blocks|key|4566842|text|我认为把记录器作为参数传递不是个好主意。您应该将全局记录器视为自己的模块，这将是最好的主意。例如：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4566843|logger.py|4566844|import+logging
log+=+logging.getLogger('')|code-block|syntax|javascript|4566845|classFoo.py|4566846|form+logger+import+log
log.debug('debug+message')|4566847|classBar.py|4566848|form+logger+import+log
log.warn('warn!')|4566849|entityMap^0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|T|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|U|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|V|8|@]|9|@]|A|$]]|$1|K|3|L|5|F|7|W|8|@]|9|@]|A|$G|H]]|$1|M|3|N|5|6|7|X|8|@]|9|@]|A|$]]|$1|O|3|P|5|F|7|Y|8|@]|9|@]|A|$G|H]]|$1|Q|3|-4|5|6|7|Z|8|@]|9|@]|A|$]]]|R|$]]

I think passing logger as parameter isn't good idea. You should consider a global logger as own module, it would be the best idea. For example: 
logger.py

<pre><code>import logging
log = logging.getLogger('')
</code></pre>

classFoo.py

<pre><code>form logger import log
log.debug('debug message')
</code></pre>

classBar.py

<pre><code>form logger import log
log.warn('warn!')
</code></pre>

blocks|key|1114185|text|也许这能帮你想出个主意？当然，您可以更好地读取配置文件或其他设置，但这是一个快速的例子。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1114186|配置日志记录的单独模块：mylogmod.py：|offset|length|style|CODE|1114187|import+logging

FILENAME+=+"mylog.log"+#+Your+logfile
LOGFORMAT+=+"%25(message)s"+#+Your+format
DEFAULT_LEVEL+=+"info"+#+Your+default+level,+usually+set+to+warning+or+error+for+production
LEVELS+=+{
++++'debug':logging.DEBUG,
++++'info':logging.INFO,
++++'warning':logging.WARNING,
++++'error':logging.ERROR,
++++'critical':logging.CRITICAL}

def+startlogging(filename=FILENAME,+level=DEFAULT_LEVEL):
++++logging.basicConfig(filename=filename,+level=LEVELS[level],+format=LOGFORMAT)|code-block|syntax|javascript|1114188|main.py：|1114189|import+logging
from+mylogmod+import+startlogging
from+myclass+import+MyClass

startlogging()

logging.info("Program+started...")
mc+=+MyClass()|1114190|具有自测试功能的模块中的类myclass.py。您可以在unittest中执行类似的操作：(请注意，不需要在单元测试中导入日志模块，只要startlogging函数就足够了。通过这种方式，可以将默认级别设置为警告或错误，将单元测试和自测试设置为调试)|1114191|import+logging

class+MyClass(object):
++++def+__init__(self):
++++++++logging.info("Initialze+MyClass+instance...")

if+__name__+==+"__main__":
++++from+mylogmod+import+startlogging
++++startlogging(level="debug")
++++logging.debug("Test+MyClass...")
++++#...+rest+of+test+code...|1114192|entityMap^0|0|C|B|0|0|0|7|0|0|D|A|1W|C|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|X|8|@$D|Y|E|Z|F|G]]|9|@]|A|$]]|$1|H|3|I|5|J|7|10|8|@]|9|@]|A|$K|L]]|$1|M|3|N|5|6|7|11|8|@$D|12|E|13|F|G]]|9|@]|A|$]]|$1|O|3|P|5|J|7|14|8|@]|9|@]|A|$K|L]]|$1|Q|3|R|5|6|7|15|8|@$D|16|E|17|F|G]|$D|18|E|19|F|G]]|9|@]|A|$]]|$1|S|3|T|5|J|7|1A|8|@]|9|@]|A|$K|L]]|$1|U|3|-4|5|6|7|1B|8|@]|9|@]|A|$]]]|V|$]]

Maybe this helps you to get an idea? Of course you can make it much better, reading settings from a config file or whatever but this is quick example.

