# `logging.config` --- 日志记录配置

源代码： Lib/logging/config.py

这一节描述了用于配置 logging 模块的 API。

## 配置函数

下列函数可配置 logging 模块。 它们位于 `logging.config` 模块中。 它们的使用是可选的 --- 要配置 logging 模块你可以使用这些函数，也可以通过调用主 API (在 `logging` 本身定义) 并定义在 `logging` 或 `logging.handlers` 中声明的处理程序。

`logging.config.``dictConfig`(config)

  > 从一个字典获取日志记录配置。 字典的内容描述见下文的 配置字典架构。

  > 如果在配置期间遇到错误，此函数将引发 `ValueError`, `TypeError`, `AttributeError` 或 `ImportError` 并附带适当的描述性消息。 下面是将会引发错误的（可能不完整的）条件列表:

  > - `level` 不是字符串或者不是对应于实际日志记录级别的字符串。

  > - `propagate` 值不是布尔类型。

  > - id 没有对应的目标。

  > - 在增量调用期间发现不存在的处理程序 id。

  > - 无效的日志记录器名称。

  > - 无法解析为内部或外部对象。

  > 解析由 `DictConfigurator` 类执行，该类的构造器可传入用于配置的字典，并且具有 `configure()` 方法。 `logging.config` 模块具有可调用属性 `dictConfigClass`，其初始值设为 `DictConfigurator`。 你可以使用你自己的适当实现来替换 `dictConfigClass` 的值。

  > `dictConfig()` 会调用 `dictConfigClass` 并传入指定的字典，然后在所返回的对象上调用 `configure()` 方法以使配置生效:

  >     def dictConfig(config):
  >         dictConfigClass(config).configure()

  > 例如，`DictConfigurator` 的子类可以在它自己的 `__init__()` 中调用 `DictConfigurator.__init__()`，然后设置可以在后续 `configure()` 调用中使用的自定义前缀。 `dictConfigClass` 将被绑定到这个新的子类，然后就可以与在默认的未定制状态下完全相同的方式调用 `dictConfig()`。

  3.2 新版功能.

`logging.config.``fileConfig`(fname, defaults=None, disable_existing_loggers=True)

  从一个 `configparser` 格式文件中读取日志记录配置。 文件格式应当与 Configuration file format 中的描述一致。 此函数可在应用程序中被多次调用，以允许最终用户在多个预设配置中进行选择（如果开发者提供了展示选项并加载选定配置的机制）。

  参数

    - fname -- 一个文件名，或一个文件类对象，或是一个派生自 `RawConfigParser` 的实例。 如果传入了一个派生自 `RawConfigParser` 的实例，它会被原样使用。 否则，将会实例化一个 `Configparser`，并且它会从作为 `fname` 传入的对象中读取配置。 如果存在 `readline()` 方法，则它会被当作一个文件类对象并使用 `read_file()` 来读取；在其它情况下，它会被当作一个文件名并传递给 `read()`。

    - defaults -- 要传递给 ConfigParser 的默认值可在此参数中指定。

    - disable_existing_loggers -- 如果指定为 `False`，则当执行此调用时已存在的日志记录器会保持启用。 默认值为 `True` 因为这将以向下兼容方式启用旧行为。 此行为是禁用任何现有的非根日志记录器除非它们或它们的上级在日志记录配置中被显式地命名。

  在 3.4 版更改: 现在接受 `RawConfigParser` 子类的实例作为 `fname` 的值。 这有助于:

  - 使用一个配置文件，其中日志记录配置只是全部应用程序配置的一部分。

  - 使用从一个文件读取的配置，它随后会在被传给 `fileConfig` 之前由使用配置的应用程序来修改（例如基于命令行参数或运行时环境的其他部分）。

`logging.config.``listen`(port=DEFAULT_LOGGING_CONFIG_PORT, verify=None)

  在指定的端口上启动套接字服务器，并监听新的配置。 如果未指定端口，则会使用模块默认的 `DEFAULT_LOGGING_CONFIG_PORT`。 日志记录配置将作为适合由 `dictConfig()` 或 `fileConfig()` 进行处理的文件来发送。 返回一个 `Thread` 实例，你可以在该实例上调用 `start()` 来启动服务器，对该服务器你可以在适当的时候执行 `join()`。 要停止该服务器，请调用 `stopListening()`。

