Python併發程式設計之執行緒池/程式池

發表於2017-01-18

引言

Python標準庫為我們提供了threading和multiprocessing模組編寫相應的多執行緒/多程式程式碼，但是當專案達到一定的規模，頻繁建立/銷燬程式或者執行緒是非常消耗資源的，這個時候我們就要編寫自己的執行緒池/程式池，以空間換時間。但從Python3.2開始，標準庫為我們提供了concurrent.futures模組，它提供了ThreadPoolExecutor和ProcessPoolExecutor兩個類，實現了對threading和multiprocessing的進一步抽象，對編寫執行緒池/程式池提供了直接的支援。

Executor和Future

concurrent.futures模組的基礎是Exectuor，Executor是一個抽象類，它不能被直接使用。但是它提供的兩個子類ThreadPoolExecutor和ProcessPoolExecutor卻是非常有用，顧名思義兩者分別被用來建立執行緒池和程式池的程式碼。我們可以將相應的tasks直接放入執行緒池/程式池，不需要維護Queue來操心死鎖的問題，執行緒池/程式池會自動幫我們排程。

Future這個概念相信有java和nodejs下程式設計經驗的朋友肯定不陌生了，你可以把它理解為一個在未來完成的操作，這是非同步程式設計的基礎，傳統程式設計模式下比如我們操作queue.get的時候，在等待返回結果之前會產生阻塞，cpu不能讓出來做其他事情，而Future的引入幫助我們在等待的這段時間可以完成其他的操作。關於在Python中進行非同步IO可以閱讀完本文之後參考我的Python併發程式設計之協程/非同步IO。

p.s: 如果你依然在堅守Python2.x，請先安裝futures模組。

pip install futures

1	pip install futures

使用submit來操作執行緒池/程式池

我們先通過下面這段程式碼來了解一下執行緒池的概念

# example1.py
from concurrent.futures import ThreadPoolExecutor
import time
def return_future_result(message):
    time.sleep(2)
    return message
pool = ThreadPoolExecutor(max_workers=2)  # 建立一個最大可容納2個task的執行緒池
future1 = pool.submit(return_future_result, ("hello"))  # 往執行緒池裡面加入一個task
future2 = pool.submit(return_future_result, ("world"))  # 往執行緒池裡面加入一個task
print(future1.done())  # 判斷task1是否結束
time.sleep(3)
print(future2.done())  # 判斷task2是否結束
print(future1.result())  # 檢視task1返回的結果
print(future2.result())  # 檢視task2返回的結果

# example1.py

from concurrent.futures import ThreadPoolExecutor

import time

def return_future_result(message):

time.sleep(2)

return message

pool = ThreadPoolExecutor(max_workers=2) # 建立一個最大可容納2個task的執行緒池

future1 = pool.submit(return_future_result, ("hello")) # 往執行緒池裡面加入一個task

future2 = pool.submit(return_future_result, ("world")) # 往執行緒池裡面加入一個task

print(future1.done()) # 判斷task1是否結束

time.sleep(3)

print(future2.done()) # 判斷task2是否結束

print(future1.result()) # 檢視task1返回的結果

print(future2.result()) # 檢視task2返回的結果

我們根據執行結果來分析一下。我們使用submit方法來往執行緒池中加入一個task，submit返回一個Future物件，對於Future物件可以簡單地理解為一個在未來完成的操作。在第一個print語句中很明顯因為time.sleep(2)的原因我們的future1沒有完成，因為我們使用time.sleep(3)暫停了主執行緒，所以到第二個print語句的時候我們執行緒池裡的任務都已經全部結束。

ziwenxie :: ~ » python example1.py
False
True
hello
world
# 在上述程式執行的過程中，通過ps命令我們可以看到三個執行緒同時在後臺執行
ziwenxie :: ~ » ps -eLf | grep python
ziwenxie      8361  7557  8361  3    3 19:45 pts/0    00:00:00 python example1.py
ziwenxie      8361  7557  8362  0    3 19:45 pts/0    00:00:00 python example1.py
ziwenxie      8361  7557  8363  0    3 19:45 pts/0    00:00:00 python example1.py

ziwenxie :: ~ » python example1.py

False

True

hello

world

# 在上述程式執行的過程中，通過ps命令我們可以看到三個執行緒同時在後臺執行

ziwenxie :: ~ » ps -eLf | grep python

ziwenxie 8361 7557 8361 3 3 19:45 pts/0 00:00:00 python example1.py

ziwenxie 8361 7557 8362 0 3 19:45 pts/0 00:00:00 python example1.py

ziwenxie 8361 7557 8363 0 3 19:45 pts/0 00:00:00 python example1.py

上面的程式碼我們也可以改寫為程式池形式，api和執行緒池如出一轍，我就不羅嗦了。

# example2.py
from concurrent.futures import ProcessPoolExecutor
import time
def return_future_result(message):
    time.sleep(2)
    return message
pool = ProcessPoolExecutor(max_workers=2)
future1 = pool.submit(return_future_result, ("hello"))
future2 = pool.submit(return_future_result, ("world"))
print(future1.done())
time.sleep(3)
print(future2.done())
print(future1.result())
print(future2.result())

