有人说测试学习多进程(或多线程)有啥用?额告诉你很有用,特别是在自己写性能测试工具时就可以用到,而且非常方便

这里只介绍非常简单的多进程模块(multiprocessing.Process)

代码如下:

#导入模块
from multiprocessing import Process
import time,os,random

#创建一个测试函数subFunc
def subFunc(param,ppid):
sleepTime = random.randint(1,10)
print('Begin %s subProcess,and will wait %s,subPID is %s,mainPID is %s'%(param,sleepTime,os.getpid(),ppid))
time.sleep(sleepTime)
print('End %s subProcess...'%param)

#主程序
if __name__ == '__main__':
param = 5
subProcessList = []
print('Begin mainProcess...')
for i in range(param):
p = Process(target=subFunc,args=(i+1,os.getpid(),))
p.start()
subProcessList.append(p) for p in subProcessList:
p.join()
print('End mainProcess...')

看着代码是不是很简单?答案是肯定的,用得多了自然也就简单了,当然这是最简单的调用

通过Process()来创建进程,然后使用start启动进程,最后使用join在所有子进程结束后再执行后面的命令,执行结果如下:

aaarticlea/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZsAAAEGCAYAAACzYDhlAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAACbPSURBVHhe7d0PsFVV3f/xJakomWUqpib+Q51IHsRS4AkNMBXz/4xpJOpU9iTTKD2oNDaT5lOKpaWjJBr2ZJGK8NPHJCQx8V+TYgYG/iUVUSM0kAIF5I/7tz/r7uVdd9+1zr0HzjrnHni/Zs7sfb5r3332Xmffs/Y+987+bJXlDAAACXUrpgAAJMNgAwBIjsEGAJAcgw0AIDkGGwBAcsHBZquttvrg0b9/f3PvvfcWLRvn/PPPL+bqq7Ov6/Z1u+22M5///OfN448/XrQAAGoh+K/Pa9asMdtvv7157733zHPPPWdOPfVUs3DhwqJ18+P2d/369eaFF14wZ555pnn66aeLVgDApgpe2egMX7bddls7HT58uJ1urtz+duvWzQ6wAIDaqvg3G3219J3vfMdceeWVRcWYBQsWmKFDh5rDDz/cPPPMM0XVmL/97W/myCOPNAcccIC56qqrzMUXX2zr7isqn6uNHz/efOELXzAvvvhi0RKmZUeOHGlOPPFEc8YZZ5ibb765aDFm+vTp5tBDDzWHHHKIeeqpp4rqxr2uBpsRI0aYa665xj53yz/77LN230455RRbF/3sEUccYQYPHtxmPYsWLTKnn3662WuvvczEiROLarzfJkyYYPbdd1/Tt29fu49OrA4ATUlfo4Wo6f33389mzpyZfetb3yqqWXbqqadm+Ydl9vLLL2f5h2pRzbJjjjkmmzdvXvb8889nQ4YMKapZtm7dOrsu3/r1623tkUceyV577bWse/fuRUuYll24cKGd5h/mbdaXDwLZ0qVLszfffDPbZ599imr1r1teVtauXWvr119/fbZy5cqi2uK0007LHn300eyxxx6z804+GGZ33HFHtnr16uykk04qqvF+O+igg7Innngie+edd2ybE6sDQDOK3q5GZ/Rqyj9wTa9evcySJUts/ROf+ITJP9jtvOo6kxddDehMfqeddjK/+MUvzKc//WlbF7cun18Ltftce3kquurQlcpDDz1krzD89YTW69di8z7VN2zYYK96fOqH119/3f5MuX/UJ/lAZp87sX7TFeGNN95oli1bZsaMGWOv0CrVAaAp5R+W7eisXE26OtBVgK4eHJ2tr1q1qnjWqn///tmDDz5or0DWrFlTVFuvJrQuR+2q6XXEnw9xm1meiq6otI1vvPFGm3o1r+v2t7xfbh2alp188snZ7Nmz7dWH5p3hw4dn999/v13nlClTimq83/KB0i47f/78rE+fPkU1XgeAZhQcbPQBq8fWW29tvxL7y1/+UrRk9qugYcOGZbvvvnt29dVXF9Usu+yyy7Jf/epX2Q9/+MPs29/+dvbCCy/YuluXHk65Vm4v85fzp6IBbv/9988uuOAC+0HvvqJy6/SXLdf8537didVF/TBw4MBs0KBBWX51VVSzbO7cudnnPve57FOf+pQdiJxYv33lK1