本文实例为大家分享了python脚本监控docker容器的方法,供大家参考,具体内容如下
脚本功能:
1、监控CPU使用率
2、监控内存使用状况
3、监控网络流量
具体代码:
#!/usr/bin/env python # --*-- coding:UTF-8 --*-- import sys import tab import re import os import time from docker import Client import commands keys_container_stats_list = ['blkio_stats', 'precpu_stats', 'Network', 'read', 'memory_stats', 'cpu_stats'] merit_list=['usage','limit','mem_use_percent','total_cpu_usage','system_cpu_usage','cpu_usage_percent','rx_bytes','tx_bytes'] returnval = None def start(container_name): global container_stats conn=Client(base_url='unix://run/docker.sock',version='1.19') generator=conn.stats(container_name) try: container_stats=eval(generator.next()) except NameError,error_msg: pass # print error_msg container_stats=eval(generator.next()) finally: conn.close() def monitor_docker(monitor_item,merit): if merit == 'mem_use_percent': start(container_name) mem_usage = container_stats['memory_stats']['usage'] mem_limit = container_stats['memory_stats']['limit'] returnval = round(float(mem_usage) / float(mem_limit),2) print returnval elif merit == 'system_cpu_usage': start(container_name) first_result = container_stats['cpu_stats']['system_cpu_usage'] start(container_name) second_result = container_stats['cpu_stats']['system_cpu_usage'] returnval = second_result - first_result print returnval elif merit == 'total_cpu_usage': start(container_name) first_result = container_stats['cpu_stats']['cpu_usage']['total_usage'] start(container_name) second_result = container_stats['cpu_stats']['cpu_usage']['total_usage'] returnval = second_result - first_result print returnval elif merit == 'cpu_usage_percent': start(container_name) system_use=container_stats['cpu_stats']['system_cpu_usage'] total_use=container_stats['cpu_stats']['cpu_usage']['total_usage'] cpu_count=len(container_stats['cpu_stats']['cpu_usage']['percpu_usage']) returnval = round((float(total_use)/float(system_use))*cpu_count*100.0,2) print returnval elif merit == 'rx_bytes': command='''docker exec -it api1 ifconfig eth1 | grep "bytes" | awk '{print $2}' | awk -F ':' '{print $2}' ''' result_one = commands.getoutput(command) time.sleep(1) command='''docker exec -it api1 ifconfig eth1 | grep "bytes" | awk '{print $2}' | awk -F ':' '{print $2}' ''' result_second = commands.getoutput(command) returnval = round((int(result_second) - int(result_one))/1024,2) print returnval elif merit == 'tx_bytes': command='''docker exec -it api1 ifconfig eth1 | grep "bytes" | awk '{print $6}' | awk -F ':' '{print $2}' ''' result_one = commands.getoutput(command) time.sleep(1) command='''docker exec -it api1 ifconfig eth1 | grep "bytes" | awk '{print $6}' | awk -F ':' '{print $2}' ''' result_second = commands.getoutput(command) returnval = round((int(result_second) - int(result_one))/1024,2) print returnval if __name__ == '__main__': command='''docker ps | awk '{print $NF}'| grep -v "NAMES"''' str=commands.getoutput(command) container_counts_list=str.split('\n') if sys.argv[1] not in container_counts_list: print container_counts_list print "你输入的容器名称错误,请重新执行脚本,并输入上述正确的容器名称." sys.exit(1) else: container_name = sys.argv[1] if sys.argv[2] not in keys_container_stats_list: print keys_container_stats_list print '你输入的容器监控项不在监控范围,请重新执行脚本,并输入上述正确的监控项.' sys.exit(1) else: monitor_item = sys.argv[2] if sys.argv[3] not in merit_list: print merit_list print "你输入的容器监控明细详细不在监控范围内,请重新执行脚本,并输入上述正确的明细监控指标." else: merit = sys.argv[3] monitor_docker(monitor_item,merit)
以上就是python脚本监控docker容器的全部代码,希望对大家的学习有所帮助。

Pythonarrayssupportvariousoperations:1)Slicingextractssubsets,2)Appending/Extendingaddselements,3)Insertingplaceselementsatspecificpositions,4)Removingdeleteselements,5)Sorting/Reversingchangesorder,and6)Listcomprehensionscreatenewlistsbasedonexistin

