On reconfiguring query execution plans in distributed object-relational DBMS

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.