Exploiting self-monitoring sample views for cardinality estimation

ABSTRACT

Good cardinality estimates are critical for generating good execution plans during query optimization. Complex predicates, correlations between columns, and user-defined functions are extremely hard to handle when using the traditional histogram approach. This demo illustrates the use of sample views for cardinality estimations as prototyped in Microsoft SQL Server. We show the creation of sample views, discuss how they are exploited during query optimization, and explain their potential effect on query plans. In addition, we also show our implementation of maintenance policies using statistical quality control techniques based on query feedback.

REFERENCES

• 1: Consolidated Database System (CDBS) by the FCC Media Bureau, available at http://www.fcc.gov/mb/databases/cdbs/, 2006.
• 2: P. Larson, W. Lehner, J. Zhou, and P. Zabback. Cardinality estimation using sample views with quality assurance. In Sigmod, 2007.
• 3: N. N. Engineering Statistics Handbook. National Institute of Standards and Technology, http://www.itl.nist.gov/div898/handbook, 2006.
• 4: F. Olken and D. Rotem. Random sampling from database files: A survey. In SSDBM, pages 92--111, 1990.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.4 Systems
          Subjects: Query processing

General Terms:
Algorithms, Performance

Keywords:
cardinality estimation, query optimization, sample views, sequential sampling, statistical quality control