Skip to Main Content
Semantic caches, originally proposed for client-server database systems, are being recently deployed to accelerate the serving of dynamic Web content by transparently caching data on edge servers. Such caches require fast query containment tests to determine if a new query is contained in the results of cached queries. Query containment checking algorithms have been studied in the context of query optimization and materialized view selection, but their scalability remains a serious limitation. We argue that application queries are usually instantiations of a smaller number of base templates and show how this can be exploited to scale up containment checking. Our contributions include (i) algorithms to detect similarity between query predicates; (ii) efficient algorithms for proving containment among similar query predicates; (iii) a technique to dynamically aggregate similar queries in the cache to support efficient search; and (iv) integration of these schemes into a two-level containment checker. We describe our approach, report on its implementation in a dynamic Web data cache, and show that it can reduce query containment cost by an order of magnitude for Web workloads.