This retrospective revisits our 2015 PVLDB paper How Good Are Query Optimizers, Really?, which challenged the prevailing notion that query optimization was a solved problem. By designing the Join Order Benchmark (JOB) and conducting a series of systematic experiments, we empirically disentangled the contributions of plan enumeration, cost modeling, and cardinality estimation. Our findings showed that cardinality estimation errors are widespread and often the dominant factor behind poor query plans, while cost models and enumeration strategies matter comparatively less. The benchmark and methodology helped refocus the community's attention on cardinality estimation and led to a resurgence of research in this area, including learned and AI-based approaches. We reflect on the role of experiments and benchmarking in database research, survey developments in query optimization over the past decade, and discuss open challenges around robustness, adaptive execution, and realistic workloads.

Predicting the biological function of molecules, be it proteins or drug-like compounds, from their atomic structure is an important and long-standing problem. The electron density field and electrostatic potential field of a molecule contain the “raw fingerprint” of how this molecule can fit to binding partners. In this paper, we show that deep learning can predict biological function of molecules directly from their raw 3D approximated electron density and electrostatic potential fields. Protein function based on Enzyme Commission numbers is predicted from the approximated electron density field. In another experiment, the activity of small molecules is predicted with quality comparable to state-of-the-art descriptor-based methods. We propose several alternative computational models for the GPU with different memory and runtime requirements for different sizes of molecules and of databases. We also propose application-specific multi-channel data representations.

Finding a good join order is crucial for query performance. In this paper, we introduce the Join Order Benchmark (JOB) and experimentally revisit the main components in the classic query optimizer architecture using a complex, real-world data set and realistic multi-join queries. We investigate the quality of industrial-strength cardinality estimators and find that all estimators routinely produce large errors. We further show that while estimates are essential for finding a good join order, query performance is unsatisfactory if the query engine relies too heavily on these estimates. Using another set of experiments that measure the impact of the cost model, we find that it has much less influence on query performance than the cardinality estimates. Finally, we investigate plan enumeration techniques comparing exhaustive dynamic programming with heuristic algorithms and find that exhaustive enumeration improves performance despite the sub-optimal cardinality estimates.