/JevfubevXXXddUY3XzzvvvGKurWrrAFBPNbvrc/7BaO666y5z8MEH26+z8rN9+1UQAAA1u4PA9ddfb0aNGmV23XVXk59N2783AAAg5NkAAJKr2ZUNAAAxDDYAgOQYbAAAyTHYAACSY7ABACTHYAMASI7BBgCQHIMNgIZbsWKFWb58efEMm6PgYKM7HbtHV4mFXrdunU3QTMHta5mrb4lx0bE+qUb5fXfrLK83Vq+1TT0O58yZY0444QR7B+/jjjvOzJ07t2ipjVgfuHr5OHR1/+HX9burO3usX7/e1juS6vf061//epttHDRoUNHSShlYzxQ5T7rT/EUXXWR69uxpRo8enSzQcFNj4/19cg+/Xu/+V5qy+l7Hp/K6nnzySVvX61922WVmjz32MD/+8Y9tTWLLJzvOdQeBMncX5PxNtjeW9HNiGkXbE9ncTfbuu+8G1+36QXd91o00+/XrV7Rs/pTfk6K/XZ+Wxeqp6U7cnX1dvf+68avuHn7fffdlhxxySNFSGxtzHLo2TR1Xy68W7E1cb7755qIlPb1ueR/ygcPmTTn+vOQfbtm4ceNsPpT8+te/ziZNmmT344EHHshuvfVWW2+UZul/3QB58eLF9vWVqeWOz6lTp2a//OUvbTbW0KFDbU1iy6c6zoNXNl0xFjplXHOPHj2Kuba25LjoHXbYoZirLdenZbF6avPnzy/mOrbLLrvYsz1lFQ0cONCmsdbSxhyHrs3vPzf/kY98xJ65/uY3v7HP6yH0e3L11VfbqxTRzXl33nlnOy/5B5pZunSpTaV17rzzTntmrf3QVdDkyZOLlsZolv5XP+6+++729ZUq7K601J8nnXSS+fCHP2xmzZplaxJbPtlxXgw67ahJD+XFvP3220U1y1588UUbO3DYYYfZrBVnwYIF2RFHHGFvi6+zFJ3NiFuPz9VuuOGG7KijjvogjqAj5fVUcuONN9orsoMPPjjL33Bb87fFnxfN/+xnP7PRADrTctxyBx54oD3L8ms6y9E++3k22pfBgwfbmAF/v1599dXsS1/6UvbJT34y+/nPf15U4/0Z2n6J1WN+97vf2awhna38+c9/tjW3/aH+175r2bPPPtsuU4nalc6qqa6AJ0yYULS0vkZItfUyLRd73dD+ipYNrd/VQ21lymrS+3rJJZfYY7x8hh6S8jh0/J93XE1n4x/96EftfCVu/WXVHm8SWo9s2LDhg6sXZ/r06TZzKh9QPmjr1avXBxlSmup5JXo9bVs+QNmIkZtuuqloqe54cLXQ74Vra4b+F+23HqJjSVcuimJRJIofGeP4y2/Mcd4Z4aMipx3vKrHQTujNiAnFKvtfU7hLXUfzOti1b8cdd1xRDb9mM8VFh2KzK/W/9v2hhx7KXn/99eC++8aOHZvlZ21ZfkaeHXvssdmFF15YtITfd6faelml160mJtzp7OvmZ4i2z9566y2b3fS9732vaIlLeRw6oTZX03u922672flKYv2zMfHksW3VfvnreOmll+znhVQabPz3MUSvpw9ITesZG++E2lytEf2vQfX3v/998SzLdtppp+y3v/2tXceMGTOyo48+umhpUV5+Y47zzoj2oNvx/LKxTWdpXm16+GccCgTTqD9gwAD7i+ILdaJfC7WHdHY50ZWWvpM866yz7NmvE3tdN683fdddd7XzEntN1XWmVqb+0WAU6jd9B1oW68/Y9sfqMXovFKCmg9bfl9j8zjvvbLdf/HqIPvA/85nP2INZy+q7dl/s56utl1V63dj+yqa+rhJTXdqq3t+Ozrgl9XEooTZX++c//2lDBTsjtJ5qjzeJbatOCrRfjtattFs9rrjiCnvFr28AtL3Lly+3y+hk7vjjj7fzMe71ylOp9njwa7H5skrrqXf/33vvvdmsWbOKZy0U8qi/H4mOWwUyOqHlN+Y474xgD7qzLR0YGuEaHQsty5Yts8vpzeuMWKyyDjod0DrAy9ugMwddYupsWVw/lPfX7ZOmZV0tLjoUm12p/10yqx5+PeTJJ5+0y3z3u9+1U+23E3rfRetTvbzPsXpIpdetJibc0RWSznx1FV+Jvh69++67bf/pTFBfoXYk5XEors39vLiaPqj1lZD+ONyRWP9Ue7xV+j3Vh16Mf2Wjs+nbb7/d9rPeEz2vRK8XmkrK2Hhxbe5nxNXq3f/65uSee+6x8zoR1nP50Y9+lE2cONGuR/05YsQIW48tvzHHeWe09r5HO61HV4mFlvLPdCQWq6zO1RXYT3/60zbrGzVqVNa3b1/7txO3v7HXjNVF/dOIuGj/KztfKDa7vP3+/MMPP2w/CHXp7NdD9Lc8teurEE39v+25n/V/3q/5bbF6TKXXrSYm3NHgpfdLXzVUorNM