Recursive Query Optimizer Iteration Estimation
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Solution Overview
Problem
Optimizing recursive database queries is challenging due to varying cardinalities across iterations, leading to suboptimal execution plans when static planning schemes are used, and providing feedback at each iteration can disrupt execution and pipelining.
Innovation Solution
Estimating the number of iterations or self-joins required for a recursive database query allows for dynamic planning of execution, using a Recursive Query Optimizer to generate optimal plans based on projected iteration counts, and adjusting plans at each iteration based on actual statistics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If static planning schemes are used for recursive queries, then execution plans can be determined in advance, but the plans become suboptimal when cardinalities vary across iterations
Solution Approach 1:
The patent implements dynamic query plan adjustment by estimating the number of iterations required for recursive queries and adapting the execution plan accordingly. The system calculates cardinality estimates for each iteration level and selects optimal execution plans dynamically based on these estimates, allowing the plan to adapt to varying cardinalities rather than using a fixed static plan.
Solution Approach 2:
The patent performs preliminary cardinality estimation by analyzing the recursive query structure and estimating the number of iterations and cardinality at each level before actual execution. This preliminary analysis allows the system to pre-determine optimal execution strategies and resource allocation, avoiding the need for trial-and-error during execution.
2Manufacturing precision
If feedback is provided at each iteration to improve optimization, then execution plans can be adjusted to actual statistics, but this disrupts execution and pipelining
Solution Approach 1:
The patent performs all necessary optimization decisions in advance by estimating iteration counts and cardinalities before execution begins. The system determines the optimal execution plan and resource allocation preliminarily, eliminating the need for iterative feedback adjustments during execution and thus maintaining continuous pipelining and high throughput.
Solution Approach 2:
The patent uses feedback from cardinality estimates and iteration predictions to refine the execution plan selection process. By incorporating these feedback mechanisms during the planning phase rather than during execution, the system achieves accurate execution plans without disrupting the execution pipeline.
3Productivity
If the number of iterations is estimated to enable dynamic planning, then execution efficiency improves, but the estimation process adds complexity to the query optimization system
Solution Approach 1:
The patent segments the complex query optimization process into distinct manageable components: parsing the recursive query, estimating iteration counts, calculating cardinality estimates for each level, and selecting optimal execution plans. This segmentation allows each component to be optimized independently, reducing overall system complexity while maintaining high execution efficiency.
Solution Approach 2:
The patent changes key parameters such as iteration count estimates and cardinality predictions to guide the optimization process. By using these parameter estimates, the system can efficiently select execution plans without requiring complex real-time analysis during execution, thus improving productivity while controlling complexity through parameter-based decision making.
Data Source
AI summary
The number of iterations or self joins required to execute a recursive database query can be estimated. It will be appreciated that this estimation can be used to plan the execution of the recursive query and can be made in various ways and for various applications. By way of example, an estimated number of iterations or self joins required to execute a recursive database query (e.g., 12) can be used as a basis to determine or plan an optimal execution plan. For example, given an estimated twelve (12) iterations, an execution plan can be determined for executing at least the first three (3) iterations or for executing every three (3) iterations, whereas for an estimated twenty (21) iterations required to complete a recursive database query, an execution plan can be determined for the first five (5) or six (6) iterations, and so on.