Going back to this thread, http://www.kx.com/ deals in financial transaction databases where they store millions of ticks. They appear to have a transactional based language with a solution that appears to be robust and fail resistant.
hmm, that is quite interesting. and apparently people out there _are_ using it for things like counter values and what not - based on their FAQ. I'd absolutely love to know more about the algorithms and math behind something like kdb+
KX publish a bunch of information about their product. Their lineage goes back to APL and the J language, both of which found most of their users in financial services. However, the general issue of time-series databases is more interesting. Google will take you to lots of research using keywords like: time-series database delta wavelet search indexing maxima Of course, don't use them all at once. To give you a flavor of the stuff that people have done, here is a slide presentation on compression and indexing that does not use averages like RRD does: http://www.cs.cmu.edu/~eugene/research/talks/major-extrema.ppt In addition to Google, it is a good idea to search CiteSeer http://citeseer.ist.psu.edu/ because it allows you to quickly track down references to other papers so you can read them all as a set. I don't think there are any full-blown open-source implementations that you could integrate into your own systems. There is stuff like Metakit http://www.equi4.com/metakit.html which stores data by column rather than by row. And people who have thought about how to efficiently store time-series probably cobbled together their own systems using bsddb or HDF5. If you are stuck in the SQL world, then check out these articles on star and snowflake schemas. http://en.wikipedia.org/wiki/Snowflake_schema http://en.wikipedia.org/wiki/Star_schema and follow up the references at the bottom of the page.