Cracking the Database Store

Query performance strongly depends on finding an execution plan that touches as few superfluous tuples as possible. The access structures deployed for this purpose, however, are non-discriminative. They assume every subset of the domain being indexed is equally important, and their structures cause a high maintenance overhead during updates. This approach often fails in decision support or scientific environments where index selection represents a weak compromise amongst many plausible plans. An alternative route, explored here, is to continuously adapt the database organization by making reorganization an integral part of the query evaluation process. Every query is first analyzed for its contribution to break the database into multiple pieces, such that both the required subset is easily retrieved and subsequent queries may benefit from the new partitioning structure. To study the potentials for this approach, we developed a small representative multi-query benchmark and ran experiments against several open-source DBMSs. The results obtained are indicative for a significant reduction in system complexity with clear performance benefits.