Computational geometry: an introduction
Computational geometry: an introduction
Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
Query optimization for parallel execution
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
Optimization of dynamic query evaluation plans
SIGMOD '94 Proceedings of the 1994 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
On-line extraction of SCSI disk drive parameters
Proceedings of the 1995 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
ACM Computing Surveys (CSUR)
Least expected cost query optimization: an exercise in utility
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Query Optimization in Database Systems
ACM Computing Surveys (CSUR)
Least expected cost query optimization: what can we expect?
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
Automating Statistics Management for Query Optimizers
IEEE Transactions on Knowledge and Data Engineering
Design and Analysis of Parametric Query Optimization Algorithms
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
LEO - DB2's LEarning Optimizer
Proceedings of the 27th International Conference on Very Large Data Bases
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
The Fittest Survives: An Adaptive Approach to Query Optimization
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Heuristic and randomized optimization for the join ordering problem
The VLDB Journal — The International Journal on Very Large Data Bases
Recovery Oriented Computing (ROC): Motivation, Definition, Techniques,
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Towards availability benchmarks: a case study of software raid systems
ATEC '00 Proceedings of the annual conference on USENIX Annual Technical Conference
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Parametric query optimization for linear and piecewise linear cost functions
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Analyzing plan diagrams of database query optimizers
VLDB '05 Proceedings of the 31st international conference on Very large data bases
On the production of anorexic plan diagrams
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
S-VFS: Searchable Virtual File System for an Intelligent Ubiquitous Storage
IEICE - Transactions on Information and Systems
DIADS: addressing the "my-problem-or-yours" syndrome with integrated SAN and database diagnosis
FAST '09 Proccedings of the 7th conference on File and storage technologies
Searchable virtual file system: toward an intelligent ubiquitous storage
GPC'06 Proceedings of the First international conference on Advances in Grid and Pervasive Computing
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Most relational query optimizers make use of information about the costs of accessing tuples and data structures on various storage devices. This information can at times be off by several orders of magnitude due to human error in configuration setup, sudden changes in load, or hardware failure. In this paper, we attempt to answer the following questions:• Are inaccurate access cost estimates likely to cause a typical query optimizer to choose a suboptimal query plan?• If an optimizer chooses a suboptimal plan as a result of inaccurate access cost estimates, how far from optimal is this plan likely to be?To address these issues, we provide a theoretical, vector-based framework for analyzing the costs of query plans under various storage parameter costs. We then use this geometric framework to characterize experimentally a commercial query optimizer. We develop algorithms for extracting detailed information about query plans through narrow optimizer interfaces, and we perform the characterization using database statistics from a published run of the TPC-H benchmark and a wide range of storage parameters.We show that, when data structures such as tables, indexes, and sorted runs reside on different storage devices, the optimizer can derive significant benefits from having accurate and timely information regarding the cost of accessing storage devices.