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.

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