Scaling access to heterogeneous data sources with DISCO

Accessing many data sources aggravates problems for users of heterogeneous distributed databases. Database administrators must deal with fragile mediators, that is, mediators with schemas and views that must be significantly changed to incorporate a new data source. When implementing translators of queries from mediators to data sources, database implementers must deal with data sources that do not support all the functionality required by mediators. Application programmers must deal with graceless failures for unavailable data sources. Queries simply return failure and no further information when data sources are unavailable for query processing. The Distributed Information Search COmponent (Disco) addresses these problems. Data modeling techniques manage the connections to data sources, and sources can be added transparently to the users and applications. The interface between mediators and data sources flexibly handles different query languages and different data source functionality. Query rewriting and optimization techniques rewrite queries so they are efficiently evaluated by sources. Query processing and evaluation semantics are developed to process queries over unavailable data sources. In this article, we describe: 1) the distributed mediator architecture of Disco; 2) the data model and its modeling of data source connections; 3) the interface to underlying data sources and the query rewriting process; and 4) query processing semantics. We describe several advantages of our system