AirIndex: Versatile Index Tuning Through Data and Storage

TL;DR

AirIndex is a systematic, data-driven approach that optimizes hierarchical index structures for specific system I/O characteristics, significantly improving lookup speed over existing indexes.

Contribution

It introduces a novel I/O-aware index builder that uses graph-based optimization to design high-speed indexes tailored to data and storage profiles.

Findings

Achieves up to 4.1x faster lookup than lightweight B-trees.

Outperforms state-of-the-art learned indexes by up to 46.3x.

Builds optimal indexes efficiently within comparable time to existing methods.

Abstract

The end-to-end lookup latency of a hierarchical index -- such as a B-tree or a learned index -- is determined by its structure such as the number of layers, the kinds of branching functions appearing in each layer, the amount of data we must fetch from layers, etc. Our primary observation is that by optimizing those structural parameters (or designs) specifically to a target system's I/O characteristics (e.g., latency, bandwidth), we can offer a faster lookup compared to the ones that are not optimized. Can we develop a systematic method for finding those optimal design parameters? Ideally, the method must have the potential to generate almost any existing index or a novel combination of them for the fastest possible lookup. In this work, we present new data and an I/O-aware index builder (called AirIndex) that can find high-speed hierarchical index designs in a principled way. Specifically, AirIndex minimizes an objective function expressing the end-to-end latency in terms of various designs -- the number of layers, types of layers, and more -- for given data and a storage profile, using a graph-based optimization method purpose-built to address the computational challenges rising from the inter-dependencies among index layers and the exponentially many candidate parameters in a large search space. Our empirical studies confirm that AirIndex can find optimal index designs, build optimal indexes within the times comparable to existing methods, and deliver up to 4.1x faster lookup than a lightweight B-tree library (LMDB), 3.3x--46.3x faster than state-of-the-art learned indexes (RMI/CDFShop, PGM-Index, ALEX/APEX, PLEX), and 2.0 faster than Data Calculator's suggestion on various dataset and storage settings.