Full support of parallelism in object-relational database systems (ORDBMSs) is desired. The parallelization techniques developed for relational database systems are not adequate for ORDBMS because of the introduction of complex abstract data types and operations on ordered domains. In this paper, we consider a data stream paradigm and develop a query parallelization framework that exploits characteristics of user-defined functions in a ORDBMS during query optimization. By introducing the concept of windows and abstract data type orderings, we develop a novel approach for parallelizing user-defined functions in a distributed ORDBMS environment. The implementation issues in providing query services in ordered domains are also discussed.

Very long running queries in database systems are not uncommon in non traditional application domains such as image processing or data warehousing analysis. Query optimization, therefore, is important. However, estimates of the query characteristics before query execution are usually inaccurate. Further, system configuration and resource availability may change during long evaluation period. As a result, queries are often evaluated with sub-optimal plan configurations. To remedy this situation, we have designed a novel approach to re-optimize suboptimal query plan configurations on-the-fly with Conquest, an extensible and distributed query processing system. A dynamic optimizer considers reconfiguration cost as well as execution cost in determining the best query plan configuration. Experimental results are presented.

Massive database sizes and growing demands for decision support and data mining result in long-running queries in extensible object-relational DBMSs, particularly in decision support and data warehousing analysis applications. Parallelization of query evaluation is often required for acceptable performance, yet queries are frequently processed suboptimally due to (1) only coarse or inaccurate estimates of the query characteristics and database statistics being available prior to query evaluation; (2) changes in system configuration and resource availability during query evaluation. In a distributed environment, dynamically reconfiguring query execution plans (QEPs), which adapts QEPs to the environment as well as to the query characteristics, is a promising means to significantly improve query evaluation performance. Based on an operator classification, we propose an algorithm to coordinate the steps in a reconfiguration and introduce alternatives for execution context checkpointing and restoring. A syntactic extension of SQL to expose the relevant characteristics of user-defined functions in support of dynamic reconfiguration is proposed. An example from the experimental system is presented.