Denormalization is a common tactic for enhancing performance of data warehouses. However, it is rarely used in main memory databases, which regards storage space as scarce resource. In this paper, we demonstrate that MMDB can actually benefit from the strategy of denormalization. We have created A-Store, a prototypical main-memory database system customized for star and snowflake schemas, which applies the strategy of denormalization to achieve highly efficient OLAP. Instead of resorting to fully materialized denormalization, A-Store applies a method called virtual denomalization, which allows query processing to be performed in a denormalized way, while without incurring additional space consumption.

Denormalization is a common tactic for enhancing performance of data warehouses, though its side-effect is quite obvious. Besides being confronted with update abnormality, denormalization has to consume additional storage space. As a result, this tactic is rarely used in main memory databases, which regards storage space, i.e., RAM, as scarce resource. Nevertheless, our research reveals that main memory database can benefit enormously from denormalization, as it is able to remarkably simplify the query processing plans and reduce the computation cost. In this paper, we present A-Store, a main memory OLAP engine customized for star/snowflake schemas. Instead of generating fully materialized denormalization, A-Store resorts to virtual denormalization by treating array indexes as primary keys. This design allows us to harvest the benefit of denormalization without sacrificing additional RAM space. A-Store uses a generic query processing model for all SPJGA queries. It applies a number of state-of-the-art optimization methods, such as vectorized scan and aggregation, to achieve superior performance. Our experiments show that A-Store outperforms the most prestigious MMDB systems significantly in star/snowflake schema based query processing.