python 日志 logging模块详细解析
Python 中的 logging 模块可以让你跟踪代码运行时的事件,当程序崩溃时可以查看日志并且发现是什么引发了错误。Log 信息有内置的层级——调试(debugging)、信息(informational)、警告(warnings)、错误(error)和严重错误(critical)。你也可以在 logging 中包含 traceback 信息。不管是小项目还是大项目,都推荐在 Python 程序中使用 logging。本文给大家介绍python 日志 logging模块 介绍。
1 基本使用
配置logging基本的设置,然后在控制台输出日志,
import logging logging.basicConfig(level = logging.INFO,format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) logger.info("Start print log") logger.debug("Do something") logger.warning("Something maybe fail.") logger.info("Finish")
运行时,控制台输出,
2016-10-09 19:11:19,434 - __main__ - INFO - Start print log
2016-10-09 19:11:19,434 - __main__ - WARNING - Something maybe fail.
2016-10-09 19:11:19,434 - __main__ - INFO - Finish
logging中可以选择很多消息级别,如debug、info、warning、error以及critical。通过赋予logger或者handler不同的级别,开发者就可以只输出错误信息到特定的记录文件,或者在调试时只记录调试信息。
例如,我们将logger的级别改为DEBUG,再观察一下输出结果,
logging.basicConfig(level = logging.DEBUG,format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s')
控制台输出,可以发现,输出了debug的信息。
2016-10-09 19:12:08,289 - __main__ - INFO - Start print log
2016-10-09 19:12:08,289 - __main__ - DEBUG - Do something
2016-10-09 19:12:08,289 - __main__ - WARNING - Something maybe fail.
2016-10-09 19:12:08,289 - __main__ - INFO - Finish
logging.basicConfig函数各参数:
filename:指定日志文件名;
filemode:和file函数意义相同,指定日志文件的打开模式,'w'或者'a';
format:指定输出的格式和内容,format可以输出很多有用的信息,
参数:作用
%(levelno)s:打印日志级别的数值
%(levelname)s:打印日志级别的名称
%(pathname)s:打印当前执行程序的路径,其实就是sys.argv[0]
%(filename)s:打印当前执行程序名
%(funcName)s:打印日志的当前函数
%(lineno)d:打印日志的当前行号
%(asctime)s:打印日志的时间
%(thread)d:打印线程ID
%(threadName)s:打印线程名称
%(process)d:打印进程ID
%(message)s:打印日志信息
datefmt:指定时间格式,同time.strftime();
level:设置日志级别,默认为logging.WARNNING;
stream:指定将日志的输出流,可以指定输出到sys.stderr,sys.stdout或者文件,默认输出到sys.stderr,当stream和filename同时指定时,stream被忽略;
2 将日志写入到文件
2.2.1 将日志写入到文件
设置logging,创建一个FileHandler,并对输出消息的格式进行设置,将其添加到logger,然后将日志写入到指定的文件中,
import logging logger = logging.getLogger(__name__) logger.setLevel(level = logging.INFO) handler = logging.FileHandler("log.txt") handler.setLevel(logging.INFO) formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') handler.setFormatter(formatter) logger.addHandler(handler) logger.info("Start print log") logger.debug("Do something") logger.warning("Something maybe fail.") logger.info("Finish")
log.txt中日志数据为,
2016-10-09 19:01:13,263 - __main__ - INFO - Start print log
2016-10-09 19:01:13,263 - __main__ - WARNING - Something maybe fail.
