python 并发请求（设定吞吐量）
需求是，每秒发起请求（异步）
前提，压测接口，response time 为3-5秒
1 from flask import Flask 2 import random 3 import time 4 5 app = Flask(__name__) 6 7 8 @app.route('/') 9 def hello_world(): 10 return 'Hello, World!' 11 12 13 @app.route('/time', methods=['POST', 'GET']) 14 def atime(): 15 a = random.randint(3, 5) 16 time.sleep(a) 17 s = 'time ---->' + str(a) 18 if a == 5: 19 s = 1 / 0 20 return s 21 22 23 if __name__ == '__main__': 24 app.run()
请求地址：http://127.0.0.1:5000/time
1.尝试使用jmeter压测，发现只能做到同步（获得结果后再发起请求）
2.python实现
#!/usr/bin/env python
# -*- coding:utf-8 -*-
from threading import Thread
import requests
# import matplotlib.pyplot as plt
import datetime
import time
import numpy as np
import json
class ThreadTest(Thread):
def __init__(self, func, args=()):
'''
:param func: 被测试的函数
:param args: 被测试的函数的返回值
'''
super(ThreadTest, self).__init__()
self.func = func
self.args = args
def run(self) -> None:
self.result = self.func(*self.args)
def getResult(self):
try:
return self.result
except BaseException as e:
return e.args[0]
def baiDu(url, code, seconds):
'''
:param code: 状态码
:param seconds: 请求响应时间
:return:
'''
r = requests.get(url=url)
code = r.status_code
seconds = r.elapsed.total_seconds()
return code, seconds
def calculationTime(startTime, endTime):
'''计算两个时间之差，单位是秒'''
return (endTime - startTime).seconds + (endTime - startTime).days*24*60*60
def getResult(seconds):
'''获取服务端的响应时间信息'''
data = {
'Max': sorted(seconds)[-1],
'Min': sorted(seconds)[0],
'Median': np.median(seconds),
'99%Line': np.percentile(seconds, 99),
'95%Line': np.percentile(seconds, 95),
'90%Line': np.percentile(seconds, 90)
}
return data
def highConcurrent(url, count, countPerS):
'''
对服务端发送高并发的请求
:param cout: 并发数
:return:
'''
global throughput
startTime = datetime.datetime.now()
sum = 0
list_count = list()
tasks = list()
results = list()
# 失败的信息
fails = []
# 成功任务数
success = []
codes = list()
seconds = list()
for i in range(int(count)):
# DispatchIDS = ['CMA01', 'DAA03', 'MPL0A', 'DCA01', 'CUE0A', 'DKA03', 'DRA51', 'MAA01', 'RAA52', 'WHJ02']
# import random
# DispatchID = random.choice(DispatchIDS)
# DispatchID = DispatchIDS[i % 10]
# url = 'http://10.50.45.240:8080/rtd/92?DispatchID=' + DispatchID
import random
alamRandint = random.randint(3,5)
url = "http://127.0.0.1:5000/time/"+ str(alamRandint)
t = ThreadTest(baiDu, args=(url, i, i))
tasks.append(t)
t.start()
print(datetime.datetime.now())
time.sleep(1 / countPerS)
for t in tasks:
t.join()
if t.getResult()[0] != 200:
fails.append(t.getResult())
results.append(t.getResult())
for item in results:
codes.append(item[0])
seconds.append(item[1])
for i in range(len(codes)):
list_count.append(i)
# 生成可视化的趋势图
# fig, ax = plt.subplots()
# ax.plot(list_count, seconds)
# ax.set(xlabel='number of times', ylabel='Request time-consuming',
# title='olap continuous request response time (seconds)')
# ax.grid()
# fig.savefig('olap.png')
# plt.show()
# print("seconds ==",seconds)
# print("list_count ==", list_count)
# print("fails ==", fails) #[]
# print("success ==",success) #[]
# print("results ==", results)
for i in seconds:
# print('i', i)
# print('type(i)', type(i))
if type(i) == type(1.0):
sum += i
else:
sum += 0
# len(list_count)-len(fails)
# print('sum', sum)
# print("len(list_count)", len(list_count))
rate = sum / len(list_count)
endTime = datetime.datetime.now()
# print('\n总共持续时间:\n',endTime-startTime)
totalTime = calculationTime(startTime=startTime, endTime=endTime)
# print('totalTime', totalTime)
if totalTime < 1:
totalTime = 1
# 吞吐量的计算
try:
print("float(len(list_count) ", float(len(list_count)))
print("totalTime", totalTime)
throughput = float(len(list_count) / totalTime)
except Exception as e:
print(e.args[0])
getResult(seconds=seconds)
errorRate = 0
if len(fails) == 0:
errorRate = 0.00
else:
errorRate = len(fails) / len(tasks) * 100
throughput = str(throughput) + '/S'
timeData = getResult(seconds=seconds)
dict1 = {
'吞吐量(tps)': throughput,
'平均响应时间(s)': rate,
'响应时间(s)': timeData,
'请求总数': len(list_count),
'错误数': len(fails),
'错误率(%)': errorRate,
'执行时间(s)': totalTime
}
return json.dumps(dict1, indent=True, ensure_ascii=False)
if __name__ == '__main__':
DispatchIDS = ['CMA01', 'DAA03', 'MPL0A', 'DCA01', 'CUE0A', 'DKA03', 'DRA51', 'MAA01', 'RAA52', 'WHJ02']
url = 'http://10.50.45.240:8080/rtd/92?DispatchID='
days = 1
hours = 1
minutes = 1
seconds = 10
countPerS = 1 # 每秒请求数量<br>
counts = days * 24 * 60 * 60 + hours * 60 * 60 + minutes * 60 + seconds
counts = counts * countPerS
# print(counts)
print(highConcurrent(url=url, count=counts, countPerS=countPerS))
'''
2023-07-11 11:30:54.941397
2023-07-11 11:30:55.455986
....
2023-07-11 11:31:24.244365
2023-07-11 11:31:24.750616
{
"吞吐量(tps)": "1.8181818181818181/S",
"平均响应时间(s)": 3.963214083333334,
"响应时间(s)": {
"Max": 5.02367,
"Min": 3.005881,
"Median": 4.009576,
"99%Line": 5.01937067,
"95%Line": 5.013791149999999,
"90%Line": 5.0105454
},
"请求总数": 60,
"错误数": 0,
"错误率(%)": 0.0,
"执行时间(s)": 33
}
nohup python my.py >> my.log 2>&1 &
# 等价于下面两行的内容
nohup python my.py 1>> my.log &
nohup python my.py 2>> my.log &
'''
返回结果如下
2023-07-08 16:24:48.524257
2023-07-08 16:24:49.027055
2023-07-08 16:24:49.539939
.......
2023-07-08 17:25:57.709663
2023-07-08 17:25:58.225109
{
"吞吐量(tps)": "1.9599782194391506/S",
"平均响应时间(s)": 4.006165397416303,
"响应时间(s)": {
"Max": 5.036263,
"Min": 3.002034,
"Median": 4.009866,
"99%Line": 5.01809934,
"95%Line": 5.0155911,
"90%Line": 5.0130394
},
"请求总数": 7199,
"错误数": 2368,
"错误率(%)": 32.893457424642314,
"执行时间(s)": 3673
}
Process finished with exit code 0
运行方式（后台运行，实时查看数据）
nohup python -u alamThreadRequests.py >> alam0712.log 2>&1 & tail -f alam0712.log
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