They are intended for (slightly) different purposes and/or requirements. CPython (a typical, mainline Python implementation) still has the global interpreter lock so a multi-threaded application (a standard way to implement parallel processing nowadays) is suboptimal. That's why multiprocessing may be preferred over threading. But not every problem may be effectively split into [almost independent] pieces, so there may be a need in heavy interprocess communications. That's why multiprocessing may not be preferred over threading in general.

asyncio(该技术不仅在Python中可用，其他语言和/或框架也有它，例如Boost.ASIO)是一种有效处理来自多个同步源的大量I/O操作的方法，而不需要并行代码执行。因此，它只是针对特定任务的解决方案(确实是一个不错的解决方案!)，而不是用于一般的并行处理。

博士TL;

做出正确的选择:

我们已经介绍了最流行的并发形式。但问题依然存在——什么时候应该选择哪一个?这实际上取决于用例。根据我的经验(和阅读)，我倾向于遵循以下伪代码:

if io_bound:
    if io_very_slow:
        print("Use Asyncio")
    else:
        print("Use Threads")
else:
    print("Multi Processing")

CPU绑定=>多处理 I/O绑定，快速I/O，有限数量的连接=>多线程 I/O受限，慢I/O，多连接=> Asyncio

参考

---

【注意】:

如果你有一个很长的调用方法(例如，一个包含睡眠时间或惰性I/O的方法)，最好的选择是asyncio, Twisted或Tornado方法(协程方法)，它与单个线程一起工作作为并发。 asyncio适用于Python3.4及更高版本。 Tornado和Twisted从Python2.7开始就准备好了 Uvloop是超快的asyncio事件循环(Uvloop使asyncio快2-4倍)。

---

(更新(2019)):

Japranto (GitHub)是一个非常快速的基于uvloop的流水线HTTP服务器。

这是基本思想:

是IO-BOUND吗?----------->使用asyncio 它的cpu量大吗?--------->使用多处理 其他的吗?---------------------->使用线程

所以基本上坚持线程，除非你有IO/CPU问题。

In multiprocessing you leverage multiple CPUs to distribute your calculations. Since each of the CPUs runs in parallel, you're effectively able to run multiple tasks simultaneously. You would want to use multiprocessing for CPU-bound tasks. An example would be trying to calculate a sum of all elements of a huge list. If your machine has 8 cores, you can "cut" the list into 8 smaller lists and calculate the sum of each of those lists separately on separate core and then just add up those numbers. You'll get a ~8x speedup by doing that.

In (multi)threading you don't need multiple CPUs. Imagine a program that sends lots of HTTP requests to the web. If you used a single-threaded program, it would stop the execution (block) at each request, wait for a response, and then continue once received a response. The problem here is that your CPU isn't really doing work while waiting for some external server to do the job; it could have actually done some useful work in the meantime! The fix is to use threads - you can create many of them, each responsible for requesting some content from the web. The nice thing about threads is that, even if they run on one CPU, the CPU from time to time "freezes" the execution of one thread and jumps to executing the other one (it's called context switching and it happens constantly at non-deterministic intervals). So if your task is I/O bound - use threading.

asyncio本质上是线程，而不是CPU，而是你，作为一个程序员(或者实际上是你的应用程序)，决定何时何地发生上下文切换。在Python中，您使用await关键字来暂停协程的执行(使用async关键字定义)。

已经有很多好的答案了。无法详细说明何时使用每种方法。这更像是两者的有趣结合。Multiprocessing + asyncio: https://pypi.org/project/aiomultiprocess/。

它的设计用例是高容量的，但仍然使用尽可能多的可用内核。Facebook使用这个库来编写某种基于python的文件服务器。Asyncio允许IO绑定流量，但multiprocessing允许多个事件循环和多个内核上的线程。

回购中的Ex代码:

import asyncio
from aiohttp import request
from aiomultiprocess import Pool

async def get(url):
    async with request("GET", url) as response:
        return await response.text("utf-8")

async def main():
    urls = ["https://jreese.sh", ...]
    async with Pool() as pool:
        async for result in pool.map(get, urls):
            ...  # process result
            
if __name__ == '__main__':
    # Python 3.7
    asyncio.run(main())
    
