Database Query Template Reuse for Processing Optimization
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Solution Overview
Problem
Existing query processing systems require significant processing time and resources for each new query, as they start from scratch, and lack efficient methods to recognize data differences and maintain audit trails effectively.
Innovation Solution
The method involves performing an initial query to retrieve records, storing them as content, creating a file of commands used, and applying these commands to subsequent queries to update information, allowing for efficient reuse of data and metadata across queries, with the ability to save and compare changes between snapshots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a new query is initiated for each data retrieval operation, then the query can be executed with complete and fresh metadata, but the processing time and resource consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by creating and storing a template query containing metadata and parameters before actual data retrieval is needed. This template is generated once and reused across multiple queries, eliminating the need to recreate query structures repeatedly. The preliminary template preparation resolves the contradiction by having query execution completeness ready in advance while reducing processing time during actual data retrieval operations.
Solution Approach 2:
The system creates a copy of the query template and reuses it across multiple data retrieval operations. Instead of creating entirely new queries each time, the system copies the established template structure and applies it to different data retrieval needs. This copying mechanism maintains query completeness while significantly reducing the time and resources required for each subsequent query operation.
2Manufacturing precision
If query metadata and parameters are recreated for each new query, then the query structure remains complete and accurate, but the amount of processing required increases
Solution Approach 1:
The query template including all metadata and parameters is prepared in advance as a preliminary structure. This template encapsulates the complete and accurate query structure needed for data retrieval, eliminating the need to recreate these elements repeatedly. The preliminary preparation maintains structural accuracy while improving processing efficiency.
Solution Approach 2:
The query template is designed as a universal structure that can serve multiple data retrieval operations. By making the query structure multi-functional and reusable across different queries, the system maintains structural accuracy and completeness while significantly improving productivity by avoiding redundant processing of metadata and parameters.
3Loss of information
If traditional audit trails are used to track database changes, then a record of queries is maintained, but the ability to recognize data differences and maintain comprehensive audit information is limited
Solution Approach 1:
The system creates copies of query templates and stores them in an audit trail with associated metadata. These copied templates serve as comprehensive records of what queries were executed, when, and with what parameters. This copying approach enhances information retention in audit trails while managing complexity by using structured, reusable template objects rather than ad-hoc query records.
Solution Approach 2:
The audit trail is prepared in advance by storing query templates and their metadata before actual query execution. This preliminary recording of query structures, parameters, and timing information ensures comprehensive audit information is captured without the limitations of traditional post-execution logging, reducing information loss while maintaining manageable complexity.
Data Source
AI summary
A query management application provides performing an initial query to retrieve records stored in a database, storing the records as initial query content in a memory, creating a file with a list of commands used during the initial query, initiating a subsequent query to obtain updated information for the records, retrieving the file and applying the list of commands to the subsequent query, performing the subsequent query, and updating the memory with results of the subsequent query.