2016-10-09 19:01:13,263 - __main__ - INFO - Finish
2.2 将日志同时输出到屏幕和日志文件
logger中添加StreamHandler,可以将日志输出到屏幕上,
import logging logger = logging.getLogger(__name__) logger.setLevel(level = logging.INFO) handler = logging.FileHandler("log.txt") handler.setLevel(logging.INFO) formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') handler.setFormatter(formatter) console = logging.StreamHandler() console.setLevel(logging.INFO) logger.addHandler(handler) logger.addHandler(console) logger.info("Start print log") logger.debug("Do something") logger.warning("Something maybe fail.") logger.info("Finish")
可以在log.txt文件和控制台中看到,
2016-10-09 19:20:46,553 - __main__ - INFO - Start print log
2016-10-09 19:20:46,553 - __main__ - WARNING - Something maybe fail.
2016-10-09 19:20:46,553 - __main__ - INFO - Finish
可以发现,logging有一个日志处理的主对象,其他处理方式都是通过addHandler添加进去,logging中包含的handler主要有如下几种,
handler名称:位置;作用
StreamHandler:logging.StreamHandler;日志输出到流,可以是sys.stderr,sys.stdout或者文件
FileHandler:logging.FileHandler;日志输出到文件
BaseRotatingHandler:logging.handlers.BaseRotatingHandler;基本的日志回滚方式
RotatingHandler:logging.handlers.RotatingHandler;日志回滚方式,支持日志文件最大数量和日志文件回滚
TimeRotatingHandler:logging.handlers.TimeRotatingHandler;日志回滚方式,在一定时间区域内回滚日志文件
SocketHandler:logging.handlers.SocketHandler;远程输出日志到TCP/IP sockets
DatagramHandler:logging.handlers.DatagramHandler;远程输出日志到UDP sockets
SMTPHandler:logging.handlers.SMTPHandler;远程输出日志到邮件地址
SysLogHandler:logging.handlers.SysLogHandler;日志输出到syslog
NTEventLogHandler:logging.handlers.NTEventLogHandler;远程输出日志到Windows NT/2000/XP的事件日志
MemoryHandler:logging.handlers.MemoryHandler;日志输出到内存中的指定buffer
HTTPHandler:logging.handlers.HTTPHandler;通过"GET"或者"POST"远程输出到HTTP服务器
2.3 日志回滚
使用RotatingFileHandler,可以实现日志回滚,
import logging from logging.handlers import RotatingFileHandler logger = logging.getLogger(__name__) logger.setLevel(level = logging.INFO) #定义一个RotatingFileHandler,最多备份3个日志文件,每个日志文件最大1K rHandler = RotatingFileHandler("log.txt",maxBytes = 1*1024,backupCount = 3) rHandler.setLevel(logging.INFO) formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') rHandler.setFormatter(formatter) console = logging.StreamHandler() console.setLevel(logging.INFO) console.setFormatter(formatter) logger.addHandler(rHandler) logger.addHandler(console) logger.info("Start print log") logger.debug("Do something") logger.warning("Something maybe fail.") logger.info("Finish")
可以在工程目录中看到,备份的日志文件,
2016/10/09 19:36 732 log.txt
2016/10/09 19:36 967 log.txt.1
2016/10/09 19:36 985 log.txt.2
2016/10/09 19:36 976 log.txt.3
2.3 设置消息的等级
可以设置不同的日志等级,用于控制日志的输出,
日志等级:使用范围
FATAL:致命错误
CRITICAL:特别糟糕的事情,如内存耗尽、磁盘空间为空,一般很少使用
ERROR:发生错误时,如IO操作失败或者连接问题
WARNING:发生很重要的事件,但是并不是错误时,如用户登录密码错误
INFO:处理请求或者状态变化等日常事务
DEBUG:调试过程中使用DEBUG等级,如算法中每个循环的中间状态
2.4 捕获traceback
Python中的traceback模块被用于跟踪异常返回信息,可以在logging中记录下traceback,
代码,
import logging logger = logging.getLogger(__name__) logger.setLevel(level = logging.INFO) handler = logging.FileHandler("log.txt") handler.setLevel(logging.INFO) formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') handler.setFormatter(formatter) console = logging.StreamHandler() console.setLevel(logging.INFO) logger.addHandler(handler) logger.addHandler(console) logger.info("Start print log") logger.debug("Do something") logger.warning("Something maybe fail.") try: open("sklearn.txt","rb") except (SystemExit,KeyboardInterrupt): raise except Exception: logger.error("Faild to open sklearn.txt from logger.error",exc_info = True) logger.info("Finish")
