Automated Statistics Collection in DB2 UDB Ashraf Aboulnaga, Peter J. Haas, Mokhtar Kandil, Sam Lightstone, Guy M. Lohman, Volker Markl, Ivan Popivanov, and Vijayshankar Raman   View Paper (PDF)   Return to INDUSTRIAL SESSION 5: XML IMPLEMENTATIONS AUTOMATIC PHYSICAL DESIGN AND INDEXING Abstract The use of inaccurate or outdated database statistics by the query optimizer in a relational DBMS often results in a poor choice of query execution plans and hence unacceptably long query processing times. Configuration and maintenance of these statistics has traditionally been a time-consuming manual operation, requiring that the database administrator (DBA) continually monitor query performance and data changes in order to determine when to refresh the statistics values and when and how to adjust the set of statistics that the DBMS maintains. In this paper we describe the new Automated Statistics Collection (ASC) component of DB2 Stinger. This autonomic technology frees the DBA from the tedious task of manually supervising the collection and maintenance of database statistics. ASC monitors both the update-delete-insert (UDI) activities on the data as well as query feedback (QF), i.e., the results of the queries that are executed on the data. ASC uses these two sources of information to automatically decide which statistics to collect and when to collect them. This combination of UDI-driven and QF-driven autonomic processes ensures that the system can handle unforeseen queries while also ensuring good performance for frequent and important queries. We present the basic concepts, architecture, and key implementation details of ASC in DB2 Stinger, and present a case study showing how the use of ASC can speed up a query workload by orders of magnitude without requiring any DBA intervention. ©2005 Association for Computing Machinery