On the propagation of errors in the size of join results
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
On the estimation of join result sizes
EDBT '94 Proceedings of the 4th international conference on extending database technology: Advances in database technology
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
Analysis and performance of inverted data base structures
Communications of the ACM
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
Accurate estimation of the number of tuples satisfying a condition
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
LEO - DB2's LEarning Optimizer
Proceedings of the 27th International Conference on Very Large Data Bases
The Volcano Optimizer Generator: Extensibility and Efficient Search
Proceedings of the Ninth International Conference on Data Engineering
The five-minute rule twenty years later, and how flash memory changes the rules
DaMoN '07 Proceedings of the 3rd international workshop on Data management on new hardware
Diagnosing Estimation Errors in Page Counts Using Execution Feedback
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
| Hi-index | 0.00 |
Most modern RDBMS depend on the query processing optimizer's cost model to choose the best execution plan for a given query. Since the physical IO (PIO) is a costly operation to execute, it naturally has an important weight in RDBMS classical cost models, which assume that the data is disk-resident and does not fit in the available main memory. However, this assumption is no longer true with the advent of cheap large main memories. In this paper, we discuss the importance of considering the buffer-cache occupancy during optimization and propose the Warm Cache Costing (WCC) model as a new technique for buffer-pool aware query optimization. The WCC-model is a novel feedback optimization technique, based on the execution statistics by learning PIO-compensation (PIOC) factors. The PIOC factor defines the average percentage of a table which is cached in the buffer pool. These PIOC factors are used in subsequent query optimizations to better estimate the PIO, thus leading to better plans. These techniques have been implemented in Sybase Adaptive Server Enterprise (ASE) database system. We have observed that they provide considerable improvements in query timings, in Decision Support environments and with almost negligible regression(if any) in other environments. This model enjoys the advantage of requiring no change to the buffer manager or other modules underlying the query processing layer and therefore is easy to implement. Also, since this model is part of an extensive feedback optimization architecture, other techniques using feedback optimization framework can be plugged in easily.