通过python脚本与linux命令结合的方式采集服务器性能数据。根据测试过程中服务器当前的tcp链接数量来决定数据采集是否结束。
脚本主要有三个操作，第一个是性能数据初步采集，通过调用linux的sar和iostat命令，将数据写入原始文件中。采集完成后，执行性能指标提取脚本，从原始指标文件提取有效的数据写入最终的文件中，并进行打包操作。
代码只是本人满足工作所需而作，算不上很好，可以满足工作所需，仅此而已
从原始文件提取数据的配置文件，根据服务器语言类型区分：
abstractConf_ch.xml—中文
abstractConf_en.xml—英文
配置文件主要是指明原始文件路径并按照需求使用linux的cat、egrep、awk命令从文件中提取数据
<?xml version='1.0' encoding='utf-8'?>
<abstract>
<res_file name="res/CPU">
<uniqflag>CPU</uniqflag>
<object_file>result/cpu_status</object_file>
<graphtitle>Cpu_Status</graphtitle>
<linelabel>%user %system</linelabel>
<x_y_label>Time(s) Cpu_Percent(%)</x_y_label>
<cmd>cat %s | egrep -v "Linux|^$|%s" | awk 'BEGIN {print "%s\n%s\n%s"}{if($2 !~/AM|PM/) print $3,$5}' >> %s</cmd>
</res_file>
...............
...............
</abstract>获取服务连接数量
# coding:utf-8
#__author__ = 'Libiao'
import subprocess
class GetLinkingNumber(object):
def __init__(self):
pass
def getLinkingNumber(serlf,servers):
ret = []
if isinstance(servers,str):
num = subprocess.Popen("netstat -tnap | grep tcp | grep %s | wc -l" %servers,stdout=subprocess.PIPE,shell=True).stdout
ret.append(int(num.readline().strip()))
elif isinstance(servers,dict):
for k,v in servers.items():
num = subprocess.Popen("netstat -tnap | grep tcp | grep %s | wc -l" %v,stdout=subprocess.PIPE,shell=True).stdout
ret.append(int(num.readline().strip()))
else:
pass
return ret需要由主程序执行的linux命令
#!/bin/bash
sar -n DEV 10 >>res/NetWork &
iostat -x -d -k 10 >>res/Disk &
sar -r 10 >>res/Memory &
sar -q 10 >>res/System_load_average &
sar -u 10 >>res/CPU &
sar -b 10 >>res/TPS &数据采集代码主方法
#-*- coding:utf-8 -*-
"""
reated on 2015年10月16日
@author: LiBiao
"""
import time,os
import subprocess
import multiprocessing
from write_log import writeLog
import del_old_file
from record_test_data import Record_Data
from server_memory_collect import serverMemoryCollect
from get_linking_number import GetLinkingNumber
#需要手动设置的参数
SERVERS_D = {'1935':'srs-rtmp','18080':'srs-hls','80':'nginx'} #可以输入srs或者nginx或者ATS
#间隔时间
INTERVAL_TIME = 10
class KPI_Collect(object):
def __init__(self):
self.getLinkNum = GetLinkingNumber()
self.TCP_COUNT = self.getLinkNum.getLinkingNumber(SERVERS_D)
self.tcpRecord = Record_Data("res/linking_number")
def getStr(self,alist):
ret = ""
for s in alist:
ret += str(s)
ret += ' '
return [ret.rstrip(' ')]
#通过调用collect.sh脚本来执行服务器性能数据采集
def sys_kpi_collect(self):
flag = '1'
cmds = ['./collect.sh']
popen = subprocess.Popen(cmds[0],stdout=subprocess.PIPE,shell=True)
pid = popen.pid
writeLog('INFO','>>>>> 性能指标采集进程执行中.....')
