39-linux-cpu内存监控

linux cpu内存监控

（1.）使用python脚本实现，启动后访问http://localhost:8000/即可查看cpu和内存使用情况

import psutil
import os
import xlwt
import xlrd
import time
from pyecharts.charts import Line
from http.server import SimpleHTTPRequestHandler
from socketserver import ThreadingTCPServer

# 获取当前运行的pid
p1 = psutil.Process(os.getpid())


# Function to generate the HTML file and add auto-refresh
def generate_html():
    # 获取折线图需要绘制的数据信息;
    data = xlrd.open_workbook("CPU.xls")
    table = data.sheets()[0]
    print(table.ncols)
    row1data = table.row_values(0)
    print(row1data)  # ['列1', '列2', '列3', '列4']

    x = []
    y1 = []
    y2 = []
    for i in range(1, table.nrows):
        print(table.row_values(i))
        x.append(table.row_values(i)[0])
        y1.append(table.row_values(i)[1])
        y2.append(table.row_values(i)[2])

    # 实例化Line类为line对象, 并添加x和y对应的点;
    line = (
        Line()
        .add_xaxis(x)
        .add_yaxis("Cpu占有率散点图", y1)
        .add_yaxis("内存占有率散点图", y2)
        # .set_global_opts(title_opts=opts.TitleOpts(title="Cpu占有率散点图"))
    )

    # Render the chart to HTML
    line.render("show.html")

    # Open the HTML file and add auto-refresh functionality
    with open("show.html", "r") as file:
        content = file.read()

    # Add a meta tag to auto-refresh the page every 10 seconds
    auto_refresh_content = f'''
    <meta http-equiv="refresh" content="5">
    {content}
    '''

    # Save the updated HTML with the refresh tag
    with open("show.html", "w") as file:
        file.write(auto_refresh_content)


# 获取当前数据并保存到Excel
def collect_data():
    myxls = xlwt.Workbook()
    sheet1 = myxls.add_sheet(u'CPU')
    sheet1.write(0, 0, '当前时间')
    sheet1.write(0, 1, 'cpu占用率')
    sheet1.write(0, 2, '内存占用率')

    i = 1
    for i in range(1, 10):
        b = psutil.cpu_percent(interval=1.0)  # cpu占用率
        a = psutil.virtual_memory().percent  # 内存占用率
        nowtime = time.strftime("%H:%M:%S", time.localtime())
        sheet1.write(i, 0, nowtime)
        print(nowtime)
        sheet1.write(i, 1, b)
        print(b)
        sheet1.write(i, 2, a)
        myxls.save('CPU.xls')
        i += 1


# HTTP server to serve the show.html file
def run_server():
    handler = SimpleHTTPRequestHandler
    # Use ThreadingTCPServer for multi-threading support
    httpd = ThreadingTCPServer(('0.0.0.0', 8000), handler)
    print("Serving at http://localhost:8000")
    httpd.serve_forever()


# Main loop
if __name__ == "__main__":
    # Run the HTTP server in a separate thread
    import threading

    server_thread = threading.Thread(target=run_server)
    server_thread.daemon = True  # Allow the server thread to exit when the main program exits
    server_thread.start()

    while True:
        # Collect data and save it to Excel
        collect_data()

        # Generate the chart and HTML with auto-refresh
        generate_html()

        # Pause for some time to avoid infinite loop too fast and avoid server overload
        time.sleep(5)  # adjust the sleep time as needed

(2.)改进版本，记录历史数据，并绘制折线图

import psutil
import os
import csv
import time
from pyecharts.charts import Line
from http.server import SimpleHTTPRequestHandler
from socketserver import ThreadingTCPServer
import threading


# Create a CSV file to store all data
def init_csv():
    with open("cpu_memory_data.csv", mode='w', newline='') as file:
        writer = csv.writer(file)
        writer.writerow(["时间", "CPU占用率", "内存占用率"])


# Function to record the data and append it to the CSV file
def collect_data():
    cpu_usage = psutil.cpu_percent(interval=1.0)  # CPU usage
    memory_usage = psutil.virtual_memory().percent  # Memory usage
    current_time = time.strftime("%H:%M:%S", time.localtime())  # Current time

    # Append data to the CSV file
    with open("cpu_memory_data.csv", mode='a', newline='') as file:
        writer = csv.writer(file)
        writer.writerow([current_time, cpu_usage, memory_usage])

    return current_time, cpu_usage, memory_usage


# Function to generate the HTML file and add auto-refresh
def generate_html():
    # Read data from the CSV file
    times, cpu_data, memory_data = [], [], []
    with open("cpu_memory_data.csv", mode='r') as file:
        reader = csv.reader(file)
        next(reader)  # Skip the header
        for row in reader:
            times.append(row[0])
            cpu_data.append(float(row[1]))
            memory_data.append(float(row[2]))

    # Generate the chart using pyecharts
    line = (
        Line()
        .add_xaxis(times)
        .add_yaxis("CPU占有率", cpu_data)
        .add_yaxis("内存占有率", memory_data)
        .set_global_opts(title_opts={"text": "CPU和内存占有率"})
    )

    # Render the chart to an HTML file
    line.render("index.html")

    # Open the HTML file and add auto-refresh functionality
    with open("index.html", "r") as file:
        content = file.read()

    # Add a meta tag to auto-refresh the page every 10 seconds
    auto_refresh_content = f'''
    <meta http-equiv="refresh" content="10">
    {content}
    '''

    # Save the updated HTML with the refresh tag
    with open("index.html", "w") as file:
        file.write(auto_refresh_content)


# HTTP server to serve the index.html file
def run_server():
    handler = SimpleHTTPRequestHandler
    # Use ThreadingTCPServer for multi-threading support
    httpd = ThreadingTCPServer(('0.0.0.0', 8000), handler)
    print("Serving at http://127.0.0.1:8000/index.html")
    httpd.serve_forever()


# Main loop
if __name__ == "__main__":
    # Initialize CSV file for storing data
    init_csv()

    # Run the HTTP server in a separate thread
    server_thread = threading.Thread(target=run_server)
    server_thread.daemon = True  # Allow the server thread to exit when the main program exits
    server_thread.start()

    while True:
        # Collect data and save it to the CSV file
        collect_data()

        # Generate the chart and HTML with auto-refresh
        generate_html()

        # Pause for some time to avoid infinite loop too fast and avoid server overload
        time.sleep(1)  # adjust the sleep time as needed

(3.)使用easyNmon，推荐使用

https://github.com/mzky/easyNmon/releases  // ./easyNmon &  启动
https://mzky.cc/post/9.html  // 使用说明，安装后访问http://localhost:9999

（4.）使用原生的nmon写文件，使用web解析文件

./nmon -f -s 5 -c 60 -C -m  // 采集数据，并写为文件

// 下载release文件，访问http://localhost:10001/nmon/，上传文件解析
https://github.com/electricbubble/nmon-analyser-releases  

参考链接

https://blog.csdn.net/weixin_42069644/article/details/104953486
https://archlinuxarm.org/packages/aarch64/nmon
https://nmon.sourceforge.io/pmwiki.php?n=Site.Download
https://www.cnblogs.com/titanstorm/p/14725029.html
https://github.com/aguther/nmonchart
https://github.com/nmonvisualizer/nmonvisualizer
https://www.cnblogs.com/arnoldlu/p/9462221.html
https://www.cnblogs.com/seamy/p/15649508.html
https://www.cnblogs.com/SunshineKimi/p/12182865.html