Oleg Broytmann wrote:
> On Wed, Jul 22, 2009 at 03:03:52PM -0400, Stef Telford wrote:
>
>> surely, please find the output attached at the bottom.
>>
>
> Thank you!
>
>
No, Honestly, Thank you :)
>> Ordered by: cumulative time
>>
>> ncalls tottime percall cumtime percall filename:lineno(function)
>> 1 0.127 0.127 83.346 83.346 sresults.py:175(__iter__)
>> 40001 0.308 0.000 79.876 0.002 dbconnection.py:649(next)
>>
>
> Hmm. Does the program try to draw the entire table in one huge slurp?
>
>
Actually, no. This page does 40k of bookings (a single object type) but
the query isn't "Sequential" (it's not grabbing 0-40,000 is what I
mean). I can do a page which has lots of different object types if this
would help ?
>> 40000 0.375 0.000 39.282 0.001 main.py:872(get)
>> 40002 10.018 0.000 39.252 0.001 {method 'fetchone' of
>> 'psycopg2._psycopg.cursor' objects}
>>
>
> Half of the time the program spends drawing rows one by one. This
> probably could be optimized by using fetchmany or fetchall.
>
>
Noted. Let me try this later tonight when I have some spare cycles :)
>> 1840006 16.887 0.000 29.234 0.000 decimal.py:515(__new__)
>>
>
> Third of the time - Decimal.__new__. There is something strange here (in
> the profiler output itself, I mean) - those Decimal calls are probably
> from DecimalCol.to_python, but the profiler didn't add those calls:
>
>
Urm. hrm. that's weird. I would have thought that they would have been
added if they were called. I have read in more than a few places, that
Decimal instantiation is slow when compared to float or gmpy :\
>> 1200000 1.856 0.000 2.769 0.000 col.py:1289(to_python)
>> 3362951 2.729 0.000 2.729 0.000 main.py:1673(instanceName)
>> 640000 0.999 0.000 1.471 0.000 col.py:657(to_python)
>>
>
> My guess is that those Decimal.__new__ calls are inside DB API driver,
> and DecimalCol.to_python gets Decimal and returns it unchanged. This means
> that the lines
>
>
>> 40002 10.018 0.000 39.252 0.001 {method 'fetchone' of
>> 'psycopg2._psycopg.cursor' objects}
>> 1840006 16.887 0.000 29.234 0.000 decimal.py:515(__new__)
>>
>
> should be read as follows: fetchone draws a row and converts values to
> Decimal, so 29.2 s are really a part of 39.2, and fetchone only waited for
> DB for 10 seconds.
>
>
10seconds to fetch from the database is not bad (in my view). The 29s
for decimal is definitely 'killer'
>> 40000 0.214 0.000 23.323 0.001 main.py:912(_init)
>> 40000 10.475 0.000 23.069 0.001 main.py:1140(_SO_selectInit)
>>
>
> _SO_selectInit is a rather simple function - it populates the row with
> values just fetched from the DB; the values are converted using calls to
> to_python. Those to_python calls are fast (if we ca believe the profiler) -
> see above. So where does _SO_selectInit spend its time?!
>
>
I wish I knew. The object in question -does- have about 40 or 50 columns
on it (don't ask.. lots of feature creep). I wonder if perhaps the
number of columns plays into the _init time ? I take it that the class
overview is cached, so that it only has to be parsed 'once'.. but what
about if it's a class through a FK or M2M ? Is SQLObject able to find
the pre-parsed class (if that makes sense)
Sorry about all this, but I really am sorta hitting my head here. I
mean, I -could- change Decimals to gmpy or something inside SO, but
floats with their (0.1 + 0.1 + 0.1 - 0.3) fiasco is pretty much a
non-starter for me I think.
Hrm. As I said, probably not being much help here :)
Regards
Stef
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