  如果指定 `verify` 参数，则它应当是一个可调用对象，该对象应当验证通过套接字接收的字节数据是否有效且应被处理。 这可以通过对通过套接字发送的内容进行加密和/或签名来完成，这样 `verify` 可调用对象就能执行签名验证和/或解密。 `verify` 可调用对象的调用会附带一个参数 —— 通过套接字接收的字节数据 —— 并应当返回要处理的字节数据，或者返回 `None` 来指明这些字节数据应当被丢弃。 返回的字节数据可以与传入的字节数据相同（例如在只执行验证的时候），或者也可以完全不同（例如在可能执行了解密的时候）。

  要将配置发送到套接字，请读取配置文件并将其作为字节序列发送到套接字，字节序列要以使用 `struct.pack('>L', n)` 打包为二进制格式的四字节长度的字符串打头。

  注解

  因为配置的各部分是通过 `eval()` 传递的，使用此函数可能让用户面临安全风险。 虽然此函数仅绑定到 `localhost` 上的套接字，因此并不接受来自远端机器的连接，但在某些场景中不受信任的代码可以在调用 `listen()` 的进程的账户下运行。 具体来说，如果如果调用 `listen()` 的进程在用户无法彼此信任的多用户机器上运行，则恶意用户就能简单地通过连接到受害者的 `listen()` 套接字并发送运行攻击者想在受害者的进程上执行的任何代码的配置的方式，安排运行几乎任意的代码。 如果是使用默认端口这会特别容易做到，即便使用了不同端口也不难做到。 要避免发生这种情况的风险，请在 `listen()` 中使用 `verify` 参数来防止未经认可的配置被应用。

  在 3.4 版更改: 添加了 `verify` 参数。

  注解

  如果你希望将配置发送给未禁用现有日志记录器的监听器，你将需要使用 JSON 格式的配置，该格式将使用 `dictConfig()` 进行配置。 此方法允许你在你发送的配置中将 `disable_existing_loggers` 指定为 `False`。

`logging.config.``stopListening`()

  停止通过对 `listen()` 的调用所创建的监听服务器。 此函数的调用通常会先于在 `listen()` 的返回值上调用 `join()`。

## 配置字典架构

描述日志记录配置需要列出要创建的不同对象及它们之间的连接；例如，你可以创建一个名为 'console' 的处理程序，然后名为 'startup' 的日志记录器将可以把它的消息发送给 'console' 处理程序。 这些对象并不仅限于 `logging` 模块所提供的对象，因为你还可以编写你自己的格式化或处理程序类。 这些类的形参可能还需要包括 `sys.stderr` 这样的外部对象。 描述这些对象和连接的语法会在下面的 Object connections 中定义。

### Dictionary Schema Details

The dictionary passed to `dictConfig()` must contain the following keys:

- version - to be set to an integer value representing the schema version. The only valid value at present is 1, but having this key allows the schema to evolve while still preserving backwards compatibility.

All other keys are optional, but if present they will be interpreted as described below. In all cases below where a 'configuring dict' is mentioned, it will be checked for the special `'()'` key to see if a custom instantiation is required. If so, the mechanism described in User-defined objects below is used to create an instance; otherwise, the context is used to determine what to instantiate.

- formatters - the corresponding value will be a dict in which each key is a formatter id and each value is a dict describing how to configure the corresponding `Formatter` instance.

  The configuring dict is searched for keys `format` and `datefmt` (with defaults of `None`) and these are used to construct a `Formatter` instance.

  在 3.8 版更改: a `validate` key (with default of `True`) can be added into the `formatters` section of the configuring dict, this is to validate the format.

- filters - the corresponding value will be a dict in which each key is a filter id and each value is a dict describing how to configure the corresponding Filter instance.

  The configuring dict is searched for the key `name` (defaulting to the empty string) and this is used to construct a `logging.Filter` instance.

- handlers - the corresponding value will be a dict in which each key is a handler id and each value is a dict describing how to configure the corresponding Handler instance.

  The configuring dict is searched for the following keys:

  - `class` (mandatory). This is the fully qualified name of the handler class.

  - `level` (optional). The level of the handler.

  - `formatter` (optional). The id of the formatter for this handler.

  - `filters` (optional). A list of ids of the filters for this handler.

