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13.12小节完成
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13.12 给内库增加日志功能
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13.12 给函数库增加日志功能
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==============================
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----------
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问题
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You would like to add a logging capability to a library, but don’t want it to interfere with
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programs that don’t use logging.
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你想给某个函数库增加日志功能,但是又不能影响到那些不使用日志功能的程序。
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解决方案
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For libraries that want to perform logging, you should create a dedicated logger object,
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and initially configure it as follows:
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对于想要执行日志操作的函数库而已,你应该创建一个专属的 ``logger`` 对象,并且像下面这样初始化配置:
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# somelib.py
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.. code-block:: python
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import logging
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log = logging.getLogger(__name__)
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log.addHandler(logging.NullHandler())
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# somelib.py
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# Example function (for testing)
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def func():
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log.critical('A Critical Error!')
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log.debug('A debug message')
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import logging
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log = logging.getLogger(__name__)
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log.addHandler(logging.NullHandler())
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With this configuration, no logging will occur by default. For example:
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# Example function (for testing)
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def func():
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log.critical('A Critical Error!')
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log.debug('A debug message')
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>>> import somelib
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>>> somelib.func()
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>>>
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使用这个配置,默认情况下不会打印日志。例如:
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However, if the logging system gets configured, log messages will start to appear. For
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example:
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.. code-block:: python
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>>> import logging
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>>> logging.basicConfig()
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>>> somelib.func()
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CRITICAL:somelib:A Critical Error!
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>>>
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>>> import somelib
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>>> somelib.func()
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>>>
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不过,如果配置过日志系统,那么日志消息打印就开始生效,例如:
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::
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>>> import logging
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>>> logging.basicConfig()
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>>> somelib.func()
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CRITICAL:somelib:A Critical Error!
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>>>
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讨论
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Libraries present a special problem for logging, since information about the environ‐
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ment in which they are used isn’t known. As a general rule, you should never write
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library code that tries to configure the logging system on its own or which makes as‐
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sumptions about an already existing logging configuration. Thus, you need to take great
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care to provide isolation.
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The call to getLogger(__name__) creates a logger module that has the same name as
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the calling module. Since all modules are unique, this creates a dedicated logger that is
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likely to be separate from other loggers.
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The log.addHandler(logging.NullHandler()) operation attaches a null handler to
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the just created logger object. A null handler ignores all logging messages by default.
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Thus, if the library is used and logging is never configured, no messages or warnings
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will appear.
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One subtle feature of this recipe is that the logging of individual libraries can be inde‐
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pendently configured, regardless of other logging settings. For example, consider the
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following code:
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>>> import logging
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>>> logging.basicConfig(level=logging.ERROR)
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>>> import somelib
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>>> somelib.func()
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CRITICAL:somelib:A Critical Error!
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>>> # Change the logging level for 'somelib' only
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>>> logging.getLogger('somelib').level=logging.DEBUG
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>>> somelib.func()
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CRITICAL:somelib:A Critical Error!
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DEBUG:somelib:A debug message
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>>>
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Here, the root logger has been configured to only output messages at the ERROR level or
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higher. However, the level of the logger for somelib has been separately configured to
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output debugging messages. That setting takes precedence over the global setting.
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The ability to change the logging settings for a single module like this can be a useful
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debugging tool, since you don’t have to change any of the global logging settings—simply
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change the level for the one module where you want more output.
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The “Logging HOWTO” has more information about configuring the logging module
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and other useful tips.
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通常来讲,你不应该在函数库代码中自己配置日志系统,或者是已经假定有个已经存在的日志配置了。
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调用 ``getLogger(__name__)`` 创建一个和调用模块同名的logger模块。
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由于模块都是唯一的,因此创建的logger也将是唯一的。
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``log.addHandler(logging.NullHandler())`` 操作将一个空处理器绑定到刚刚已经创建好的logger对象上。
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一个空处理器默认会忽略调用所有的日志消息。
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因此,如果使用该函数库的时候还没有配置日志,那么将不会有消息或警告出现。
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还有一点就是对于各个函数库的日志配置可以是相互独立的,不影响其他库的日志配置。
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例如,对于如下的代码:
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.. code-block:: python
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>>> import logging
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>>> logging.basicConfig(level=logging.ERROR)
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>>> import somelib
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>>> somelib.func()
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CRITICAL:somelib:A Critical Error!
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>>> # Change the logging level for 'somelib' only
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>>> logging.getLogger('somelib').level=logging.DEBUG
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>>> somelib.func()
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CRITICAL:somelib:A Critical Error!
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DEBUG:somelib:A debug message
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>>>
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在这里,根日志被配置成仅仅输出ERROR或更高级别的消息。
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不过 ,``somelib`` 的日志级别被单独配置成可以输出debug级别的消息,它的优先级比全局配置高。
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像这样更改单独模块的日志配置对于调试来讲是很方便的,
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因为你无需去更改任何的全局日志配置——只需要修改你想要更多输出的模块的日志等级。
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`Logging HOWTO <https://docs.python.org/3/howto/logging.html>`_
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详细介绍了如何配置日志模块和其他有用技巧,可以参阅下。

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