Detailed explanation of server performance monitoring and alarm in Python automation operation and maintenance

1. Basic monitoring architecture design

Monitoring indicator selection

• Core resources: CPU utilization, memory utilization, disk space and I/O, network traffic, process status, etc.
• Business indicators: HTTP service status code, database connection number, application response time, etc.
• Containerized scenarios: Docker/Kubernetes container resource usage, Pod health status.

Tools and library selection

• Data collection：psutil(System Resources),requests(HTTP status),docker(Container Monitoring).
• Alarm notification：smtplib(mail),requests（Webhook）、twilio(Short message).
• Data storage and visualization: Prometheus (time sequence database), Grafana (dashboard), InfluxDB (lightweight storage).

2. Core code implementation and configuration

Scenario 1: Basic resource monitoring and alarm

Configuration instructions：

usepsutilCollect data and send email alerts through SMTP protocol.

Timing tasks:passcrontabExecute the script every 5 minutes:

*/5 * * * * /usr/bin/python3 /path/to/

Scenario 2: HTTP service status monitoring

import requests
import sys

def check_http_status(url, expected_code=200):
    try:
        response = (url, timeout=10)
        if response.status_code != expected_code:
            send_alert(f"HTTPAbnormal status：{url} return {response.status_code}")
    except Exception as e:
        send_alert(f"Unreachable service：{url}，mistake：{str(e)}")

def send_alert(message):
    # Integrated Webhook (such as DingTalk, Enterprise WeChat)    webhook_url = "/robot/send?access_token=xxx"
    headers = {'Content-Type': 'application/json'}
    data = {"msgtype": "text", "text": {"content": message}}
    (webhook_url, json=data, headers=headers)

# Call examplecheck_http_status("/api/health")

Extended configuration：

• Integrated Zabbix: Use script output as a custom monitoring item and configure Trigger to trigger alarms.
• Prometheus Monitoring:useprometheus-clientThe library exposes the indicators for Prometheus to pull.

Scenario 3: Log Analysis and Anomaly Detection

import re
from collections import defaultdict

def analyze_logs(log_path, pattern=r'ERROR: (.*)'):
    error_counts = defaultdict(int)
    with open(log_path, 'r') as f:
        for line in f:
            match = (pattern, line)
            if match:
                error_type = (1)
                error_counts[error_type] += 1
    # Trigger threshold alarm    for error, count in error_counts.items():
        if count > 10:
            send_alert(f"Error Type {error} Appears in the log {count} Second-rate")

# Example: Monitoring Nginx error logsanalyze_logs('/var/log/nginx/')

Optimization solution：

• useloguruorELKStack (Elasticsearch + Logstash + Kibana) implements log aggregation.

3. Advanced scenarios and integration

1. Container monitoring

usedockerLibrary gets container status:

import docker
client = docker.from_env()
for container in ():
    stats = (stream=False)
    print(f"container {} CPUUsage rate：{stats['cpu_percent']}%")

Integration of Kubernetes: viakubernetesThe library monitors Pod resources.

2. Automatic repair

Automatically clean up old logs when insufficient disk space is detected:

if  > 90:
    ("find /var/log -name '*.log' -mtime +7 -exec rm {} \;")

3. Visualize the dashboard

Grafana configuration: Store data to InfluxDB and configure the dashboard to display real-time metrics.

4. Recommended complete tool chain

5. Summary

Implementing automated operation and maintenance monitoring through Python, it is necessary to select a tool chain based on specific scenarios:

• Basic monitoring：psutil+SMTP alarm meets stand-alone needs.
• Distributed Systems: Prometheus+Grafana implements cluster monitoring.
• Log and business monitoring: Regular analysis + ELK stack improves the inspection efficiency.
• Automated repair: Trigger predefined scripts (such as cleaning files, restarting services) after a problem is detected.

Things to note：

• Security: Sensitive information (such as passwords) should be stored using environment variables or encrypted.
• Performance overhead: Monitoring scripts need to optimize resource usage to avoid affecting business.
• Alarm convergence: Avoid alarm storms through tools such as AlertManager.

This is the article about the detailed explanation of server performance monitoring and alarms in Python automation operation and maintenance. For more related contents of Python server performance monitoring and alarms, please search for my previous articles or continue browsing the related articles below. I hope everyone will support me in the future!