Budget-Constrained Index Tuning With Selective What-If Analysis
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Solution Overview
Problem
Index tuning in database systems is resource-intensive and time-consuming due to extensive 'what-if' analyses, with existing solutions often allocating resources suboptimally, leading to wasted computational effort.
Innovation Solution
A method that calculates derived costs for query-configuration pairs, identifies minimal gaps with actual costs, and reallocates budget to critical pairs for detailed evaluation, optimizing index configurations while minimizing unnecessary what-if calls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive what-if analyses are performed to evaluate numerous hypothetical index configurations, then index tuning accuracy is improved, but computational resource consumption increases significantly
Solution Approach 1:
The patent segments the index tuning process into two distinct phases: a filtering phase that uses derived cost estimates to quickly eliminate unpromising query-configuration pairs, and a refinement phase that applies expensive what-if analyses only to the remaining candidate pairs. This segmentation allows the system to maintain tuning accuracy while dramatically reducing overall computational resource consumption by avoiding unnecessary what-if calls.
Solution Approach 2:
The patent performs preliminary cost derivation for all query-configuration pairs before conducting what-if analyses. This preliminary action identifies and filters out pairs with minimal cost improvement potential, ensuring that subsequent what-if analyses are applied only to promising candidates. This approach preserves accuracy for critical evaluations while reducing total computational overhead.
2Productivity
If budget constraints are applied to limit the number of what-if calls during index tuning, then resource efficiency is improved, but allocation of the limited budget remains suboptimal
Solution Approach 1:
The patent implements a feedback mechanism where the results of what-if analyses are used to update and refine the derived cost estimates for remaining candidate pairs. This feedback loop allows the system to progressively improve its filtering accuracy, ensuring that the limited budget is allocated to the most promising configurations while dynamically adjusting evaluation strategies based on accumulated information.
Solution Approach 2:
The patent changes the parameter of cost estimation by introducing derived cost calculations that provide quick approximations without full what-if analysis. This parameter change enables the system to efficiently evaluate and filter configurations, allocating the limited budget to pairs where the derived cost indicates potential significant improvement, thereby optimizing resource efficiency.
3Loss of time
If derived cost approximation is used to avoid what-if calls, then computational time is reduced, but accuracy of cost estimation may be compromised
Solution Approach 1:
The patent employs a dynamic evaluation strategy where the system adaptively switches between derived cost approximation and full what-if analysis based on the specific characteristics of each query-configuration pair. Pairs showing high potential for cost improvement undergo rigorous what-if analysis, while others use faster derived cost estimation. This dynamic approach balances computational time savings with maintained accuracy for critical decisions.
Data Source
AI summary
This document relates to budget-constrained index tuning in database systems. A method for index tuning within a database system includes receiving a budget constraint, generating a set of candidate indexes for a workload of queries, and determining a derived cost for each query-configuration pair (QCP). The method further involves identifying a subset of QCPs where the derived cost and a what-if cost have a delta value below a predefined difference threshold and allocating the budget constraint to these identified QCPs to avoid what-if calls. Additionally, the method reallocates a saved portion of the budget constraint to other QCPs where the delta value exceeds the difference threshold, evaluates these other QCPs using what-if calls to select an optimal index configuration, and applies the selected index configuration to the database system.