Python进程间通讯与进程池超详细讲解 python进程池的作用

def putElement(name, qu: multiprocessing.Queue):
    try:
        for i in range(10):
            qu.put(f"{name}-{i + 1}")
            time.sleep(.1)
    except ValueError:
        print("queue closed")
    print(f"{name}: put complete")
def getElement(name, qu: multiprocessing.Queue):
    try:
        while True:
            r = qu.get()
            print(f"{name} recv: {r}")
    except ValueError:
        print("queue closed")
    print(f"{name}: get complete")
if __name__ == '__main__':
    qu = multiprocessing.Queue(100)
    puts = [multiprocessing.Process(target=putElement, args=(f"send{i}", qu)) for i in range(10)]
    gets = [multiprocessing.Process(target=getElement, args=(f"recv{i}", qu), daemon=True) for i in range(2)]
    list(map(lambda f: f.start(), puts))
    list(map(lambda f: f.start(), gets))
    for f in puts:
        f.join()
    print("To close")
    qu.close() # 只是main中的close了，其他进程中的并没有

def pipeProc(pipe):
    outPipe, inPipe = pipe
    inPipe.close() # 必须关闭，否则结束时不会收到EOFError异常
    try:
        while True:
            r = outPipe.recv()
            print("Recv:", r)
    except EOFError:
        print("RECV end")
if __name__ == '__main__':
    outPipe, inPipe = multiprocessing.Pipe()
    sub = multiprocessing.Process(target=pipeProc, args=((outPipe, inPipe),))
    sub.start()
    outPipe.close() # 必须在进程成功运行后，才可关闭
    with inPipe:
        for x in range(10):
            inPipe.send(x)
            time.sleep(.1)
    print("send complete")
    sub.join()

def poolWorker():
    print(f"worker in process {os.getpid()}")
    time.sleep(1)
def poolWorkerOne(name):
    print(f"worker one {name} in process {os.getpid()}")
    time.sleep(random.random())
    return name
def poolWorkerTwo(first, second):
    res = first + second
    print(f"worker two {res} in process {os.getpid()}")
    time.sleep(1./(first+1))
    return res
def poolInit():
    print("pool init")
if __name__ == '__main__':
    workers = multiprocessing.Pool(5, poolInit) # poolInit会被调用5次（线程启动时）
    with workers:
        for i in range(5):
            workers.apply_async(poolWorker)
        arg = [(i, i) for i in range(10)]
        workers.map_async(poolWorkerOne, arg)
        results = workers.starmap_async(poolWorkerTwo, arg) # 每个元素（元组）会被拆分为独立的参数
        print("Starmap:", results.get())
        results = workers.imap_unordered(poolWorkerOne, arg)
        for r in results: # r是乱序的（若使用imap，则与输入arg的顺序相同）
            print("Unordered:", r)
    # 必须保证workers已close了
    workers.join()