我正在努力理解Python中的线程。我看过文档和示例,但坦率地说,许多示例过于复杂,我很难理解它们。
如何清楚地显示为多线程划分的任务?
我正在努力理解Python中的线程。我看过文档和示例,但坦率地说,许多示例过于复杂,我很难理解它们。
如何清楚地显示为多线程划分的任务?
当前回答
这里是多线程,有一个简单的例子会很有帮助。您可以运行它并轻松了解多线程在Python中的工作方式。我使用了一个锁来防止访问其他线程,直到前面的线程完成它们的工作。通过使用这行代码,
t锁定=线程。有界信号量(值=4)
您可以一次允许多个进程,并保留将在稍后或完成之前的进程后运行的其余线程。
import threading
import time
#tLock = threading.Lock()
tLock = threading.BoundedSemaphore(value=4)
def timer(name, delay, repeat):
print "\r\nTimer: ", name, " Started"
tLock.acquire()
print "\r\n", name, " has the acquired the lock"
while repeat > 0:
time.sleep(delay)
print "\r\n", name, ": ", str(time.ctime(time.time()))
repeat -= 1
print "\r\n", name, " is releaseing the lock"
tLock.release()
print "\r\nTimer: ", name, " Completed"
def Main():
t1 = threading.Thread(target=timer, args=("Timer1", 2, 5))
t2 = threading.Thread(target=timer, args=("Timer2", 3, 5))
t3 = threading.Thread(target=timer, args=("Timer3", 4, 5))
t4 = threading.Thread(target=timer, args=("Timer4", 5, 5))
t5 = threading.Thread(target=timer, args=("Timer5", 0.1, 5))
t1.start()
t2.start()
t3.start()
t4.start()
t5.start()
print "\r\nMain Complete"
if __name__ == "__main__":
Main()
其他回答
使用线程/多处理的最简单方法是使用更多高级库,如autothread。
import autothread
from time import sleep as heavyworkload
@autothread.multithreaded() # <-- This is all you need to add
def example(x: int, y: int):
heavyworkload(1)
return x*y
现在,您可以为函数提供int列表。Autothread将为您处理所有事务,并只提供并行计算的结果。
result = example([1, 2, 3, 4, 5], 10)
Alex Martelli的回答对我有所帮助。不过,这里有一个我认为更有用的修改版本(至少对我来说)。
更新:可在Python 2和Python 3中使用
try:
# For Python 3
import queue
from urllib.request import urlopen
except:
# For Python 2
import Queue as queue
from urllib2 import urlopen
import threading
worker_data = ['http://google.com', 'http://yahoo.com', 'http://bing.com']
# Load up a queue with your data. This will handle locking
q = queue.Queue()
for url in worker_data:
q.put(url)
# Define a worker function
def worker(url_queue):
queue_full = True
while queue_full:
try:
# Get your data off the queue, and do some work
url = url_queue.get(False)
data = urlopen(url).read()
print(len(data))
except queue.Empty:
queue_full = False
# Create as many threads as you want
thread_count = 5
for i in range(thread_count):
t = threading.Thread(target=worker, args = (q,))
t.start()
大多数文档和教程都使用Python的“线程和队列”模块,对于初学者来说,它们可能会让人不知所措。
也许可以考虑Python 3的concurrent.futures.ThreadPoolExecutor模块。
结合子句和列表理解,这可能是一个真正的魅力。
from concurrent.futures import ThreadPoolExecutor, as_completed
def get_url(url):
# Your actual program here. Using threading.Lock() if necessary
return ""
# List of URLs to fetch
urls = ["url1", "url2"]
with ThreadPoolExecutor(max_workers = 5) as executor:
# Create threads
futures = {executor.submit(get_url, url) for url in urls}
# as_completed() gives you the threads once finished
for f in as_completed(futures):
# Get the results
rs = f.result()
借用本文,我们了解了如何在多线程、多处理和异步/异步之间进行选择及其用法。
Python 3有一个新的内置库,以实现并发和并行-concurrent.futures
因此,我将通过一个实验演示如何通过线程池运行四个任务(即.sleep()方法):
from concurrent.futures import ThreadPoolExecutor, as_completed
from time import sleep, time
def concurrent(max_worker):
futures = []
tic = time()
with ThreadPoolExecutor(max_workers=max_worker) as executor:
futures.append(executor.submit(sleep, 2)) # Two seconds sleep
futures.append(executor.submit(sleep, 1))
futures.append(executor.submit(sleep, 7))
futures.append(executor.submit(sleep, 3))
for future in as_completed(futures):
if future.result() is not None:
print(future.result())
print(f'Total elapsed time by {max_worker} workers:', time()-tic)
concurrent(5)
concurrent(4)
concurrent(3)
concurrent(2)
concurrent(1)
输出:
Total elapsed time by 5 workers: 7.007831811904907
Total elapsed time by 4 workers: 7.007944107055664
Total elapsed time by 3 workers: 7.003149509429932
Total elapsed time by 2 workers: 8.004627466201782
Total elapsed time by 1 workers: 13.013478994369507
[注]:
正如您在上面的结果中看到的,最好的情况是这四项任务有3名员工。如果有进程任务而不是I/O绑定或阻塞(多处理而不是线程),则可以将ThreadPoolExecutor更改为ProcessPoolExecutoor。
这里是使用线程导入CSV的一个非常简单的示例。(图书馆的收录可能因不同的目的而有所不同。)
助手函数:
from threading import Thread
from project import app
import csv
def import_handler(csv_file_name):
thr = Thread(target=dump_async_csv_data, args=[csv_file_name])
thr.start()
def dump_async_csv_data(csv_file_name):
with app.app_context():
with open(csv_file_name) as File:
reader = csv.DictReader(File)
for row in reader:
# DB operation/query
驾驶员功能:
import_handler(csv_file_name)