Parallel approaches to database management

Abstract only given, as follows. A variety of parallel approaches have been used to support database processing across a spectrum of machine architectures. We begin by describing areas where parallelism is potentially important in dealing with very large databases, including loading, query/update, and database administration. We then discuss hardware tradeoffs, including multicomputers versus multiprocessors, distributed versus centralized memory, and specialised versus general-purpose architectures. At the software level, we cover a number of approaches, including running multiple transactions in parallel, decomposing queries into parallel subqueries, executing low-level query operations in parallel, running multiple instances of the DBMS, and partitioning data over disks. We characterise the impact of these approaches on performance, scalability, and ease of use, for both decision support and transaction processing. Finally, the approaches taken in several commercial DBMSs are described, as well as extensions such as the Kendall Square Query Decomposer.<>