We have a fairly unique need for a local, in-memory cache. This will store data aggregated from other sources. Generating the data only takes a few minutes, and it is updated often. There will be some fairly expensive queries of arbitrary complexity run at a fairly high rate. We're looking for high concurrency and reasonable performance throughout. The entire data set is roughly 20 MB in size. We've tried Carbonado in front of SleepycatJE only to discover that it chokes at a fairly low concurrency and that Carbonado's rule-based optimizer is wholly insufficient for our needs. We've also tried Carbonado's Map Repository which suffers the same problems. I've since moved the backend database to a local PostgreSQL instance hoping to take advantage of PostgreSQL's superior performance at high concurrency. Of course, at the default settings, it performs quite poorly compares to the Map Repository and Sleepycat JE. My question is how can I configure the database to run as quickly as possible if I don't care about data consistency or durability? That is, the data is updated so often and it can be reproduced fairly rapidly so that if there is a server crash or random particles from space mess up memory we'd just restart the machine and move on. I've never configured PostgreSQL to work like this and I thought maybe someone here had some ideas on a good approach to this. -- Sent via pgsql-performance mailing list (pgsql-performance@xxxxxxxxxxxxxx) To make changes to your subscription: http://www.postgresql.org/mailpref/pgsql-performance
