Well, if you need a fast database, you can use in-memory database like http://www.ispras.ru/~knizhnik/fastdb.html (it's written in C++ for C++, so you will need to develop a glue layer from C to Python) But note, that all of your data should fit in RAM in such case. Also note, that embedded databases delivers some delays during commits in calling threads, so you should handle that issues too. On Thu, 14 Jun 2007 15:29:08 -0400 Jonathan Vanasco <twisted-python@2xlp.com> wrote:
note: this is less about twisted than a project i've built on twisted that is having some issues. everyone here is smart, so i thought this would be a good list to ask on.
i've got a small twisted daemon that is working as a proxy for hosting content that we store on amazon-s3 long story short: archive to s3 (redundancy, cheap storage), twisted daemon on our network fetches to cache & serve. we have cheaper bandwidth, plus do a lot of file monitoring / name abstraction that amazon's network won't support. currently there are 10k documents on s3, which are accessible via 150k+ 'keys'. the cache basiscally proxies the right doc for each key
I'm running into 2 issues with it: short term- keys are mapped to s3 files via a ton of 'hints' that i store in bdb after fetching from postgresql. the hints need to be refreshed every 1-5 minutes or so. does anyone have a good suggestion on how to do that? i could do this really easily with sqlite, but i need to use bdb -- as sqlite isn't nearly fast enough; while bdb is.
long term- everything is fine for now -- we only have about 3gb of data. but thats going to be growing to about 20gb soon, and we want to limit the cache to an active 10gb using an Adaptive Replacement Cache algorithm with a bdb datastore. has anyone done something similar in twisted or python in general?
any input would be appreciated. thanks!
// Jonathan Vanasco
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