1 logging模块简介
logging模块是Python内置的标准模块,主要用于输出运行日志,可以设置输出日志的等级、日志保存路径、日志文件回滚等;相比print,具备如下优点:
1.可以通过设置不同的日志等级,在release版本中只输出重要信息,而不必显示大量的调试信息;
2.print将所有信息都输出到标准输出中,严重影响开发者从标准输出中查看其它数据;logging则可以由开发者决定将信息输出到什么地方,以及怎么输出;
logging框架中主要由四个部分组成:
- Loggers: 可供程序直接调用的接口
- Handlers: 决定将日志记录分配至正确的目的地
- Filters: 提供更细粒度的日志是否输出的判断
- Formatters: 制定最终记录打印的格式布局
2 logging模块使用
2.1 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,
复制代码 代码如下: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;
2.2 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
2.2.2 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
2.2.3 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()
以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持。