    # Python 3.6
    # loop = asyncio.get_event_loop()
    # loop.run_until_complete(main())

只是和加法在这里，不会工作在说jupyter笔记本很好，因为笔记本已经有一个asyncio循环运行。只是给你留个小纸条，别扯头发。

许多答案建议如何只选择一个选项，但为什么不能使用所有三个选项呢?在这个回答中，我将解释如何使用asyncio来管理所有三种并发形式的组合，以及在需要时在它们之间轻松切换。

简短的回答

---

Many developers that are first-timers to concurrency in Python will end up using processing.Process and threading.Thread. However, these are the low-level APIs which have been merged together by the high-level API provided by the concurrent.futures module. Furthermore, spawning processes and threads has overhead, such as requiring more memory, a problem which plagued one of the examples I showed below. To an extent, concurrent.futures manages this for you so that you cannot as easily do something like spawn a thousand processes and crash your computer by only spawning a few processes and then just re-using those processes each time one finishes.

这些高级api是通过concurrent.futures提供的。然后由concurrent.future . processpoolexecutor和concurrent.future . threadpoolexecutor实现。在大多数情况下，您应该在多处理中使用这些。进程和线程。线程，因为将来当你使用并发时，从一个转换到另一个更容易。你不需要学习每种期货的详细区别。

由于它们共享统一的接口，您还会发现使用多处理或线程的代码将经常使用concurrent.futures。Asyncio也不例外，并提供了一种通过以下代码使用它的方法:

import asyncio
from concurrent.futures import Executor
from functools import partial
from typing import Any, Callable, Optional, TypeVar

T = TypeVar("T")

async def run_in_executor(
    executor: Optional[Executor],
    func: Callable[..., T],
    /,
    *args: Any,
    **kwargs: Any,
) -> T:
    """
    Run `func(*args, **kwargs)` asynchronously, using an executor.

    If the executor is None, use the default ThreadPoolExecutor.
    """
    return await asyncio.get_running_loop().run_in_executor(
        executor,
        partial(func, *args, **kwargs),
    )

# Example usage for running `print` in a thread.
async def main():
    await run_in_executor(None, print, "O" * 100_000)

asyncio.run(main())

事实上，使用线程和asyncio是如此普遍，以至于在Python 3.9中他们添加了asyncio。to_thread(func， *args， **kwargs)来缩短默认的ThreadPoolExecutor。

长话短说

---

这种方法有什么缺点吗?

是的。使用asyncio，最大的缺点是异步函数与同步函数不同。如果你没有从一开始就考虑到asyncio，这可能会让asyncio的新用户陷入困境，并导致大量的返工。

另一个缺点是代码的用户也将被迫使用asyncio。所有这些必要的返工通常会让首次使用asyncio的用户感到不快。

这样做有什么非性能优势吗?

Yes. Similar to how using concurrent.futures is advantageous over threading.Thread and multiprocessing.Process for its unified interface, this approach can be considered a further abstraction from an Executor to an asynchronous function. You can start off using asyncio, and if later you find a part of it you need threading or multiprocessing, you can use asyncio.to_thread or run_in_executor. Likewise, you may later discover that an asynchronous version of what you're trying to run with threading already exists, so you can easily step back from using threading and switch to asyncio instead.

这样做是否有性能优势?

是的……也没有。最终取决于任务本身。在某些情况下，它可能没有帮助(尽管它可能没有伤害)，而在其他情况下，它可能有很大的帮助。这个答案的其余部分解释了为什么使用asyncio来运行Executor可能是有利的。

-结合多个执行程序和其他异步代码

Asyncio本质上为并发性提供了更多的控制，代价是你需要更多地控制并发性。如果你想使用ThreadPoolExecutor同时运行一些代码和使用ProcessPoolExecutor同时运行一些代码，那么使用同步代码管理这些代码就不那么容易了，但是使用asyncio就非常容易。

import asyncio
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor

async def with_processing():
    with ProcessPoolExecutor() as executor:
        tasks = [...]
        for task in asyncio.as_completed(tasks):
            result = await task
            ...

async def with_threading():
    with ThreadPoolExecutor() as executor:
        tasks = [...]
        for task in asyncio.as_completed(tasks):
            result = await task
            ...

async def main():
    await asyncio.gather(with_processing(), with_threading())

asyncio.run(main())

How does this work? Essentially asyncio asks the executors to run their functions. Then, while an executor is running, asyncio will go run other code. For example, the ProcessPoolExecutor starts a bunch of processes, and then while waiting for those processes to finish, the ThreadPoolExecutor starts a bunch of threads. asyncio will then check in on these executors and collect their results when they are done. Furthermore, if you have other code using asyncio, you can run them while waiting for the processes and threads to finish.

