How do I prevent rate limiting in async web scrapers?

The skill includes implementation patterns for using asyncio.Semaphore to limit concurrent requests and manage throughput to avoid being blocked by servers.

When should I use async over threading in Python?

Asyncio is preferred for I/O-bound tasks where the program spends most time waiting for network or disk responses, while threading is typically better for parallelizing CPU-bound tasks.

Does this skill help with FastAPI performance?

Yes, it provides the fundamental async patterns and concurrency controls, like semaphores and task management, required to build highly efficient FastAPI endpoints.

Does this cover async database connections?

Yes, the skill provides patterns for using async context managers to manage database connections and execute concurrent queries without blocking the event loop.

Async Python Patterns

Name: Async Python Patterns
Author: ovachiever

byovachiever

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API Development

Implements high-performance Python applications using asyncio, concurrent programming patterns, and non-blocking I/O architectures.

About

This skill provides a comprehensive toolkit for mastering Python's asynchronous ecosystem, enabling developers to build highly scalable, non-blocking applications. It offers expert guidance on core asyncio concepts such as the event loop, coroutines, and tasks, alongside advanced implementation patterns like producer-consumer queues, rate limiting with semaphores, and concurrent database operations. Whether you are building real-time WebSocket servers, high-throughput APIs with FastAPI, or efficient web scrapers, this skill ensures best practices for resource management and performance optimization.

Key Features

Robust error handling and timeout management for async operations
Implementation of async context managers and iterators
Resource synchronization using async locks and semaphores
Advanced concurrency patterns including gather, tasks, and futures
5 GitHub stars
Practical templates for aiohttp scraping and async database access

Use Cases

Optimizing I/O-bound microservices and database communication layers
Building high-throughput web APIs with FastAPI, Sanic, or aiohttp
Developing efficient web scrapers that handle thousands of concurrent requests

About

Key Features

Robust error handling and timeout management for async operations
Implementation of async context managers and iterators
Resource synchronization using async locks and semaphores
Advanced concurrency patterns including gather, tasks, and futures
5 GitHub stars
Practical templates for aiohttp scraping and async database access

Use Cases

Optimizing I/O-bound microservices and database communication layers
Building high-throughput web APIs with FastAPI, Sanic, or aiohttp
Developing efficient web scrapers that handle thousands of concurrent requests