jmeter 测试ES并发

Jmeter 测试

Jmeter安装

• 下载安装包解压即可。http://jmeter.apache.org/download_jmeter.cgi

Jmeter配置

• 配置线程组

  测试计划->添加->threads->线程组：

  如图所示则是200个用户，Ranmp-up Period是这么多用户在10s内提交完毕，循环10次

image.png

• 配置参数文件

  线程组->添加->配置元件->CSV Data Set Config

  配置相应的属性

image.png

• 配置HTTP请求

  线程组->添加->Sampler->HTTP请求

  配置相应的属性

image.png

• 添加输出报告

  线程组->添加->监听器->summary report

• 添加察看结果树

  线程组->添加->监听器->察看结果树

  通过结果树可以看到发送到服务器的请求，以及返回的结果。可以通过这个组件调试请求。

image.png

结果分析

结果如图所示：

image.png

samples：运行的线程总数5000。
Average：平均响应时间192ms。
Error：错误率0。
Throughout：吞吐量，每秒钟处理了1831个请求。

遇到的问题

• 编码问题

  1）查看参数文件的编码，一般用utf-8,。如果是txt文件，且编码不是utf-8，打开文件，另存为，设置编码格式为utf-8

  2）在CSV Data Set Config配置时，设置解析文件的格式为utf-8

  3）在HTTP请求配置，设置Content encoding为utf-8

Python脚本测试

import sys
import os
import threading
import random
import time
import json 

def parse_param():
    args = sys.argv
    thread_num = args[1]
    times = args[2]
    keyword_fname = args[3]
    rfile = open(keyword_fname) 
    keywords = rfile.read().split("\n")
    rfile.close()
    return int(thread_num),int(times),keywords

def get_file_data(fname):
    rfile = open(fname)
    keywords = rfile.read().split("\n")
    rfile.close()
    return keywords

class myThread(threading.Thread):
    def __init__(self,cmd,cost_time):
        self.cmd =cmd
        self.cost_time = cost_time
        threading.Thread.__init__(self)

    def run(self):
        result = os.popen(self.cmd).read()
        result =json.loads(result)
        if "error" in result.keys():
            print "error:%s" % result
        self.cost_time.append(int(result["took"]))
        #self.cost_time += [int(result["took"])]
        #if result[0:10].find("error") != -1:
        #    print "error"
        #print result

def run(thread_num,times,cmds):
    threads=[]
    start_time = time.time()
    t = 0
    cost_times =[]
    for i in range(0,times):
        for j in range(0,thread_num):
            tmp_thread = myThread(cmds[t],cost_times)
            t = t+1
            threads.append(tmp_thread)
        for val in threads:
            val.start()

        for val in threads:
            val.join()
        threads[:] = []
    print len(cost_times)
    avg_time = sum(cost_times)/len(cost_times)
    #avg_time = float((time.time()-start_time))/times
    output = float(thread_num*times)/(time.time()-start_time)
    return avg_time,output

def clear_cache():
    cmd = "curl 100.110.11.131:9200/_cache/clear"
    result = os.popen(cmd).read()
    print result

def deal_once(thread_num,times,keywords):
    #thread_num,times,keywords=parse_param()
    clear_cache()
    ip_list = ["xxx","xxx","xxx","xxx"]
    #cmd = "curl xxx:9200/customer/_search -d '{\"size\":0,\"query\":{\"should\":[";
    cmds =[]
    for val in keywords:
        cmd = "curl %s:9200/customer32/_search -d '{\"size\":0,\"query\":{\"bool\":{\"should\":[" % random.sample(ip_list,1)[0]
        keyword = val.split("\t")
        for key in keyword:
            tmp = "{\"match_phrase\":{\"strdescription\":\"%s\"}}," % key
            cmd = cmd + tmp
        cmd = cmd[:-1] + "]}}}'"
        cmds.append(cmd)
    print cmds[0]
    avg_time,output = run(thread_num,times,cmds)
    print "avg_time:%f" % avg_time
    print "output/s:%f" % output


def deal():
    #thread_num = [100,500,1000,2000,3000]
    #thread_num = [100,200,400,500]
    thread_num = [5000]
    times =1
    files = ["2_prefix.txt","6_prefix.txt","11_prefix.txt"]
    files = ["2_prefix.txt","6_prefix.txt"]
    files = ["11_prefix.txt"]
    for file in files:
        for num in thread_num:
            print "thread_num:%d\t file is %s" % (num,file)
            keywords = get_file_data(file)
            deal_once(num,times,keywords)
            time.sleep(1)


deal()