Most OLAP has that exponential falloff in data use with time. I once extracted all the date strings from recent queries on a data warehouse and found the same distribution.<p>Zynga once built a kind of time-series database with very similar metric namespace issues: about 24M metrics/minute reducing to about 1M unique names with heavy skew. They did almost everything wrong in implementing it; I was considering blogging about it once but let it go.<p>It turned out that the basic aggregation (they were in a hierarchy, so they needed to rollup to each level with counts and uniques) could be done in a few seconds with a string sort. But nothing could solve the problem of middle management.