Batch Data Query Optimization via Static Dependency Analysis
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Solution Overview
Problem
Current big-data query systems face inefficiencies in query optimization due to the conventional use of single query statements, which limits optimization opportunities and requires dynamic data dependency analysis, making it inefficient for batch query scenarios.
Innovation Solution
A batch data query method that determines symbol identifiers and dependencies among multiple query statements based on operators and operands, allowing for inter-query optimization using preset rules, thereby improving optimization efficiency and reducing overheads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If single query statement is used as basic unit for parsing and optimization, then query processing is simple, but optimization opportunities are insufficient
Solution Approach 1:
The patent merges multiple query statements into a batch query group, treating them as a unified optimization unit. The query optimization module analyzes dependencies among multiple queries and performs joint optimization, combining previously separate optimization opportunities into a coordinated process that improves overall query efficiency while enabling inter-query optimizations.
2Adaptability or versatility
If dynamic data dependency analysis is performed through monitoring and feedback, then query optimization can be adaptive, but system overhead increases
Solution Approach 1:
The patent performs preliminary dependency analysis by collecting static dependency information during query compilation and parsing phases. By pre-analyzing data record access patterns, table relationships, and potential conflicts before query execution, the system establishes optimization strategies in advance, reducing the need for complex real-time monitoring and feedback mechanisms during runtime.
Solution Approach 2:
The patent creates dependency relationship models and query execution plans that replicate the actual query execution flow. These models capture data dependencies, conflict relationships, and optimization opportunities without requiring continuous monitoring of the actual execution. The copied dependency information is then used to guide optimization decisions, reducing overhead from real-time monitoring.
3Productivity
If dynamic optimization is executed based on monitored data dependencies, then query performance can be improved, but optimization can only be applied to specific inputs
Solution Approach 1:
The patent develops a universal query optimization framework that analyzes dependency relationships in a general manner applicable to multiple query inputs. By identifying patterns in data access, table relationships, and conflict scenarios that transcend specific input data, the system creates optimization strategies that can be reused across different query batches. The dependency analysis module captures structural relationships rather than input-specific characteristics, enabling the same optimization logic to apply universally.
Data Source
AI summary
A batch data query method and apparatus are provided. A data query server determines symbol identifiers of operands in N query statements according to operators and the operands in the N query statements, where the symbol identifiers include version numbers of the operands; determines dependency among the N query statements according to the version numbers of the operands in the N query statements; and then, performs inter-query optimization on the N query statements according to the dependency among the N query statements and a preset optimization rule.