I get these results:

C:\Python26\python.exe F:/Experimental/web2py_272/web2py/test_executesql.py
> Times are for 100 iterations
> Preparing....
> started bench
> speed_executesql() total=1.132703 avg=0.011327
> speed_normal_rows() total=5.320367 avg=0.053204
> speed_rows_with_cacheable() total=5.349665 avg=0.053497
> speed_executesql_with_colnames() total=1.083485 avg=0.010835
> speed_rows_with_simple_processor() total=1.202628 avg=0.012026
> Process finished with exit code 0


Pretty similar results...
Although a little slower overall, same gaps appear.


Then I tested it with timeit using your approach:

Times are for 10 iterations
> Preparing....
> started bench
> speed_normal_rows() total=47.711369 avg=4.771137
> speed_executesql_with_colnames() total=0.513249 avg=0.051325


That is a *x92.959318 times* (!) faster
Almost full 2 orders of magnitude.


On Tuesday, November 19, 2013 10:45:52 PM UTC+2, Niphlod wrote:
>
> if only those announcements came with actual reproducible tests..... life 
> would be easier for us :-P
> I'd guess your usecase is either 1k rows with 50 columns or a really slow 
> python server, because I can't reproduce those kind of "advertised 
> magnitude gains". Just a 4x that is quite expected since Rows carries 
> around lots of helpers.
>
> CPython 2.7.3
> speed_executesql() total=0.846748 avg=0.008467
> speed_normal_rows() total=4.214647 avg=0.042146
> speed_rows_with_cacheable() total=3.705196 avg=0.037052
> speed_executesql_with_colnames() total=0.844287 avg=0.008443
> speed_rows_with_simple_processor() total=0.891577 avg=0.008916
>
> pypy 1.9.0
> speed_executesql() total=3.344496 avg=0.033445
> speed_normal_rows() total=3.832903 avg=0.038329
> speed_rows_with_cacheable() total=3.472066 avg=0.034721
> speed_executesql_with_colnames() total=2.808536 avg=0.028085
> speed_rows_with_simple_processor() total=3.268108 avg=0.032681
>
> Attached the test code.
>
>

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