Operator Maps for Database Query Plan Robustness Landmarks
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Solution Overview
Problem
Database query optimizers face challenges in selecting efficient query plans due to disparities between expected and actual runtime conditions, leading to potential performance degradation, especially under adverse conditions such as data skew and resource contention.
Innovation Solution
The creation and use of an operator map to evaluate database query plan robustness by measuring performance across a range of runtime conditions, including resource availability and data characteristics, identifying 'robustness landmarks' where performance degrades, and applying risk premium-based analysis to mitigate risks and improve predictability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a query optimizer selects a query plan based on expected runtime conditions, then query execution efficiency is improved under normal conditions, but performance degrades significantly when actual conditions differ from expectations (e.g., data skew, resource contention)
Solution Approach 1:
The system performs preliminary actions by creating operator maps that evaluate operator performance across a range of runtime conditions before actual query execution. This includes measuring and mapping performance for selected ranges of runtime conditions such as resource availability and data characteristics, and identifying robustness landmarks that indicate where performance may degrade. When a query is executed, the system looks up predicted performance using these pre-computed maps and landmarks, allowing it to anticipate performance issues before they occur.
Solution Approach 2:
The system makes the query execution system dynamic by adapting query plan selection based on current runtime conditions. Instead of relying solely on static cost-based optimization, the system dynamically adjusts plan selection by referencing operator maps that capture performance across varying conditions. The system can identify when current conditions approach robustness landmarks and adjust execution accordingly, making the optimizer responsive to actual runtime environment rather than relying on expected conditions.
2Productivity
If the query optimizer examines all possible query plans to find the most efficient one, then query execution efficiency is improved, but the complexity and time required for query optimization increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-computing operator maps that evaluate operator performance across many different runtime conditions before actual query execution. This includes measuring and mapping performance for selected ranges of runtime conditions such as resource availability and data characteristics. By having this performance information pre-computed and stored, the system avoids the need to re-examine all possible query plans at query execution time, significantly reducing optimization complexity while maintaining the ability to select efficient plans.
Solution Approach 2:
The system creates simplified representations (copies) of the complex query plan evaluation process through operator maps. Instead of examining all possible query plans for each query, the system uses pre-computed maps that capture the essential performance characteristics of operators under various conditions. These maps serve as simplified models that allow the optimizer to make informed decisions without performing exhaustive analysis, reducing complexity while preserving the ability to identify efficient execution plans.
3Productivity
If the system uses cost-based query optimization to select the plan with smallest estimated cost, then query execution efficiency is improved under expected conditions, but the system fails to account for actual runtime condition disparities
Solution Approach 1:
The system introduces feedback by using actual runtime conditions to inform query plan selection. The operator maps contain performance information gathered from actual execution under various conditions, and this information is fed back into the query optimization process. When a query is being optimized, the system looks up predicted performance using the current runtime conditions and the pre-computed maps, allowing the optimizer to adjust plan selection based on feedback from actual system behavior rather than relying solely on theoretical cost estimates.
Solution Approach 2:
The system changes the parameters used for query optimization from static cost estimates to dynamic performance predictions based on actual runtime conditions. Instead of using only the cost-based metrics that assume expected conditions, the system incorporates performance data from operator maps that reflect actual system behavior under varying conditions. This includes considering resource availability, data characteristics, and other runtime parameters when selecting query plans, making the optimization process adaptive to actual conditions rather than relying on fixed cost models.
Data Source
AI summary
Computer-implemented systems and associated operating methods create and use an operator map to identify and evaluate database query plan robustness landmarks. The computer-implemented system comprises logic that creates a map evaluating performance of an implementation of a database engine operator during execution under actual operating conditions. The logic measures and maps performance for a selected range of runtime conditions including resource availability and data characteristics.