并发编程-线程
并发编程-线程
- 一个进程是操作系统中运行的一个任务，进程独立拥有CPU、内存等资源
- 一个线程是一个进程中运行的一个任务，线程之间共享CPU、内存等资源，进程里的每一个任务都是线程。
线程创建
创建线程：使用threading模块中的Thread类来创建线程
- target表示线程执行的任务
- args表示任务的参数，是一个元组
- start()方法指启动线程
- join()方法指等待线程结束
from threading import Thread
def task(count:int):
...
thread1 = Thread(target=task, args=(10,)) # 创建子线程thread1
thread1.start() # 启动子线程
启动两个线程执行task任务
from threading import Thread
def task(count: int):
for i in range(count):
print(i)
thread1 = Thread(target=task, args=(10,))
thread2 = Thread(target=task, args=(20,))
thread1.start() # 启动线程1
thread2.start() # 启动线程2
print("main thread is end") # 执行到此处，主线程无任务，自行结束
输出结果：
0
1
2
3
4
5
06main thread is end
7
8
9
1
2
3
4
5
6
7
8
9
等待主线程结束：
from threading import Thread
def task():
for i in range(10):
print(i)
thread1 = Thread(target=task)
thread2 = Thread(target=task)
thread1.start()
thread2.start()
thread1.join() # 等待线程执行结束
thread2.join() # 等待线程执行结束
print("main thread is end") # 等到线程1和线程2任务执行完毕，主线程才会结束
输出结果：
0
1
2
3
4
50
6
7
8
9
1
2
3
4
5
6
7
8
9
main thread is end
通过继承创建线程
通过继承Thread类来创建线程
打印混乱的原因：多线程执行时，print函数打印内容后会再打印换行，导致换行来不及打印，出现打印混乱
解决办法：行尾手动加换行，print函数end参数设置为空字符串
print(f"{self.name} - {i}\n", end="")
from threading import Thread
class MyThread(Thread):
def run(self) -> None:
# 线程的任务脚本
pass
import time
from threading import Thread
class MyThread(Thread):
def __init__(self, name: str, count: int):
super().__init__()
# 该方法已弃用，使用name属性替换
# self.setName()
self.name = name # 设置线程名称
self.count = count
def run(self) -> None:
# 线程的任务脚本
for i in range(self.count):
print(f"{self.name} - {i}\n", end="")
# 休眠10毫秒
time.sleep(0.01)
t1 = MyThread("a", 10)
t2 = MyThread("b", 10)
t1.start()
t2.start()
输出结果：
D:\Python3.11\python.exe F:/Code/python_code/high_python/python_advance/thread/demo01_class.py
a - 0
b - 0
a - 1
b - 1
b - 2
a - 2
a - 3
b - 3
b - 4
a - 4
b - 5
a - 5
b - 6
a - 6
b - 7
a - 7
b - 8
a - 8
b - 9
a - 9
守护线程
主线程结束，守护线程会自动结束，这就叫守护线程
- 守护线程会在主线程结束时候自动结束
- 主线程则需要等待到所有的非守护线程结束才能结束
- 守护线程一般用于非关键性的线程，比如：日志
from threading import Thread
def task():
for i in range(10):
print(i)
thread = Thread(target=task, daemon=True)
thread.start()
输出结果：
0
Process finished with exit code 0
等待线程结束
from threading import Thread
def task():
for i in range(10):
print(i)
thread = Thread(target=task, daemon=True)
thread.start()
thread.join()
输出结果：
D:\Python3.11\python.exe F:/Code/python_code/high_python/python_advance/thread/demo03_daemon_thread.py
0
1
2
3
4
5
6
7
8
9
Process finished with exit code 0
继承类设置守护线程
class MyThread(Thread):
def __init__(self, name: str, count: int):
super().__init__()
# 该方法已弃用，使用name属性替换
# self.setName()
self.name = name # 设置线程名称
self.count = count
self.daemon = True
def run(self) -> None:
# 线程的任务脚本
for i in range(self.count):
print(f"{self.name} - {i}\n", end="")
# 休眠10毫秒
time.sleep(0.01)
t1 = MyThread("a", 10)
t2 = MyThread("b", 10)
t1.start()
t2.start()
t1.join() # 等待t1结束，结束主线程
线程安全队列
queue模块中的Queue类提供了线程安全队列功能
- put(item, block=False) # 队列元素满时，不阻塞，会抛出异常，为True时，会让线程阻塞等待， 可能会导致线程卡死
- put(item, timeout=3) # 队列元素满时，等待timeout时间，如果超出时间则抛出异常
