Python asyncio library in-depth analysis (including complete code and comments)

1. The core principle of asyncio

1. Event Loop

• effect: Event loop isasyncioThe core scheduler is responsible for listening and managing all asynchronous tasks.
• Implementation principle：
  • Maintain a task queue (Task Queue), execute coroutines according to priority.
  • Concurrency is handled through non-blocking I/O and callback mechanisms.
  • useepoll（Linux）、kqueue(macOS) orselect(Cross-platform) to achieve efficient I/O multiplexing.

2. Coroutine

• definition:passasync defDefined functions that can be suspended and restored.
• Key mechanisms：
  • useawaitPush the coroutine and hand over control to the event loop.
  • The coroutine status is saved ingeneratorIn the object, continue execution from the suspended point when recovering.

3. Future and Task

• Future: Indicates the final result of an asynchronous operation (similar to Promise).
• Task: Inherited fromFuture, used to wrap coroutines and schedule execution.

2. The difference between synchronous vs asynchronous

1. Comparison of execution modes

2. Code comparison example

Synchronous blocking code

import time

def sync_task():
    (1)  # Blocking threads    print("Sync task done")

start = ()
sync_task()
sync_task()
print(f"Total time consumption: {() - start:.2f}s")  # The output is about 2.0s

Asynchronous non-blocking code

import asyncio

async def async_task():
    await (1)  # Non-blocking hang    print("Async task done")

async def main():
    start = ()
    await (async_task(), async_task())
    print(f"Total time consumption: {() - start:.2f}s")  # The output is about 1.0s
(main())

3. Detailed explanation of asyncio working mechanism

1. Task scheduling process

1. Create an event loop and start.
2. Wrapping coroutines asTaskObjects join the queue.
3. The event loop polls the task status and executes the ready task.
4. meetawait hangs the current task，Switch to other tasks。
5. Recover tasks via callback when I/O is completed.

2. Coroutine life cycle

Created → Pending → Running → Suspended (at await) → Resumed → Completed/Failed

4. Application fields and complete code examples

1. High-performance network crawler

import aiohttp
import asyncio

async def fetch(url):
    async with () as session:
        async with (url) as response:
            return await ()

async def main():
    urls = [
        "",
        "",
        ""
    ]
    tasks = [fetch(url) for url in urls]
    results = await (*tasks)
    for url, content in zip(urls, results):
        print(f"{url} Content length: {len(content)}")

(main())

2. Real-time Web Services (FastAPI Integration)

from fastapi import FastAPI
from  import HTMLResponse
import asyncio

app = FastAPI()

async def background_task():
    while True:
        await (5)
        print("Background tasks are running...")

@app.on_event("startup")
async def startup_event():
    asyncio.create_task(background_task())

@("/", response_class=HTMLResponse)
async def read_root():
    await (1)  # Simulate asynchronous database query    return "<h1>Hello Async World!</h1>"

3. Real-time data processing (WebSocket chat room)

import websockets
import asyncio

connected = set()

async def chat_server(websocket):
    (websocket)
    try:
        async for message in websocket:
            await (
                *[(f"User said: {message}") for client in connected]
            )
    finally:
        (websocket)

async def main():
    async with (chat_server, "localhost", 8765):
        await ()  # Permanently run
(main())

V. Advanced features and best practices

1. Coroutine synchronization mechanism

• Lock (Lock): Prevent competition for shared resources.

lock = ()
async def safe_update():
    async with lock:
        # Modify shared resources

2. Error handling

async def risky_task():
    try:
        await async_operation()
    except Exception as e:
        print(f"Error capture: {e}")

async def main():
    task = asyncio.create_task(risky_task())
    await task

3. Performance tuning

• Limit the concurrency amount (if used）：

sem = (10)
async def limited_task():
    async with sem:
        await heavy_operation()

6. Summary of applicable scenarios

7. Summary

asyncioThrough event loops and coroutine mechanisms, Python provides efficient asynchronous programming capabilities, suitable for I/O-intensive scenarios. Understanding its underlying principles (such as task scheduling, coroutine life cycle) is the key to optimizing performance. Selecting synchronous or asynchronous models in combination with specific scenarios can significantly improve program efficiency.

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