Optimally Leveraging Density and Locality to Support LIMIT Queries
Existing database systems are not optimized for queries with a LIMIT clause---operating instead in an all-or-nothing manner. In this paper, we propose a fast LIMIT query evaluation engine, called NeedleTail, aimed at letting analysts browse a small sample of the query results on large datasets as 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) to quickly locate promising blocks, trading off locality and density. In settings where the samples are used to compute aggregates, we extend techniques from survey sampling to mitigate the bias in our samples. Our experimental results demonstrate that NeedleTail returns results 4x faster on HDDs and 9x faster on SSDs on average, while occupying up to 23x less memory than existing techniques.