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Existing database systems are not optimized for queries with a LIMITclause---operating instead in an all-or-nothing manner. In this paper, wepropose a fast LIMIT query evaluation engine, called NeedleTail, aimed atletting analysts browse a small sample of the query results on large datasetsas quickly as possible, independent of the overall size of the result set.NeedleTail introduces density maps, a lightweight in-memory indexing structure,and a set of efficient algorithms (with desirable theoretical guarantees) toquickly locate promising blocks, trading off locality and density. In settingswhere the samples are used to compute aggregates, we extend techniques fromsurvey sampling to mitigate the bias in our samples. Our experimental resultsdemonstrate that NeedleTail returns results 4x faster on HDDs and 9x faster onSSDs on average, while occupying up to 23x less memory than existingtechniques.

Optimally Leveraging Density and Locality to Support LIMIT Queries