How I learned to stop blocking the event loop and start building faster apps
4 min read1 day ago
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Concurrency in Python was one of those things I avoided for years. Threads felt messy, processes were overkill, and async looked like magic I didn’t fully understand. But once I finally dove into asyncio, it completely changed how I built applications.
In this article, I’ll break down how I scaled Python applications using concurrency and asynchronous programming. We’ll go from the basics of async to advanced patterns with queues, tasks, and third-party libraries that make async easier to handle.
1. Why Asyncio Matters in Python
Traditional Python programs run synchronously. That means every task waits its turn, like people queuing for coffee. The problem? When your app…
How I learned to stop blocking the event loop and start building faster apps
4 min read1 day ago
–
Concurrency in Python was one of those things I avoided for years. Threads felt messy, processes were overkill, and async looked like magic I didn’t fully understand. But once I finally dove into asyncio, it completely changed how I built applications.
In this article, I’ll break down how I scaled Python applications using concurrency and asynchronous programming. We’ll go from the basics of async to advanced patterns with queues, tasks, and third-party libraries that make async easier to handle.
1. Why Asyncio Matters in Python
Traditional Python programs run synchronously. That means every task waits its turn, like people queuing for coffee. The problem? When your app makes network requests or reads files, it spends a lot of time waiting.
Asyncio fixes this by allowing Python to handle other work while waiting. Instead of blocking, it cooperatively multitasks. Perfect for I/O-heavy workloads like:
- Web scraping thousands of URLs
- Chatbots handling multiple conversations
- Servers responding to client requests