I came across an interesting VLDB 2005 paper, "C-Store: A Column-oriented DBMS" (PDF).
What attracted me to this paper, other than that Mike Stonebraker is lead author, was that the goals seem to have a lot in common with what appeared to motivate Google's BigTable.
C-Store is column-oriented (values for a column are stored contiguously) instead of row-oriented like most databases. It is optimized for reads. It is designed for sparse table structures and compresses data. It is designed for high availability on a large cluster. It has relaxed consistency on reads to minimize lock contention. It is extremely fast, two orders of magnitude faster than normal row-oriented databases on reads in their preliminary tests.
Google's BigTable is also column-oriented (storing compressed <row, column, timestamp> triples in the SSTable structures). It optimized for reads. It is designed for sparse table structures and compresses data. It has relaxed consistency. It is extremely fast.
There are some big differences. BigTable is not designed to support arbitrary SQL; it is a very large, distributed map. BigTable emphasizes massive data and high availability on very large clusters more than C-Store. BigTable is designed to support historical queries (e.g. get data as it looked at time X). BigTable does not require explicit table definitions and strings are the only data type.
These unusual databases implementations are fascinating. I am not familiar with any other very large scale, high availability, distributed map like BigTable, nor have I heard of any RDMS with the same very large scale, high availability, read-optimized goals of C-Store.
See also my previous post, "I want a big, virtual database".
Update: Speaking of Michael Stonebraker, his new startup, Vertica, just raised $16.5M to build a new database that "provides extremely fast ad hoc SQL query performance, even for very large databases." The underlying technology apparently is based on C-Store.