Every SQL statement is limited to return a single, possibly denormalized table. This approximately 50-year-old design decision has far-reaching consequences. The most apparent problem is the redundancy introduced through denormalization, which can result in long transfer times of query results and high memory usage for materializing intermediate results. Additionally, regardless of their goals, users are forced to fit query computations into one single result, mixing the data retrieval and transformation aspect of SQL. Moreover, both problems violate the principles and core ideas of normal forms. In this paper, we argue for eliminating the single-table limitation of SQL. We extend SQL's SELECT clause by the keyword 'RESULTDB' to support returning a result subdatabase. Our extension has clear semantics, i.e., by annotating any existing SQL statement with the RESULTDB keyword, the DBMS returns the tables participating in the query, each restricted to the relevant tuples that occur in the traditional single-table query result. Thus, we do not denormalize the query result in any way. Our approach has significant, far-reaching consequences, impacting the querying of hierarchical data, materialized views, and distributed databases, while maintaining backward compatibility. In addition, our proposal paves the way for a long list of exciting future research opportunities. We propose multiple algorithms to integrate our feature into both closed-source and open-source database systems. For closed-source systems, we provide several SQL-based rewrite methods. In addition, we present an efficient algorithm for cyclic and acyclic join graphs that we integrated into an open-source database system. We conduct a comprehensive experimental study. Our results show that returning multiple individual result sets can significantly decrease the result set size. Furthermore, our rewrite methods and algorithm introduce minimal overhead and can even outperform single-table execution in certain cases.

Index structures are a building block of query processing and computer science in general. Since the dawn of computer technology there have been index structures. And since then, a myriad of index structures are being invented and published each and every year. In this paper we argue that the very idea of "inventing an index" is a misleading concept in the first place. It is the analogue of "inventing a physical query plan". This paper is a paradigm shift in which we propose to drop the idea to handcraft index structures (as done for binary search trees over B-trees to any form of learned index) altogether. We present a new automatic index breeding framework coined Genetic Generic Generation of Index Structures (GENE) . It is based on the observation that almost all index structures are assembled along three principal dimensions: (1) structural building blocks, e.g., a B-tree is assembled from two different structural node types (inner and leaf nodes), (2) a couple of invariants, e.g., for a B-tree all paths have the same length, and (3) decisions on the internal layout of nodes (row or column layout, etc.). We propose a generic indexing framework that can mimic many existing index structures along those dimensions. Based on that framework we propose a generic genetic index generation algorithm that, given a workload and an optimization goal, can automatically assemble and mutate, in other words 'breed' new index structure 'species'. In our experiments we follow multiple goals. We reexamine some good old wisdom from database technology. Given a specific workload, will GENE even breed an index that is equivalent to what our textbooks and papers currently recommend for such a workload? Or can we do even more? Our initial results strongly indicate that generated indexes are the next step in designing index structures.