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 (G3oI). 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 G3oI 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".

中文翻译：

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没有“索引”这样的东西！或：接下来的 500 篇索引论文

索引结构通常是查询处理和计算机科学的构建块。自从计算机技术出现以来，就有了索引结构。从那时起，每年都有无数的索引结构被发明和发布。在本文中，我们认为“发明索引”的想法首先是一个误导性的概念。它类似于“发明物理查询计划”。这篇论文是一种范式转变，我们建议完全放弃手工制作索引结构的想法（就像对 B 树上的二叉搜索树所做的那样，到任何形式的学习索引）。我们提出了一个新的自动索引繁殖框架，它创造了索引结构的遗传通用生成 (G3oI)。它基于观察到几乎所有索引结构都沿三个主要维度组装：（1）结构构建块，例如 B 树由两种不同的结构节点类型（内部节点和叶节点）组装，(2)几个不变量，例如，对于 B 树，所有路径都具有相同的长度，以及 (3) 对节点内部布局（行或列布局等）的决定。我们提出了一个通用索引框架，可以沿这些维度模仿许多现有的索引结构。基于该框架，我们提出了一种通用遗传索引生成算法，在给定工作量和优化目标的情况下，该算法可以自动组装和变异，换句话说，“培育”新的索引结构“物种”。在我们的实验中，我们遵循多个目标。我们重新审视了数据库技术中的一些古老智慧。给定特定的工作量，G3oI 是否会产生一个与我们目前针对此类工作量推荐的教科书和论文相当的索引？或者我们可以做得更多吗？我们的初步结果强烈表明生成索引是设计“索引结构”的下一步。