# example2.py

from concurrent.futures import ProcessPoolExecutor

import time

def return_future_result(message):

time.sleep(2)

return message

pool = ProcessPoolExecutor(max_workers=2)

future1 = pool.submit(return_future_result, ("hello"))

future2 = pool.submit(return_future_result, ("world"))

print(future1.done())

time.sleep(3)

print(future2.done())

print(future1.result())

print(future2.result())

下面是執行結果

ziwenxie :: ~ » python example2.py
False
True
hello
world
ziwenxie :: ~ » ps -eLf | grep python
ziwenxie      8560  7557  8560  3    3 19:53 pts/0    00:00:00 python example2.py
ziwenxie      8560  7557  8563  0    3 19:53 pts/0    00:00:00 python example2.py
ziwenxie      8560  7557  8564  0    3 19:53 pts/0    00:00:00 python example2.py
ziwenxie      8561  8560  8561  0    1 19:53 pts/0    00:00:00 python example2.py
ziwenxie      8562  8560  8562  0    1 19:53 pts/0    00:00:00 python example2.py

ziwenxie :: ~ » python example2.py

False

True

hello

world

ziwenxie :: ~ » ps -eLf | grep python

ziwenxie 8560 7557 8560 3 3 19:53 pts/0 00:00:00 python example2.py

ziwenxie 8560 7557 8563 0 3 19:53 pts/0 00:00:00 python example2.py

ziwenxie 8560 7557 8564 0 3 19:53 pts/0 00:00:00 python example2.py

ziwenxie 8561 8560 8561 0 1 19:53 pts/0 00:00:00 python example2.py

ziwenxie 8562 8560 8562 0 1 19:53 pts/0 00:00:00 python example2.py

使用map/wait來操作執行緒池/程式池

除了submit，Exectuor還為我們提供了map方法，和內建的map用法類似，下面我們通過兩個例子來比較一下兩者的區別。

使用submit操作回顧

# example3.py
import concurrent.futures
import urllib.request
URLS = ['http://httpbin.org', 'http://example.com/', 'https://api.github.com/']
def load_url(url, timeout):
    with urllib.request.urlopen(url, timeout=timeout) as conn:
        return conn.read()
# We can use a with statement to ensure threads are cleaned up promptly
with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
    # Start the load operations and mark each future with its URL
    future_to_url = {executor.submit(load_url, url, 60): url for url in URLS}
    for future in concurrent.futures.as_completed(future_to_url):
        url = future_to_url[future]
        try:
            data = future.result()
        except Exception as exc:
            print('%r generated an exception: %s' % (url, exc))
        else:
            print('%r page is %d bytes' % (url, len(data)))

# example3.py

import concurrent.futures

import urllib.request

URLS = ['http://httpbin.org', 'http://example.com/', 'https://api.github.com/']

def load_url(url, timeout):

with urllib.request.urlopen(url, timeout=timeout) as conn:

return conn.read()

# We can use a with statement to ensure threads are cleaned up promptly

with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:

# Start the load operations and mark each future with its URL

future_to_url = {executor.submit(load_url, url, 60): url for url in URLS}

for future in concurrent.futures.as_completed(future_to_url):

url = future_to_url[future]

try:

data = future.result()

except Exception as exc:

print('%r generated an exception: %s' % (url, exc))

else:

print('%r page is %d bytes' % (url, len(data)))

從執行結果可以看出，as_completed不是按照URLS列表元素的順序返回的。

ziwenxie :: ~ » python example3.py
'http://example.com/' page is 1270 byte
'https://api.github.com/' page is 2039 bytes
'http://httpbin.org' page is 12150 bytes

ziwenxie :: ~ » python example3.py

'http://example.com/' page is 1270 byte

'https://api.github.com/' page is 2039 bytes

'http://httpbin.org' page is 12150 bytes

使用map

# example4.py
import concurrent.futures
import urllib.request
URLS = ['http://httpbin.org', 'http://example.com/', 'https://api.github.com/']
def load_url(url):
    with urllib.request.urlopen(url, timeout=60) as conn:
        return conn.read()
# We can use a with statement to ensure threads are cleaned up promptly
with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
    for url, data in zip(URLS, executor.map(load_url, URLS)):
        print('%r page is %d bytes' % (url, len(data)))

