Database Optimizer Dynamic Confidence Threshold Control
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Solution Overview
Problem
Current database management systems lack proactive mechanisms for controlling the optimizer's pursuit of robustness for individual workloads or queries, often resulting in a tradeoff between query robustness and performance, with existing approaches being inherently passive and reactive.
Innovation Solution
A system and method for automatic and dynamic control of optimizer settings, which includes a confidence threshold parameter to adjust query execution plans based on observed inaccuracies, allowing for more aggressive or conservative strategies depending on performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If an aggressive query execution plan is selected to improve performance, then query response time is reduced, but predictability and consistency of performance deteriorate when cost estimates are inaccurate
Solution Approach 1:
The system dynamically adjusts the aggressiveness of query execution plans based on runtime conditions and observed performance. The query optimizer transitions from static plan selection to dynamic adaptation, modifying execution strategies during query processing based on actual system state and cost estimation accuracy.
Solution Approach 2:
The system changes key parameters such as cost thresholds and confidence levels to control the tradeoff between aggressive and conservative query execution. By adjusting these parameters dynamically, the system can shift between performance-optimized and predictability-optimized execution modes based on workload characteristics and system state.
2Reliability
If a conservative query execution plan is selected to improve predictability, then performance consistency is improved, but query response time increases
Solution Approach 1:
The system dynamically adjusts the aggressiveness of query execution plans based on runtime conditions and observed performance. The query optimizer transitions from static plan selection to dynamic adaptation, modifying execution strategies during query processing based on actual system state and cost estimation accuracy.
Solution Approach 2:
The system changes key parameters such as cost thresholds and confidence levels to control the tradeoff between aggressive and conservative query execution. By adjusting these parameters dynamically, the system can shift between performance-optimized and predictability-optimized execution modes based on workload characteristics and system state.
3Reliability
If proactive control mechanisms are implemented to manage optimizer aggressiveness, then predictability and performance balance is improved, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms where actual query execution performance is monitored and fed back to the query optimizer. This feedback loop enables the optimizer to learn from past performance and adjust future plan selection, automatically balancing aggressiveness without requiring complex manual configuration.
Solution Approach 2:
The query optimizer performs self-adjustment based on observed performance patterns and cost estimation accuracy. The system automatically modifies its own behavior and parameters without external intervention, reducing the complexity of manual control while maintaining predictable performance through adaptive learning.
Data Source
AI summary
A system, method, and computer-readable medium that provide automatic and dynamic control of optimizer settings in a large complex system environment are provided. The optimizer may pursue more aggressive query execution plans and switch to a more conservative approach in cases of actual observed inaccuracies.