Autonomic View of Query Optimizers in Database Management Systems

The growing complexity of applications, huge data volume and the data structures to process massive data are becoming challenging issue. Query optimizer is a major component of a Database Management System (DBMS) that executes queries through different strategies and techniques efficiently. These techniques select the best optimal execution plan from the candidate plans according to the available resources and environment. Traditionally, skilled database administrators are required to tune DBMS for efficient query processing. Recently it has been realized to develop DBMSs having autonomic capabilities. Autonomic DBMS (ADBMS) are now being developed to reduce this dependency on an expensive skilled human resource. The paper analyzes the autonomic capabilities of query optimizers in three well-known DBMSs – DB2, Oracle and SQL Server being used in the industry. The research is focused to find and earmark those areas in query optimizers where the human intervention is required. Query Optimizers are compares with their autonomic capabilities, explores their strengths and weaknesses, and provides the basis for improving the current state of autonomic computing in query optimizers. The autonomic behavior of query optimizers is observed by designing and executing different queries through experiments and some recommendations are given.