yes, it provides the fundamental async/await patterns and best practices required for building high-performance services with frameworks like FastAPI, Sanic, and aiohttp.

Can this help with web scraping?

Absolutely. It includes specific patterns for concurrent requests, timeout handling, and rate limiting using semaphores to build scalable and polite web scrapers.

Async Python Patterns

Name: Async Python Patterns
Author: Krosebrook

byKrosebrook

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Desarrollo de API

Implements high-performance asynchronous Python patterns using asyncio for non-blocking I/O and concurrent processing.

Acerca de

This skill provides comprehensive guidance for mastering Python's asyncio library and concurrent programming paradigms. It enables developers to build high-performance, scalable applications by implementing non-blocking patterns such as async/await, task management, and producer-consumer architectures. Whether building modern web APIs with FastAPI, high-concurrency scrapers with aiohttp, or real-time communication systems, this skill ensures best practices for error handling, rate limiting, and resource management in complex asynchronous environments.

Características Principales

Asynchronous context managers and iterators for resource safety
Robust error handling and timeout patterns for resilient systems
Async/await syntax and coroutine lifecycle management
1 GitHub stars
Concurrent task execution and result gathering using asyncio.gather
Advanced concurrency primitives including semaphores and locks

Casos de Uso

Developing concurrent web scrapers that handle thousands of simultaneous requests
Implementing real-time data processing pipelines and background task workers
Building high-throughput REST or GraphQL APIs with FastAPI or Sanic

Acerca de

Características Principales

Asynchronous context managers and iterators for resource safety
Robust error handling and timeout patterns for resilient systems
Async/await syntax and coroutine lifecycle management
1 GitHub stars
Concurrent task execution and result gathering using asyncio.gather
Advanced concurrency primitives including semaphores and locks

Casos de Uso

Developing concurrent web scrapers that handle thousands of simultaneous requests
Implementing real-time data processing pipelines and background task workers
Building high-throughput REST or GraphQL APIs with FastAPI or Sanic