Python Logging Libraries Comparison: logging vs loguru

1. Loguru Simplifies Logging

In Python development, logging is a crucial tool. It helps developers record the running status of programs, debug issues, and monitor the health of systems. Python has the built - in logging library. However, with the changing requirements, many people have started to use loguru as an alternative. This article will compare these two libraries to help you choose a more suitable logging solution.

Loguru is a popular third - party logging library. It has become a powerful substitute for logging by simplifying the configuration process, supporting chained calls, and providing more abundant features.

Advantages of Loguru

• Simple Configuration: Loguru doesn't require creating complex configurations. You can complete complex logging configurations with just a few lines of code.
• Chained Calls: It supports chained calls, making logging more intuitive.
• Multi - target Output: It can easily achieve logging output to both the console and files, and also supports rich format configurations.
• Extra Features: It supports features such as automatic log compression, log file rotation, and log retention days.

Basic Example of Loguru

from loguru import logger

# Configure logging
logger.add("app.log", rotation="500 MB")  # The file will rotate automatically when it exceeds 500 MB

# Log messages
logger.info("This is an info message.")
logger.warning("This is a warning message.")
logger.error("This is an error message.")

In this example, we don't need to configure multiple handlers additionally. Just calling logger.add() can easily complete the file logging configuration.

Output to Both File and Console

Loguru can very conveniently achieve output to both the file and the console:

from loguru import logger
import sys

# Add logging output to file and console
logger.add("app.log", rotation="500 MB", retention="10 days")  # File rotation and retention for 10 days
logger.add(sys.stdout, level="INFO")  # Output to the console

# Log messages
logger.info("This is an info message.")
logger.warning("This is a warning message.")
logger.error("This is an error message.")

Here, logger.add(sys.stdout, level="INFO") can display the logs on the console without additional configuration.

2. Advantages and Disadvantages of Python's Built - in logging

Advantages

• Part of the Standard Library: logging is part of the Python standard library, so there is no need for additional installation, and it is cross - platform.
• Highly Customizable: logging provides powerful customization features, allowing flexible control of log formats, levels, and targets (files, consoles, remote servers, etc.).
• Strong Compatibility: Many third - party libraries also use logging, enabling seamless integration of various logs.

Disadvantages

• Complex Configuration: The basic use of logging is relatively simple, but slightly more complex configurations become lengthy and non - intuitive, especially when it is necessary to output to multiple targets (such as files and consoles) simultaneously.
• No Chained Calls: logging does not support chained calls like loguru and requires layer - by - layer configuration.

Basic Example

A simple logging example of logging is as follows:

import logging

# Configure logging
logging.basicConfig(
    level = logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    filename='app.log',
    filemode='a'
)

# Log messages
logging.info("This is an info message.")
logging.warning("This is a warning message.")
logging.error("This is an error message.")

In this example, the logs will be recorded in the app.log file but will not be displayed in the console. If we want to display the logs in both the console and the file, we need to configure StreamHandler additionally.

Configuration for Output to File and Console

To output logs to both the console and the file, we need to configure multiple Handlers. The code is as follows:

import logging

# Get the logger
logger = logging.getLogger()
logger.setLevel(logging.INFO)

# File handler
file_handler = logging.FileHandler('app.log')
file_handler.setFormatter(logging.Formatter('%(asctime)s - %(levelname)s - %(message)s'))

# Console handler
console_handler = logging.StreamHandler()
console_handler.setFormatter(logging.Formatter('%(asctime)s - %(levelname)s - %(message)s'))

# Add handlers to the logger
logger.addHandler(file_handler)
logger.addHandler(console_handler)

# Log messages
logger.info("This is an info message.")
logger.warning("This is a warning message.")
logger.error("This is an error message.")

It can be seen that to achieve a relatively simple function, we need to create different Handlers and configure them one by one.

3. Detailed Comparison between logging and loguru

4. Recommended Application Scenarios

• Simple Applications and Rapid Development: Loguru is a better choice. It is concise and intuitive, suitable for rapid prototyping and small projects.
• Complex Applications and Multi - module Projects: The highly customizable features provided by logging are more suitable for complex systems that require multi - level configurations, especially those projects that rely on third - party libraries and hope to unify log management.

5. Summary

Both loguru and logging have their advantages and disadvantages. For most Python projects, the concise syntax and powerful features of loguru make it the first choice for rapid development. For larger projects, the compatibility and flexibility of the standard library logging are more suitable. I hope this article can help you choose the appropriate logging tool for your project.

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