ASLM: Adaptive Single Layer Model for Learned Index

Abstract

Index structures such as B-trees are important tools that DBAs use to enhance the performance of data access. However, with the approaching of the big data era, the amount of data generated in different domains have exploded. A recent study has shown that indexes consume about 55% of total memory in a state-of-the-art in-memory DBMS. Building indexes in traditional ways have encountered a bottleneck. Recent work proposes to use neural network models to replace B-tree and many other indexes. However, the proposed model is heavy, inaccuracy, and has failed to consider model updating. In this paper, a novel, simple learned index called adaptive single layer model is proposed to replace the B-tree index. The proposed model, using two data partition methods, is well-organized and can be applied to different workloads. Updating is also taken into consideration. The proposed model incorporates two data partition methods is evaluated in two datasets. The results show that the prediction error is reduced by around 50% and demonstrate that the proposed model is more accurate, stable and effective than the currently existing model.