A separate module to configure the logging: <code>mylogmod.py</code> :

<pre><code>import logging

FILENAME = "mylog.log" # Your logfile
LOGFORMAT = "%(message)s" # Your format
DEFAULT_LEVEL = "info" # Your default level, usually set to warning or error for production
LEVELS = {
 'debug':logging.DEBUG,
 'info':logging.INFO,
 'warning':logging.WARNING,
 'error':logging.ERROR,
 'critical':logging.CRITICAL}

def startlogging(filename=FILENAME, level=DEFAULT_LEVEL):
 logging.basicConfig(filename=filename, level=LEVELS[level], format=LOGFORMAT)
</code></pre>

The <code>main.py</code> :

<pre><code>import logging
from mylogmod import startlogging
from myclass import MyClass

startlogging()

logging.info("Program started...")
mc = MyClass()
</code></pre>

A class <code>myclass.py</code> from a module with self test. You can do something similar in a unittest: (Note that you don't need to import the logging module in a unittest, just the <code>startlogging</code> function is enough. This way you can set the default level to warning or error and the unittests and self tests to debug)

<pre><code>import logging

class MyClass(object):
 def __init__(self):
 logging.info("Initialze MyClass instance...")

if __name__ == "__main__":
 from mylogmod import startlogging
 startlogging(level="debug")
 logging.debug("Test MyClass...")
 #... rest of test code...
</code></pre>

blocks|key|3734413|text|通常不会；它通常不打算作为参数传递。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3734414|约定是在每个模块的顶部使用log+=+logging.getLogger(__name__)。每个模块的__name__值是不同的。然后，__name__的结果值可以反映在每个日志消息中。|offset|length|style|CODE|3734415|entityMap^0|0|D|X|1G|8|1X|8|0^^$0|@$1|2|3|4|5|6|7|J|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|K|8|@$D|L|E|M|F|G]|$D|N|E|O|F|G]|$D|P|E|Q|F|G]]|9|@]|A|$]]|$1|H|3|-4|5|6|7|R|8|@]|9|@]|A|$]]]|I|$]]

Not usually; it is typically not meant to be passed as a parameter.

The convention is to use <code>log = logging.getLogger(__name__)</code> in the top of each module. The value of <code>__name__</code> is different for each module. The resultant value of <code>__name__</code> can then be reflected in each log message.

A Python application we're developing requires a logger. A coworker argues that the logger should be created and configured in every class that's using it. My opinion is that it should be created and configured on application start and passed as a constructor-parameter.

Both variants have their merits and we're unsure what the best practice is.

Should a Python logger be passed as parameter?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

文章

问答

视频

学习中心

腾讯云实验室

直播

竞赛

腾讯云代码分析专区

腾讯iOA零信任安全管理系统专区

腾讯云架构师技术同盟交流圈

腾讯云数据库专区

腾讯云顾问专区

腾讯云原生专区

腾讯混元专区

腾讯云TCE专区

腾讯云Lighthouse专区

腾讯云HAI专区

腾讯云Edgeone专区

腾讯云存储专区

腾讯云智能专区

腾讯轻联专区 

腾讯云开发专区

TAPD专区

腾讯轻量云游戏服专区

腾讯云最具价值专家

腾讯云架构师技术同盟

腾讯云创作之星

腾讯云开发者先锋

腾讯云代码助手

云原生构建

TAPD 敏捷项目管理

Cloud Studio

SDK中心

API中心

命令行工具

涵盖代码开发、场景应用、自动测试全流程，助你从零构建专属AI助手

一站式MCP教程库，解锁AI应用新玩法

我们正在开发的Python应用程序需要一个记录器。一位同事认为，记录器应该在使用它的每个类中创建和配置。我的观点是，应该在应用程序启动时创建和配置它，并将其作为构造函数参数传递。这两个变体都有其优点，我们不确定最佳实践是什么。

问是否应该将Python记录器作为参数传递？
EN

Stack Overflow用户

社区

活动

圈层

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问是否应该将Python记录器作为参数传递？EN