Barry Warsaw writes:
> On Feb 16, 2017, at 03:20 PM, M.-A. Lemburg wrote:
>
> >I know some people will disagree, but IMO using "assert" is the wrong
> >approach in such situations - it's meant for development and testing
> >only, not as short-cut to avoid having to write a proper error
> >ha
> On 16 Feb 2017, at 21:03, Abe Dillon wrote:
>
> I personally don't see why you can't use floats for log levels, but I'm
> interested to know how people are using logs such that they need dozens of
> levels. That, however; is tangential to the discussion about conditional
> execution of an e
>
> You are right that Python logging infrastructure can deal with logs at a
> finer grain, but the code required to do it is more verbose than logical
> switches
The default behavior of filters seems pretty useful to me (filtering by
hierarchy), but, then again, so do the default log levels. I h
On Feb 16, 2017, at 03:20 PM, M.-A. Lemburg wrote:
>I know some people will disagree, but IMO using "assert" is the wrong
>approach in such situations - it's meant for development and testing
>only, not as short-cut to avoid having to write a proper error
>handler :-)
I use assertions for "things
On 2017-02-15 16:06, Abe Dillon wrote:
> On 15.02.2017, 20:39 Kyle Lahnakoski wrote:
>
> Log "levels" never made sense to me; how can a single dimension be
> useful substitute for a number of binary switches? With log
> "levels", you either don't have enough logging, or you drown in
>
On Thu, Feb 16, 2017 at 1:55 PM, M.-A. Lemburg wrote:
> Some comments:
>
> 1. you don't need a preprocessor for this: simply put your
> logging code into an "if __debug__:" block:
>
> https://docs.python.org/3.6/reference/simple_stmts.html?
> the-assert-statement#grammar-token-assert_stmt
>
On 16.02.2017 14:23, Victor Stinner wrote:
> 2017-02-16 13:55 GMT+01:00 M.-A. Lemburg :
>> 1. you don't need a preprocessor for this: simply put your
>> logging code into an "if __debug__:" block:
>
> The problem with -O is that it also disables assertions, whereas you
> may want to keep them
2017-02-16 13:55 GMT+01:00 M.-A. Lemburg :
> 1. you don't need a preprocessor for this: simply put your
> logging code into an "if __debug__:" block:
The problem with -O is that it also disables assertions, whereas you
may want to keep them at runtime on production for good reasons.
Victor
_
Some comments:
1. you don't need a preprocessor for this: simply put your
logging code into an "if __debug__:" block:
https://docs.python.org/3.6/reference/simple_stmts.html?the-assert-statement#grammar-token-assert_stmt
and then run your production code with "python -O"
(the trick h
Yeah, I had a similar issue in a previous company. A colleague wrote a
script using a regex to remove these debug logs in the .py code.
IHMO the clean design for that would be to support officially preprocessors
in Python. My PEP opens the gate for that:
https://www.python.org/dev/peps/pep-0511/
> On 15 Feb 2017, at 21:06, Abe Dillon wrote:
>
> On 15.02.2017, 20:39 Kyle Lahnakoski wrote:
> Log "levels" never made sense to me; how can a single dimension be useful
> substitute for a number of binary switches? With log "levels", you either
> don't have enough logging, or you drown in to
So if python converted:
debugLog( <# ‘format string %r’ % (expensive(),) #> )
Into:
def __tmp__():
yield ‘format string %r’ % (expensive(),)
debugLog( __tmp__ )
Then debugLog can detect the generator and call __next__ only if logging is
enabled.
I gues
On 15.02.2017, 20:39 Kyle Lahnakoski wrote:
> Log "levels" never made sense to me; how can a single dimension be useful
> substitute for a number of binary switches? With log "levels", you either
> don't have enough logging, or you drown in too much logging (or you manage
> a number of loggers, w
On 15.02.2017 12:55, Barry Scott wrote:
The lazy eval sound like a generator.
Exactly my thought.
Sven
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On Wednesday, 15 February 2017 22:18:31 GMT Steven D'Aprano wrote:
> On Tue, Feb 14, 2017 at 03:51:05PM +, Barry Scott wrote:
> > A common pattern I use is to have logging calls for debug and information
> > with my applications. The logging calls can be separately enabled and
> > disabled.
>
On Wed, Feb 15, 2017 at 10:47 PM, Barry Scott wrote:
> __repr__ is interesting however.
>
> Typically I describe in a string why the value is being logged. That means
> that the object is always a string. I cannot recall using debugLog( obj )
> in production.
