document: Performance of compressed inverted list caching in search engines Due to the rapid growth in the size of the web , web search engines are facing enormous performance challenges . The larger engines in particular have to be able to process tens of thousands of queries per second on tens of billions of documents , making query throughput a critical issue . To satisfy this heavy workload , search engines use a variety of performance optimizations including index compression , caching , and early termination . We focus on two techniques , inverted index compression and index caching , which play a crucial rule in web search engines as well as other high-performance information retrieval systems . We perform a comparison and evaluation of several inverted list compression algorithms , including new variants of existing algorithms that have not been studied before . We then evaluate different inverted list caching policies on large query traces , and finally study the possible performance benefits of combining compression and caching . The overall goal of this paper is to provide an updated discussion and evaluation of these two techniques , and to show how to select the best set of approaches and settings depending on parameter such as disk speed and main memory cache size .
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