控制台和日志文件log.txt中输出,
Start print log Something maybe fail. Faild to open sklearn.txt from logger.error Traceback (most recent call last): File "G:\zhb7627\Code\Eclipse WorkSpace\PythonTest\test.py", line 23, in <module> open("sklearn.txt","rb") IOError: [Errno 2] No such file or directory: 'sklearn.txt' Finish
也可以使用logger.exception(msg,_args),它等价于logger.error(msg,exc_info = True,_args),
将
logger.error("Faild to open sklearn.txt from logger.error",exc_info = True)
替换为,
logger.exception("Failed to open sklearn.txt from logger.exception")
控制台和日志文件log.txt中输出,
Start print log Something maybe fail. Failed to open sklearn.txt from logger.exception Traceback (most recent call last): File "G:\zhb7627\Code\Eclipse WorkSpace\PythonTest\test.py", line 23, in <module> open("sklearn.txt","rb") IOError: [Errno 2] No such file or directory: 'sklearn.txt' Finish
2.5 多模块使用logging
主模块mainModule.py,
import logging import subModule logger = logging.getLogger("mainModule") logger.setLevel(level = logging.INFO) handler = logging.FileHandler("log.txt") handler.setLevel(logging.INFO) formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') handler.setFormatter(formatter) console = logging.StreamHandler() console.setLevel(logging.INFO) console.setFormatter(formatter) logger.addHandler(handler) logger.addHandler(console) logger.info("creating an instance of subModule.subModuleClass") a = subModule.SubModuleClass() logger.info("calling subModule.subModuleClass.doSomething") a.doSomething() logger.info("done with subModule.subModuleClass.doSomething") logger.info("calling subModule.some_function") subModule.som_function() logger.info("done with subModule.some_function")
子模块subModule.py,
import logging module_logger = logging.getLogger("mainModule.sub") class SubModuleClass(object): def __init__(self): self.logger = logging.getLogger("mainModule.sub.module") self.logger.info("creating an instance in SubModuleClass") def doSomething(self): self.logger.info("do something in SubModule") a = [] a.append(1) self.logger.debug("list a = " + str(a)) self.logger.info("finish something in SubModuleClass") def som_function(): module_logger.info("call function some_function")
执行之后,在控制和日志文件log.txt中输出,
2016-10-09 20:25:42,276 - mainModule - INFO - creating an instance of subModule.subModuleClass
2016-10-09 20:25:42,279 - mainModule.sub.module - INFO - creating an instance in SubModuleClass
2016-10-09 20:25:42,279 - mainModule - INFO - calling subModule.subModuleClass.doSomething
2016-10-09 20:25:42,279 - mainModule.sub.module - INFO - do something in SubModule
2016-10-09 20:25:42,279 - mainModule.sub.module - INFO - finish something in SubModuleClass
2016-10-09 20:25:42,279 - mainModule - INFO - done with subModule.subModuleClass.doSomething
2016-10-09 20:25:42,279 - mainModule - INFO - calling subModule.some_function
2016-10-09 20:25:42,279 - mainModule.sub - INFO - call function some_function
2016-10-09 20:25:42,279 - mainModule - INFO - done with subModule.some_function
首先在主模块定义了logger'mainModule',并对它进行了配置,就可以在解释器进程里面的其他地方通过getLogger('mainModule')得到的对象都是一样的,不需要重新配置,可以直接使用。定义的该logger的子logger,都可以共享父logger的定义和配置,所谓的父子logger是通过命名来识别,任意以'mainModule'开头的logger都是它的子logger,例如'mainModule.sub'。