-缩小代码的哪些部分需要执行器

在你的代码中有很多执行程序是不常见的，但是当人们使用线程/进程时，我看到的一个常见问题是，他们会把他们的整个代码推到一个线程/进程中，期望它能工作。例如，我曾经看到以下代码(大约):

from concurrent.futures import ThreadPoolExecutor
import requests

def get_data(url):
    return requests.get(url).json()["data"]

urls = [...]

with ThreadPoolExecutor() as executor:
    for data in executor.map(get_data, urls):
        print(data)

有趣的是，这段代码有并发时比没有并发时要慢。为什么?因为生成的json很大，许多线程消耗大量内存是灾难性的。幸运的是，解决方法很简单:

from concurrent.futures import ThreadPoolExecutor
import requests

urls = [...]

with ThreadPoolExecutor() as executor:
    for response in executor.map(requests.get, urls):
        print(response.json()["data"])

现在每次只有一个json被卸载到内存中，一切正常。

这里的教训是什么?

你不应该试图把所有的代码都放到线程/进程中，你应该关注代码的哪一部分真正需要并发性。

但是，如果get_data不是一个如此简单的函数呢?如果我们必须在函数中间的某个地方应用执行程序呢?这就是asyncio的用武之地:

import asyncio
import requests

async def get_data(url):
    # A lot of code.
    ...
    # The specific part that needs threading.
    response = await asyncio.to_thread(requests.get, url, some_other_params)
    # A lot of code.
    ...
    return data

urls = [...]

async def main():
    tasks = [get_data(url) for url in urls]
    for task in asyncio.as_completed(tasks):
        data = await task
        print(data)

asyncio.run(main())

尝试同样的并发。期货一点也不漂亮。您可以使用回调、队列等，但这将比基本的asyncio代码更难管理。

多处理可以并行运行。 多线程和asyncio不能并行运行。

使用英特尔(R)酷睿(TM) i7-8700K CPU @ 3.70GHz和32.0 GB RAM，我用2个进程、2个线程和2个异步任务计算了2到100000之间有多少素数，如下所示。*这是CPU限制计算:

因为多处理可以并行运行，所以如上所示，多处理比多线程和asyncio快两倍。

我使用了以下3组代码:

多处理:

# "process_test.py"

from multiprocessing import Process
import time
start_time = time.time()

def test():
    num = 100000
    primes = 0
    for i in range(2, num + 1):
        for j in range(2, i):
            if i % j == 0:
                break
        else:
            primes += 1
    print(primes)

if __name__ == "__main__": # This is needed to run processes on Windows
    process_list = []

    for _ in range(0, 2): # 2 processes
        process = Process(target=test)
        process_list.append(process)

    for process in process_list:
        process.start()

    for process in process_list:
        process.join()

    print(round((time.time() - start_time), 2), "seconds") # 23.87 seconds

结果:

...
9592
9592
23.87 seconds

多线程:

# "thread_test.py"

from threading import Thread
import time
start_time = time.time()

def test():
    num = 100000
    primes = 0
    for i in range(2, num + 1):
        for j in range(2, i):
            if i % j == 0:
                break
        else:
            primes += 1
    print(primes)

thread_list = []

for _ in range(0, 2): # 2 threads
    thread = Thread(target=test)
    thread_list.append(thread)
    
for thread in thread_list:
    thread.start()

for thread in thread_list:
    thread.join()

print(round((time.time() - start_time), 2), "seconds") # 45.24 seconds

结果:

...
9592
9592
45.24 seconds

Asyncio:

# "asyncio_test.py"

import asyncio
import time
start_time = time.time()

async def test():
    num = 100000
    primes = 0
    for i in range(2, num + 1):
        for j in range(2, i):
            if i % j == 0:
                break
        else:
            primes += 1
    print(primes)

async def call_tests():
    tasks = []

    for _ in range(0, 2): # 2 asyncio tasks
        tasks.append(test())

    await asyncio.gather(*tasks)

asyncio.run(call_tests())

print(round((time.time() - start_time), 2), "seconds") # 44.77 seconds

结果:

...
9592
9592
44.77 seconds

多处理 每个进程都有自己的Python解释器，并且可以在处理器的独立内核上运行。Python multiprocessing是一个包，它支持使用类似于threading模块的API生成进程。多处理包提供了真正的并行性，通过使用子进程而不是线程，有效地避开了全局解释器锁。

