Using Big Data Analysis to Improve Cache Performance in Search Engines

Abstract

Web Search Engines process huge amounts of data to support search but must run under strong performance requirements (to answer a query in a fraction of a second). To meet that performance they implement different optimization techniques such as caching, that may be implemented at several levels. One of these caching levels is the intersection cache, that attempts to exploit frequently occurring pairs of terms by keeping in the memory of the search node the results of intersecting the corresponding inverted lists. In this work we propose an optimization step to decide which items should be cached and which not by introducing the usage of data mining techniques. Our preliminary results show that it is possible to achieve extra cost savings in this already hyper-optimized field.