Python logging: why printf-style string formatting may be better thanf-strings

栏目: IT技术 · 发布时间: 6年前

内容简介:Python provides more than one way to format strings:

Python provides more than one way to format strings: %-formatting , str.format() , string.Template and f-strings . What format developers use is the matter of personal aesthetic reasons rather than anything else. Still there are use cases where good old printf-style %-formatting may have advantages over other formats. Python’s logging module is one of these cases. Let’s see what are the main reasons to use %-formatting for logging in Python.

Python logging optimization: format string until it cannot be avoided

Python logging methods like debug get in message argument along with optional args and kwargs . Arguments used for message formatting. Formatting of these arguments is deferred until it cannot be avoided. That means the final message is not evaluated if its log level is below the logger’s log level. On the other hand, f-string is really an expression that evaluated at runtime and it lacks logging’s optimizations. Let’s see an example:

In [1]: import logging
   ...: logging.basicConfig(level=logging.INFO)
   ...: logger = logging.getLogger('TestLogger')

In [2]: class A:
   ...:     def __str__(self):
   ...:         print('Unnecessary slow computations are done here!')
   ...:         return self.__class__.__name__
   ...:
   ...: obj = A()

In [3]: logger.debug("log level below INFO with args: %s", obj)

In [4]: logger.debug(f"log level below INFO with f-string: {obj}")
Unnecessary slow computations are done here!

In [5]: logger.info("log level INFO with args: %s", obj)
Unnecessary slow computations are done here!
INFO:TestLogger:log level INFO with args: A

As you can see in step 3 message is not logged as root logger’s level is higher than debug . That’s why that message is not formatted. In step 4 f-string is evaluated even though it’s not getting logged either.

Logging module has other nice optimizations that worth learning.

Sentry integration with logging

Sentry is a popular error tracking solution. Sentry integrates with the Python logging module. Error aggregation is another great feature of Sentry. Sentry looks at the event’s stacktrace, exception, or message and group it with existing ones if they are the same. If you get a hundred of exceptions that are all the same they get grouped into one with nice statistics.

When integrating Sentry with Python logging, it’s important to use %-formatting , so that Sentry can group your messages. As an example, let’s log failed attempts to retrieve an URL :

logger.error("Failed to retrieve URL %s", url)

With printf-style formatting your messages will be grouped together no matter what value url variable holds. But in this case:

logger.error(f"Failed to retrieve URL {url}")

you get a separate event in Sentry for every single unique URL . If you get 1000 unique URL , your Sentry dashboard might get messy.

Conclusions

Although Python is not Perl, there’s still more than one way to do it. Which string formatting style to use is an open question. Your project may not use Sentry, or you may prefer f-strings for readability reasons. You may see deferred formatting in logging for printf-style strings as a futile micro-optimization that won’t improve your app performance. But still, there may be cases where using an oldie but goldie %-formatting is beneficial, even at price of string formatting inconsistency.


以上就是本文的全部内容,希望本文的内容对大家的学习或者工作能带来一定的帮助,也希望大家多多支持 码农网

查看所有标签

猜你喜欢:

本站部分资源来源于网络,本站转载出于传递更多信息之目的,版权归原作者或者来源机构所有,如转载稿涉及版权问题,请联系我们

软技能

软技能

John Sonmez / 王小刚 / 人民邮电出版社 / 2016-7 / 59.00元

这是一本真正从“人”(而非技术也非管理)的角度关注软件开发人员自身发展的书。书中论述的内容既涉及生活习惯,又包括思维方式,凸显技术中“人”的因素,全面讲解软件行业从业人员所需知道的所有“软技能”。本书聚焦于软件开发人员生活的方方面面,从揭秘面试的流程到精耕细作出一份杀手级简历,从创建大受欢迎的博客到打造你,从提高自己工作效率到与如何与“拖延症”做斗争,甚至包括如何投资不动产,如何关注自己的健康。本......一起来看看 《软技能》 这本书的介绍吧!

JSON 在线解析
JSON 在线解析

在线 JSON 格式化工具

HTML 编码/解码
HTML 编码/解码

HTML 编码/解码

SHA 加密
SHA 加密

SHA 加密工具