>
> dlog('This is the state of obj at
On Wednesday, 15 February 2017 22:33:42 GMT Chris Angelico wrote:
> On Wed, Feb 15, 2017 at 10:18 PM, Steven D'Aprano
wrote:
> > Python doesn't have thunks, but there is a relatively heavyweight
> > solution for delayed evaluation: wrap the code in a function.
> >
> > debugLog( ‘info is %r’, lam
On Wed, Feb 15, 2017 at 10:18 PM, Steven D'Aprano wrote:
> Python doesn't have thunks, but there is a relatively heavyweight
> solution for delayed evaluation: wrap the code in a function.
>
> debugLog( ‘info is %r’, lambda: expensiveFunction() )
>
>
> and then adjust debugLog so that if the argum
On Tuesday, 14 February 2017 22:00:31 GMT Barry wrote:
> > On 14 Feb 2017, at 17:39, Kyle Lahnakoski wrote:
> >
> >
> > Can you wrap the expensive functions in lambdas? And have your logger
> > evaluate it, only if required?
> >
> >> debugLog( ‘info is %r’ % (lambda: expensiveFunction(),) )
>
On Tue, Feb 14, 2017 at 03:51:05PM +, Barry Scott wrote:
> A common pattern I use is to have logging calls for debug and information
> with my applications.
> The logging calls can be separately enabled and disabled.
[...]
> What would be nice is to be able to avoid evaluation the tuple of arg
> On 15 Feb 2017, at 02:39, Kyle Lahnakoski wrote:
>
>
> On 2017-02-14 19:51, Abe Dillon wrote:
>> The point is that the cost of creating the msg argument can be very high.
>>
>> At the point that logging decides to skip output it is to late to save
>> the cost of creating the arg
On 2017-02-14 19:51, Abe Dillon wrote:
>The point is that the cost of creating the msg argument can be very
> high.
>
> At the point that logging decides to skip output it is to late to
> save the cost of creating the arg tuple.
>
> This sounds like an optimization that's sufficiently
The point is that the cost of creating the msg argument can be very
high.
At the point that logging decides to skip output it is to late to save
the cost of creating the arg tuple.
This sounds like an optimization that's sufficiently rare and complex to
warrant a custom fix or a 3rd p
> On 14 Feb 2017, at 17:03, Cory Benfield wrote:
>
>
>> On 14 Feb 2017, at 15:51, Barry Scott wrote:
>> And have the evaluation of the argument skipped unless its dbg_log is
>> enabled.
>>
>> I cannot see how to do this with python as it stands.
>>
>> Something would have to be added to all
> On 14 Feb 2017, at 17:39, Kyle Lahnakoski wrote:
>
>
> Can you wrap the expensive functions in lambdas? And have your logger
> evaluate it, only if required?
>
>> debugLog( ‘info is %r’ % (lambda: expensiveFunction(),) )
Interesting idea. I will bench mark and see what the cost of the lamba
There are several mechanisms in the logging module to handle this use-case.
First, note that logging functions can take multiple arguments (
https://docs.python.org/3/library/logging.html?highlight=logging#logging.Logger.debug
):
>>> import logging
>>> msg = "hello, %s! %s to %s you!"
>>> args = (
On 2017-02-14 15:51, Barry Scott wrote:
A common pattern I use is to have logging calls for debug and information with
my applications.
The logging calls can be separately enabled and disabled.
For example:
debug_log_enabled = False
def debugLog( msg ):
If debug_log_enabled:
Seems slightly simpler to just make debugLog accept a callable as an
alternative to a string.
debugLog(lambda:( ‘info is %s’ % expensiveFunction()) )
Op 14 feb. 2017 18:42 schreef "Kyle Lahnakoski" :
Can you wrap the expensive functions in lambdas? And have your logger
evaluate it, only if req
Can you wrap the expensive functions in lambdas? And have your logger
evaluate it, only if required?
> debugLog( ‘info is %r’ % (lambda: expensiveFunction(),) )
On 2017-02-14 10:51, Barry Scott wrote:
> A common pattern I use is to have logging calls for debug and information
> with my applica
> On 14 Feb 2017, at 15:51, Barry Scott wrote:
> And have the evaluation of the argument skipped unless its dbg_log is enabled.
>
> I cannot see how to do this with python as it stands.
>
> Something would have to be added to allow python to short circuit the
> argument tuple evaluation.
>
>
A common pattern I use is to have logging calls for debug and information with
my applications.
The logging calls can be separately enabled and disabled.
For example:
debug_log_enabled = False
def debugLog( msg ):
If debug_log_enabled:
print( ‘Debug: %s’ % (msg,) )
Then the ca
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