I stumbled over an algorithmic / data-structures question over on SO, which I'll short quote:
(...) opinions regarding best of breed data structures to be used for indexing time-series (aka column-wise data, aka flat linear).
Queries that will be required:
All values in the time range [t0,t1] All values in the time range [t0,t1] that are greater/less than v0 All values in the time range [t0,t1] that are in the value range[v0,v1]
The data sets consist of summarized time-series (...) The data set(s) in question are about 15-20TB in size, hence processing is performed in a distributed manner - because some of the queries described above will result in datasets larger than the physical amount of memory available on any one system.
Distributed processing in this context also means dispatching the required data specific computation along with the time-series query, so that the computation can occur as close to the data as is possible - so as to reduce node to node communications (somewhat similar to map/reduce paradigm) - in short proximity of computation and data is very critical.
I'll readily admit that problems of this scale are way over my head, but my first hunch (even with the data sizes mentioned) given this problem would have been to ask whether they checked if large-scale RDBMS (well, I guess Oracle, or Oracle, right?) could handle this in a sane way.
So here's the question: Can a (enterprise?) RDBMS handle this kind of problem today with acceptable performance vs. a "hand-coded" solution.
Note: Hope this isn't too vague, and feel free to re-tag as appropriate :-)