  All other keys are passed through as keyword arguments to the handler's constructor. For example, given the snippet:

      handlers:
        console:
          class : logging.StreamHandler
          formatter: brief
          level   : INFO
          filters: [allow_foo]
          stream  : ext://sys.stdout
        file:
          class : logging.handlers.RotatingFileHandler
          formatter: precise
          filename: logconfig.log
          maxBytes: 1024
          backupCount: 3

  the handler with id `console` is instantiated as a `logging.StreamHandler`, using `sys.stdout` as the underlying stream. The handler with id `file` is instantiated as a `logging.handlers.RotatingFileHandler` with the keyword arguments `filename='logconfig.log', maxBytes=1024, backupCount=3`.

- loggers - the corresponding value will be a dict in which each key is a logger name and each value is a dict describing how to configure the corresponding Logger instance.

  The configuring dict is searched for the following keys:

  - `level` (optional). The level of the logger.

  - `propagate` (optional). The propagation setting of the logger.

  - `filters` (optional). A list of ids of the filters for this logger.

  - `handlers` (optional). A list of ids of the handlers for this logger.

  The specified loggers will be configured according to the level, propagation, filters and handlers specified.

- root - this will be the configuration for the root logger. Processing of the configuration will be as for any logger, except that the `propagate` setting will not be applicable.

- incremental - whether the configuration is to be interpreted as incremental to the existing configuration. This value defaults to `False`, which means that the specified configuration replaces the existing configuration with the same semantics as used by the existing `fileConfig()` API.

  If the specified value is `True`, the configuration is processed as described in the section on Incremental Configuration.

- disable_existing_loggers - whether any existing non-root loggers are to be disabled. This setting mirrors the parameter of the same name in `fileConfig()`. If absent, this parameter defaults to `True`. This value is ignored if incremental is `True`.

### Incremental Configuration

It is difficult to provide complete flexibility for incremental configuration. For example, because objects such as filters and formatters are anonymous, once a configuration is set up, it is not possible to refer to such anonymous objects when augmenting a configuration.

Furthermore, there is not a compelling case for arbitrarily altering the object graph of loggers, handlers, filters, formatters at run-time, once a configuration is set up; the verbosity of loggers and handlers can be controlled just by setting levels (and, in the case of loggers, propagation flags). Changing the object graph arbitrarily in a safe way is problematic in a multi-threaded environment; while not impossible, the benefits are not worth the complexity it adds to the implementation.

Thus, when the `incremental` key of a configuration dict is present and is `True`, the system will completely ignore any `formatters` and `filters` entries, and process only the `level` settings in the `handlers` entries, and the `level` and `propagate` settings in the `loggers` and `root` entries.

Using a value in the configuration dict lets configurations to be sent over the wire as pickled dicts to a socket listener. Thus, the logging verbosity of a long-running application can be altered over time with no need to stop and restart the application.

### Object connections

The schema describes a set of logging objects - loggers, handlers, formatters, filters - which are connected to each other in an object graph. Thus, the schema needs to represent connections between the objects. For example, say that, once configured, a particular logger has attached to it a particular handler. For the purposes of this discussion, we can say that the logger represents the source, and the handler the destination, of a connection between the two. Of course in the configured objects this is represented by the logger holding a reference to the handler. In the configuration dict, this is done by giving each destination object an id which identifies it unambiguously, and then using the id in the source object's configuration to indicate that a connection exists between the source and the destination object with that id.

So, for example, consider the following YAML snippet:

    formatters:
      brief:
        # configuration for formatter with id 'brief' goes here
      precise:
        # configuration for formatter with id 'precise' goes here
    handlers:
      h1: #This is an id
       # configuration of handler with id 'h1' goes here
       formatter: brief
      h2: #This is another id
       # configuration of handler with id 'h2' goes here
       formatter: precise
    loggers:
      foo.bar.baz:
        # other configuration for logger 'foo.bar.baz'
        handlers: [h1, h2]

(Note: YAML used here because it's a little more readable than the equivalent Python source form for the dictionary.)

The ids for loggers are the logger names which would be used programmatically to obtain a reference to those loggers, e.g. `foo.bar.baz`. The ids for Formatters and Filters can be any string value (such as `brief`, `precise` above) and they are transient, in that they are only meaningful for processing the configuration dictionary and used to determine connections between objects, and are not persisted anywhere when the configuration call is complete.

The above snippet indicates that logger named `foo.bar.baz` should have two handlers attached to it, which are described by the handler ids `h1` and `h2`. The formatter for `h1` is that described by id `brief`, and the formatter for `h2` is that described by id `precise`.

