Automatic plan choice validation using performance statistics
Proceedings of the 1st international workshop on Testing database systems
A pay-as-you-go framework for query execution feedback
Proceedings of the VLDB Endowment
Query optimizers: time to rethink the contract?
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
General Database Statistics Using Entropy Maximization
DBPL '09 Proceedings of the 12th International Symposium on Database Programming Languages
Consistent histograms in the presence of distinct value counts
Proceedings of the VLDB Endowment
Warm cache costing: a feedback optimization technique for buffer pool aware costing
Proceedings of the 13th International Conference on Extending Database Technology
Understanding cardinality estimation using entropy maximization
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Understanding cardinality estimation using entropy maximization
ACM Transactions on Database Systems (TODS)
| Hi-index | 0.00 |
Errors in estimating page counts can lead to poor choice of access methods and in turn to poor quality plans. Although there is past work in using execution feedback for accurate cardinality estimation, the problem of inaccurate estimation of page counts has not been addressed. In this paper, we present novel mechanisms for diagnosing errors in page count by monitoring query execution at low overhead. Detection of inaccuracy in the optimizer estimates of page count can be leveraged by database administrators to improve plan quality. We have prototyped our techniques in the Microsoft SQL Server engine, and our experiments demonstrate the ability to estimate page counts accurately using execution feedback with low overhead. For queries on several real world databases, we observe significant improvement in plan quality when page counts obtained from execution feedback are used instead of the traditional optimizer estimations.