It has been a recognized fact for many years that query execution can benefit from pushing group-by operators down in the operator tree and applying them before a join. This so-called eager aggregation reduces the size(s) of the join argument(s), making join evaluation faster. Lately, the idea enjoyed a revival when it was applied to outer joins for the first time and incorporated in a state-of-the-art plan generator. However, this recent approach is highly dependent on the use of heuristics because of the exponential growth of the search space that goes along with eager aggregation. Finding an optimal solution for larger queries calls for effective optimality preserving pruning mechanisms to reduce the search space size as far as possible. By a more thorough investigation of functional dependencies and keys, we provide a set of new pruning criteria and evaluate their effectiveness with respect to the runtime and memory consumption of the resulting plan generator.

We fill two gaps in the literature. First, we give a comprehensive set of equivalences allowing reordering of grouping with non-inner joins. Second, we show how to incorporate the optimal placement of grouping into a state-of-the-art dynamic programming (DP)-based plan generator.

Reordering more than traditional joins (e.g. outerjoins, antijoins) requires some care, since not all reorderings are valid. To prevent invalid plans, two approaches have been described in the literature. We show that both approaches still produce invalid plans.