


Best Practices for the Python Logging Module: Writing Clean, Maintainable Code
Log level
LogThe level determines which messages will be output to the log. python The Logging module provides 6 log levels (from low to high):
DEBUG INFO WARNING ERROR CRITICAL FATAL
Generally, the following levels are recommended:
- DEBUG: Used for debugging information, only used during the development phase
- INFO: Used for general information, recording system status and operations
- WARNING: For potential problems that may cause problems but will not immediately disrupt the system
- ERROR: Used for errors and exceptions that require attention and resolution
- CRITICAL: Used for serious errors that will cause system interruption
Log format
The log format determines the information contained in the log message. Python The Logging module provides a variety of predefined formatters, such as:
logging.FORMatter() logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")
You can also customize the formatter to add additional information such as process ID, thread name, or call stack.
Log processing
Log processing determines how log messages are output and processed. The Python Logging module provides a variety of processors, such as:
- StreamHandler: Output log messages to stdout or stderr
- FileHandler: Output log messages to file
- RotatingFileHandler: Output log messages to a file and automatically scroll when the file reaches a certain size
- SocketHandler: Send log messages to the remote host over the network
You can use multiple processors at the same time to process log messages in different ways.
Best Practices
The following are some best practices for the Python Logging module:
- Use the appropriate log level: Select the appropriate log level based on the importance of the message.
- Customized log format: Add other information as needed to improve the readability and traceability of the log.
- Use multiple processors: Use multiple processors at the same time to process log messages in different ways, such as logging error messages to a file and outputting debugging information to the console.
- Using the logging.config module: Use this module to configure complex logging settings, such as using a configuration file or the dictConfig() function.
- Follow logging conventions: Use consistent log formats and levels throughout your code base to improve code readability and maintainability.
- Logging stack trace of exception: When logging an error or exception, include the stack trace to aid debugging.
- Use debug logs sensibly: Use DEBUG level only when needed, otherwise a lot of noise will be generated.
- Review logs regularly: Review logs regularly for errors, warnings, or other information that requires attention.
Code Example
The following is a simple example using the Python Logging module:
import logging # 创建一个 logger,传递名称为 my_app logger = logging.getLogger("my_app") # 设置日志级别为 INFO logger.setLevel(logging.INFO) # 创建一个流处理器,将日志消息输出到 stdout handler = logging.StreamHandler() handler.setLevel(logging.INFO) # 创建一个格式器 formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s") handler.setFormatter(formatter) # 将处理器添加到 logger logger.addHandler(handler) # 记录一條 INFO 級別的日誌信息 logger.info("This is an info message")
By following these best practices, you can effectively use the Python Logging module to record events in your application and improve the maintainability, readability, and debuggability of your code.
The above is the detailed content of Best Practices for the Python Logging Module: Writing Clean, Maintainable Code. For more information, please follow other related articles on the PHP Chinese website!

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.

Python's real-world applications include data analytics, web development, artificial intelligence and automation. 1) In data analysis, Python uses Pandas and Matplotlib to process and visualize data. 2) In web development, Django and Flask frameworks simplify the creation of web applications. 3) In the field of artificial intelligence, TensorFlow and PyTorch are used to build and train models. 4) In terms of automation, Python scripts can be used for tasks such as copying files.

Python is widely used in data science, web development and automation scripting fields. 1) In data science, Python simplifies data processing and analysis through libraries such as NumPy and Pandas. 2) In web development, the Django and Flask frameworks enable developers to quickly build applications. 3) In automated scripts, Python's simplicity and standard library make it ideal.

Python's flexibility is reflected in multi-paradigm support and dynamic type systems, while ease of use comes from a simple syntax and rich standard library. 1. Flexibility: Supports object-oriented, functional and procedural programming, and dynamic type systems improve development efficiency. 2. Ease of use: The grammar is close to natural language, the standard library covers a wide range of functions, and simplifies the development process.

Python is highly favored for its simplicity and power, suitable for all needs from beginners to advanced developers. Its versatility is reflected in: 1) Easy to learn and use, simple syntax; 2) Rich libraries and frameworks, such as NumPy, Pandas, etc.; 3) Cross-platform support, which can be run on a variety of operating systems; 4) Suitable for scripting and automation tasks to improve work efficiency.

Yes, learn Python in two hours a day. 1. Develop a reasonable study plan, 2. Select the right learning resources, 3. Consolidate the knowledge learned through practice. These steps can help you master Python in a short time.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Safe Exam Browser
Safe Exam Browser is a secure browser environment for taking online exams securely. This software turns any computer into a secure workstation. It controls access to any utility and prevents students from using unauthorized resources.

WebStorm Mac version
Useful JavaScript development tools

SAP NetWeaver Server Adapter for Eclipse
Integrate Eclipse with SAP NetWeaver application server.

MinGW - Minimalist GNU for Windows
This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

Atom editor mac version download
The most popular open source editor