1.线程queue :会有锁
q=queue.Queue(3)
q.get()
q.put() 先进先出 队列
后进先出 堆栈
优先级队列
 """先进先出 队列"""
import queue
q=queue.Queue(3) #先进先出->队列 q.put('first')
q.put(2)
# q.put('third')
# q.put(4)
q.put(4,block=False) #q.put_nowait(4)
# q.put_nowait(4)
# q.put(4,block=True) # True 阻塞 False 不阻塞 直接告诉你 队列满了
# q.put(4,block=True,timeout=3) # 阻塞等待3秒 还没有拿走数据就抛异常
#
print(q.get())
print(q.get())
print(q.get())
print(q.get(block=True,timeout=2)) # false 不阻塞没有数据就抛异常 默认是阻塞 block=True
print(q.get_nowait()) # 相当于block=false
# def get(self, block=True, timeout=None): """后进先出 堆栈"""
import queue
q=queue.LifoQueue(3) #后进先出->堆栈
q.put('first')
q.put(2)
q.put('third') print(q.get())
print(q.get())
print(q.get()) """优先级队列 """
import queue
q=queue.PriorityQueue(3) #优先级队列 q.put((10,{'alice':12})) # 数字越小 优先级越高 优先拿出来
q.put((40,'two'))
q.put((30,'three')) print(q.get())
print(q.get())
print(q.get())
2.线程池进程池:
client server 是IO 操作应该用多线程
计算密集型: 用多进程
io密集型:用多线程 池:对数目加以限制,保证机器正常运行
 from concurrent.futures import ProcessPoolExecutor,ThreadPoolExecutor
import os,time,random def task(name):
print('name:%s pid:%s run' %(name,os.getpid()))
time.sleep(random.randint(1,3)) if __name__ == '__main__':
pool=ProcessPoolExecutor(4) # 不指定 默认是cpu的核数
# pool=ThreadPoolExecutor(5) for i in range(10):
pool.submit(task,'egon%s' %i) # 异步调用池子收了10个任务,但同一时间只有4个任务在进行 pool.shutdown(wait=True) # 类似join 代表往池子里面丢任务的入口封死了 计数器-1 print('主')
"""
主 # # 异步调用池子收了10个任务,但同一时间只有4个任务在进行
name:egon0 pid:60056 run # 只有4个pid
name:egon1 pid:64700 run
name:egon2 pid:59940 run
name:egon3 pid:60888 run name:egon4 pid:60888 run name:egon5 pid:60056 run
name:egon6 pid:60888 run name:egon7 pid:60056 run
name:egon8 pid:64700 run
name:egon9 pid:59940 run
"""
# pool.shutdown(wait=True) # 代表往池子里面丢任务的入口封死了 计数器-1
"""
name:egon0 pid:57124 run
name:egon1 pid:62252 run
name:egon2 pid:55736 run
name:egon3 pid:62060 run
name:egon4 pid:57124 run
name:egon5 pid:62252 run
name:egon6 pid:55736 run
name:egon7 pid:55736 run
name:egon8 pid:62060 run
name:egon9 pid:55736 run

""" from concurrent.futures import ProcessPoolExecutor,ThreadPoolExecutor
from threading import currentThread
import os,time,random def task():
print('name:%s pid:%s run' %(currentThread().getName(),os.getpid()))
time.sleep(random.randint(1,3)) if __name__ == '__main__':
pool=ThreadPoolExecutor(5) for i in range(10):
pool.submit(task) pool.shutdown(wait=True) print('主')
"""
name:ThreadPoolExecutor-0_0 pid:61508 run
name:ThreadPoolExecutor-0_1 pid:61508 run
name:ThreadPoolExecutor-0_2 pid:61508 run
name:ThreadPoolExecutor-0_3 pid:61508 run
name:ThreadPoolExecutor-0_4 pid:61508 run
name:ThreadPoolExecutor-0_2 pid:61508 run
name:ThreadPoolExecutor-0_4 pid:61508 run
name:ThreadPoolExecutor-0_0 pid:61508 run
name:ThreadPoolExecutor-0_3 pid:61508 run
name:ThreadPoolExecutor-0_1 pid:61508 run

"""
3.异步调用与回调机制:
提交任务的两种方式:
同步调用:提交完任务后,就在原地等待任务执行完毕,拿到结果,再执行下一行代码,导致程序是串行执行,效率低
异步调用:提交完任务后,不等待任务执行完毕。异步调用+回调机制 自动触发叫回调
 """同步调用"""
from concurrent.futures import ThreadPoolExecutor
import time
import random def la(name):
print('%s is laing' %name)
time.sleep(random.randint(3,5))
res=random.randint(7,13)*'#'
return {'name':name,'res':res} def weigh(shit):
name=shit['name']
size=len(shit['res'])
print('%s 拉了 《%s》kg' %(name,size)) if __name__ == '__main__':
pool=ThreadPoolExecutor(13) shit1=pool.submit(la,'alex').result()
weigh(shit1) shit2=pool.submit(la,'wupeiqi').result()
weigh(shit2) shit3=pool.submit(la,'yuanhao').result()
weigh(shit3) """异步调用 + 回调机制 自动触发叫回调"""
from concurrent.futures import ThreadPoolExecutor
import time
import random def la(name):
print('%s is laing' %name)
time.sleep(random.randint(3,5))
res=random.randint(7,13)*'#'
return {'name':name,'res':res}
# weigh({'name':name,'res':res}) # 这样写不好 所有功能 写在一起了 def weigh(shit):
shit=shit.result() # 拿到是 对象 需要result()
name=shit['name']
size=len(shit['res'])
print('%s 拉了 《%s》kg' %(name,size)) if __name__ == '__main__':
pool=ThreadPoolExecutor(13) # pool.submit(la, 'alex')
# pool.submit(la, 'wupeiqi')
# pool.submit(la, 'yuanhao') pool.submit(la,'alex').add_done_callback(weigh) # 实现了程序的解耦合
pool.submit(la,'wupeiqi').add_done_callback(weigh)
pool.submit(la,'yuanhao').add_done_callback(weigh)
4.异步调用与回调机制应用:
pip3 install requests
requests 异步调用+回调机制的 应用场景:
from concurrent.futures import ThreadPoolExecutor
import requests
import time def get(url): # io操作 基于线程 数目有限 用线程池
print('GET %s' %url)
response=requests.get(url)
time.sleep(3)
return {'url':url,'content':response.text} def parse(res):
res=res.result()
print('%s parse res is %s' %(res['url'],len(res['content']))) if __name__ == '__main__':
urls=[
'http://www.cnblogs.com/linhaifeng',
'https://www.python.org',
'https://www.openstack.org',
] pool=ThreadPoolExecutor(2) for url in urls:
pool.submit(get,url).add_done_callback(parse)

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