### User-defined objects

The schema supports user-defined objects for handlers, filters and formatters. (Loggers do not need to have different types for different instances, so there is no support in this configuration schema for user-defined logger classes.)

Objects to be configured are described by dictionaries which detail their configuration. In some places, the logging system will be able to infer from the context how an object is to be instantiated, but when a user-defined object is to be instantiated, the system will not know how to do this. In order to provide complete flexibility for user-defined object instantiation, the user needs to provide a 'factory' - a callable which is called with a configuration dictionary and which returns the instantiated object. This is signalled by an absolute import path to the factory being made available under the special key `'()'`. Here's a concrete example:

    formatters:
      brief:
        format: '%(message)s'
      default:
        format: '%(asctime)s %(levelname)-8s %(name)-15s %(message)s'
        datefmt: '%Y-%m-%d %H:%M:%S'
      custom:
          (): my.package.customFormatterFactory
          bar: baz
          spam: 99.9
          answer: 42

The above YAML snippet defines three formatters. The first, with id `brief`, is a standard `logging.Formatter` instance with the specified format string. The second, with id `default`, has a longer format and also defines the time format explicitly, and will result in a `logging.Formatter` initialized with those two format strings. Shown in Python source form, the `brief` and `default` formatters have configuration sub-dictionaries:

    {
      'format' : '%(message)s'
    }

和:

    {
      'format' : '%(asctime)s %(levelname)-8s %(name)-15s %(message)s',
      'datefmt' : '%Y-%m-%d %H:%M:%S'
    }

respectively, and as these dictionaries do not contain the special key `'()'`, the instantiation is inferred from the context: as a result, standard `logging.Formatter` instances are created. The configuration sub-dictionary for the third formatter, with id `custom`, is:

    {
      '()' : 'my.package.customFormatterFactory',
      'bar' : 'baz',
      'spam' : 99.9,
      'answer' : 42
    }

and this contains the special key `'()'`, which means that user-defined instantiation is wanted. In this case, the specified factory callable will be used. If it is an actual callable it will be used directly - otherwise, if you specify a string (as in the example) the actual callable will be located using normal import mechanisms. The callable will be called with the remaining items in the configuration sub-dictionary as keyword arguments. In the above example, the formatter with id `custom` will be assumed to be returned by the call:

    my.package.customFormatterFactory(bar='baz', spam=99.9, answer=42)

The key `'()'` has been used as the special key because it is not a valid keyword parameter name, and so will not clash with the names of the keyword arguments used in the call. The `'()'` also serves as a mnemonic that the corresponding value is a callable.

### Access to external objects

There are times where a configuration needs to refer to objects external to the configuration, for example `sys.stderr`. If the configuration dict is constructed using Python code, this is straightforward, but a problem arises when the configuration is provided via a text file (e.g. JSON, YAML). In a text file, there is no standard way to distinguish `sys.stderr` from the literal string `'sys.stderr'`. To facilitate this distinction, the configuration system looks for certain special prefixes in string values and treat them specially. For example, if the literal string `'ext://sys.stderr'` is provided as a value in the configuration, then the `ext://` will be stripped off and the remainder of the value processed using normal import mechanisms.

The handling of such prefixes is done in a way analogous to protocol handling: there is a generic mechanism to look for prefixes which match the regular expression `^(?P<prefix>[a-z]+)://(?P<suffix>.*)$` whereby, if the `prefix` is recognised, the `suffix` is processed in a prefix-dependent manner and the result of the processing replaces the string value. If the prefix is not recognised, then the string value will be left as-is.

### Access to internal objects

As well as external objects, there is sometimes also a need to refer to objects in the configuration. This will be done implicitly by the configuration system for things that it knows about. For example, the string value `'DEBUG'` for a `level` in a logger or handler will automatically be converted to the value `logging.DEBUG`, and the `handlers`, `filters` and `formatter` entries will take an object id and resolve to the appropriate destination object.