# example4.py

import concurrent.futures

import urllib.request

URLS = ['http://httpbin.org', 'http://example.com/', 'https://api.github.com/']

def load_url(url):

with urllib.request.urlopen(url, timeout=60) as conn:

return conn.read()

# We can use a with statement to ensure threads are cleaned up promptly

with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:

for url, data in zip(URLS, executor.map(load_url, URLS)):

print('%r page is %d bytes' % (url, len(data)))

從執行結果可以看出，map是按照URLS列表元素的順序返回的，並且寫出的程式碼更加簡潔直觀，我們可以根據具體的需求任選一種。

ziwenxie :: ~ » python example4.py
'http://httpbin.org' page is 12150 bytes
'http://example.com/' page is 1270 bytes
'https://api.github.com/' page is 2039 bytes

ziwenxie :: ~ » python example4.py

'http://httpbin.org' page is 12150 bytes

'http://example.com/' page is 1270 bytes

'https://api.github.com/' page is 2039 bytes

第三種選擇wait

wait方法接會返回一個tuple(元組)，tuple中包含兩個set(集合)，一個是completed(已完成的)另外一個是uncompleted(未完成的)。使用wait方法的一個優勢就是獲得更大的自由度，它接收三個引數FIRST_COMPLETED, FIRST_EXCEPTION 和ALL_COMPLETE，預設設定為ALL_COMPLETED。

我們通過下面這個例子來看一下三個引數的區別

from concurrent.futures import ThreadPoolExecutor, wait, as_completed
from time import sleep
from random import randint
def return_after_random_secs(num):
    sleep(randint(1, 5))
    return "Return of {}".format(num)
pool = ThreadPoolExecutor(5)
futures = []
for x in range(5):
    futures.append(pool.submit(return_after_random_secs, x))
print(wait(futures))
# print(wait(futures, timeout=None, return_when='FIRST_COMPLETED'))

from concurrent.futures import ThreadPoolExecutor, wait, as_completed

from time import sleep

from random import randint

def return_after_random_secs(num):

sleep(randint(1, 5))

return "Return of {}".format(num)

pool = ThreadPoolExecutor(5)

futures = []

for x in range(5):

futures.append(pool.submit(return_after_random_secs, x))

print(wait(futures))

# print(wait(futures, timeout=None, return_when='FIRST_COMPLETED'))

如果採用預設的ALL_COMPLETED，程式會阻塞直到執行緒池裡面的所有任務都完成。

ziwenxie :: ~ » python example5.py
DoneAndNotDoneFutures(done={
<Future at 0x7f0b06c9bc88 state=finished returned str>,
<Future at 0x7f0b06cbaa90 state=finished returned str>,
<Future at 0x7f0b06373898 state=finished returned str>,
<Future at 0x7f0b06352ba8 state=finished returned str>,
<Future at 0x7f0b06373b00 state=finished returned str>}, not_done=set())

ziwenxie :: ~ » python example5.py

DoneAndNotDoneFutures(done={

<Future at 0x7f0b06c9bc88 state=finished returned str>,

<Future at 0x7f0b06cbaa90 state=finished returned str>,

<Future at 0x7f0b06373898 state=finished returned str>,

<Future at 0x7f0b06352ba8 state=finished returned str>,

<Future at 0x7f0b06373b00 state=finished returned str>}, not_done=set())

如果採用FIRST_COMPLETED引數，程式並不會等到執行緒池裡面所有的任務都完成。

ziwenxie :: ~ » python example5.py
DoneAndNotDoneFutures(done={
<Future at 0x7f84109edb00 state=finished returned str>,
<Future at 0x7f840e2e9320 state=finished returned str>,
<Future at 0x7f840f25ccc0 state=finished returned str>},
not_done={<Future at 0x7f840e2e9ba8 state=running>,
<Future at 0x7f840e2e9940 state=running>})

ziwenxie :: ~ » python example5.py

DoneAndNotDoneFutures(done={

<Future at 0x7f84109edb00 state=finished returned str>,

<Future at 0x7f840e2e9320 state=finished returned str>,

<Future at 0x7f840f25ccc0 state=finished returned str>},

not_done={<Future at 0x7f840e2e9ba8 state=running>,

<Future at 0x7f840e2e9940 state=running>})

思考題

寫一個小程式對比multiprocessing.pool(ThreadPool)和ProcessPollExecutor(ThreadPoolExecutor)在執行效率上的差距，結合上面提到的Future思考為什麼會造成這樣的結果。

References

DOCUMENTATION OF CONCURRENT-FUTURES

歡迎大家拍磚、提意見。相互交流，共同進步！