Optimal histograms for limiting worst-case error propagation in the size of join results
ACM Transactions on Database Systems (TODS)
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
Least expected cost query optimization: what can we expect?
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Proceedings of the 17th International Conference on Data Engineering
The VLDB Journal — The International Journal on Very Large Data Bases
Towards a robust query optimizer: a principled and practical approach
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Analyzing plan diagrams of database query optimizers
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Plan selection based on query clustering
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Primitives for workload summarization and implications for SQL
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Foundations and Trends in Databases
Main-memory scan sharing for multi-core CPUs
Proceedings of the VLDB Endowment
Efficiently approximating query optimizer plan diagrams
Proceedings of the VLDB Endowment
Progressive Parametric Query Optimization
IEEE Transactions on Knowledge and Data Engineering
Constant-Time Query Processing
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Query optimizers: time to rethink the contract?
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Predictable performance for unpredictable workloads
Proceedings of the VLDB Endowment
Using functional dependencies for reducing the size of a data cube
FoIKS'12 Proceedings of the 7th international conference on Foundations of Information and Knowledge Systems
PARAS: a parameter space framework for online association mining
Proceedings of the VLDB Endowment
FIRE: interactive visual support for parameter space-driven rule mining
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Query optimization over crowdsourced data
Proceedings of the VLDB Endowment
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Parameterized queries are commonly used in database applications. In a parameterized query, the same SQL statement is potentially executed multiple times with different parameter values. In today's DBMSs the query optimizer typically chooses a single execution plan that is reused for multiple instances of the same query. A key problem is that even if a plan with low average cost across instances is chosen, its variance can be high, which is undesirable in many production settings. In this paper, we describe techniques for selecting a plan that can better address the trade-off between the average and variance of cost across instances of a parameterized query. We show how to efficiently compute the skyline in the average-variance cost space. We have implemented our techniques on top of a commercial DBMS. We present experimental results on benchmark and real-world decision support queries.