线程池类似于多处理池？

Question

工作线程是否有Pool类,类似于多处理模块的Pool类？

我喜欢例如并行化地图功能的简单方法

def long_running_func(p):
    c_func_no_gil(p)

p = multiprocessing.Pool(4)
xs = p.map(long_running_func, range(100))

但是我想在没有创建新流程的开销的情况下这样做.

我知道GIL.但是,在我的用例中,该函数将是一个IO绑定的C函数,python包装器将在实际函数调用之前释放GIL.

我是否必须编写自己的线程池？

Answer 1

我刚刚发现模块中确实 存在一个基于线程的Pool接口multiprocessing,但是它有些隐藏,没有正确记录.

它可以通过导入

from multiprocessing.pool import ThreadPool

它是使用包装python线程的虚拟Process类实现的.可以multiprocessing.dummy在文档中简要提及这个基于线程的Process类.据推测,这个虚拟模块基于线程提供整个多处理接口.

Answer 2

在Python 3中你可以使用concurrent.futures.ThreadPoolExecutor,即:

executor = ThreadPoolExecutor(max_workers=10)
a = executor.submit(my_function)

有关更多信息和示例,请参阅文档.

Answer 3

是的,它似乎(或多或少)具有相同的API.

import multiprocessing

def worker(lnk):
    ....    
def start_process():
    .....
....

if(PROCESS):
    pool = multiprocessing.Pool(processes=POOL_SIZE, initializer=start_process)
else:
    pool = multiprocessing.pool.ThreadPool(processes=POOL_SIZE, 
                                           initializer=start_process)

pool.map(worker, inputs)
....

Answer 4

对于非常简单和轻量级的东西(从这里略微修改):

from Queue import Queue
from threading import Thread


class Worker(Thread):
    """Thread executing tasks from a given tasks queue"""
    def __init__(self, tasks):
        Thread.__init__(self)
        self.tasks = tasks
        self.daemon = True
        self.start()

    def run(self):
        while True:
            func, args, kargs = self.tasks.get()
            try:
                func(*args, **kargs)
            except Exception, e:
                print e
            finally:
                self.tasks.task_done()


class ThreadPool:
    """Pool of threads consuming tasks from a queue"""
    def __init__(self, num_threads):
        self.tasks = Queue(num_threads)
        for _ in range(num_threads):
            Worker(self.tasks)

    def add_task(self, func, *args, **kargs):
        """Add a task to the queue"""
        self.tasks.put((func, args, kargs))

    def wait_completion(self):
        """Wait for completion of all the tasks in the queue"""
        self.tasks.join()

if __name__ == '__main__':
    from random import randrange
    from time import sleep

    delays = [randrange(1, 10) for i in range(100)]

    def wait_delay(d):
        print 'sleeping for (%d)sec' % d
        sleep(d)

    pool = ThreadPool(20)

    for i, d in enumerate(delays):
        pool.add_task(wait_delay, d)

    pool.wait_completion()

要支持任务完成时的回调,您只需将回调添加到任务元组即可.

Answer 5

您好在Python中使用线程池,您可以使用此库:

from multiprocessing.dummy import Pool as ThreadPool

然后使用,这个库就是这样的:

pool = ThreadPool(threads)
results = pool.map(service, tasks)
pool.close()
pool.join()
return results

线程是您想要的线程数,任务是大多数映射到服务的任务列表.

Answer 6

另一种方法是将进程添加到线程队列池中

import concurrent.futures
with concurrent.futures.ThreadPoolExecutor(max_workers=cpus) as executor:
    for i in range(10):
        a = executor.submit(arg1, arg2,....)

Answer 7

这是我最终使用的结果。它是上述dgorissen类的修改版本。

文件： threadpool.py

from queue import Queue, Empty
import threading
from threading import Thread


class Worker(Thread):
    _TIMEOUT = 2
    """ Thread executing tasks from a given tasks queue. Thread is signalable, 
        to exit
    """
    def __init__(self, tasks, th_num):
        Thread.__init__(self)
        self.tasks = tasks
        self.daemon, self.th_num = True, th_num
        self.done = threading.Event()
        self.start()

    def run(self):       
        while not self.done.is_set():
            try:
                func, args, kwargs = self.tasks.get(block=True,
                                                   timeout=self._TIMEOUT)
                try:
                    func(*args, **kwargs)
                except Exception as e:
                    print(e)
                finally:
                    self.tasks.task_done()
            except Empty as e:
                pass
        return

    def signal_exit(self):
        """ Signal to thread to exit """
        self.done.set()


class ThreadPool:
    """Pool of threads consuming tasks from a queue"""
    def __init__(self, num_threads, tasks=[]):
        self.tasks = Queue(num_threads)
        self.workers = []
        self.done = False
        self._init_workers(num_threads)
        for task in tasks:
            self.tasks.put(task)

    def _init_workers(self, num_threads):
        for i in range(num_threads):
            self.workers.append(Worker(self.tasks, i))

    def add_task(self, func, *args, **kwargs):
        """Add a task to the queue"""
        self.tasks.put((func, args, kwargs))

    def _close_all_threads(self):
        """ Signal all threads to exit and lose the references to them """
        for workr in self.workers:
            workr.signal_exit()
        self.workers = []

    def wait_completion(self):
        """Wait for completion of all the tasks in the queue"""
        self.tasks.join()

    def __del__(self):
        self._close_all_threads()


def create_task(func, *args, **kwargs):
    return (func, args, kwargs)

使用游泳池

from random import randrange
from time import sleep

delays = [randrange(1, 10) for i in range(30)]

def wait_delay(d):
    print('sleeping for (%d)sec' % d)
    sleep(d)

pool = ThreadPool(20)
for i, d in enumerate(delays):
    pool.add_task(wait_delay, d)
pool.wait_completion()

Answer 8

是的，有一个类似于多处理池的线程池，但是，它有点隐藏并且没有正确记录。您可以通过以下方式导入它：-

from multiprocessing.pool import ThreadPool

我只是给你展示一个简单的例子

def test_multithread_stringio_read_csv(self):
        # see gh-11786
        max_row_range = 10000
        num_files = 100

        bytes_to_df = [
            '\n'.join(
                ['%d,%d,%d' % (i, i, i) for i in range(max_row_range)]
            ).encode() for j in range(num_files)]
        files = [BytesIO(b) for b in bytes_to_df]

        # read all files in many threads
        pool = ThreadPool(8)
        results = pool.map(self.read_csv, files)
        first_result = results[0]

        for result in results:
            tm.assert_frame_equal(first_result, result)