Python Logging: loguru vs logging

Python log library comparison: logging vs loguru

1. Loguru simplifies logging

Logging is a crucial tool in Python development. It helps developers record program running status, debug problems, and monitor system health. Python comes with a logging library. However, as needs changed, many people started using loguru as an alternative. This article will compare these two libraries and help you choose a more suitable logging solution.

Loguru is a popular third-party logging library. It becomes a powerful alternative to logging by simplifying the configuration process, supporting chained calls, and providing richer functionality.

Advantages of Loguru

• Simple configuration: Loguru does not require creating complex configurations. Complex log configuration can be completed with just a few lines of code.
• Chained calls: It supports chained calls to make logging more intuitive.
• Multi-target output: It can easily output logs to the console and files at the same time, and supports rich format configuration.
• Extra features: It supports functions such as automatic log compression, log file rotation and log retention days.

Basic example of Loguru

from loguru import logger

# 配置日志
logger.add("app.log", rotation="500 MB")  # 文件大小超过 500 MB 时自动轮转

# 记录日志消息
logger.info("这是一个信息消息。")
logger.warning("这是一个警告消息。")
logger.error("这是一个错误消息。")

In this example, we do not need to configure multiple additional processors. File log configuration is easily accomplished by simply calling logger.add().

Output to file and console simultaneously

Loguru can easily output to files and console at the same time:

from loguru import logger
import sys

# 添加日志输出到文件和控制台
logger.add("app.log", rotation="500 MB", retention="10 days")  # 文件轮转和保留 10 天
logger.add(sys.stdout, level="INFO")  # 输出到控制台

# 记录日志消息
logger.info("这是一个信息消息。")
logger.warning("这是一个警告消息。")
logger.error("这是一个错误消息。")

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

2. Advantages and disadvantages of Python’s built-in logging library

Advantages

• Part of the standard library: logging is part of the Python standard library, so no additional installation is required and it is cross-platform.
• Highly customizable: logging provides powerful customization capabilities, allowing flexible control of log format, level and destination (file, console, remote server, 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 can become verbose and unintuitive, especially when output needs to be output to multiple targets at the same time (such as files and consoles) )hour.
• Does not support chain calls: logging does not support chain calls like loguru and needs to be configured layer by layer.

Basic example

A simple log example of

logging is as follows:

import logging

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

# 记录日志消息
logging.info("这是一个信息消息。")
logging.warning("这是一个警告消息。")
logging.error("这是一个错误消息。")

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

At the same time output to the configuration of the file and console

In order to output log output to the console and files at the same time, we need to configure multiple Handler. The code is as follows:

from loguru import logger

# 配置日志
logger.add("app.log", rotation="500 MB")  # 文件大小超过 500 MB 时自动轮转

# 记录日志消息
logger.info("这是一个信息消息。")
logger.warning("这是一个警告消息。")
logger.error("这是一个错误消息。")

It can be seen that in order to achieve a relatively simple feature, we need to create different Handler and configure them one by one.

3. Logging and Loguru's detailed comparison

4. The recommended application scenario

• Simple applications and rapid development :: Loguru is a better choice. It is simple and intuitive, suitable for fast prototype design and small projects.
Complex applications and multi -module projects
• :: Logging provided by the height customization function is more suitable for complex systems that require multi -level configuration, especially those projects that depend on third -party libraries and hope to manage uniform log management Essence
5. Summary

Loguru and Logging each have advantages and disadvantages. For most Python projects, Loguru's simple grammar and powerful functions make it the first choice for rapid development. For large projects, the compatibility and flexibility of the standard library Logging are more applicable. I hope this article can help you choose the right log tool for your project.

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