OLAP query processing in a database cluster

The efficient execution of OLAP queries, which are typically read-only and heavy-weight, is a hard problem which has been traditionally solved using tightly-coupled multiprocessors. Considering a database cluster as a cost-effective alternative, we propose an efficient, yet simple, solution, called fined-grained virtual partitioning to OLAP parallel query processing. We designed this solution for a shared-nothing database cluster architecture that can scale up to very large configurations and support black-box DBMS using non intrusive, simple techniques. To validate our solution, we implemented a Java prototype on a 16 node cluster system and ran experiments with typical queries of the TPC-H benchmark. The results show that our solution yields linear, and sometimes super-linear, speedup. With 16 nodes, it outperforms traditional virtual partitioning by a factor of 6.