As Python developers, we're always looking for ways to make our code faster and more efficient. One way to accomplish this is by using async code. But when should you actually use async in Python?
What is Async?
Async stands for "asynchronous" and refers to code that can run separately from the main program flow. Here's a quick example:
import asyncio
async def my_async_func():
# Do some work here
await some_async_call()
asyncio.run(my_async_func())
The asyncio.run() function runs the my_async_func coroutine in an event loop separately from the main thread. This allows that work to happen concurrently.
The key benefit is that while the async function is waiting on some I/O or network request, the main thread can continue executing other code. This makes async programming very useful for I/O bound tasks.
When Should You Use Async?
Here are some good opportunities to leverage async in your Python code:
Network Requests: Calling APIs, web scraping, database access, etc. All network I/O is asynchronous by nature, so async code plays very nicely here.
File I/O: Reading and writing files on disk can block the main thread. Async file handling like asyncio.to_thread() prevents blocking.
Concurrency Needed: If your program needs to juggle several tasks at once, async programming is a perfect fit.
Improve Perceived Performance: Async can make your program feel faster by preventing blocking even if CPU bound.
Common Async Use Cases
Here are some practical examples of times where async Python really shines:
Web Scraping: Fetching data from multiple sites concurrently speeds up scrapers dramatically:
APIs: Async HTTP clients like aiohttp prevent blocking when making API calls:
Databases: Async DB drivers like aiopg enable concurrent database queries.
Common Pitfalls to Avoid
While async is powerful, you have to avoid some common traps:
Don't use async code for CPU heavy tasks as it provides no benefit there. Use multiprocessing instead.
Beware of mixing async and sync code which can cause nasty deadlocks. Use asyncio.to_thread() to bridge when needed.
Debugging async code can be challenging due to its non-linear execution flow. Use purpose-built tools like aiomonitor.
Key Takeaways
Here are some key points on when to use async in Python:
Use it for I/O bound tasks - networking, files, databases, etc.
Employ it when concurrency and parallelism is needed.
Avoid it for purely CPU intensive work.
Bridge carefully between sync and async with helpers like asyncio.to_thread().
Debug with purpose-built async tools like aiomonitor.
Following these guidelines will let you maximize the power of async in your Python apps! Async programming opens up performance gains and new possibilities - have fun exploring it!
Browse by tags:
Browse by language:
The easiest way to do Web Scraping
Get HTML from any page with a simple API call. We handle proxy rotation, browser identities, automatic retries, CAPTCHAs, JavaScript rendering, etc automatically for you
