论文标题
自动找到最佳索引结构
Automatically Finding Optimal Index Structure
论文作者
论文摘要
现有的学习指数（例如RMI，Alex，PGM）优化了每个节点的内部回归器，而不是整体结构，例如索引高度，每层层的大小等。在本文中，我们分享了我们最近的发现，我们可以通过优化结构和内部回归器来优化查找速度，以实现大幅更快的速度。具体而言，我们的方法（称为AirIndex）将端到端的查找时间表示为新的目标函数，并使用专用构建的优化器搜索最佳设计决策。在我们对最先进方法的实验中，AirIndex可以更快地查找存储在本地SSD上的数据，并且在Azure Cloud Storage上的数据中更快地查找数据。
Existing learned indexes (e.g., RMI, ALEX, PGM) optimize the internal regressor of each node, not the overall structure such as index height, the size of each layer, etc. In this paper, we share our recent findings that we can achieve significantly faster lookup speed by optimizing the structure as well as internal regressors. Specifically, our approach (called AirIndex) expresses the end-to-end lookup time as a novel objective function, and searches for optimal design decisions using a purpose-built optimizer. In our experiments with state-of-the-art methods, AirIndex achieves 3.3x-7.7x faster lookup for the data stored on local SSD, and 1.4x-3.0x faster lookup for the data on Azure Cloud Storage.
