Python 日志打印之logging.config.dictConfig使用总结 / 开普饭

#实践环境

WIN 10

Python 3.6.5

#函数说明

logging.config.dictConfig(config)

dictConfig函数位于logging.config模块，该函数通过字典参数config对logging进行配置。3.2版本新增的函数

##参数说明

config 字典类型，包含以下key：

version - 表示版本，该键值为从1开始的整数。该key必选，除此之外，其它key都是可选。

formatters - 日志格式化器，其value值为一个字典，该字典的每个键值对都代表一个Formatter，键值对中，key代表Formatter ID(自定义ID)，value为字典，描述如何配置相应的Formatter实例。默认格式为 '%(message)s’

filters - 日志过滤器，其value值为一个字典，该字典的每个键值对都代表一个Filter，键值对中，key代表Filter ID(自定义ID)，value为字典，描述如何配置相应的Filter实例。

handlers - 日志处理器，其value值为一个字典，该字典的每个键值对都代表一个Handler，键值对中，key代表Handler ID(自定义ID)，value为字典，描述如何配置相应的Handler实例，包含以下配置key：

class (必选). 日志处理器类全称
level (可选). 指定该日志处理器需要处理哪些级别的日志，低于该级别的日志将不被该handler处理。level可以为代表日志级别的整数或者表大写字符串，字符串日志级别和数字日志级别对应关系如下：

　　　　CRITICAL = 50

　　　　FATAL = CRITICAL

　　　　ERROR = 40

　　　　WARNING = 30

　　　　WARN = WARNING

　　　　INFO = 20

　　　　DEBUG = 10

　　　　NOTSET = 0

　　　　下同，不再赘述.

formatter (可选). 指定该日志处理器使用的日志格式化器
filters (可选). 制定该日志处理器使用的日志过滤器

# 上述的class配置项的值，可以使用自定义Handler类，此时，如果自定义Handler类的__init__构造函数还需要其它参数来初始化类实例，可以继续添自定义参数，这些自定义参数被当做关键字参数会自动传递给构造函数。

一个例子：

"handlers": {        "console":{            "class":"study.MyLogHandler",            "formatter":"brief",            "level":"INFO"        },        "file": {            "class": "logging.handlers.RotatingFileHandler",            "formatter": "precise",      "filename": "logconfig.log",      "maxBytes": 1024,      "backupCount": 3        }    }

id为console的日志处理器被实例化为一个logging.StreamHandler，使用sys.stout作为基础实例流。id为file的日志处理器则被实例化为具有关键字参数filename ='logconfig.log'，maxBytes = 1024，backupCount = 3的 logging.handlers.RotatingFileHandler

loggers - 日志记录器，其value值为一个字典，该字典的每个键值对都代表一个Handler，键值对中，key代表Handler ID，value为字典，描述如何配置相应的Logger实例，包含以下配置key：

level (可选). 指定该日志记录器需要记录哪些级别的日志，低于该级别的日志将不被该logger记录。

propagate (可选). 指定该日志记录器的propagation配置，为布尔值，即True 或 False，用于控制是否向上遍历父辈日志打印器，进而控制当前日志打印器是否共享父辈打印器的日志处理器。True，向上遍历，否则不向上遍历。

filters (可选). 指定该日志记录器使用的日志过滤器

handlers (可选). 制定该日志记录器使用的日志处理器

root - root logger配置。除了不支持propagate配置项以外，该配置的处理过程同处理其它logger的配置一样，配置规则也一样

incremental - 用于判断该config配置是否解释为现有配置的增量配置，还是覆盖原有配置。默认为False，即使用现有fileConfig（）API使用的相同语义替换现有配置

disable_existing_loggers - 其value为布尔值，表示是否禁用现有日志记录器(root logger除外)，默认值为True，即禁用。如果incremental 键值为True，则忽略该配置项

#代码示例1

study.py

study.py#!/usr/bin/env python# -*- coding:utf-8 -*-'''@CreateTime: 2020/12/29 14:08@Author : shouke'''import loggingimport logging.configLOGGING_CONFIG = {    "version": 1,    "formatters": {        "default": {            'format':'%(asctime)s %(filename)s %(lineno)s %(levelname)s %(message)s',        },        "plain": {            "format": "%(message)s",        },    },    "handlers": {        "console": {            "class": "logging.StreamHandler",            "level": "INFO",            "formatter": "default",        },        "console_plain": {            "class": "logging.StreamHandler",            "level":logging.INFO,            "formatter": "plain"        },        "file":{            "class": "logging.FileHandler",            "level":20,            "filename": "./log.txt",            "formatter": "default",        }    },    "loggers": {        "console_logger": {            "handlers": ["console"],            "level": "INFO",            "propagate": False,        },        "console_plain_logger": {            "handlers": ["console_plain"],            "level": "DEBUG",            "propagate": False,        },        "file_logger":{            "handlers": ["file"],            "level": "INFO",            "propagate": False,        }    },    "disable_existing_loggers": True,}# 运行测试logging.config.dictConfig(LOGGING_CONFIG)logger = logging.getLogger("console_logger")logger.debug('debug message')logger.info('info message')logger.warn('warning message')logger.error('error message')logger.critical('critical message')

