Python's asyncio module provides a way to write non-blocking, asynchronous code that can execute faster than equivalent threaded code. Here's why asyncio shines and how it achieves better performance.
Blocking Calls Slow Things Down
The Python threads model suffers from something called the Global Interpreter Lock (GIL). This lock prevents multiple threads from running Python bytecodes at once, meaning only one CPU core can execute Python code at a time.
When a Python thread makes a blocking call, like requesting data from a web API, the entire Python process stalls until that call completes. Other threads cannot run during that blocking call no matter how many cores are available.
# Thread 1 makes a blocking call, stalling all other threads
response = requests.get(url) Asyncio Uses Cooperative Multitasking
The asyncio module provides an event loop that can juggle multiple tasks cooperatively. Each task explicitly gives control back to the event loop periodically so other tasks get a turn.
import asyncio
async def fetch_data():
# Pause here cooperatively to allow other tasks to run
await asyncio.sleep(0)
return requests.get(url)
loop = asyncio.get_event_loop()
loop.run_until_complete(fetch_data())Since blocking calls like
Asyncio Maximizes CPU Utilization
While a threaded app is stuck at one Python thread during I/O, an asyncio app can continue executing other tasks. The event loop constantly switches tasks to maximize CPU usage.
Asyncio also minimizes expensive Python GIL contention. Threads in Python must acquire the GIL before running Python bytecodes, which is a bottleneck. But asyncio tasks like I/O waits don't need the GIL, freeing it for other work.
So asyncio allows efficient cooperative multitasking in Python. By minimizing blocking calls and GIL contention, it can achieve much higher throughput than threads.