Thanks for the quick reply . I apologize for not being clear . I do not have a specific web app or use case in my mind . I just wanted to know whether it can be used for large-scale projects (yes . I understand that now) . However i could not interpret some points of your reply . 1. Database - is the DAL provided by web2py is not fast enough ? (now that it also supports MongoDB ) . could you provide some sort of benchmarks ? if not DAL , should we switch to db-specific python API's provided by the DB (like pymysql etc) . 2 .by default web2py handles 1k requests but can be modified to handle 2.5m requests . what part of web2py (what kind of optimisations ) should be modified to achieve this ? Also how do these optimisations differ for the different scenarios you mentioned back there ? (many small requests , less requests but requesting a large chunk of data) . 3. I could not understand the meaning of reduce models . can you please elaborate on that
On Sunday, September 2, 2012 12:14:52 AM UTC+5:30, Niphlod wrote: > > is the sky blue, really really blue ? those kind of questions really makes > me wonder. > What is "high volume of traffic" ? 1k concurrent requests ? 10GB/hour of > traffic ? many small requests? less requests but requesting a large chunk > of data? The data returned to the users will be the result of many tables > joined? questions like yours, to have back an answer given with a grain of > salt, is going to require a lot more details. > Anyway: > 1. Database large: not a web2py problem, in fact, web2py is not a database > (:-P). Be sure your db engine (that is often the bottleneck in almost every > app) handles your traffic > 2. have you tested your app with 10 users ? test it with 1000 and see if > you app is scalable. The way you code it makes it scalable, not the > framework. Again, web2py could handle 1k requests per minute, but if your > db is going to kneel down at 200 requests per minute, don't blame the > framework for being slow. > 3. have you something (I mean, code) that runs faster on other frameworks > to have web2py to blame ? > 4. Inspect your idea of app. If that's going to be an api with 1m > concurrent users, with no forms, no sessions, no auth, no model, probably > you'll find other frameworks faster than web2py. If it's going to require > all of those, or a generous subset, having your app in web2py saves you a > lot of time (and errors) > > General advices working with web2py: reduce models, use lazy tables > (available in the last release), use session.forget() wisely, and a lot of > bunch of advices you can find in the book. Want some proofs? Some users in > this list have huge sites working on web2py. From what I can recall howesc > has web2py handling a site servicing 2.5m requests per day. > > On Saturday, September 1, 2012 6:48:01 PM UTC+2, Webtechie wrote: >> >> >> I would like to use web2py for a web application which has large >> databases (really large) , expects high volume of traffic . Are there any >> ways to make web2py apps run faster ? (like really faster ) , (looking for >> solutions apart from pooling more hardware and replacing Cpython wth pypy , >> running on a non-blocking server like tornado ) . How can i optimise web2py >> for my needs ? are web2py applications scalable ? >> > --
