Just another last question . 1. Is it advantageous to use Database Abstraction layer provided by web2py or use the vendor specific db module ? (I know it depends on the type of db,but the general rule)
On Sunday, September 2, 2012 3:57:46 AM UTC+5:30, Niphlod wrote: > > This is a discussion with no ends if you don't provide a complete app. > 1. read your preferred manual/book/guide on how to tune your db engine > (whatever engine will you choose). Find out if it performs better with more > RAM, or faster disks, or more CPU, on what OS, etc. and plan your resources > accordingly > 2. let's say you don't need session, you call session.forget() in your app > and all the related machinery in web2py will be disabled. This speeds up > web2py, but if you need sessions you can't turn them off, can you ? ^_^ > > On Sunday, September 2, 2012 12:15:05 AM UTC+2, Webtechie wrote: >> >> 1. Apart from building tables so that joins are minimum and building >> indexes .. what are other optimisations to make that possible ? >> 2 . I understand that some tuning should be done . could you be more >> specific about the type of tuning that should be done ? what should be >> changed exactly ? can you please explain with a specific use case ? >> >> On Saturday, September 1, 2012 10:18:01 PM UTC+5:30, 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 ? >>> >> --
