Query Plan Robustness Mapping for Database Performance
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Solution Overview
Problem
Database systems face performance degradation due to the disparity between expected and actual runtime conditions, particularly when multiple queries execute simultaneously, leading to unexpected performance issues as data skew and resource availability vary significantly, causing selected query plans to be suboptimal under adverse conditions.
Innovation Solution
A computer-implemented system evaluates the robustness of query plans by measuring performance across a range of runtime conditions, producing maps that compare the selected plan to alternative plans, identifying landmarks where performance degrades, and analyzing these maps to predict and analyze performance continuity under varying conditions, thereby enabling the selection of more robust query execution plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a query optimizer selects a query plan based on estimated cost and expected runtime conditions, then the plan appears optimal under expected conditions, but performance degrades unexpectedly when actual runtime conditions differ from estimates
Solution Approach 1:
The system performs preliminary evaluation of query plans across a predetermined range of runtime conditions before actual query execution. By pre-measuring performance under various conditions and creating robustness maps, the system prepares advance knowledge about plan behavior that can be used during actual execution without requiring re-evaluation at runtime.
Solution Approach 2:
The system uses feedback from measuring query plan performance across different runtime conditions to create robustness maps. These maps capture the relationship between runtime conditions and performance outcomes, providing feedback information that helps the query optimizer make more reliable plan selections by understanding how plans behave under varying conditions rather than relying solely on static cost estimates.
2Productivity
If the query optimizer examines all possible query plans to find the most efficient one, then the selected plan is optimal under expected conditions, but the search space becomes very large due to query complexity
Solution Approach 1:
The system performs preliminary measurements of query plan performance across a predetermined range of runtime conditions before actual query execution. By pre-evaluating plans under various conditions and storing the results in robustness maps, the system eliminates the need to re-examine all possible plans during actual execution, significantly reducing the effective search space while maintaining optimality.
Solution Approach 2:
The system creates virtual copies of query plans evaluated under different runtime conditions by measuring performance across predetermined conditions and storing results in robustness maps. These copies allow the system to understand plan behavior under various conditions without actually executing all possible plans, reducing the effective search space while maintaining comprehensive performance knowledge.
3Reliability
If the system measures query plan performance across a wide range of runtime conditions, then the robustness map provides comprehensive performance insight, but the measurement and analysis process becomes more complex
Solution Approach 1:
The system segments the runtime condition space into a predetermined range of conditions that are systematically measured. By dividing the complex continuous space of runtime conditions into discrete, manageable segments (such as different data cardinalities, resource availability levels, etc.), the system can comprehensively measure performance without creating an unmanageably complex evaluation process.
Solution Approach 2:
The system performs preliminary measurements of query plan performance across different runtime conditions before actual query execution. By pre-evaluating plans under various conditions and storing the results in robustness maps, the system prepares advance performance data that can be queried during actual execution, eliminating the need for complex real-time analysis while maintaining comprehensive performance insight.
Data Source
AI summary
Computer-implemented and associated operating methods evaluate robustness of a query plan by measuring performance with regard to a range of runtime conditions and producing a map of relative performance of the given query plan when compared with alternative plans for a range of conditions. The computer-implemented system comprises logic that evaluates the selected query plan in comparison to multiple alternative query plans in a predetermined range of runtime conditions that include data characteristics. The logic produces a set of performance measurements and analyzes the measured performance to map performance of the selected query plan in comparison to performance of one or more alternative query plans.