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We describe the design of Mariposa, an experimental distributed data management system that provides high performance in an environment of high data mobility and heterogeneous host capabilities. The Mariposa design unifies the approaches taken by distributed file systems and distributed databases. In addition, Mariposa provides a general, flexible platform for the development of new algorithms for distributed query optimization, storage management, and scalable data storage structures. This flexibility is primarily due to a unique rule-based design that permits autonomous, local-knowledge decisions to be made regarding data placement, query execution location, and storage management.>

Four implementations that support large objects in the POSTGRES database system are presented. The four implementations offer varying levels of support for security, transactions, compression, and time travel. All are implemented using the POSTGRES abstract data type paradigm, support user-defined operators and functions, and allow file-oriented access to large objects in the database. The support for user-defined storage managers available in POSTGRES is also detailed. The performance of all four large object implementations on two different storage devices is described.>

The authors present two algorithms for maintaining B-tree index consistency in a database management system which does not use write-ahead logging (WAL). One algorithm is similar to shadow paging, but improves performance by integrating shadow meta-data with index meta-data. The other algorithm uses a two-phase page reorganization scheme to reduce the space overhead caused by shadow paging. Although designed for the POSTGRES storage system, these algorithms should also be useful in a WAL-based storage system, as support for logical logging. Measurements and analysis of a prototype implementation suggest that the algorithms will have little impact on data manager performance.>