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    <p>IMHO, for messages that are very likely to be displayed, such as those given to <code>error</code> or <code>warn</code> it does not make much of a difference.</p> <p>For messages that are less likely displayed, I would definitely go for the second version, mainly for performance reasons. I often give large objects as a parameter to <code>info</code>, which implement a costly <code>__str__</code> method. Clearly, sending this pre-formatted to <code>info</code> would be a performance waste.</p> <p><strong>UPDATE</strong></p> <p>I just checked the source code of the <code>logging</code> module and, indeed, formatting is done <em>after</em> checking the log level. For example:</p> <pre><code>class Logger(Filterer): # snip def debug(self, msg, *args, **kwargs): # snip if self.isenabledfor(debug): self._log(debug, msg, args, **kwargs) </code></pre> <p>One can observe that <code>msg</code> and <code>args</code> are untouched between calling <code>log</code> and checking the log level.</p> <p><strong>UPDATE 2</strong></p> <p>Spired by Levon, let me add some tests for objects that have a costly <code>__str__</code> method:</p> <pre><code>$ python -m timeit -n 1000000 -s "import logging" -s "logger = logging.getLogger('foo')" -s "logger.setLevel(logging.ERROR)" "logger.warn('%s', range(0,100))" 1000000 loops, best of 3: 1.52 usec per loop $ python -m timeit -n 1000000 -s "import logging" -s "logger = logging.getLogger('foo')" -s "logger.setLevel(logging.ERROR)" "logger.warn('%s' % range(0,100))" 1000000 loops, best of 3: 10.4 usec per loop </code></pre> <p>In practice, this could give a fairly high performance boost.</p>
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    1. POIdiomatic Python logging: format string + args list vs. inline string formatting - which is preferred?
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    1. POIdiomatic Python logging: format string + args list vs. inline string formatting - which is preferred?
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    1. COAnother time this is likely to make a difference is if the logging is in a performance critical loop.
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      1. USSteven Rumbalski
    2. COThat's what I expected based on observation. Thanks for going source diving!
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