Hi, I use Beaker in production to help speed up the delivery of game content. We've seen enormous (seconds to milliseconds) speed ups for caching large queries that don't change.
We don't use it at the query level but as a way to cache whole results from sqlalchemy. As long as you remember to merge all the objects back into the Session you're fine. We currently don't use the 'memcache' but the 'memory' setting, it's the easiest to setup and gives very good results. The only caveat I have about the memory caching is that there is no way of setting how big it gets, if that's a problem use memcache instead. For me, I just set the lifetime of objects to an hour and that stops the memory growing without bounds. File caching doesn't give as good results because you have to hit the disk and that will lead to IO problems especially if you are using a database which will be using the disk as well. I hope this helps, Ben Hesketh On Jun 8, 2:43 pm, Łukasz Czuja <[email protected]> wrote: > Hi, > > I reviewed a couple of messages on the list concerning caching. While: > > http://www.sqlalchemy.org/trac/browser/examples/beaker_caching > > is quite comprehensive, it does not include any information about > performance gains or wether this example works for more complicated > queries, joining couple of tables, returning agregates or diffrent > objects from the same query. Are there any limitations on data types > that cannot be cached? Also there is no information as wether this > example would work if beaker is setup with memcache as storage and > wether it would be faster than file storage and if so by how much? > > Can any one provide some numbers on this concept? Does anyone have a > success story to share? > > I'm dying to see how this performs and if it is usable on production > environment. > > Any input is appreciated. Thanks in advance. -- You received this message because you are subscribed to the Google Groups "sqlalchemy" group. To post to this group, send email to [email protected]. To unsubscribe from this group, send email to [email protected]. For more options, visit this group at http://groups.google.com/group/sqlalchemy?hl=en.
