The EXODUS optimizer generator
SIGMOD '87 Proceedings of the 1987 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
Eddies: continuously adaptive query processing
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
LEO - DB2's LEarning Optimizer
Proceedings of the 27th International Conference on Very Large Data Bases
User-Optimizer Communication using Abstract Plans in Sybase ASE
Proceedings of the 27th International Conference on Very Large Data Bases
A Blackboard Architecture for Query Optimization in Object Bases
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
Query processing and optimization in Oracle Rdb
The VLDB Journal — The International Journal on Very Large Data Bases
Robust query processing through progressive optimization
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Towards a robust query optimizer: a principled and practical approach
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Consistent selectivity estimation via maximum entropy
The VLDB Journal — The International Journal on Very Large Data Bases
Foundations and Trends in Databases
A pay-as-you-go framework for query execution feedback
Proceedings of the VLDB Endowment
Statistical Analysis and Data Mining
Power Hints for Query Optimization
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Self-Tuning for SQL Performance in Oracle Database 11g
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Automated SQL tuning through trial and (sometimes) error
Proceedings of the Second International Workshop on Testing Database Systems
Tuning database configuration parameters with iTuned
Proceedings of the VLDB Endowment
PerfXplain: debugging MapReduce job performance
Proceedings of the VLDB Endowment
Efficient processing of top-k join queries by attribute domain refinement
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
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The need to improve a suboptimal execution plan picked by the query optimizer for a repeatedly run SQL query arises routinely. Complex expressions, skewed or correlated data, and changing conditions can cause the optimizer to make mistakes. For example, the optimizer may pick a poor join order, overlook an important index, use a nested-loop join when a hash join would have done better, or cause an expensive, but avoidable, sort to happen. SQL tuning is also needed while tuning multi-tier services to meet service-level objectives. The difficulty of SQL tuning can be lessened considerably if users and higher-level tuning tools can tell the optimizer: "I am not satisfied with the performance of the plan p being used for the query Q that runs repeatedly. Can you generate a (δ%) better plan?" This paper designs, implements, and evaluates Xplus which, to our knowledge, is the first query optimizer to provide this feature. Xplus goes beyond the traditional plan-first-execute-next approach: Xplus runs some (sub)plans proactively, collects monitoring data from the runs, and iterates. A nontrivial challenge is in choosing a small set of plans to run. Xplus guides this process efficiently using an extensible architecture comprising SQL-tuning experts with different goals, and a policy to arbitrate among the experts. We show the effectiveness of Xplus on real-life tuning scenarios created using TPC-H queries on a PostgreSQL database.