Techniques for Inverted Index Compression
This work addresses the need for efficient index compression in large-scale search engines to enhance query processing speed and reduce infrastructure costs, but it is incremental as it focuses on surveying and evaluating existing techniques.
The paper surveys encoding algorithms for compressing inverted indexes in search engines and characterizes their performance through experimentation, aiming to improve memory hierarchy exploitation and reduce storage requirements.
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.