Joins have traditionally been the most expensive database operator, but they are required to query normalized schemas. In turn, normalized schemas are necessary to minimize update costs and space usage. Joins can be avoided altogether by using a denormalized schema instead of a normalized schema; this improves analytical query processing times at the tradeoff of increased update overhead, loading cost, and storage requirements.
In our work, we show that we can achieve the best of both worlds by leveraging partial, incremental, and dynamic denormalized tables to avoid join operators, resulting in fast query performance while retaining the minimized loading, update, and storage costs of a normalized schema. We introduce adaptive denormalization for modern main memory systems. We replace the traditional join operations with efficient scans over the relevant partial universal tables without incur- ring the prohibitive costs of full denormalization.