However, a more generic mechanism is needed for user-defined objects which are not known to the `logging` module. For example, consider `logging.handlers.MemoryHandler`, which takes a `target` argument which is another handler to delegate to. Since the system already knows about this class, then in the configuration, the given `target` just needs to be the object id of the relevant target handler, and the system will resolve to the handler from the id. If, however, a user defines a `my.package.MyHandler` which has an `alternate` handler, the configuration system would not know that the `alternate` referred to a handler. To cater for this, a generic resolution system allows the user to specify:

    handlers:
      file:
        # configuration of file handler goes here

      custom:
        (): my.package.MyHandler
        alternate: cfg://handlers.file

The literal string `'cfg://handlers.file'` will be resolved in an analogous way to strings with the `ext://` prefix, but looking in the configuration itself rather than the import namespace. The mechanism allows access by dot or by index, in a similar way to that provided by `str.format`. Thus, given the following snippet:

    handlers:
      email:
        class: logging.handlers.SMTPHandler
        mailhost: localhost
        fromaddr: my_app@domain.tld
        toaddrs:
          - support_team@domain.tld
          - dev_team@domain.tld
        subject: Houston, we have a problem.

in the configuration, the string `'cfg://handlers'` would resolve to the dict with key `handlers`, the string `'cfg://handlers.email` would resolve to the dict with key `email` in the `handlers` dict, and so on. The string `'cfg://handlers.email.toaddrs[1]` would resolve to `'dev_team.domain.tld'` and the string `'cfg://handlers.email.toaddrs[0]'` would resolve to the value `'support_team@domain.tld'`. The `subject` value could be accessed using either `'cfg://handlers.email.subject'` or, equivalently, `'cfg://handlers.email[subject]'`. The latter form only needs to be used if the key contains spaces or non-alphanumeric characters. If an index value consists only of decimal digits, access will be attempted using the corresponding integer value, falling back to the string value if needed.

Given a string `cfg://handlers.myhandler.mykey.123`, this will resolve to `config_dict['handlers']['myhandler']['mykey']['123']`. If the string is specified as `cfg://handlers.myhandler.mykey[123]`, the system will attempt to retrieve the value from `config_dict['handlers']['myhandler']['mykey'][123]`, and fall back to `config_dict['handlers']['myhandler']['mykey']['123']` if that fails.

### Import resolution and custom importers

Import resolution, by default, uses the builtin `__import__()` function to do its importing. You may want to replace this with your own importing mechanism: if so, you can replace the `importer` attribute of the `DictConfigurator` or its superclass, the `BaseConfigurator` class. However, you need to be careful because of the way functions are accessed from classes via descriptors. If you are using a Python callable to do your imports, and you want to define it at class level rather than instance level, you need to wrap it with `staticmethod()`. For example:

    from importlib import import_module
    from logging.config import BaseConfigurator

    BaseConfigurator.importer = staticmethod(import_module)

You don't need to wrap with `staticmethod()` if you're setting the import callable on a configurator instance.

## Configuration file format

The configuration file format understood by `fileConfig()` is based on `configparser` functionality. The file must contain sections called `[loggers]`, `[handlers]` and `[formatters]` which identify by name the entities of each type which are defined in the file. For each such entity, there is a separate section which identifies how that entity is configured. Thus, for a logger named `log01` in the `[loggers]` section, the relevant configuration details are held in a section `[logger_log01]`. Similarly, a handler called `hand01` in the `[handlers]` section will have its configuration held in a section called `[handler_hand01]`, while a formatter called `form01` in the `[formatters]` section will have its configuration specified in a section called `[formatter_form01]`. The root logger configuration must be specified in a section called `[logger_root]`.

注解

The `fileConfig()` API is older than the `dictConfig()` API and does not provide functionality to cover certain aspects of logging. For example, you cannot configure `Filter` objects, which provide for filtering of messages beyond simple integer levels, using `fileConfig()`. If you need to have instances of `Filter` in your logging configuration, you will need to use `dictConfig()`. Note that future enhancements to configuration functionality will be added to `dictConfig()`, so it's worth considering transitioning to this newer API when it's convenient to do so.

Examples of these sections in the file are given below.

    [loggers]
    keys=root,log02,log03,log04,log05,log06,log07

    [handlers]
    keys=hand01,hand02,hand03,hand04,hand05,hand06,hand07,hand08,hand09

    [formatters]
    keys=form01,form02,form03,form04,form05,form06,form07,form08,form09

The root logger must specify a level and a list of handlers. An example of a root logger section is given below.

    [logger_root]
    level=NOTSET
    handlers=hand01

The `level` entry can be one of `DEBUG, INFO, WARNING, ERROR, CRITICAL` or `NOTSET`. For the root logger only, `NOTSET` means that all messages will be logged. Level values are `eval()`uated in the context of the `logging` package's namespace.