9bOuRnWS8NxzzxUt8f6v13Hot7v5z372s/bDSh9kHSmvw4ltf0xsPaIP/hB9nXb55Zdn1157bbZkyRJ7Nj1mzBh7lT169Gj73An1s3ut8lSqOR7KNf+5X3di7W6+3v2vz2t/PfrqXnRVo89kXaHoa3tdEEhs+UrH+aYgFnozkB/M5qijjjL5GVxRQT3R//VBPze3mg029913n8mvakx+RWMHnPzM2Bx99NFFK1LSv4b269fP5FdNRQX1RP/XB/3c3IiFBgAkx73RAADJMdgAAJJjsAEAJMdgAwBIjsEGAJAcgw0AIDkGGwBAcgw2AGqOmGeUBQcbF2vqok0bHQu9MBJfWituX8tcXeFCxEJXr/y+u3WW1xur19rmdhy6uv/w6/rdrXcstOPHPMdihrVdobji2PK11tn9df3Z1fs/dnzG+jm2fLL+1x0EytxdS3UDPN1AsqM8idR0E7lQfGmtEAvdHrHQ7XXF49C1aeq4WiNioUWhb37Ms7Y3FDMciyuOLd8ozdL/seOTWOgqxOJLa4VY6PaIhW6vKx6Hrs3vPzffiFjo/AOqXcxzLGY4FlecOn67Ws3S/7Hjk1jo/FFtLLT48aWVpIzjdTWd5RALveXEQvu6wnHo+D/vuJrOxusZSxyKedYt7UMxw9pHnVGX44pjy8dou7VtxEK38o/PWD/7/OWr7f/Oar93Be14V4uFLseXVqJMllRxvMRCt9jSYqGdrnIcOqE2V9N77SfGxsT6p5rjLRbzHIsZjsUVVxtLrO3WB6SmxEITC13MtfJrofayUHxpJbE41djrunm96cRCEwsd09WOQwm1uVo9Y4m1rJJH9fBjnpUw6VIu9XvhjnOFDIbiimPLx7jtLk+l2uPBr8Xmyyqth1joVsEedGdbOuA1wjU6FlpXBKH40kpSxvG6fdK0jFjoFqH3XbQ+1cv7HKuHVHrdamKAnc7GQne141Bcm/t5cbVGxEI7/pVNLGY4FldcbSyxtjs0lWqOh9jvhevPrt7/seOTWGivVm4vi8WXVlKvON4y9Y/OJIiFbvsa4tf8tlg9plGx0JWOw1j/1+s49NvdfCNioaUc86yz81DMsM629VmhM2c/rji2vIT62W1zeSrVHA/lmv/crzuxdjdPLHRbxEJvBvKDmbjcBqL/64N+bm7EQm8GiMttLPq/Pujn5kYsNAAgOe6NBgBIjsEGAJAcgw0AIDkGGwBAcgw2AIDkGGwAAMkx2AAAkmOwAVBzK2oUC12r9aDxgoONQnTcQ9GmjY6FnjFjhhk2bJgN9Rk+fLiZPXt20VIbbl/LXF3hQsRCV6/8vrt1ltcbq9daqtjdWon1gauXj0NX9x9+Xb+7XSEW2t8+Pfr06WPra9euNRdddJHp2bOnGT16dDCg0F9PrXV2f912d/X+jx2fen1ioTtJN4/797//bbdHdy3VDQxriVjo9oiFbk83MyQWumPlWOhY/yoaYtKkSXZ7FUh26623Fi0tyutplGbp/9jxSSx0Fe6++26z44476oi10awajWuJWOj2iIVuLxajWyubayx0jOKKdQat7R00aJC995lTzXpSa5b+jx2fxELnj2pjofUz3/jGNzqVeZIyjtfVdJZDLDSx0JWkPA4d/+cdV9PZeKNjobVepdrqVv/63dB7J7rlvbZPNPVDukLridH6tW3EQrfyj08dS7pyIRa6ilhofc1w2223ZRdffHFRiVMmS6o4XmKhWxAL3bGUx6ETanM1vdfKJelIrH+qOd5isdDTpk3Lli1bZr+WUd2FwpUHG/d+xdYTo+3WB6SmxEITC13MtfJrofYY/XLuueeexbO4WJxq7HXdvN50YqGJhY4hFjpOyyp5VA8/Ftqn318FgYm2a/ny5XZeJ23HH3+8ne/Menxuu8tTqfZ48Gux+bJK6yEWulWwB93Zlg54jXCNjoXWGb86S6+rkbYzl5Mp43jdPmlaRix0i9D7Llqf6uV9jtVDKr0usdCtta4SCz1y5Ej7+6vjTr+/LpZY87fffrutq+/1vKyzVzahqRALTSx0m1q5vUwdor9p6Irm3HPPtf9B0ZF6xfGWqX+IhQ73kV/z22L1GGKhW/h1v93Nd5VYaP39RR9guorX+/HKK6/Y5XTWPGbMGHs1PXr0aPvcV16PhPrZbXN5KsRCEwuNGsoPZuJyG4j+rw/6ubkRC70ZmExcbkPR//VBPzc3YqEBAMlxbzQAQHIMNgCA5BhsAADJMdgAAJJjsAEAJMdgAwBIjsEGAJBc0w02K4iJBYCmExxsXKypHoo2bXQstC9FTKzb1zJXV7gQsdDVK7/vbp3l9cbqtbapx6G/nXr0KeKNa8Wtt8zVy8ehq/sPv67f3XrHQq9bty4Ybhiq+