Cracking In-Memory Database Index A Case Study for Adaptive Radix Tree Index

TL;DR

This paper investigates the feasibility of index cracking for in-memory databases by proposing an algorithm to crack the Adaptive Radix Tree (ART) index, aiming to reduce index creation and update costs.

Contribution

It introduces a novel algorithm using auxiliary data structures to crack the ART index, addressing the gap in applying cracking techniques to complex in-memory index structures.

Findings

Demonstrates the feasibility of cracking the ART index in-memory

Proposes an auxiliary data structure-based cracking algorithm for ART

Lays groundwork for future research on in-memory index cracking

Abstract

Indexes provide a method to access data in databases quickly. It can improve the response speed of subsequent queries by building a complete index in advance. However, it also leads to a huge overhead of the continuous updating during creating the index. An in-memory database usually has a higher query processing performance than disk databases and is more suitable for real-time query processing. Therefore, there is an urgent need to reduce the index creation and update cost for in-memory databases. Database cracking technology is currently recognized as an effective method to reduce the index initialization time. However, conventional cracking algorithms are focused on simple column data structure rather than those complex index structure for in-memory databases. In order to show the feasibility of in-memory database index cracking and promote to future more extensive research, this paper conducted a case study on the Adaptive Radix Tree (ART), a popular tree index structure of in-memory databases. On the basis of carefully examining the ART index construction overhead, an algorithm using auxiliary data structures to crack the ART index is proposed.