Sparse" data, in which relations have many attributes that are null for most tuples, presents a challenge for relational database management systems. If one uses the normal "horizontal" schema to store such data sets in any of the three leading commercial RDBMS, the result is tables that occupy vast amounts of storage, most of which is devoted to nulls. If one attempts to avoid this storage blowup by using a "vertical" schema, the storage utilization is indeed better, but query performance is orders of magnitude slower for certain classes of queries. In this paper, we argue that the proper way to handle sparse data is not to use a vertical schema, but rather to extend the RDBMS tuple storage format to allow the representation of sparse attributes as interpreted fields. The addition of interpreted storage allows for efficient and transparent querying of sparse data, uniform access to all attributes, and schema scalability. We show, through an implementation in PostgreSQL, that the interpreted storage approach dominates in query efficiency and ease-of-use over the current horizontal storage and vertical schema approaches over a wide range of queries and sparse data sets.
Flash solid-state drives (SSDs) are changing the I/O landscape, which has largely been dominated by traditional hard disk drives (HDDs) for the last 50 years. In this paper we propose and systematically explore designs for using an SSD to improve the performance of a DBMS buffer manager. We propose three alternatives that differ mainly in the way that they deal with the dirty pages evicted from the buffer pool. We implemented these alternatives, as well another recently proposed algorithm for this task (TAC), in SQL Server, and ran experiments using a variety of benchmarks (TPC-C, E and H) at multiple scale factors. Our empirical evaluation shows significant performance improvements of our methods over the default HDD configuration (up to 9.4X), and up to a 6.8X speedup over TAC.
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