9uth+tPbW8oxrjWOru/bvu6ev/HYp5j/Rlbfs6WHAvtpIqJJRa6PWKh20u9fZtDLLReN7QPoXpoOYnFGDdKs/S/brZJLHQN5DueLCaWWOj2iIWuv80hFjr2e1LN708sxrhRmqX/YzHPxELnj2pioauJiZWUcbyuprMcYqG3nFhoLROKN64k5XHo+D/vuJrOxusdSxxaj5Treh7qT+27zrRVj8UY+7QebRux0K38mOfO9Ke/vCIItthY6GpjYkWZLKnieImFbrGlxULH4o0rSXkcOqE2V9N77SfGxsT6p9rjTWLbWq7H+rOjGOMyrVcfkJoSC00sdDHXyq+F2n3VxsRKLE419rpuXm86sdDEQndEg4QCrTqS+jiUUJur1TuWWGLbWmkf/P6sFGMc4tZbnkq1x4Nfi82XVVoPsdCtgj3ozrZ0wGuEa3QstK+zVzYp43jdPmlaRix0i9D7Llqf6uV9jtVDGhULHYs3riTlcSiuzf28uFojYqF1paL16EPWF6rH+jMWYxyj9YamQiw0sdBtauX2mFBMbEy94njL1D/EQof7yK/5bbF6TKNioWPxxhLr/3odh367m29ELHRsPaF6rD81AOkzpBxjLKF+dussT4VYaGKhUUP5wUxcbgPR//VBPzc3YqE3A5OJy20o+r8+6OfmRiw0ACA5bsQJAEiOwQYAkByDDQAgOQYbAEByDDYAgOQYbAAAyTHYAACSY7ABACQXHGxcrKmLNm10LLS/PXr0aXAc75Yg1ifVKL/vbp3l9cbqtbapx6Ful5IyrjjWB65ePg5d3X/4df3udoVY6Fi/vfrqq+a0004ze++9txk9erRZuXKlrS+MxBXXWmf31/VnV+//WL/F+j+2/BYdCx3ZzJohFro9YqHbSx1XvLnGQsf67ctf/nJ211132e3VHYd1d2LRTSNDccWN4vqzq/d/rN+6Six09LfMHTAabJQH0UjlgzeF2GuorhA53YF3SxpsJFW/V+rrenO3+O8M3QlXt8pPqVLfxI7D0M+4mm7pX6tbxHeGTlDL2xPrNz/jSR94ulu6KHrAUV3xJY3WDP0f67dY/8eWV0qpu2O97qh+4okn2vlN1b6XCuosPbpCLLSW7UpxvK6msxxiobecWGgdGzoDrBSvW5byOHT8n3dcTWfjjY6FjvWbfneUHKmrS/1OKAyvzI8rjtHraduIhW7l91tnjlt/eUUQbLGx0F0tjpdY6BZbWix0tXHFkvI4dEJtrqb3WlcQHYn1T7XHm5TXE+u3P/7xjzZxVSeoej819ZXjimP0evqA1JRYaGKhi7lWfi3UHqNfTgUJdSR1HK/qxEJvWbHQ1cYVS+rjUEJtrlbvWGIpr6cz/aYrf//rmlBccYx7vfJUqj0e/FpsvqzSeurd/8RCF+ty1K6aXkf8+ZCuFsfr9knTMmKhW4Ted9H6VC/vc6weUul1U8ZCVxtXLCmPQ3Ft7ufF1bpKLHSlftPvhdtOfVMg+gYgFFcco9cLTYVYaGKh29TK7WWV4nhj6hXHW6b+IRY63Ed+zW+L1WMqvW7KWGid8OjY7mxcsdTrOPTb3XxXiYWu1G86znQF99WvfjVbvny5rcXiiiXUz+61ylMhFppYaNRQfjATl9tA9H990M/NjVjozQBxuY1F/9cH/dzciIUGACTHvdEAAMkx2AAAkmOwAQAkx2ADAEiOwQYAkByDDQAgOQYbAEByDDYAgOSCg40fc6rHpZdeWrR0zP1MJQsTx77GtsHVt8SYZwBopOBgs3r1ajtdt26dee+998w+++xjn3fGyiJHvBLlcl9zzTU2g/zcc8813/zmN4uW2nj33XeLubbcfqn9xhtvNKNGjbLPAQBpBQcbnfmLPpy7d+9uvva1r9nnnbHDDjsUc3FLly41u+++u32dU045pcMroWr16NGjmGvL7Ve3bt3sIAoAqI+Kf7PZcccdi7kW7muo8ePH26+/dHdnZ+7cueaQQw4x55xzTlGJmzRpUjFnzIMPPmh+8IMfFM/iJkyYYPbdd1/Tt29fM3LkSFtz21Oed3T10qdPH7ttPg02I0aMsFdX4n722WefNUceeaQdAB3t4xFHHGEGDx7cZn8XLVpkTj/9dLPXXnuZiRMnFlVjFixYYIYOHWoOP/xw88wzzxTV8PZLrA4AmxXdiDNETcqX8BdRLoOeK4xoU+KEnc7GvooyVlLF6zZTzDMANKPoXZ91ph9q8uv+/C677GL+8Y9/mG222Sb6s75p06bZr9x0FdAZysbRlcqyZcvMmDFj7FWUxLbHzSsDY4899jBvvfVWm3qZ6hs2bLBXPT79E0M+gNqf6dWrl1myZMkHdV3d6GtGn+pvvvmmndfyWkZi2x+rA8DmJPg12po