Physical database design for relational databases
ACM Transactions on Database Systems (TODS)
Sequential sampling procedures for query size estimation
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
Adaptive selectivity estimation using query feedback
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
AutoAdmin “what-if” index analysis utility
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Implications of certain assumptions in database performance evauation
ACM Transactions on Database Systems (TODS)
Towards a robust query optimizer: a principled and practical approach
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Goals and benchmarks for autonomic configuration recommenders
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
The history of histograms (abridged)
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
DB2 design advisor: integrated automatic physical database design
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Automatic SQL tuning in oracle 10g
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Automated statistics collection in DB2 UDB
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Self-tuning database systems: a decade of progress
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Robustness in automatic physical database design
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
A critical look at the TAB benchmark for physical design tools
ACM SIGMOD Record
A Benchmark for Online Index Selection
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
CoPhy: a scalable, portable, and interactive index advisor for large workloads
Proceedings of the VLDB Endowment
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The explosion of available data in the last few years has increased the importance of physical database design, since the selection of proper physical structures (e.g. indices, partitions and materialized views) may improve query execution performance by several orders of magnitude. Commercial DBMS vendors have recognized this need and offered automated physical design tools as part of their products. These tools use what-if interfaces to simulate the presence of different physical structures and recommend physical designs that minimize the estimated execution time of a given workload. Along with the recommended design, they deliver an estimation of the expected improvement the new design brings. In this paper, we examine the output of physical designers, i. e., whether what we see as a result of the tuning (the estimation of the improvement) is indeed what we may expect after applying the design (the actual improvement). We evaluate three commercial physical designers by varying their input parameters on real and synthetic data sets. Our results show that all three physical designers exhibit highly unpredictable behavior in certain cases, indicating that there is still significant room for improvement in terms of their predictability and consequently, their quality.