Adaptive selectivity estimation using query feedback
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Efficient mid-query re-optimization of sub-optimal query execution plans
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Self-tuning histograms: building histograms without looking at data
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Exploiting statistics on query expressions for optimization
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Dynamic Query Optimization in Rdb/VMS
Proceedings of the Ninth International Conference on Data Engineering
LEO - DB2's LEarning Optimizer
Proceedings of the 27th International Conference on Very Large Data Bases
Automating Statistics Management for Query Optimizers
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Conditional selectivity for statistics on query expressions
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Adaptive ordering of pipelined stream filters
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
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
ISOMER: Consistent Histogram Construction Using Query Feedback
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Multiprocessor hash-based join algorithms
VLDB '85 Proceedings of the 11th international conference on Very Large Data Bases - Volume 11
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Automated statistics collection in DB2 UDB
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Diagnosing Estimation Errors in Page Counts Using Execution Feedback
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
Xplus: a SQL-tuning-aware query optimizer
Proceedings of the VLDB Endowment
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Past work has suggested that query execution feedback can be useful in improving the quality of plans by correcting cardinality estimation errors in the query optimizer. The state-of-the-art approach for obtaining execution feedback is "passive" monitoring which records the cardinality of each operator in the execution plan. We observe that there are many cases where even after repeated executions of the same query with use of feedback from passive monitoring, suboptimal choices in the execution plan cannot be corrected. We present a novel "pay-as-you-go" framework in which a query potentially incurs a small overhead on each execution but obtains cardinality information that is not available with passive monitoring alone. Such a framework can significantly extend the reach of query execution feedback in obtaining better plans. We have implemented our techniques in Microsoft SQL Server, and our evaluation on real world and synthetic queries suggests that plan quality can improve significantly compared to passive monitoring even at low overheads.