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pool_vs_gevent.py
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137 lines (116 loc) · 2.69 KB
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# encoding: utf-8
__author__ = 'zhanghe'
import time
from tools import time_log
# import gevent
# from gevent import monkey
# monkey.patch_all()
from multiprocessing import Pool
# @time_log.time_log
def foo_cpu(name):
"""
模拟CPU密集场景（CPU单独占用2S）
:return:
"""
start_time = time.time()
while 1:
end_time = time.time()
if (end_time - start_time) >= 2:
print name
break
@time_log.time_log
def run_cpu_gevent():
threads = [gevent.spawn(foo_cpu, i) for i in range(9)]
gevent.joinall(threads)
@time_log.time_log
def run_cpu_pool():
p = Pool()
for i in range(9):
p.apply_async(foo_cpu, args=(i,))
p.close()
p.join()
# @time_log.time_log
def foo_io(name):
"""
模拟IO密集场景（CPU挂起2S）
:return:
"""
while 1:
time.sleep(2)
print name
break
@time_log.time_log
def run_io_gevent():
threads = [gevent.spawn(foo_io, i) for i in range(9)]
gevent.joinall(threads)
@time_log.time_log
def run_io_pool():
p = Pool()
for i in range(9):
p.apply_async(foo_io, args=(i,))
p.close()
p.join()
if __name__ == '__main__':
# CPU密集场景（适用于pool）
# run_cpu_gevent()
# run_cpu_pool() # 需要注释掉gevent
# IO密集场景（适用于gevent）
# run_io_gevent()
run_io_pool() # 需要注释掉gevent
# CPU密集场景下gevent测试结果：
# 方法run_cpu_gevent开始时间：Thu Apr 30 13:11:01 2015
# 0
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# 方法run_cpu_gevent结束时间：Thu Apr 30 13:11:19 2015
# 方法run_cpu_gevent运行时间：18.00S
# CPU密集场景下pool测试结果：
# 方法run_cpu_pool开始时间：Thu Apr 30 13:09:08 2015
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# 方法run_cpu_pool结束时间：Thu Apr 30 13:09:14 2015
# 方法run_cpu_pool运行时间：6.04S
# IO密集场景下gevent测试结果：
# 方法run_io_gevent开始时间：Thu Apr 30 13:12:44 2015
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# 方法run_io_gevent结束时间：Thu Apr 30 13:12:46 2015
# 方法run_io_gevent运行时间：2.00S
# IO密集场景下pool测试结果：
# 方法run_io_pool开始时间：Thu Apr 30 13:13:54 2015
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# 方法run_io_pool结束时间：Thu Apr 30 13:14:00 2015
# 方法run_io_pool运行时间：6.12S
# 测试机器为4核心
# pool 不能与 gevent同时存在，一个同步，一个异步。所以当测试pool时，需要注释掉gevent及补丁
# 以上结果可以分析出：
# 一、对于CPU密集运算的场景，gevent无用武之地，需要多进程来支持
# 二、对于IO密集运算的场景，gevent再好不过了，pool提升不明显