1a+xU/yCgP/jfcsst9rn7O4drFzevr4Hmz59v5syZY5/7y5Qpl+L999+3A42met6RmTNnmnHjxpmxY8fa/2Rz8isD+4Hu/qPNf91XXnnFzJs3zxx66KH2uWtz/yjgaJCR0CA0cOBA+zWcHpp3+vfvb0OctM6pU6cWVWMGDBhgVq1aZdflBhqJbX+sDgCblfxDsR2V/Yey3st1/7lUEydcKfY1pl7xumWNink+77zzirm2qq0DQFdAeBoAILmK/40GAEAtMNgAAJJjsAEAJMdgAwBIjsEGAJAcgw0AIDkGGwBAcgw2AIDkGGwAAMkx2AAAkgsONroDsv+odSz0jBkzzLBhw+ydoocPH25mz55dtNRGbBtcnVhoAKiv4GDj7oqcKhZaYWP33HOPWbx4sbnwwgvN2WefXbTUBrHQANC1BAeb1LHQd999t00B1T1AlR9T61vrEwsNAF1Lxb/ZpIqFdvThf+edd5qLL764qMQRCw0ATUwRAyFqSh0LrVjn2267LcsHm6ISRyw0ADSvhsVCO0q67N27t3njjTeKShix0ADQvIJfo7n45FSx0PlVgV2v/iY0ZcoUM2TIkKIljlhoAGhi+YdiOyr7j1rHQivKWXHNe+65Z3buuedmixcvLlriiIVuQSw0gGZELDQAILmK/40GAEAtMNgAAJJjsAEAJMdgAwBIjsEGAJAcgw0AIDkGGwBAcgw2AIDkGGwAAMk1fLBZsWKFWb58efEMALA5Cg42ugOy/6h1LLTvqquuapP7UguxbXB1YqEBoL6Cg427K3KqWGhHd1IuB7TVArHQANC1BAeb1LHQolvzL1261KZU1hqx0ADQtVT8m03KWOhZs2bZ+OTOIhYaAJqYDRoIUFOqWOiXXnope/755+385MmTbexyR4iFBoDm1ZBYaEUhu/9A+8Mf/mB23XVXc8wxx5i9997b1kKIhQaAJpZ/iLbjrhJ0xr9ixYps4sSJtr5mzRpbV7v480OGDLGJmHr49Y509spm/Pjxdp3z58/P+vTpU1RbrgxeffXVbPbs2e22TVcK2p5jjz3W1tSm+qpVq+xzx12xaVp28skn23Xr6kPzzvDhw7P777/frnPKlClFNbNXOeX1S2z7Y3UA2JwEBxt98PqPWsdCO/o67fLLL8+uvfbaLL9iKKphxEK3IBYaQDMiFhoAkFzF/0YDAKAWGGwAAMkx2AAAkmOwAQAkx2ADAEiOwQYAkByDDQAgOQYbAEByDDYAgOQYbAAAyQUHG90B2X/UOhbaX7ceypypJbfeMlcnFhoA6is42KyuQyy0bsnmHs8991xRrQ1ioQGgawkONvWIhU6JWGgA6Foq/s0mZSz0DTfcYHr37m0jl+fPn19U44iFBoAmpoiBEDWlioWeNm1atmzZMhvGpvA0F25WCbHQANC8GhIL7csHHLPffvuZxYsXF5UwYqEBoHkFv0bTACD6BwH9wf+WW26xz93fOVy7uHl9DaSvw+bMmWOf+8uUnXXWWXa9Wt+UKVPMkCFDipa4mTNnmnHjxpmxY8eaM888s6gak18Z2A/0J5980j73X/eVV14x8+bNM4ceeqh97trcPwo4GmQkNAgNHDjQfg2nh+ad/v37m/wKz65z6tSpRdWYAQMGmFWrVtl1uYFGYtsfqwPAZiX/UGxHZf9R61jo6dOnZ4MHD8522203+1VTPigULXHEQrcgFhpAMyIWGgCQXMX/RgMAoBYYbAAAyTHYAACSY7ABACTHYAMASI7BBgCQHIMNACA5BhsAQHIMNgCA5BhsAADJBQcb3QHZf9Q6Flp0k89UN56MbYOrEwsNAPUVHGzcXZFTxkJvu+225vbbby+e1Rax0ADQtQQHm3rEQqeMZSYWGgC6lop/s0kZC60rm2oQCw0ATcwGDQSoKVUstNPZ5YRYaABoXg2Nhe7sckIsNAA0r+DXaC4+OVUstLz99tt2unTpUjvtCLHQANDE8g/FdlT2H7WOhRa3TEfLOcRCtyAWGkAzIhYaAJBcxf9GAwCgFhhsAADJMdgAAJJjsAEAJMdgAwBIjsEGAJAcgw0AIDkGGwBAcgw2AIDkGGwAAMkFBxvdAdl/pIiF3hjnn39+MVeZ2wbinwGgawgONu6uyCljoTfGDTfcUMxV5raf+GcA6BqCg009YqFTIv4ZALqWin+zSRULrXUoAvnEE080Z5xxhrn55puLFmOmT59u82e0rqeeeqqohr+eq7Q9QvwzAHQRNmggQE2pYqHVvnDhQjvNP8zbLJ8PAtnSpUuzN998M9tnn32KapatW7eu3XorbU95WSH+GQAaoyGx0K69PBVddehKJR+47BWGv57Qev1abN6nOvHPAFBfwa/RXHxyyljoGH0Q66uvBx98sKi0cNHN+dWMnUpse9xz4p8BoIvIPyzbUdl/1DoW2v95fyr5IJPtv//+2QUXXJANHz78g6+o3Dr9Zcs1/7lfd2J1If4ZANIhFhoAkFzF/0YDAKAWGGwAAMkx2AAAkmOwAQAkx2ADAEiOwQYAkByDDQAgOQYbAEByDDYAgOQYbAAAyTHYAACSY7ABACTHYAMASI7BBgCQHIMNACA5BhsAQHIMNgCA5BhsAADJMdgAAJJjsAEAJMdgAwBIjsEGAJAcgw0AIDkGGwBAcgw2AIDkGGwAAMkx2AAAkmOwAQAkx2ADAEguONhstdX37WO77b5vPv/58ebxx18uWprLnDmLzLBhPzMf//gPzHnnTS6qxpx//pRirnM6u7zrt42xKT/r1GM7q1FtP9fSpuzj2rXrzUUX3WV69vyhGT16qnnvvXVFC4CNtVWWK+Y/sGbNWrP99lea9esvNS+88A9z5pmTzdNPX1i0No///M/rzXXXnWAOO2zf/INnq6Kajuu3LKv+Q+6dd9aYj3zkqo362UpWr15revRou02bsp2bKrQ9KWxKf06a9ER+vBhz2mmfMX/840vm73//lznnnEFFK4CNEbyy2W67be20W7et8rO69Xa+GT3++Nt1G2jE9dvG2GGH7Yq52po//41irtWmbOemCm1PCpvSn3feOc+ccELfvJ+2MYMG7WcmT/5r0QJgY1X8m023bpebESPuNNdc80X73H018eyzfzdHHjnenHLKRFuXF19cYo444gYzePANdt5ZtGiZOf30/zV77XWlmTjxsaJqzIIFS8zQoePN4YdfZ555pvUDaMKER8y++15l+va9xowc+euiGq/HuK9QtA/+1yluH3yuNn78Q+YLX7ixzfaHlpdK2/OTnzxgDjrox+b//m9uUYnv79y5i8whh/wkP3OeVFTitB36sNb06adfMzfd9GjREt/OAQNati3UFtrOmND2a53f/OYdZuHCpe1ev/zcqbQ9IbHjJ3a8xfoz1v8h8+cvy6+KtrfzGnCee265nQewCfQ1WogxlxVzrdauXW/r118/K1u5cnVRbXHaab/IHn10QfbYYwvsvHPGGb/M7rhjdrZ69XvZSSf9vKhm2amnTszyX/rs5ZffyvIPk6KaZfmHX/bEEy9n77yzxrY5sXoloX1Yt65lH3zr12+wtUceeTF77bVlWffure2h5SW2PVpWfbB48fJs//2vKqrx/T3uuJuyhx56IXv99WXB1/GNHXt39pvfPJH16PH97NhjJ2QXXvj/ipb4dkqoHtvOmND2/+tfq+x2XHTRXdnAgddly5e/a+tS7fbExI6f2PEW689Y/4f06nWlPSZE0332GWfnAWy8qv4bbZttPmSn3/rWkHZfUzz22D/MwIH75WeO+9l55+GH/25OPbW//ermt7/9RlE15k9/etMcfPBEs//+PzNPPNF6Zjpt2tlm8uS5ZtSoO82KFauLarxera23btkH34c+1NINRx55YH4G/XHz3nv2qRVaXiptz+DBB5jdd/+Yefnl1npsf5988i3zuc/1Np/85MeLStx//Mce5tpr/2S6d+9m7r9/ienXb8+iJb6dlYS2Mya0/R/96PbmyiuPy69855lLLhliPvaxHrYuG7M9IbHjJ3a8xfoz1v8hffrsZFauXGPn9TemT3+64/cGQGXBwWbNmpb/vtEvmm/DhvftNB+k7NQ3cGBP+xWGHpp3+vff2TzyyAK7zqlTnyqq+jqlp1m16rv2D7iLFl1SVI2ZOfN5M27ciWbs2GHmzDPvLKrxekx+Rmqn+s8in9sH1y7uv43cfov+iC6h5SW0Pe5nQuuJ7W/fvjvZr8b0n3Pilg858MCe5i9/+Xc+wPWzzw86aDc7ldh2So8eW5m33lphHnjgWfu80nbGhLY/v7Ix//M/M/M+6GcuvfQPZvHif9m6VLM9lcSOn9jxFuvPWP+HjBjRz8yY8Yw9LvSfmKef3rdoAbCxgoPN9ttfYaf6ryHf1lv/T5up74orvmhGj55m/vu/f5ef7R5fVE3+gfzF/ANpljn00OtMr16tZ4hXXvlFc8IJE80ee1yRnxk/UFR1Broo/8C4zhxzzK/Mf/3XZ4pqvB6zzTY/sNPu3X/Y5u8Dbttdu2y3Xcv+uv0W/beWhJaX0Pa4nwmtJ7a/3//+0eYrX5li7rlnnn3ulg/p3bvlQ/Wssw6z0wMOaP2QjW2nPPzw2eaUU27NTx5aBpdK2xkT2v6ddvpxfrXxd3POOQPMX/+60uy553W2LtVsTyWx4yd2vMX6M9b/IV/+8mHmqadez/fnKjN9+vP2OYBNE/zXZwAAaseY/w/2EkQQ4ciWswAAAABJRU5ErkJggg==" alt="" />

