| Current database workloads often consist of a mixture of short online transaction processing (OLTP) queries and large complex queries such as those typical of online analytical processing (OLAP). OLAP queries usually involve multiple joins, arithmetic operations, nested sub-queries, and other system or user-defined functions and they typically operate on large data sets. These resource intensive queries can monopolize the database system resources and negatively impact the performance of smaller, possibly more important, queries.;In this thesis, we present an approach to managing the execution of large queries that involves the decomposition of large queries into an equivalent set of smaller queries and then scheduling the smaller queries so that the work is accomplished with less impact on other queries. We describe a prototype implementation of our approach for IBM DB2(TM) and present a set of experiments to evaluate the effectiveness of the approach. |
