Techniques for Inverted Index Compression

TL;DR

This paper surveys various encoding algorithms for compressing inverted indexes in search engines and evaluates their performance to improve query efficiency and storage reduction.

Contribution

It provides a comprehensive survey of inverted index compression techniques and experimentally characterizes their performance in real-world scenarios.

Findings

Different encoding algorithms vary in compression ratio and speed.

Compression improves query processing efficiency.

Experimental results guide optimal index compression choices.

Abstract

The data structure at the core of large-scale search engines is the inverted index, which is essentially a collection of sorted integer sequences called inverted lists. Because of the many documents indexed by such engines and stringent performance requirements imposed by the heavy load of queries, the inverted index stores billions of integers that must be searched efficiently. In this scenario, index compression is essential because it leads to a better exploitation of the computer memory hierarchy for faster query processing and, at the same time, allows reducing the number of storage machines. The aim of this article is twofold: first, surveying the encoding algorithms suitable for inverted index compression and, second, characterizing the performance of the inverted index through experimentation.