实际开发一个application,首先可以通过logging配置文件编写好这个application所对应的配置,可以生成一个根logger,如'PythonAPP',然后在主函数中通过fileConfig加载logging配置,接着在application的其他地方、不同的模块中,可以使用根logger的子logger,如'PythonAPP.Core','PythonAPP.Web'来进行log,而不需要反复的定义和配置各个模块的logger。
3 通过JSON或者YAML文件配置logging模块
尽管可以在Python代码中配置logging,但是这样并不够灵活,最好的方法是使用一个配置文件来配置。在Python 2.7及以后的版本中,可以从字典中加载logging配置,也就意味着可以通过JSON或者YAML文件加载日志的配置。
3.1 通过JSON文件配置
JSON配置文件,
{ "version":1, "disable_existing_loggers":false, "formatters":{ "simple":{ "format":"%(asctime)s - %(name)s - %(levelname)s - %(message)s" } }, "handlers":{ "console":{ "class":"logging.StreamHandler", "level":"DEBUG", "formatter":"simple", "stream":"ext://sys.stdout" }, "info_file_handler":{ "class":"logging.handlers.RotatingFileHandler", "level":"INFO", "formatter":"simple", "filename":"info.log", "maxBytes":"10485760", "backupCount":20, "encoding":"utf8" }, "error_file_handler":{ "class":"logging.handlers.RotatingFileHandler", "level":"ERROR", "formatter":"simple", "filename":"errors.log", "maxBytes":10485760, "backupCount":20, "encoding":"utf8" } }, "loggers":{ "my_module":{ "level":"ERROR", "handlers":["info_file_handler"], "propagate":"no" } }, "root":{ "level":"INFO", "handlers":["console","info_file_handler","error_file_handler"] } }
通过JSON加载配置文件,然后通过logging.dictConfig配置logging,
import json import logging.config import os def setup_logging(default_path = "logging.json",default_level = logging.INFO,env_key = "LOG_CFG"): path = default_path value = os.getenv(env_key,None) if value: path = value if os.path.exists(path): with open(path,"r") as f: config = json.load(f) logging.config.dictConfig(config) else: logging.basicConfig(level = default_level) def func(): logging.info("start func") logging.info("exec func") logging.info("end func") if __name__ == "__main__": setup_logging(default_path = "logging.json") func()
3.2 通过YAML文件配置
通过YAML文件进行配置,比JSON看起来更加简介明了,
version: 1 disable_existing_loggers: False formatters: simple: format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s" handlers: console: class: logging.StreamHandler level: DEBUG formatter: simple stream: ext://sys.stdout info_file_handler: class: logging.handlers.RotatingFileHandler level: INFO formatter: simple filename: info.log maxBytes: 10485760 backupCount: 20 encoding: utf8 error_file_handler: class: logging.handlers.RotatingFileHandler level: ERROR formatter: simple filename: errors.log maxBytes: 10485760 backupCount: 20 encoding: utf8 loggers: my_module: level: ERROR handlers: [info_file_handler] propagate: no root: level: INFO handlers: [console,info_file_handler,error_file_handler]
通过YAML加载配置文件,然后通过logging.dictConfig配置logging,
import yaml import logging.config import os def setup_logging(default_path = "logging.yaml",default_level = logging.INFO,env_key = "LOG_CFG"): path = default_path value = os.getenv(env_key,None) if value: path = value if os.path.exists(path): with open(path,"r") as f: config = yaml.load(f) logging.config.dictConfig(config) else: logging.basicConfig(level = default_level) def func(): logging.info("start func") logging.info("exec func") logging.info("end func") if __name__ == "__main__": setup_logging(default_path = "logging.yaml") func()
到此这篇关于python 日志 logging模块 详细解析的文章就介绍到这了,更多相关python logging模块内容请搜索亿速云以前的文章或继续浏览下面的相关文章希望大家以后多多支持亿速云!