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import psutil
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import time
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import threading
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from flask import Flask, Response
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from prometheus_client import generate_latest, CONTENT_TYPE_LATEST, Gauge
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app = Flask(__name__)
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# --- 1. 定义全局变量存储 CPU 数据 ---
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# 初始化一个变量，默认 0.0
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CURRENT_CPU_USAGE = 0.0
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# --- 2. 定义指标 ---
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cpu_usage_gauge = Gauge(
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    'my_app_cpu_usage_percent',
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    'Current CPU usage percentage of the application'
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)
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# --- 3. 后台任务：每秒更新一次 CPU 使用率 ---
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def background_cpu_monitor():
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    global CURRENT_CPU_USAGE
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    while True:
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        CURRENT_CPU_USAGE = psutil.cpu_percent(interval=1)
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        cpu_usage_gauge.set(CURRENT_CPU_USAGE)
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# --- 4. 启动后台线程 ---
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# 设置为守护线程(daemon=True)，这样主程序退出时它也会自动关闭
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thread = threading.Thread(target=background_cpu_monitor, daemon=True)
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thread.start()
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# --- 5. 暴露 /metrics 接口 ---
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@app.route('/metrics')
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def metrics():
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    return Response(generate_latest(), mimetype=CONTENT_TYPE_LATEST)
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# --- 6. 模拟业务接口 ---
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@app.route('/')
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def index():
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    return {"status": "running", "msg": "Check /metrics for real data"}
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if __name__ == "__main__":
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    print("启动服务... 访问 http://127.0.0.1:5000/metrics")
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    app.run(host='127.0.0.1', port=5000, debug=False)

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import psutil
2
import time
3
import threading
4
from flask import Flask
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from prometheus_flask_exporter import PrometheusMetrics, Gauge
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app = Flask(__name__)
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# 1. 初始化扩展库
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# 注意：这一步会自动注入默认的 Python/Process 指标，并自动注册 /metrics 路由
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metrics = PrometheusMetrics(app)
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# 2. 定义自定义指标
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cpu_gauge = Gauge('cpu_usage_custom', 'Custom CPU Usage')
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# 3. 定义后台更新函数
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def update_cpu_loop():
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    while True:
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        cpu_percent = psutil.cpu_percent(interval=1)
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        # 更新指标
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        cpu_gauge.set(cpu_percent)
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# 4. 启动后台线程
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# 设置为守护线程 (daemon=True)，这样主程序退出时，它也会自动关闭
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thread = threading.Thread(target=update_cpu_loop, daemon=True)
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thread.start()
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# 5. 业务路由
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@app.route('/')
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def index():
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    return "Server is running. Check /metrics for data."
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if __name__ == "__main__":
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    print("启动服务... 访问 http://127.0.0.1:5000/metrics")
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    app.run(port=5000)

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python app.py