Python的logging模塊通過日誌級別、處理器、格式化和層級式記錄器提供靈活的日誌記錄功能。它支持五種日誌級別（DEBUG、INFO、WARNING、ERROR、CRITICAL），允許開發(fā)者根據(jù)嚴(yán)重程度分類信息，並通過設(shè)置全局級別控制輸出內(nèi)容；例如，在生產(chǎn)環(huán)境中僅顯示W(wǎng)ARNING及以上級別的日誌以保持簡潔。此外，可通過添加多個處理器將日誌發(fā)送到不同目標(biāo)，如文件、郵件或控制臺，每個處理器可獨(dú)立配置級別與格式；例如一個處理器將所有DEBUG及以上級別的日誌寫入文件，另一個僅在控制臺顯示W(wǎng)ARNING及以上級別。同時，formatter用於自定義日誌格式，如包含時間戳、日誌級別、模塊名等字段，提升日誌可讀性。最後，命名記錄器支持按代碼結(jié)構(gòu)組織日誌系統(tǒng)，實(shí)現(xiàn)對不同模塊的獨(dú)立配置，例如myapp.moduleA和myapp.moduleB可分別設(shè)定不同的日誌級別，從而滿足複雜項目中的多樣化需求。

Python's logging module is built to help developers track events in their code—like errors, warnings, or general status updates—in a structured and customizable way. Instead of scattering print statements throughout your code, logging gives you a consistent framework that scales well from simple scripts to complex applications.

Here's how it offers that flexibility:

Multiple Log Levels for Different Needs

One of the core features is the use of log levels. These let you categorize messages by severity: DEBUG , INFO , WARNING , ERROR , and CRITICAL . You can control which level of messages get recorded or displayed.

For example:

• Use logging.debug() for detailed info useful during development.
• Use logging.warning() when something unexpected happens but isn't critical.
• Use logging.error() or logging.critical() when things go wrong and action may be needed.

You can set the logging level globally so only messages at or above that level are processed.
Say you're in production—you might only want to see WARNING and above:

 logging.basicConfig(level=logging.WARNING)

This keeps logs cleaner and more relevant.

Handlers Let You Send Logs Wherever You Want

By default, logs just show up on the console. But what if you want them written to a file? Or sent over the network? Handlers make this possible.

You can attach multiple handlers to a logger. For instance:

• One handler writes logs to a file.
• Another sends error logs to an email address via SMTP.
• Yet another outputs logs to the console with different formatting.

Each handler can also have its own level. So maybe you log everything to a file ( DEBUG and up), but only show warnings and errors in the console.

Example of adding a file handler:

 handler = logging.FileHandler('app.log')
formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
logging.getLogger().addHandler(handler)

Flexible Formatting for Readable Output

Log messages don't all have to look the same. With formatters, you define what each message includes—like timestamp, logger name, severity level, or even the function name where the log originated.

Want to include the time and level in every message?

 logging.basicConfig(format='%(asctime)s - %(levelname)s: %(message)s')

Or maybe you want more context in debug logs (like the module name and line number) and keep it simpler in error logs. Formatters give you that control per handler.

Loggers Organized Like Your Code Structure

Python's logging system supports hierarchical loggers. That means you can create named loggers like myapp.moduleA and myapp.moduleB , and configure them individually or as a group ( myapp ).

This helps in large apps where different components might need different logging behavior. For example:

• Set myapp.database to log at DEBUG level for troubleshooting.
• Keep myapp.ui at INFO to avoid noise unless needed.

Getting a named logger is easy:

 logger = logging.getLogger('myapp.moduleA')
logger.debug('This only shows if moduleA's logging level allows it.')

All these pieces—levels, handlers, formatters, and named loggers—work together so you can build a logging setup that fits your exact needs without rewriting the wheel. It's not flashy, but once you understand how they connect, it becomes a powerful tool in debugging and monitoring applications.

That's the basic idea—and honestly, most of what you need to start using logging effectively.

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