Database Dependency Metadata Extraction from Execution Plans
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Solution Overview
Problem
Current methods for retrieving dependency metadata in databases are inefficient, as they often require manual identification of object names and do not effectively utilize the information available in database statement execution plans.
Innovation Solution
The proposed solution involves retrieving and parsing database statement execution plans to identify references to objects, assembling metadata that reflects dependencies, and augmenting this metadata with other dependency data, thereby eliminating the need for manual object name identification and enhancing data accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual identification of object names is used to retrieve dependency metadata, then the process is simple to implement, but the efficiency and productivity are low
Solution Approach 1:
The system enables self-service by automatically extracting dependency metadata from execution plans without requiring manual identification of object names. The execution plan parser autonomously identifies object references and assembles dependency information, eliminating the need for manual intervention and significantly improving retrieval efficiency
Solution Approach 2:
The patent replaces the manual mechanical process of identifying object names with an automated computational system. The execution plan parser uses computational methods to extract object references from XML-formatted execution plans, substituting human manual work with automated text parsing and data extraction algorithms
2Measurement precision
If execution plans are parsed to extract dependency metadata, then data accuracy and completeness are improved, but the device complexity increases
Solution Approach 1:
The execution plan serves as an intermediary that already contains structured information about object dependencies. By parsing this intermediate representation (XML-formatted execution plan), the system extracts accurate dependency metadata without directly analyzing the complex database statement syntax, thereby improving accuracy while managing complexity through the use of this intermediate data structure
Solution Approach 2:
The patent segments the metadata extraction process into distinct modular components: retrieving the execution plan, parsing the XML structure, identifying object references, and assembling dependency metadata. This segmentation allows each component to be independently developed and tested, managing overall system complexity while achieving high measurement precision through specialized processing at each stage
3Extent of automation
If execution plans are retrieved and parsed automatically, then the extent of automation is improved, but the loss of information may increase due to parsing errors
Solution Approach 1:
The system implements feedback mechanisms to verify parsing accuracy and ensure information completeness. The parser validates extracted object references against the original execution plan structure, and the assembled dependency metadata can be cross-checked for consistency, providing feedback loops that prevent information loss while maintaining high automation levels
Data Source
AI summary
An execution plan for a database statement can be retrieved from a database server. References to objects can be identified in the execution plan, and metadata from the references can be assembled in a data structure on computer readable storage media. The metadata can reflect dependencies on the objects. Additionally, other dependency metadata can be augmented with the metadata from the references.