The latest in Python concurrent programming: Learn about the latest innovations and trends

Multiprocessing

Python's multiprocessing module allows you to create independent processes, each with its own memory and resources. This is useful for processing large data sets in parallel or performing io intensive tasks.

import multiprocessing

def worker(num):
print(f"Worker {num}: started")
# 执行一些耗时的任务
print(f"Worker {num}: finished")

if __name__ == "__main__":
jobs = []
for i in range(5):
p = multiprocessing.Process(target=worker, args=(i,))
jobs.append(p)
p.start()

for job in jobs:
job.join()

Asynchronous programming

The asyncio module provides support for asynchronous programming, allowing you to write non-blocking code that runs in an event loop. This is useful for handling network requests, websocket and real-time applications.

import asyncio

async def main():
# 执行一些异步任务
await asyncio.gather(
fetch_data(),
process_data(),
send_results()
)

asyncio.run(main())

Thread

Python's Threads module allows you to create lightweight threads that run in the same process. They are useful for processing small tasks in parallel and performing concurrent operations.

import threading

def worker(num):
print(f"Thread {num}: started")
# 执行一些耗时的任务
print(f"Thread {num}: finished")

threads = []
for i in range(5):
t = threading.Thread(target=worker, args=(i,))
threads.append(t)
t.start()

for thread in threads:
thread.join()

Concurrency Framework

In addition to standard library functions, there are many third-party concurrency frameworks, which provide advanced functions:

• Celery: For distributed task queue and asynchronous task execution
• Luigi: For creating reproducible complex workflows
• Dask: Used for large-scale distributed computing

Event Loop

Python's event loop is the core of handling concurrent operations. It's an infinite loop that polls for events and calls the appropriate handler. Understanding the event loop is crucial to writing efficient concurrent code.

Performance Optimization Tips

• Use Parallelism: Take advantage of multiprocessing and threads to process tasks in parallel.
• Asynchronous programming: For IO-intensive operations, use asyncio to avoid blocking.
• Reduce GIL contention: Python's global interpreter Locks (GIL) can limit concurrency, use C extensions or Cython to avoid this problem.
• Using concurrency frameworks: Third-party concurrency frameworks provide optimization tools and advanced functions.

Summarize

Recent advances in

PythonConcurrency Programming provide powerful tools to improve application performance and take advantage of modern hardware. By understanding multiprocessing, asynchronous programming, threads, concurrency frameworks, and event loops, you can create efficient, scalable concurrent systems.

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