Database Data Usage Tracking via SQL Query Analysis
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Solution Overview
Problem
Current systems lack effective methods to track and analyze data usage in database systems, leading to inefficient performance and storage issues due to unknown usage patterns by applications and users.
Innovation Solution
A computer system and method that generates data usage statistics by comparing SQL queries to predefined characteristics, using an interface to receive SQL queries and characteristics, and storing them to determine data usage categories, which are then analyzed to increment usage counters, providing insights for optimizing database performance and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed tracking of data usage is implemented, then measurement precision of data usage behavior is improved, but device complexity increases due to additional monitoring components and processing requirements
Solution Approach 1:
The database management system automatically monitors and analyzes its own data usage patterns without requiring external monitoring tools. The system uses built-in components to capture SQL queries, track data access patterns, and generate usage statistics, making the monitoring infrastructure self-contained and reducing overall system complexity.
Solution Approach 2:
The patent introduces an intermediary analysis component that sits between the database operations and the monitoring system. This intermediary captures query information and usage patterns without interfering with actual data operations, enabling precise measurement while keeping the monitoring logic separate from core database functions.
2Loss of information
If comprehensive data usage monitoring is implemented, then information completeness about usage patterns is improved, but loss of time increases due to additional processing overhead
Solution Approach 1:
The system implements monitoring at selective points in the data access workflow rather than attempting to track every single operation. It focuses on capturing key SQL queries and data access patterns at critical junctures, providing sufficient usage information without the overhead of comprehensive granular tracking of every data operation.
Solution Approach 2:
The system pre-defines data usage categories and characteristics before monitoring begins. By establishing the monitoring framework, categories, and analysis rules in advance, the system avoids the computational overhead of dynamically creating monitoring structures during data access operations, thus reducing processing time while maintaining information completeness.
3Measurement precision
If data usage analysis is performed on all stored data, then measurement precision is improved, but use of energy increases due to computational requirements
Solution Approach 1:
The system extracts only the essential usage information needed for analysis rather than processing complete detailed logs of all data operations. It focuses on capturing key query patterns and data access characteristics, discarding redundant information, thereby reducing computational energy requirements while maintaining sufficient measurement precision for optimization decisions.
4Productivity
If detailed data usage tracking is implemented, then productivity optimization potential is improved, but device complexity increases due to additional system components
Solution Approach 1:
The patent combines multiple monitoring functions into a unified analysis component that handles query capture, usage pattern recognition, and statistics generation simultaneously. By merging these functions into a single integrated system rather than separate components, the solution reduces overall system complexity while maintaining comprehensive monitoring capabilities for productivity optimization.
Data Source
AI summary
Systems and methods are described for determining data usage behavior in a database system. The systems and methods can include receiving, from one or more applications, a plurality of SQL queries, storing the received SQL queries, receiving one or more characteristics, each characteristic relating to one or more respective fields, determining one or more data usage categories, wherein a data usage category has one or more characteristics values corresponding to one or more stored field values of the one or more respective fields, comparing the data selection condition of at least one stored SQL query with the determined one or more data usage categories, and incrementing a data usage counter associated with a identified data usage category if the data selection condition of the at least one stored SQL query indicates at least one query access to stored data including field values in accordance with the identified data usage category.