Analytics on Historical Data Using a Clustered Insert-Only In-Memory Column Database

In the field of OLAP and Data Warehousing, column stores and compressed main-memory data storage technology have successfully been implemented in products that enable a significant speed improvement of analytical queries with special performance requirements. We could soon see the majority of analytical workloads move to such main-memory based systems. Having one specialized OLAP DBMS explicitly aimed at performing ad-hoc queries on an ever-growing database requires the capability of an in-memory database to retain historical states so that applications can calculate consistent values based on previous states of the database, a requirement often found in financial and production planning analytical applications. This paper describes Rock, an in-memory analytics cluster based on a Column store database, and proposes an architecture for historical query support as well as the prototypical implementation in Rock.