The `handlers` entry is a comma-separated list of handler names, which must appear in the `[handlers]` section. These names must appear in the `[handlers]` section and have corresponding sections in the configuration file.

For loggers other than the root logger, some additional information is required. This is illustrated by the following example.

    [logger_parser]
    level=DEBUG
    handlers=hand01
    propagate=1
    qualname=compiler.parser

The `level` and `handlers` entries are interpreted as for the root logger, except that if a non-root logger's level is specified as `NOTSET`, the system consults loggers higher up the hierarchy to determine the effective level of the logger. The `propagate` entry is set to 1 to indicate that messages must propagate to handlers higher up the logger hierarchy from this logger, or 0 to indicate that messages are not propagated to handlers up the hierarchy. The `qualname` entry is the hierarchical channel name of the logger, that is to say the name used by the application to get the logger.

Sections which specify handler configuration are exemplified by the following.

    [handler_hand01]
    class=StreamHandler
    level=NOTSET
    formatter=form01
    args=(sys.stdout,)

The `class` entry indicates the handler's class (as determined by `eval()` in the `logging` package's namespace). The `level` is interpreted as for loggers, and `NOTSET` is taken to mean 'log everything'.

The `formatter` entry indicates the key name of the formatter for this handler. If blank, a default formatter (`logging._defaultFormatter`) is used. If a name is specified, it must appear in the `[formatters]` section and have a corresponding section in the configuration file.

The `args` entry, when `eval()`uated in the context of the `logging` package's namespace, is the list of arguments to the constructor for the handler class. Refer to the constructors for the relevant handlers, or to the examples below, to see how typical entries are constructed. If not provided, it defaults to `()`.

The optional `kwargs` entry, when `eval()`uated in the context of the `logging` package's namespace, is the keyword argument dict to the constructor for the handler class. If not provided, it defaults to `{}`.

    [handler_hand02]
    class=FileHandler
    level=DEBUG
    formatter=form02
    args=('python.log', 'w')

    [handler_hand03]
    class=handlers.SocketHandler
    level=INFO
    formatter=form03
    args=('localhost', handlers.DEFAULT_TCP_LOGGING_PORT)

    [handler_hand04]
    class=handlers.DatagramHandler
    level=WARN
    formatter=form04
    args=('localhost', handlers.DEFAULT_UDP_LOGGING_PORT)

    [handler_hand05]
    class=handlers.SysLogHandler
    level=ERROR
    formatter=form05
    args=(('localhost', handlers.SYSLOG_UDP_PORT), handlers.SysLogHandler.LOG_USER)

    [handler_hand06]
    class=handlers.NTEventLogHandler
    level=CRITICAL
    formatter=form06
    args=('Python Application', '', 'Application')

    [handler_hand07]
    class=handlers.SMTPHandler
    level=WARN
    formatter=form07
    args=('localhost', 'from@abc', ['user1@abc', 'user2@xyz'], 'Logger Subject')
    kwargs={'timeout': 10.0}

    [handler_hand08]
    class=handlers.MemoryHandler
    level=NOTSET
    formatter=form08
    target=
    args=(10, ERROR)

    [handler_hand09]
    class=handlers.HTTPHandler
    level=NOTSET
    formatter=form09
    args=('localhost:9022', '/log', 'GET')
    kwargs={'secure': True}

Sections which specify formatter configuration are typified by the following.

    [formatter_form01]
    format=F1 %(asctime)s %(levelname)s %(message)s
    datefmt=
    class=logging.Formatter

The `format` entry is the overall format string, and the `datefmt` entry is the `strftime()`-compatible date/time format string. If empty, the package substitutes something which is almost equivalent to specifying the date format string `'%Y-%m-%d %H:%M:%S'`. This format also specifies milliseconds, which are appended to the result of using the above format string, with a comma separator. An example time in this format is `2003-01-23 00:29:50,411`.

The `class` entry is optional. It indicates the name of the formatter's class (as a dotted module and class name.) This option is useful for instantiating a `Formatter` subclass. Subclasses of `Formatter` can present exception tracebacks in an expanded or condensed format.

注解

Due to the use of `eval()` as described above, there are potential security risks which result from using the `listen()` to send and receive configurations via sockets. The risks are limited to where multiple users with no mutual trust run code on the same machine; see the `listen()` documentation for more information.

参见

模块 `logging`

  日志记录模块的 API 参考。

`logging.handlers` 模块

  日志记录模块附带的有用处理器。