当你有CPU密集型任务时，使用多处理。

多线程 Python多线程允许在进程中生成多个线程。这些线程可以共享进程的相同内存和资源。在CPython中，由于全局解释器锁，在任何给定的时间都只能运行一个线程，因此你不能利用多个内核。由于GIL的限制，Python中的多线程并不能提供真正的并行性。

Asyncio Asyncio致力于协作多任务概念。Asyncio任务运行在同一个线程上，因此没有并行性，但它为开发人员提供了更好的控制，而不是操作系统，这是多线程的情况。

关于asyncio相对于线程的优点，在这个链接上有一个很好的讨论。

Python摘要中的多处理VS线程VS AsyncIO

我不是一个专业的Python用户，但作为一个计算机体系结构的学生，我想我可以分享一些我在多处理和多线程之间选择的考虑因素。此外，其他一些答案(甚至在那些投票较高的答案中)都是滥用技术术语，所以我认为也有必要对这些问题进行一些澄清，我将首先进行澄清。

多处理和多线程之间的根本区别在于它们是否共享相同的内存空间。线程共享对相同虚拟内存空间的访问，因此线程交换计算结果(零复制，完全用户空间执行)是高效且容易的。

Processes on the other hand have separate virtual memory spaces. They cannot directly read or write the other process’ memory space, just like a person cannot read or alter the mind of another person without talking to him. (Allowing so would be a violation of memory protection and defeat the purpose of using virtual memory. ) To exchange data between processes, they have to rely on the operating system’s facility (e.g. message passing), and for more than one reasons this is more costly to do than the “shared memory” scheme used by threads. One reason is that invoking the OS’ message passing mechanism requires making a system call which will switch the code execution from user mode to kernel mode, which is time consuming; another reason is likely that OS message passing scheme will have to copy the data bytes from the senders’ memory space to the receivers’ memory space, so non-zero copy cost.

说一个多线程程序只能使用一个CPU是不正确的。很多人这么说的原因是由于CPython实现的一个工件:全局解释器锁(GIL)。由于GIL的存在，CPython进程中的线程是序列化的。因此，多线程python程序似乎只使用了一个CPU。

但是多线程计算机程序通常不局限于一个核心，对于Python来说，不使用GIL的实现确实可以并行运行多个线程，也就是说，同时在多个CPU上运行。(见https://wiki.python.org/moin/GlobalInterpreterLock)。

考虑到CPython是Python的主要实现，可以理解为什么多线程Python程序通常等同于绑定到单核。

With Python with GIL, the only way to unleash the power of multicores is to use multiprocessing (there are exceptions to this as mentioned below). But your problem better be easily partition-able into parallel sub-problems that have minimal intercommunication, otherwise a lot of inter-process communication will have to take place and as explained above, the overhead of using the OS’ message passing mechanism will be costly, sometimes so costly the benefits of parallel processing are totally offset. If the nature of your problem requires intense communication between concurrent routines, multithreading is the natural way to go. Unfortunately with CPython, true, effectively parallel multithreading is not possible due to the GIL. In this case you should realize Python is not the optimal tool for your project and consider using another language.

还有一种替代解决方案，即在用C(或其他语言)编写的外部库中实现并发处理例程，并将该模块导入Python。CPython GIL不会阻塞由该外部库生成的线程。

So, with the burdens of GIL, is multithreading in CPython any good? It still offers benefits though, as other answers have mentioned, if you’re doing IO or network communication. In these cases the relevant computation is not done by your CPU but done by other devices (in the case of IO, the disk controller and DMA (direct memory access) controller will transfer the data with minimal CPU participation; in the case of networking, the NIC (network interface card) and DMA will take care of much of the task without CPU’s participation), so once a thread delegates such task to the NIC or disk controller, the OS can put that thread to a sleeping state and switch to other threads of the same program to do useful work.

在我的理解中，asyncio模块本质上是用于IO操作的多线程的特定情况。

所以: cpu密集型程序，可以很容易地分区到多个进程上运行，但通信有限:如果GIL不存在，则使用多线程(如Jython)，如果GIL存在，则使用多进程(如CPython)。

cpu密集型程序，需要在并发例程之间进行密集的通信:如果GIL不存在，则使用多线程，或者使用其他编程语言。

大量的IO: asyncio