Improved techniques for result caching in web search engines

TLDR

Query processing is a major cost factor in large web search engines, and while prior work has focused on cache hit ratios, this paper argues that total cost savings must also be considered due to variable query processing costs. The study investigates query result caching as a weighted caching problem to optimize query processing performance. The authors evaluate several weighted result caching algorithms and analyze their performance under Zipf-based query distributions. Feature‑based cache eviction policies introduced in this work significantly outperform prior methods and also yield performance gains for inverted list caching.

Abstract

Query processing is a major cost factor in operating large web search engines. In this paper, we study query result caching, one of the main techniques used to optimize query processing performance. Our first contribution is a study of result caching as a weighted caching problem. Most previous work has focused on optimizing cache hit ratios, but given that processing costs of queries can vary very significantly we argue that total cost savings also need to be considered. We describe and evaluate several algorithms for weighted result caching, and study the impact of Zipf-based query distributions on result caching. Our second and main contribution is a new set of feature-based cache eviction policies that achieve significant improvements over all previous methods, substantially narrowing the existing performance gap to the theoretically optimal (clairvoyant) method. Finally, using the same approach, we also obtain performance gains for the related problem of inverted list caching.