Evaluation of optimization techniques for aggregation

Aggregations are almost always done at the top of operator tree after all selections and joins in a SQL query. But actually they can be done before joins and make later joins much cheaper when used properly. Although some enumeration algorithms considering eager aggregation are proposed, no sufficient evaluations are available to guide the adoption of this technique in practice. And no evaluations are done for real data sets and real queries with estimated cardinalities. That means it is not known how eager aggregation performs in the real world. In this thesis, a new estimation method for group by and join combining traditional estimation method and index-based join sampling is proposed and evaluated. Two enumeration algorithms considering eager aggregation are implemented and compared in the context of estimated cardinality. We find that the new estimation method works well with little overhead and that under certain conditions, eager aggregation can dramatically accelerate queries