This rows = db(db.cat.id>0).select(orderby=db.cat.name,limitby=(1,10))
works on GAE and it is fast. mind that you should start counting at 0, not 1. On Jan 15, 12:44 am, toan75 <toa...@gmail.com> wrote: > On RDBS, i have code: > rows = db(db.cat.id>0).select(orderby=db.cat.name,limitby=(1,10)) > but on GAE: > rows = db(db.cat.id>0).select() > rows = rows.sort(lambda row:row.name) > rows = rows[1:10] > It spend long time in a large datasets. > Is there some other way i can use to make the performance? > > Regards, > Toan. > > On Jan 15, 12:30 pm, "mr.freeze" <nat...@freezable.com> wrote: > > > WebGrid can handle large datasets as it limits the query by the page > > size if the datasource is a Set or Table(s). The performance hit will > > come in when the filter row is enabled since each filter is a query > > for all distinct values in a field. I would try disabling the filter > > row with: > > > grid.enabled_rows.remove('filter') > > > On Jan 14, 9:12 am, Johann Spies <johann.sp...@gmail.com> wrote: > > > > Is web2py suitable if I want to work with large datasets? > > > > I am currently developing a database and want to use web2py to make it > > > available to the client. > > > > Up to now I was using the shell and appadmin interfaces to the databasis. > > > > When trying out thewebgrid-slice > > > fromhttp://www.web2pyslices.com/main/slices/take_slice/39andalsothe > > > "Quick Table Management Snippet" > > > fromhttp://www.web2pyslices.com/main/slices/take_slice/42todevelopan > > > interface to one of the tables containing about 168800 records python > > > used up all the resources on my computer (more than 3.4G of memory) > > > and I had to kill the process. > > > > In both cases I referred to the table as the datasource. > > > > What I do not understand is that in the appadmin interface I do not > > > have the same problem. > > > > How do I prevent web2py loading whole dataset into memory? After all > > > what is the use of a sql database if the everything is loaded into > > > RAM? > > > > Regards > > > Johann > >
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