Parallel database

A parallel database system seeks to improve performance through parallelization of various operations, such as loading data, building indexes and evaluating queries. Although data may be stored in a distributed fashion, the distribution is governed solely by performance considerations. Parallel databases improve processing and input/output speeds by using multiple CPUs and disks in parallel. Centralized and client-server database systems are not powerful enough to handle such applications. In parallel processing, many operations are performed simultaneously, as opposed to serial processing, in which the computational steps are performed sequentially.

Parallel databases can be roughly divided into three categories:

• Shared memory architecture, where multiple processors share the main memory space, as well as mass storage (e.g. hard disk drives).
• Shared disk architecture, where each node has its own main memory, but all nodes share mass storage, usually a storage area network. In practice, each node usually also has multiple processors.
• Shared nothing architecture, where each node has its own mass storage as well as main memory.

Example parallel databases

• Objectivity/DB
• IBM_DB2

References

Communications of the ACM: Parallel database systems: the future of high performance database systems