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python监控进程内存和cpu使用情况

用Python来编写脚本简化日常的运维工作是Python的一个重要用途。在Linux下,有许多系统命令可以让我们时刻监控系统运行的状态,如ps,top,free等等。要获取这些系统信息,Python可以通过subprocess模块调用并获取结果。但这样做显得很麻烦,尤其是要写很多解析代码。

接下来我们利用psutil来监控进程的cpu和内存负载,并将监控的数据定时push到监控系统中,这样就很方便我们每天登录到监控平台去查看业务进程的情况。

pip install psutil
# 监控代码如下

#!/usr/bin/env python
# -*- coding:utf-8 -*-

"""
@Author: 风哥
@Mail: gujiwork@outlook.com
@File:MonitorProcess.py
@Time:2020/4/24 22:32
"""

import psutil
import re
import requests
import time
import json

MONITOR_API_URL = "http://localhost:2058/api/collector/push"
APP_NAME = "Dzookeeper.log.dir=/data/apps/zookeeper-3.5.3-beta_2/bin/"
MONITOR_TAG_NAME = "trip-corp-web"
TIME_INTERVAL = 20


class MonitorProcessInfo(object):
    pid_number = None
    process = None

    @classmethod
    def __init__(cls, process_name):
        """
        Get process ID number according to process name
        :param process_name:
        """
        pids = psutil.process_iter()

        for pid in pids:

            # Find PID by process name
            '''
            str_pid = str(pid)
            f = re.compile(process_name, re.I)
            if f.search(str_pid):
                global pid_number, process
                pid_number = int(str_pid.split('pid=')[1].split(',')[0])
                process = psutil.Process(pid_number)
            '''

            # Find PID based on process name CmdLine
            if process_name in ''.join(pid.cmdline()):
                global pid_number, process
                pid_number = pid.pid
                process = psutil.Process(pid_number)

    @classmethod
    def process_memory(cls):
        """
        Get process memory usage
        :return:
        """
        process_memory_percent = process.memory_percent()
        process_memory_info = process.memory_info()

        return process_memory_percent, process_memory_info.rss

    @classmethod
    def process_cpu(cls):
        """
        Get the CPU usage of the process
        :return:
        """
        process_cpu_percent = process.cpu_percent(interval=1.0)

        return process_cpu_percent

    @classmethod
    def process_io(cls):
        """
        Get process IO status
        :return:
        """
        process_io_count = process.io_counters()

        return process_io_count.read_bytes, process_io_count.write_bytes

    @classmethod
    def process_threads(cls):
        """
        Get Process Threads
        :return:
        """
        process_threads = process.num_threads()
        return process_threads


class PushMonitorProcessInfo(MonitorProcessInfo):

    def __init__(self, process_name, tag_name):
        """Monitoring indicators
            - memory_rss :      Memory size used by process
            - memory_percent:   Percentage of process memory used
            - process_threads:  Number of process threads
            - io_read_bytes:    Process IO read operation
            - io_write_bytes:   Process IO write operation
            - cpu_percent:      Percentage of CPU used by process

        :param process_name:
        :param tag_name:
        """
        # py3 super
        # super().__init__(process_name)

        """When super inherits the parent class in py2, the parent class needs to add the object attribute, 
        MonitorProcessInfo (object), otherwise it will be thrown incorrectly
        TypeError: must be type, not classobj"""
        super(PushMonitorProcessInfo, self).__init__(process_name)

        cpu_percent = PushMonitorProcessInfo.process_cpu()
        memory_percent, memory_rss = PushMonitorProcessInfo.process_memory()
        io_read_bytes, io_write_bytes = PushMonitorProcessInfo.process_io()
        thread_number = PushMonitorProcessInfo.process_threads()

        pro_data = {
            "memory_rss": memory_rss,
            "memory_percent": memory_percent,
            "io_read_bytes": io_read_bytes,
            "io_write_bytes": io_write_bytes,
            "cpu_percent": cpu_percent,
            "threads": thread_number
        }
        payload = []
        t = int(time.time())
        for k, v in pro_data.items():
            metric_data = {
                "metric": "process.%s" % k,
                "endpoint": "10.86.12.13",
                "tags": "tomcat_name=%s" % tag_name,
                "value": int(v),
                "timestamp": t,
                "step": TIME_INTERVAL
            }

            payload.append(metric_data)

        headers = {
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 \
                                        (KHTML, like Gecko) Chrome/80.0.3987.122 Safari/537.36",
            "Content-Type": "application/json"
        }

        rsp = requests.post(url=MONITOR_API_URL, data=json.dumps(payload), headers=headers)
        print(rsp.text)


while True:
    push_data = PushMonitorProcessInfo(APP_NAME, MONITOR_TAG_NAME)
    time.sleep(TIME_INTERVAL)

脚本以死循环方式每隔20秒将数据上报到监控平台,登陆监控平台就可以很愉快的查看图表了

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