学习索引研究综述

Due to the explosive growth of data in the era of big data, it is difficult for the traditional index structures to handle this huge and complex data. In order to solve this problem, the learned index has emerged and become one of the most popular research topics in the database. Learned indexes employ machine learning models for index construction. By training and learning the relationship between data and physical location, the learning model can be obtained so as to master the distribution characteristics between the two to realize the improvement and optimization of the traditional index. Extensive experiments show that learned indexes can adapt to large-scale data sets-and provide better search performance with lower memory requirements than traditional indexes. This paper introduces the applications of learned indexes and reviews the existing learned index models. According to data types, learned indexes are divided into two categories: one-dimensional and multi-dimensional. The advantages, disadvantages, and supported searches of learned index models in each category are also introduced and analyzed in detail. Finallysome future research directions of learned indexes are prospected to provide references for related researches.