{'fraction_non_alphanumeric': 0.024012393493415957, 'fraction_numerical': 0.0, 'mean_word_length': 5.4278606965174125, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Optimal Top-Down Join Enumeration (extended version)

Most contemporary database systems perform cost-based join enumeration using some variant of System-R's bottom-up dynamic programming method. The notable exceptions are systems based on the top-down transformational search of Volcano/Cascades. As recent work has demonstrated, bottom-up dynamic programming can attain optimality with respect to the shape of the join graph; no comparable results have been published for transformational search. However, transformational systems leverage benefits of top-down search not available to bottom-up methods.

In this paper we describe a top-down join enumeration algorithm that is optimal with respect to the join graph. We present performance results demonstrating that a combination of optimal enumeration with search strategies such as branch-and-bound yields an algorithm significantly faster than those previously described in the literature. Although our algorithm enumerates the search space top-down, it does not rely on transformations and thus retains much of the architecture of traditional dynamic programming. As such, this work provides a migration path for existing bottom-up optimizers to exploit top-down search without drastically changing to the transformational paradigm.