self.to_stop_subprocess(flag,popen)
#停止sys_kpi_collect执行的程序的popen句柄
def to_stop_subprocess(self,flag,popen):
curr_tcpnum = self.getLinkNum.getLinkingNumber(SERVERS_D)
self.tcpRecord.recordData(["srs&nginx Linking","%s %s %s" %tuple(SERVERS_D.values()),"Time(s) Numbers"])
self.tcpRecord.recordData(self.getStr(self.TCP_COUNT))
if flag is '1':
loops = 0
while True:
if sum(curr_tcpnum) <= sum(self.TCP_COUNT):
if loops == 15:
#15s内当前连接数小于初始化连接数，退出程序
#删除还存在于系统中的sar和iostat进程
names = ['sar','iostat']
cmd = "killall -9 %s %s" %tuple(names)
subprocess.call(cmd,shell=True)
#终止子进程
popen.kill()
if subprocess.Popen.poll(popen) is not None:
break
else:
writeLog("INFO",r">>>>> 等待子进程终止")
else:
loops += 5
time.sleep(5)
else:
loops = 0
time.sleep(INTERVAL_TIME)#等待INTERVAL_TIME时间
curr_tcpnum = self.getLinkNum.getLinkingNumber(SERVERS_D)
self.tcpRecord.recordData(self.getStr(curr_tcpnum))
writeLog("INFO",r">>>>> 性能指标采集完成")
else:
while True:
if subprocess.Popen.poll(popen) is not None:
break
else:
writeLog("INFO",r">>>>> 等待子进程终止")
writeLog("INFO",r">>>>> 性能指标采集完成")
#判断系统中是否还存留sar和iostat进程
def is_process_exists(self,name):
cmd = "ps ax | grep %s | grep -v grep" %name
p = subprocess.Popen(cmd,stdout=subprocess.PIPE,shell=True)
p.wait()
if p.stdout.readline():
return 1
return 0
def main_start(self):
start_times = 0.0
timeRecord = Record_Data("res/timeConsum")
for server,num in zip(SERVERS_D.values(),self.TCP_COUNT):
writeLog("INFO",r">>>>> 初始 %s 服务连接数 %d" %(server,num))
curr_tcpN = self.getLinkNum.getLinkingNumber(SERVERS_D)
time.sleep(10)
while True:
if not sum(curr_tcpN) <= sum(self.TCP_COUNT):
start_times = time.time()
for server,num in zip(SERVERS_D.values(),curr_tcpN):
writeLog("INFO",r">>>>> 指标采集任务开始，当前 %s 连接数 %d" %(server,num))
#删除旧的kpi文件
del_old_file.Del_Old_File("res/").del_old_file()
#单独线程执行其他服务（srs、nginx等）进程内存指标采集任务
for port,server in SERVERS_D.items():
multiprocessing.Process(target=serverMemoryCollect,args=([port,server],INTERVAL_TIME,sum(self.TCP_COUNT),self.getLinkNum)).start()
#采集服务器系统kpi指标
self.sys_kpi_collect()
writeLog("INFO",r">>>>> 性能数据采集结束！")
time_consum = time.time() - start_times
timeRecord.recordData(["%s" %str(time_consum)])
break
else:
time.sleep(1)
curr_tcpN = self.getLinkNum.getLinkingNumber(SERVERS_D)
if __name__ == '__main__':
kpiCollect = KPI_Collect()
kpiCollect.main_start()
采集其他服务进程消耗内存的代码
#-*- coding:utf-8 -*-
"""
reated on 2015年10月16日
@author: LiBiao
"""
import time
import subprocess
from write_log import writeLog
from record_test_data import Record_Data
#Record the memory of server used
def serverMemoryCollect(servers,intervaltime,tcpNum,getLinkObj):
getLinkNum = getLinkObj
memRecord = Record_Data("res/%s" %(servers[1]+":"+servers[0]))
cmd = "ps -ef | grep %s | grep -v grep | awk \'{print $2}\'" %servers[1]
f = subprocess.Popen(cmd,stdout=subprocess.PIPE,shell=True)
writeLog("INFO",">>>>> %s 指标采集进程执行中....." %servers[1])
