If so, let me rephrase it...having 16gb of data and have to access all 16 gb at on time is a daunting task. if you have 16gb of data and you work only on a piece of that, let's say, an indexed tree and a client requests just a node, you can try with redis. the problem arises only when you have to put into python process's memory 16 gb of data alltogether.
On 6 Feb, 22:09, "Sebastian E. Ovide" <sebastian.ov...@gmail.com> wrote: > On Mon, Feb 6, 2012 at 7:39 PM, Bruno Rocha <rochacbr...@gmail.com> wrote: > > 16 GB shared across requests is called "Database", to run a memory like > > database you should go with Redis! > > :D it sounds a lot... but it is not anymore... specially if you want to > serve a lot of requests in realtime ! > > we are using two machines with 36G for a real commercial application. We > use so much memory for implementing a tree for fast research of addresses > using phonetics (28M addresses)... using Oracle (a big machine optimized by > two DBA experts) was two slow for us (around 1 second per query)... A big > improvement was obtained using special indexes (created by lucene) stored > in SSD... but still to slow for us... so the only solution was to use a > "special" tree all in memory.... > > just investigating if there is something else in the open source that could > save us Weblogic licences.... > > -- > Sebastian E. Ovide