对于需要创建大量子进程的程序来说,不能这么写,因为我们可以使用进程池模块(Pool)来实现

具体代码如下:

#导入模块
from multiprocessing import Pool
import time,os,random #创建一个测试函数subFunc
def subFunc(param,ppid):
sleepTime = random.randint(1,10)
print('Begin %s subProcess,and will wait %s,subPID is %s,mainPID is %s'%(param,sleepTime,os.getpid(),ppid))
time.sleep(sleepTime)
print('End %s subProcess...'%param) if __name__ == '__main__':
param = 5
print('Begin mainProcess...')
#创建进程池
p = Pool(5)
for i in range(param):
p.apply_async(subFunc,args=(i+1,os.getpid(),))
#关闭进程
p.close()
#等待子进程结束后在执行后续命令
p.join() print('End mainProcess...')

也是很简单的,每步的动作注释代码里都有,就不一一解释了,执行结果如下(和上面的Process类似):

aaarticlea/png;base64,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" alt="" />

还是那句话,测试人员要向多维度多方向发展,特别是向开发靠拢,把学到的开发知识应用于测试当中,最终成为一个测试开发的复合型人才,这才是未来测试的出路,只做单一的测试只会被淘汰

python之进程(multiprocess)的更多相关文章

  1. Python之进程 3 - 进程池和multiprocess.Poll

    一.为什么要有进程池? 在程序实际处理问题过程中,忙时会有成千上万的任务需要被执行,闲时可能只有零星任务.那么在成千上万个任务需要被执行的时候,我们就需要去创建成千上万个进程么?首先,创建进程需要消耗 ...