pids = [pid.strip() for pid in f.stdout]
heard = [servers[1],'used','Linking_Number Memory_Capacity(MB)']
try:
memRecord.recordData(heard)
curr_tcpN = sum(getLinkNum.getLinkingNumber(servers[0]))
loops = 0
while True:
vrss = []
for p in pids:
cmd2 = "cat /proc/%s/status | grep VmRSS | awk \'{print $2}\'" %p
rss = subprocess.Popen(cmd2,stdout=subprocess.PIPE,shell=True).stdout
vrss.append(int(rss.readline().strip()))
memRecord.recordData(['%s' %str((sum(vrss)/1024))])
if curr_tcpN <= tcpNum:
if loops == 15:
#15s之内，当前连接数小于初始化连接数，程序退出
break
else:
loops += 5
time.sleep(5)
else:
loops = 0
time.sleep(intervaltime)
curr_tcpN = sum(getLinkNum.getLinkingNumber(servers[0]))
writeLog("INFO",r">>>>> %s 进程内存采集完成" %servers[1])
except IOError as err:
writeLog("INFO","File error: " + str(err))
return 0
从原始数据文件提取有效数据并写入新的文件
# -*- coding: utf-8 -*-
'''
Created on 2015年9月14日
@author: LiBiao
'''
import os,time
import subprocess
import getCmds
import del_old_file
from write_log import writeLog
#需要手动配置的数据
#SERVER_NAME = ['srs_2.0.0.','nginx']#'nginx' #可以输入nginx或者srs
SERVERS_D = {'1935':'srs-rtmp','18080':'srs-hls','80':'nginx'}
#系统语言编码
LANG = "en_US.UTF-8"
#获取系统当前使用的语言
def getSysLANG():
popen = subprocess.Popen('echo $LANG',stdout=subprocess.PIPE,shell=True)
return popen.stdout.read().strip()
# 根据系统语言编码获取对应配置文件路径
def getConfPath():
if getSysLANG() == LANG:
return "./conf/abstractConf_en.xml"
return "./conf/abstractConf_ch.xml"
class AbstractKPI(object):
def __init__(self,*args):
(self.cmds,) = args
def abstract_kpi(self):
for cmd in self.cmds:
# print cmd
subprocess.Popen(cmd,stdout=subprocess.PIPE,shell=True)
#获取本机ip地址，用来产生区别于其他机器的数据
def get_local_ip():
try:
ip = os.popen("ifconfig | grep 'inet addr' | awk '{print $2}'").read()
ip = ip[ip.find(':') + 1:ip.find('\n')]
except Exception,e:
print e
return ip
#将最终采集数据打包
def to_tar():
ip = get_local_ip()
times = time.strftime("%Y-%m-%d-%H-%M-%S",time.localtime())
subprocess.call("cp res/linking_number res/timeConsum " +"res/%s "*len(SERVERS_D.items()) %tuple([v + "\:" + k for k,v in SERVERS_D.items()]) + "result/",shell=True)
files = ["result/" + filename for filename in os.listdir("result/")]
cmd = 'tar -cf SYS_KPI_'+ ip + "_" + times + '.tar' + ' %s'*len(files) %tuple(files)
try:
subprocess.call(cmd,shell=True)
except Exception as err:
writeLog("ERROR",r">>>>> 文件压缩出现错误 %s" %str(err))
exit()
writeLog("INFO",r">>>>> 指标文件打包完成")
#脚本主入口函数
def main_start():
#删除旧的kpi文件
del_old_file.Del_Old_File("result/").del_old_file()
#获取到配置文件路径
confpath = getConfPath()
#调用getCmds获取解析kpi文件的命令
cmds = getCmds.Get_Cmds(confpath).getcmds()
#从原始指标文件提取有用的数据
AbstractKPI(cmds).abstract_kpi()
#将result目录下的解析后的kpi文件打包
to_tar()
writeLog("INFO",r">>>>> 指标数据提取并打包完成")
if __name__ == '__main__':
main_start()
脚本中采集数据的命令是linux的，其实这并不是最合适的处理方式，之前只是为了满足工作所需。目前正在使用python第三方模块psutil中的一些方法来执行服务器性能数据的采集，这样的话，脚本就会更加符合python开发的模式。