Home Backend Development Python Tutorial python脚本监控docker容器

python脚本监控docker容器

Jun 10, 2016 pm 03:05 PM
docker python container monitor

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)
Copy after login

 

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。

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

PHP and Python: Different Paradigms Explained PHP and Python: Different Paradigms Explained Apr 18, 2025 am 12:26 AM

PHP is mainly procedural programming, but also supports object-oriented programming (OOP); Python supports a variety of paradigms, including OOP, functional and procedural programming. PHP is suitable for web development, and Python is suitable for a variety of applications such as data analysis and machine learning.

Python vs. JavaScript: The Learning Curve and Ease of Use Python vs. JavaScript: The Learning Curve and Ease of Use Apr 16, 2025 am 12:12 AM

Python is more suitable for beginners, with a smooth learning curve and concise syntax; JavaScript is suitable for front-end development, with a steep learning curve and flexible syntax. 1. Python syntax is intuitive and suitable for data science and back-end development. 2. JavaScript is flexible and widely used in front-end and server-side programming.

Choosing Between PHP and Python: A Guide Choosing Between PHP and Python: A Guide Apr 18, 2025 am 12:24 AM

PHP is suitable for web development and rapid prototyping, and Python is suitable for data science and machine learning. 1.PHP is used for dynamic web development, with simple syntax and suitable for rapid development. 2. Python has concise syntax, is suitable for multiple fields, and has a strong library ecosystem.

PHP and Python: A Deep Dive into Their History PHP and Python: A Deep Dive into Their History Apr 18, 2025 am 12:25 AM

PHP originated in 1994 and was developed by RasmusLerdorf. It was originally used to track website visitors and gradually evolved into a server-side scripting language and was widely used in web development. Python was developed by Guidovan Rossum in the late 1980s and was first released in 1991. It emphasizes code readability and simplicity, and is suitable for scientific computing, data analysis and other fields.

How to run python with notepad How to run python with notepad Apr 16, 2025 pm 07:33 PM

Running Python code in Notepad requires the Python executable and NppExec plug-in to be installed. After installing Python and adding PATH to it, configure the command "python" and the parameter "{CURRENT_DIRECTORY}{FILE_NAME}" in the NppExec plug-in to run Python code in Notepad through the shortcut key "F6".

How to use VSCode How to use VSCode Apr 15, 2025 pm 11:21 PM

Visual Studio Code (VSCode) is a cross-platform, open source and free code editor developed by Microsoft. It is known for its lightweight, scalability and support for a wide range of programming languages. To install VSCode, please visit the official website to download and run the installer. When using VSCode, you can create new projects, edit code, debug code, navigate projects, expand VSCode, and manage settings. VSCode is available for Windows, macOS, and Linux, supports multiple programming languages ​​and provides various extensions through Marketplace. Its advantages include lightweight, scalability, extensive language support, rich features and version

Golang vs. Python: Performance and Scalability Golang vs. Python: Performance and Scalability Apr 19, 2025 am 12:18 AM

Golang is better than Python in terms of performance and scalability. 1) Golang's compilation-type characteristics and efficient concurrency model make it perform well in high concurrency scenarios. 2) Python, as an interpreted language, executes slowly, but can optimize performance through tools such as Cython.

Python vs. C  : Learning Curves and Ease of Use Python vs. C : Learning Curves and Ease of Use Apr 19, 2025 am 12:20 AM

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

See all articles