在Python中，设计并发编程模式需要考虑多种因素，如线程安全、进程间通信、任务调度等。以下是一些常见的并发编程设计模式：
多线程模式是最常见的并发编程模式之一，适用于I/O密集型任务。
import threading
def worker():
print(f"Thread {threading.current_thread().name} is working")
threads = []
for i in range(5):
t = threading.Thread(target=worker, name=f"Thread-{i}")
threads.append(t)
t.start()
for t in threads:
t.join()
多进程模式适用于CPU密集型任务，因为Python的全局解释器锁（GIL）限制了多线程的并行计算能力。
import multiprocessing
def worker():
print(f"Process {multiprocessing.current_process().name} is working")
processes = []
for i in range(5):
p = multiprocessing.Process(target=worker, name=f"Process-{i}")
processes.append(p)
p.start()
for p in processes:
p.join()
异步编程模式适用于I/O密集型任务，可以提高程序的并发性能。Python的asyncio库是实现异步编程的常用工具。
import asyncio
async def worker():
print(f"Task {asyncio.current_task().name} is working")
await asyncio.sleep(1)
async def main():
tasks = []
for i in range(5):
task = asyncio.create_task(worker(), name=f"Task-{i}")
tasks.append(task)
await asyncio.gather(*tasks)
asyncio.run(main())
线程池模式可以有效地管理线程资源，避免频繁创建和销毁线程的开销。Python的concurrent.futures.ThreadPoolExecutor提供了线程池的实现。
from concurrent.futures import ThreadPoolExecutor
def worker():
print(f"Thread {threading.current_thread().name} is working")
with ThreadPoolExecutor(max_workers=5) as executor:
for i in range(5):
executor.submit(worker, f"Task-{i}")
进程池模式可以有效地管理进程资源，避免频繁创建和销毁进程的开销。Python的concurrent.futures.ProcessPoolExecutor提供了进程池的实现。
from concurrent.futures import ProcessPoolExecutor
def worker():
print(f"Process {multiprocessing.current_process().name} is working")
with ProcessPoolExecutor(max_workers=5) as executor:
for i in range(5):
executor.submit(worker, f"Task-{i}")
任务队列模式适用于生产者-消费者模型，可以有效地解耦生产者和消费者。Python的queue模块提供了任务队列的实现。
import threading
import queue
def worker(q):
while True:
item = q.get()
if item is None:
break
print(f"Worker is processing {item}")
q.task_done()
q = queue.Queue()
for i in range(5):
q.put(i)
threads = []
for i in range(5):
t = threading.Thread(target=worker, args=(q,), name=f"Worker-{i}")
threads.append(t)
t.start()
q.join()
for _ in threads:
q.put(None)
for t in threads:
t.join()
事件驱动模式适用于需要响应特定事件的场景。Python的threading模块提供了事件对象的支持。
import threading
def worker(event):
print(f"Worker is waiting for event")
event.wait()
print("Worker has received event")
event = threading.Event()
t = threading.Thread(target=worker, args=(event,), name="Worker")
t.start()
print("Main thread is setting event")
event.set()
t.join()
管道通信模式适用于进程间通信，Python的multiprocessing模块提供了管道的实现。
import multiprocessing
def sender(conn):
conn.send(["Hello", "from", "sender"])
conn.close()
def receiver(conn):
msg = conn.recv()
print("Received:", msg)
conn.close()
parent_conn, child_conn = multiprocessing.Pipe()
t1 = multiprocessing.Process(target=sender, args=(child_conn,), name="Sender")
t2 = multiprocessing.Process(target=receiver, args=(parent_conn,), name="Receiver")
t1.start()
t2.start()
t1.join()
t2.join()
Python提供了多种并发编程模式，选择哪种模式取决于具体的应用场景和需求。多线程适用于I/O密集型任务，多进程适用于CPU密集型任务，异步编程适用于I/O密集型任务，线程池和进程池可以有效地管理资源，任务队列适用于生产者-消费者模型，事件驱动模式适用于响应特定事件的场景，管道通信适用于进程间通信。
