In this article, we survey recent research on column-oriented database systems, or column-stores, where each attribute of a table is stored in a separate file or region on storage. Such databases have seen a resurgence in recent years with a rise in interest in analytic queries that perform scans and aggregates over large portions of a few columns of a table. The main advantage of a column-store is that it can access just the columns needed to answer such queries. We specifically focus on three influential research prototypes, MonetDB, VectorWise, and C-Store. These systems have formed the basis for several well-known commercial column-store implementations. We describe their similarities and differences and discuss their specific architectural features for compression, late materialization, join processing, vectorization and adaptive indexing (database cracking).