Query Optimization Merging Subtasks Across Tasks
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Solution Overview
Problem
Existing query optimization technologies can only optimize query logic within a single query task, leading to repeated execution of similar query logics across different tasks, resulting in low query efficiency and wasted resources.
Innovation Solution
A method and apparatus for query optimization that combines multiple query tasks based on their dependence relationships, merging subtasks across tasks to create a combined task, which allows for the execution of merged subtasks to obtain results for multiple tasks efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If query optimization is performed only within a single query task, then the complexity of the optimization system is kept simple, but repeated query logics across different tasks cannot be merged, resulting in low query efficiency
Solution Approach 1:
The patent merges multiple query tasks into a unified query task by identifying and combining common subtasks across different tasks. This allows repeated query logics to be executed once and shared across multiple original tasks, significantly improving query efficiency while managing complexity through systematic task consolidation
Solution Approach 2:
The optimized query task structure enables a single query logic to serve multiple purposes across different original tasks. By creating a universal subtask that can be shared and reused, the system achieves multi-functionality where one optimized query path benefits multiple downstream tasks simultaneously
2Productivity
If multiple query tasks are combined and subtasks are merged, then repeated query logics are optimized and query efficiency is improved, but the complexity of task management and dependence relationships increases
Solution Approach 1:
The patent segments query tasks into independent subtasks that can be individually optimized and then recombined. By breaking down complex query tasks into smaller, manageable subtasks with clear dependence relationships, the system can merge common subtasks across tasks while maintaining manageable complexity through modular structure
Solution Approach 2:
The system performs preliminary analysis to identify common subtasks across multiple query tasks before execution. By pre-processing the task structure, identifying shared components, and establishing dependence relationships in advance, the system prepares the merged task structure beforehand, reducing runtime complexity and improving execution efficiency
Data Source
AI summary
A query optimization method includes: obtaining a plurality of query tasks to be optimized, each query task including at least one subtask, and when a same query task includes at least two subtasks, a query dependence relationship existing between the at least two subtasks; combining the plurality of query tasks based on the query dependence relationship to obtain a combined task; and merging at least two subtasks in different query tasks of the plurality of query tasks based on the combined task to obtain a first merged subtask, the first merged subtask being executed to obtain query results of the at least two subtasks in the different query tasks.