Monitoring Tips in Python

Python is a powerful, easy-to-learn and easy-to-use programming language that has become a standard tool in the fields of data science and machine learning. With the rise of cloud computing, Python's application scenarios are becoming more and more widespread, and it is often used in fields such as server management, network monitoring, and data security. In these scenarios, monitoring skills are crucial. This article will introduce monitoring techniques in Python from the following aspects.

Log monitoring

The log is an important tool for recording system behavior and an important basis for troubleshooting problems. Python's built-in logging module can help us implement complex log management functions, such as setting logs at different levels, writing logs to files or databases, triggering alarms at specific times or scenarios, etc.

For example, we can use the following code to implement a simple logging program:

import logging

logging.basicConfig(filename='example.log', level=logging.DEBUG)

logging.debug('This is a debug message')
logging.info('This is an info message')
logging.warning('This is a warning message')
logging.error('This is an error message')
logging.critical('This is a critical message')

After running this program, a file named example.log will be generated in the current directory. file and write five levels of log information into it. We can customize the format, level and output destination of logging by modifying the parameters of logging.basicConfig.

Network Monitoring

The network is the core of modern computers and the most vulnerable part. Python's socket module can help us implement functions such as sending and receiving data over the network, finding host information, and controlling network traffic.

Taking network port scanning as an example, we can use the following code to scan all open ports of the target host:

import socket

host = '127.0.0.1'
ports = range(1, 1025)

for port in ports:
    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
    try:
        s.connect((host, port))
        print(f"Port {port} is open")
    except:
        pass
    s.close()

This program uses socket.socket to create a TCP/IP socket and try to connect to each port of the target host. If the connection is successful, it means that the port is open and we can add it to the result list.

Process Monitoring

In the field of server management and security, process monitoring is a vital task. Python's os module and psutil module provide access and control interfaces to system processes, which can help us obtain, modify and terminate process information.

Taking process status query as an example, we can use the following code to obtain the status of the specified PID process:

import psutil

pid = 1234
p = psutil.Process(pid)
print(p.name())
print(p.status())
print(p.cpu_percent())

This program uses psutil.Process to obtain the process object of the specified PID , and output information such as its process name, status, and CPU usage.

Behavior Monitoring

In the field of data analysis and machine learning, behavior monitoring is an important task that can help us discover and predict user behavior, abnormal behavior, fraudulent behavior, etc. Python's pandas module and scikit-learn module provide a wealth of data processing and machine learning tools that can help us with data collection, feature extraction, model training, and prediction.

Taking anomaly detection as an example, we can use the following code to identify abnormal samples in the data set:

import pandas as pd
from sklearn.ensemble import IsolationForest

data = pd.read_csv('dataset.csv')
X = data.drop('label', axis=1)
clf = IsolationForest(random_state=42)
clf.fit(X)
pred = clf.predict(X)
print(sum(pred == -1))

This program uses pd.read_csv to read the data set in CSV format , and exclude the label column from the feature. Subsequently, use IsolationForest to train an anomaly detection model and make predictions on the entire data set. Finally, output the number of all abnormal samples.

Summary

As an excellent programming tool, Python has been widely used in monitoring, server management, security, data science, machine learning and other fields. This article introduces various monitoring techniques in Python, including log monitoring, network monitoring, process monitoring and behavior monitoring. I hope that readers can understand the basic principles and implementation methods of these techniques through the introduction of this article, and apply them in actual work to improve their programming level and work efficiency.

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