- get(block=False) # 从队列取元素，如果队列为空则不阻塞，会抛出异常
- get(timeout=10) # 从队列取元素，如果队列为空则则赛等待10秒，超时抛出异常
- qsize() # 队列大小
- empty() # 判断队列是否为空
- full() # 判断队列是否满的
生产者消费者线程实例
from threading import Thread
from queue import Queue
class Producer(Thread):
"""生产者"""
def __init__(self, name: str, count: int, queue: Queue) -> None:
super(Producer, self).__init__()
# 线程自带属性
self.name = name
# 自定义属性
self.count = count
self.queue = queue
def run(self) -> None:
for n in range(self.count):
msg = f"{self.name} - {n}"
self.queue.put(msg, block=True)
class Consumer(Thread):
"""消费者"""
def __init__(self, name: str, queue: Queue) -> None:
super().__init__()
self.name = name
self.daemon = True
self.queue = queue
def run(self) -> None:
while True:
msg = self.queue.get(block=True)
print(f"{self.name} - {msg}\n", end="")
queue = Queue(maxsize=3)
threads = [
Producer("p1", 10, queue),
Producer("p2", 10, queue),
Producer("p3", 10, queue),
Consumer("c1", queue),
Consumer("c2", queue),
]
for t in threads:
t.start()
线程池
- 线程的创建和销毁相对比较耗费资源
- 频繁的创建和销毁线程不利于高性能
- 线程池是python提供的便于线程管理和提高性能的工具
python提供两个类来管理线程
concurrent.futures.ThreadPoolExecutor- submit() # 启动/执行一个任务，返回结果是一个Future对象
- map() # 多个任务执行，将不同参数分配到每一个任务中
- shutdown() # 关闭线程池
- Future
- result() # 任务执行结果
- exception() # 任务异常信息
方式一：适用于不同任务
from concurrent.futures import ThreadPoolExecutor
import time
def task(name: str):
print(f"{name} - step 1\n", end="")
time.sleep(1)
print(f"{name} - step 2\n", end="")
return f"{name} complete"
with ThreadPoolExecutor() as executor:
result_1 = executor.submit(task, "A")
result_2 = executor.submit(task, "B")
print(result_1.result()) # result()会等待有结果再返回
print(result_2.result())
"""
A - step 1
B - step 1
A - step 2
A complete
B - step 2
B complete
"""
方式二：map()适用于同一任务，不同参数
from concurrent.futures import ThreadPoolExecutor
import time
def task(name: str) -> str:
print(f"{name} - step 1\n", end="")
time.sleep(1)
print(f"{name} - step 2\n", end="")
return f"{name} complete"
with ThreadPoolExecutor() as executor:
results = executor.map(task, ["C", "D"])
for r in results:
print(r)
"""
C - step 1
D - step 1
D - step 2
C - step 2
C complete
D complete
"""
多线程案例：下载图片
from concurrent.futures import ThreadPoolExecutor
from urllib.request import urlopen, Request
import os
def download_img(url: str):
site_url = Request(url, headers={})
with urlopen(site_url) as web_file:
img_data = web_file.read()
if not img_data:
raise Exception(f"Error: can not load the image from {url}")
file_name = os.path.basename(url)
with open(file_name, "wb") as f:
f.write(img_data)
return "Download image successfully, {}".format(url)
urls = [
"https://img0.bdstatic.com/static/searchresult/img/baseimg3_4f26a23.png",
# "..."
]
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36",
"content-type": "Content-Type: image/jpeg"
}
with ThreadPoolExecutor() as ececutor:
results = ececutor.map(download_img, urls)
for r in results:
print(r)
执行结果：
PS F:\Code\python_code\high_python\python_advance\thread> python .\thread_pool_demo.py
Download image successfully, https://img0.bdstatic.com/static/searchresult/img/baseimg3_4f26a23.png