NumPyarraysareessentialforapplicationsrequiringefficientnumericalcomputationsanddatamanipulation.Theyarecrucialindatascience,machinelearning,physics,engineering,andfinanceduetotheirabilitytohandlelarge-scaledataefficiently.Forexample,infinancialanaly

useanArray.ArarayoveralistinpythonwhendeAlingwithHomeSdata,performance-Caliticalcode,orinterFacingWithCcccode.1)同质性data:arrayssavememorywithtypedelements.2)绩效code-performance-clitionalcode-clitadialcode-critical-clitical-clitical-clitical-clitaine code:araysofferferbetterperperperformenterperformanceformanceformancefornalumericalicalialical.3)

不,notalllistoperationsareSupportedByArrays,andviceversa.1)arraysdonotsupportdynamicoperationslikeappendorinsertwithoutresizing,wheremactssperformance.2)listssdonotguaranteeconeeconeconstanttanttanttanttanttanttanttanttimecomplecomecomecomplecomecomecomecomecomecomplecomectaccesslikearrikearraysodo。

toAccesselementsInapythonlist,useIndIndexing,负索引,切片,口头化。1)indexingStartSat0.2)否定indexingAccessesessessessesfomtheend.3)slicingextractsportions.4)iterationerationUsistorationUsisturessoreTionsforloopsoreNumeratorseforeporloopsorenumerate.alwaysCheckListListListListlentePtotoVoidToavoIndexIndexIndexIndexIndexIndExerror。

Arraysinpython,尤其是Vianumpy,ArecrucialInsCientificComputingfortheireftheireffertheireffertheirefferthe.1)Heasuedfornumerericalicerationalation,dataAnalysis和Machinelearning.2)Numpy'Simpy'Simpy'simplementIncressionSressirestrionsfasteroperoperoperationspasterationspasterationspasterationspasterationspasterationsthanpythonlists.3)inthanypythonlists.3)andAreseNableAblequick

你可以通过使用pyenv、venv和Anaconda来管理不同的Python版本。1)使用pyenv管理多个Python版本:安装pyenv,设置全局和本地版本。2)使用venv创建虚拟环境以隔离项目依赖。3)使用Anaconda管理数据科学项目中的Python版本。4)保留系统Python用于系统级任务。通过这些工具和策略,你可以有效地管理不同版本的Python,确保项目顺利运行。

numpyarrayshaveseveraladagesoverandastardandpythonarrays:1)基于基于duetoc的iMplation,2)2)他们的aremoremoremorymorymoremorymoremorymoremorymoremoremory,尤其是WithlargedAtasets和3)效率化,效率化,矢量化函数函数函数函数构成和稳定性构成和稳定性的操作,制造


热AI工具

Undresser.AI Undress
人工智能驱动的应用程序,用于创建逼真的裸体照片

AI Clothes Remover
用于从照片中去除衣服的在线人工智能工具。

Undress AI Tool
免费脱衣服图片

Clothoff.io
AI脱衣机

Video Face Swap
使用我们完全免费的人工智能换脸工具轻松在任何视频中换脸!

热门文章

热工具

SublimeText3 Linux新版
SublimeText3 Linux最新版

MinGW - 适用于 Windows 的极简 GNU
这个项目正在迁移到osdn.net/projects/mingw的过程中,你可以继续在那里关注我们。MinGW:GNU编译器集合(GCC)的本地Windows移植版本,可自由分发的导入库和用于构建本地Windows应用程序的头文件;包括对MSVC运行时的扩展,以支持C99功能。MinGW的所有软件都可以在64位Windows平台上运行。

Atom编辑器mac版下载
最流行的的开源编辑器

mPDF
mPDF是一个PHP库,可以从UTF-8编码的HTML生成PDF文件。原作者Ian Back编写mPDF以从他的网站上“即时”输出PDF文件,并处理不同的语言。与原始脚本如HTML2FPDF相比,它的速度较慢,并且在使用Unicode字体时生成的文件较大,但支持CSS样式等,并进行了大量增强。支持几乎所有语言,包括RTL(阿拉伯语和希伯来语)和CJK(中日韩)。支持嵌套的块级元素(如P、DIV),

SublimeText3 Mac版
神级代码编辑软件(SublimeText3)