AI Query Execution Plan Refinement for Error-Driven Granularity
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Solution Overview
Problem
Existing artificial intelligence assistants face challenges in efficiently generating and executing execution plans for complex queries due to computational inefficiencies and abstraction gaps between high-level planners and low-level assistants, leading to resource-intensive replanning and errors.
Innovation Solution
A method involving a high-level planner generating an initial plan, which is iteratively refined by a plan refinement machine learning model using reinforcement learning techniques to generate granular execution plans, minimizing computational expense and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a high-level planner generates an initial execution plan, then the query can be executed with fewer steps, but execution errors occur due to abstraction gaps between high-level and low-level components
Solution Approach 1:
The execution plan is segmented into multiple refinement levels. The high-level planner creates an initial plan at a coarse granularity, which is then iteratively refined by the plan refinement model to add detailed steps. This segmentation allows the system to benefit from both high-level efficiency and low-level precision, resolving the contradiction between execution speed and accuracy.
Solution Approach 2:
The plan refinement model performs preliminary refinement of the execution plan before actual execution. By anticipating potential execution errors and refining the plan in advance, the system corrects abstraction gaps before they cause failures, thereby maintaining both speed and reliability.
2Reliability
If the plan is refined iteratively using reinforcement learning, then execution accuracy improves, but computational overhead increases
Solution Approach 1:
The plan refinement model applies partial refinement only when necessary - specifically when execution errors are detected. Instead of always performing full refinement, the system selectively refines plans that show signs of potential failure, reducing unnecessary computational overhead while maintaining high accuracy when needed.
Solution Approach 2:
The reinforcement learning mechanism uses feedback from execution results to guide refinement. When execution succeeds, no further refinement is needed. When errors occur, the feedback triggers targeted refinement. This feedback-driven approach optimizes the balance between accuracy and computational cost by performing refinement only when the system state indicates it is necessary.
3Reliability
If a fine-grained execution plan is generated directly, then execution accuracy improves, but the planning process becomes computationally expensive
Solution Approach 1:
The planning process is segmented into two distinct phases: high-level plan generation and iterative refinement. The high-level planner creates a coarse plan quickly with low computational complexity, while the refinement phase adds detail only when execution errors indicate it is necessary. This segmentation avoids the prohibitive cost of generating fully detailed plans from scratch.
Solution Approach 2:
The plan granularity is dynamic rather than static. The system starts with a coarse-grained plan and dynamically refines it to finer granularity based on execution feedback. This dynamic adaptation allows the system to maintain simplicity when possible while achieving detail when necessary, resolving the contradiction between accuracy and planning complexity.
Data Source
AI summary
Certain aspects provide techniques and apparatus for executing queries in a computing system using machine learning models. An example method generally includes receiving a plan to satisfy a request in the computing system and event log data associated with execution of the plan. The plan generally specifies a first plurality of actions to be performed by the computing system at a first level of granularity. Using a plan refinement machine learning model, a refined plan is generated when the event log data indicates that execution of the generated plan results in one or more execution errors and the one or more execution errors are solvable. Generally, the refined plan specifies a second plurality of actions to be performed by the computing system at a second level of granularity, the second level of granularity being finer than the first level of granularity.