python-线程池
********线程池********Python标准模块--Concurrent.futures
1.介绍
Concurrent.futures模块提供了高度封装的异步调用接口
ThreadPoolExecutor:线程池，提供异步调用
ProcessPoolExecutor:进程池，提供异步调用
Both implement the same interface, which is defined by the abstract Executor class.
2.基本方法
#submit(fn, *args, **kwargs)
异步提交任务
#map(func, *iterables, timeout=None, chunksize=1)
取代for循环submit的操作
#shutdown(wait=True)
相当于进程池的pool.close()+pool.join()操作
wait=True，等待池内所有任务执行完毕回收完资源后才继续
wait=False，立即返回，并不会等待池内的任务执行完毕
但不管wait参数为何值，整个程序都会等到所有任务执行完毕
submit和map必须在shutdown之前
#result(timeout=None)
取得结果
#add_done_callback(fn)
回调函数
****ProcessPoolExecutor****
# 介绍
'''
The ProcessPoolExecutor class is an Executor subclass that uses a pool of processes
to execute calls asynchronously. ProcessPoolExecutor uses the multiprocessing module,
which allows it to side-step the Global Interpreter Lock but also means that only
picklable objects can be executed and returned.
class concurrent.futures.ProcessPoolExecutor(max_workers=None, mp_context=None)
An Executor subclass that executes calls asynchronously using a pool of at most
max_workers processes. If max_workers is None or not given, it will default to
the number of processors on the machine. If max_workers is lower or equal to 0,
then a ValueError will be raised.
'''
# 用法
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import os, time, random
def task(n):
print('%s is runing' % os.getpid())
time.sleep(random.randint(1, 3))
return n ** 2
if __name__ == '__main__':
executor = ProcessPoolExecutor(os.cpu_count() + 1)
futures = []
for i in range(11):
future = executor.submit(task, i)
futures.append(future)
executor.shutdown(True)
print('+++>')
for future in futures:
print(future.result())
*****ThreadPoolExecutor****
#介绍
'''
ThreadPoolExecutor is an Executor subclass that uses a pool of threads to execute calls asynchronously.
class concurrent.futures.ThreadPoolExecutor(max_workers=None, thread_name_prefix='')
An Executor subclass that uses a pool of at most max_workers threads to execute calls asynchronously.
Changed in version 3.5: If max_workers is None or not given, it will default to the number of processors
on the machine, multiplied by 5, assuming that ThreadPoolExecutor is often used to overlap I/O instead of
CPU work and the number of workers should be higher than the number of workers for ProcessPoolExecutor.
New in version 3.6: The thread_name_prefix argument was added to allow users to control the threading.
Thread names for worker threads created by the pool for easier debugging.
'''
#用法
#与ProcessPoolExecutor相同
*****map****
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import os, time, random
def task(n):
print('%s is runing' % os.getpid())
time.sleep(random.randint(1, 3))
return n ** 2
if __name__ == '__main__':
executor = ThreadPoolExecutor(os.cpu_count() * 5)
# for i in range(41):
# future=executor.submit(task,i)
executor.map(task, range(1, 42)) # map取代了for+submit
****回调函数****
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
from multiprocessing import Pool
import requests
import json
import os
def get_page(url):
print('<进程%s> get %s' % (os.getpid(), url))
respone = requests.get(url)
if respone.status_code == 200:
return {'url': url, 'text': respone.text}
def parse_page(res):
res = res.result()
print('<进程%s> parse %s' % (os.getpid(), res['url']))
parse_res = 'url:<%s> size:[%s]\n' % (res['url'], len(res['text']))
with open('db.txt', 'a') as f:
f.write(parse_res)
if __name__ == '__main__':
urls = [
'https://www.baidu.com',
'https://www.python.org',
'https://www.openstack.org',
'https://help.github.com/',
'http://www.sina.com.cn/'
]
# p=Pool(3)
# for url in urls:
# p.apply_async(get_page,args=(url,),callback=pasrse_page)
# p.close()
# p.join()
p = ProcessPoolExecutor(3)
for url in urls:
p.submit(get_page, url).add_done_callback(parse_page) # parse_page拿到的是一个future对象obj，需要用obj.result()拿到结果
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