  2. {Python之进程} 背景知识 什么是进程 进程调度 并发与并行 同步\异步\阻塞\非阻塞 进程的创建与结束 multiprocess模块 进程池和mutiprocess.Poll

    Python之进程 进程 本节目录 一 背景知识 二 什么是进程 三 进程调度 四 并发与并行 五 同步\异步\阻塞\非阻塞 六 进程的创建与结束 七 multiprocess模块 八 进程池和mut ...

  3. Python:进程

    由于GIL的存在,python一个进程同时只能执行一个线程.因此在python开发时,计算密集型的程序常用多进程,IO密集型的使用多线程 1.多进程创建: #创建方法1:将要执行的方法作为参数传给Pr ...

  4. Python之进程

    进程 进程(Process)是计算机中的程序关于某数据集合上的一次运行活动,是系统进行资源分配和调度的基本单位,是操作系统结构的基础.在早期面向进程设计的计算机结构中,进程是程序的基本执行实体:在当代 ...

  5. Python之进程 进阶 下

    在python程序中的进程操作 之前我们已经了解了很多进程相关的理论知识,了解进程是什么应该不再困难了,刚刚我们已经了解了,运行中的程序就是一个进程.所有的进程都是通过它的父进程来创建的.因此,运行起 ...

  6. 【Python】使用Supervisor来管理Python的进程

    来源 : http://blog.csdn.net/xiaoguaihai/article/details/44750073     1.问题描述 需要一个python的服务程序在后台一直运行,不能让 ...

  7. python 守护进程 daemon

    python 守护进程 daemon # -*-coding:utf-8-*- import sys, os '''将当前进程fork为一个守护进程 注意:如果你的守护进程是由inetd启动的,不要这 ...

  8. Python的进程与线程--思维导图

    Python的进程与线程--思维导图

  9. Python守护进程和脚本单例运行

    Python 守护进程 守护进程简介 进程运行有时候需要脱离当前运行环境,尤其是Linux和Unix环境中需要脱离Terminal运行,这个时候就要用到守护进程.守护进程可以脱离当前环境要素来执行,这 ...

  10. python开发进程:共享数据&进程池

    一,共享数据 展望未来,基于消息传递的并发编程是大势所趋 即便是使用线程,推荐做法也是将程序设计为大量独立的线程集合 通过消息队列交换数据.这样极大地减少了对使用锁定和其他同步手段的需求, 还可以扩展 ...

随机推荐

  1. C++ is_same

    is_same template< class T, class U > struct is_same; 如果T与U具有同一const-volatile限定的相同类型,则is_same&l ...

  2. redis、mysql、mongdb的比较

    特点: 1-1 MySQL:1. 使用c和c++编写,并使用了多种编译器进行测试,保证源代码的可移植性2. 支持多种操作系统3. 为多种编程语言提供可API4. 支持多线程,充分利用CPU资源优化的S ...

  3. 堆优化dij

    #include<iostream> #include<cstdio> #include<queue> using namespace std; ],head[], ...

  4. flex布局实现elment容器布局

    一.flex布局是什么 flex布局,意为"弹性布局",是一种响应式的布局方法 采用 Flex 布局的元素,称为 Flex 容器,它的所有子元素自动成为容器成员. 先放上一个ele ...

  5. Python中使用RabbitMQ

    一 RabbitMQ简介 RabbitMQ是一个在AMQP基础上完整的,可复用的企业消息系统.他遵循Mozilla Public License开源协议. MQ全称为Message Queue, 消息 ...

  6. PHP开发——常量

    概念 l  常量就是值永远不变的量.如:圆周率.身份证号码等. l  所谓常量值永远不变的量,是指在一次完整的HTTP请求过程中. l  常量在程序运行过程中,不能修改.也不能删除. l  常量比变量 ...

  7. 17. pt-online-schema-change

    在平时MySQL的运维过程中,经常会遇到表结构的变更.在表比较小的时候,直接进行变更,时间较短,但是当表非常大的时候,这么做会导致应用卡死,服务不可用.目前InnoDB引擎是通过以下步骤来进行DDL的 ...

  8. leveldb 学习记录(八) compact

    随着运行时间的增加,memtable会慢慢 转化成 sstable. sstable会越来越多 我们就需要进行整合 compact 代码会在写入查询key值 db写入时等多出位置调用MaybeSche ...

  9. 使用Maven搭建SpringMVC

    1.创建Maven Project 注意选择webapp 2.添加Maven依赖 <project xmlns="http://maven.apache.org/POM/4.0.0&q ...

  10. 学习Acegi应用到实际项目中(5)

    实际企业应用中,用户密码一般都会进行加密处理,这样才能使企业应用更加安全.既然密码的加密如此之重要,那么Acegi(Spring Security)作为成熟的安全框架,当然也我们提供了相应的处理方式. ...