运行study.py，结果输出如下

2021-01-09 10:01:59,123 study.py 66 INFO info message

2021-01-09 10:01:59,123 study.py 67 WARNING warning message

2021-01-09 10:01:59,123 study.py 68 ERROR error message

2021-01-09 10:01:59,123 study.py 69 CRITICAL critical message

#代码示例2

基于代码示例1，修改LOGGING_CONFIG及getLogger函数参数

LOGGING_CONFIG = {    "version": 1,    "formatters": {        "default": {            'format':'%(asctime)s %(filename)s %(lineno)s %(levelname)s %(message)s',        }    },    "handlers": {        "console": {            "class": "logging.StreamHandler",            "level": "INFO",            "formatter": "default",        }    },    "disable_existing_loggers": True,    "root": {        "handlers": ["console"],        "level": "DEBUG"    },}# 运行测试logging.config.dictConfig(LOGGING_CONFIG)logger = logging.getLogger("root")logger.debug('debug message')logger.info('info message')logger.warn('warning message')logger.error('error message')logger.critical('critical message')

运行study.py，结果输出如下

2021-01-09 10:33:03,456 study.py 38 INFO info message

2021-01-09 10:33:03,456 study.py 39 WARNING warning message

2021-01-09 10:33:03,456 study.py 40 ERROR error message

2021-01-09 10:33:03,456 study.py 41 CRITICAL critical message

# 源码的角度分析propagate配置项

Logger类，位于logging/__init__.py

class Logger(Filterer):      #...略      def debug(self, msg, *args, **kwargs):        """        Log 'msg % args' with severity 'DEBUG'.        To pass exception information, use the keyword argument exc_info with        a true value, e.g.        logger.debug("Houston, we have a %s", "thorny problem", exc_info=1)        """        if self.isEnabledFor(DEBUG):            self._log(DEBUG, msg, args, **kwargs)    def info(self, msg, *args, **kwargs):        """        Log 'msg % args' with severity 'INFO'.        To pass exception information, use the keyword argument exc_info with        a true value, e.g.        logger.info("Houston, we have a %s", "interesting problem", exc_info=1)        """        if self.isEnabledFor(INFO):            self._log(INFO, msg, args, **kwargs)        #...略     def _log(self, level, msg, args, exc_info=None, extra=None, stack_info=False):        """        Low-level logging routine which creates a LogRecord and then calls        all the handlers of this logger to handle the record.        """        sinfo = None        if _srcfile:            #IronPython doesn't track Python frames, so findCaller raises an            #exception on some versions of IronPython. We trap it here so that            #IronPython can use logging.            try:                fn, lno, func, sinfo = self.findCaller(stack_info)            except ValueError: # pragma: no cover                fn, lno, func = "(unknown file)", 0, "(unknown function)"        else: # pragma: no cover            fn, lno, func = "(unknown file)", 0, "(unknown function)"        if exc_info:            if isinstance(exc_info, BaseException):                exc_info = (type(exc_info), exc_info, exc_info.__traceback__)            elif not isinstance(exc_info, tuple):                exc_info = sys.exc_info()        record = self.makeRecord(self.name, level, fn, lno, msg, args,                                 exc_info, func, extra, sinfo)        self.handle(record)    def handle(self, record):        """        Call the handlers for the specified record.        This method is used for unpickled records received from a socket, as        well as those created locally. Logger-level filtering is applied.        """        if (not self.disabled) and self.filter(record):            self.callHandlers(record)    def hasHandlers(self):        """        See if this logger has any handlers configured.        Loop through all handlers for this logger and its parents in the        logger hierarchy. Return True if a handler was found, else False.        Stop searching up the hierarchy whenever a logger with the "propagate"        attribute set to zero is found - that will be the last logger which        is checked for the existence of handlers.        """        c = self        rv = False        while c:            if c.handlers:                rv = True                break            if not c.propagate:                break            else:                c = c.parent        return rv    def callHandlers(self, record):        """        Pass a record to all relevant handlers.        Loop through all handlers for this logger and its parents in the        logger hierarchy. If no handler was found, output a one-off error        message to sys.stderr. Stop searching up the hierarchy whenever a        logger with the "propagate" attribute set to zero is found - that        will be the last logger whose handlers are called.        """        c = self        found = 0        while c:            for hdlr in c.handlers:                found = found + 1                if record.levelno >= hdlr.level:                    hdlr.handle(record)            if not c.propagate:                 c = None    #break out            else:                c = c.parent        if (found == 0):            if lastResort:                if record.levelno >= lastResort.level:                    lastResort.handle(record)            elif raiseExceptions and not self.manager.emittedNoHandlerWarning:                sys.stderr.write("No handlers could be found for logger"                                 " \"%s\"\n" % self.name)                self.manager.emittedNoHandlerWarning = True

默认的，当通过logger.debug，logger.info的方式打印日志时，会先判断对应日志级别是否开启，如果开启，则调用logger实例的_log方法，接着经过一连串的函数调用（self._log() -> self.handle -> self.callHandlers），如上，self.callHandlers中，会先遍历当前日志打印器自身的所有日志处理器，处理日志消息，然后判断propagate属性是否为True，如果为True，则获取上级日志打印器，继续遍历其日志处理器，处理消息，否则不遍历上级

另外，查看hasHandlers函数可知，判断一个logger是否有日志处理器，也用到了propagate，如果propagate为True，则遍历父级日志打印器，看其是否存在日志处理器，如果父级或者父辈日志打印器存在日志处理器，则判断该logger拥有日志处理器。

由此可见，propagate功能就是用于控制是否向上遍历父辈日志打印器，进而控制当前日志打印器是否共享父辈打印器的日志处理器。

Python 日志打印之logging.config.dictConfig使用总结

相关推荐