Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
Optimization of dynamic query evaluation plans
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
Eddies: continuously adaptive query processing
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Database System Implementation
Database System Implementation
XXL - A Library Approach to Supporting Efficient Implementations of Advanced Database Queries
Proceedings of the 27th International Conference on Very Large Data Bases
Adapting to source properties in processing data integration queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Dynamic plan migration for continuous queries over data streams
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
When can we trust progress estimators for SQL queries?
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Lifting the burden of history from adaptive query processing
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Foundations and Trends in Databases
Dynamic plan migration for snapshot-equivalent continuous queries in data stream systems
EDBT'06 Proceedings of the 2006 international conference on Current Trends in Database Technology
A foundation for the replacement of pipelined physical join operators in adaptive query processing
EDBT'06 Proceedings of the 2006 international conference on Current Trends in Database Technology
Run-time adaptivity for search computing
Search computing
Hi-index | 0.00 |
In adaptive query processing, the way in which a query is evaluated is changed in the light of feedback obtained from the environment during query evaluation. Such feedback may, for example, establish that misleading selectivity estimates were used when the query was compiled, leading to the optimizer choosing an inappropriate join order or unsuitable join algorithms. This paper describes how joins can be reordered, and the join algorithms used replaced, while they are being evaluated in pipelined plans. Where joins are reordered and/or replaced during their evaluation, the approach avoids duplicating work that has already been carried out, by resuming from where the previous plan left off. The approach has been evaluated empirically, and shown to be effective for improving query performance in the light of misleading selectivity estimates.