Database Schema Auto-Adjustment for Heterogeneous Storage
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Solution Overview
Problem
Database management systems face challenges in portability across different database management systems and in optimizing database schema to efficiently store and manage varying input data, particularly in terms of storage requirements and query performance.
Innovation Solution
A database selection system that identifies optimal database types for input data, automatically adjusts database schema, and dynamically scales storage based on input data, using a processor and memory to select the ideal database from a set of distributed heterogeneous databases, and applies uniform query languages to optimize key-value stores for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a database schema is manually optimized for specific database management systems, then query performance is improved, but portability across different database management systems deteriorates
Solution Approach 1:
The patent implements a universal database schema optimization system that automatically adapts to multiple database management systems (Oracle, MySQL, PostgreSQL, SQL Server) through a common optimization framework. The system uses database-agnostic metadata analysis and generates portable optimization recommendations that work across different DBMS platforms without requiring manual re-optimization for each system.
Solution Approach 2:
The system enables databases to self-optimize by automatically analyzing their own schemas, query patterns, and performance characteristics. The optimization engine continuously monitors database operations and autonomously generates and applies schema adjustments without requiring external manual intervention or system-specific expertise.
2Quantity of substance
If database schema is manually adjusted to optimize storage, then storage efficiency is improved, but system complexity and maintenance difficulty increase
Solution Approach 1:
The database system automatically monitors storage usage patterns, query access frequencies, and data growth trends to self-adjust schema configurations. The optimization engine continuously evaluates storage efficiency metrics and autonomously implements schema changes such as partitioning, indexing, and data archiving without requiring manual intervention.
Solution Approach 2:
The system implements continuous feedback loops where performance metrics and storage statistics are collected, analyzed, and used to generate optimization recommendations. The system monitors the impact of applied optimizations and automatically adjusts further based on observed outcomes, creating a closed-loop control system for storage optimization.
3Stability of the object's composition
If fixed database schema is used, then system stability is improved, but adaptability to changing data requirements deteriorates
Solution Approach 1:
The patent implements dynamic schema evolution capabilities where the database structure can automatically adapt to changing data requirements while maintaining operational stability. The system uses version control for schema changes, allows incremental migrations, and maintains backward compatibility through automated transformation layers that handle both old and new data formats simultaneously.
4Productivity
If extensive manual optimization of database schema is performed, then query performance is improved, but time and resources required for optimization increase
Solution Approach 1:
The system automatically analyzes query workloads, identifies performance bottlenecks, and generates optimization recommendations without requiring manual performance tuning expertise. The optimization engine continuously monitors database operations and autonomously implements schema adjustments, eliminating the need for dedicated DBA time for routine optimization tasks.
Solution Approach 2:
The system performs preliminary analysis of data patterns, query frequencies, and access modes during database initialization and ongoing operations. By pre-calculating optimization opportunities and preparing schema adjustments in advance, the system minimizes disruption to normal operations and reduces the time required for performance optimization interventions.
Data Source
AI summary
Disclosed are a database selection system and/or a method to automatically adjust a database schema based on an input data. The method of the database selection system includes identifying an attribute of the input data. An optimal database type is determined in which the input data is to be stored based on a match between the optimal database type and the attribute. The method selects an ideal database based on the optimal database type from a set of distributed heterogeneous databases. The method determines if an existing database schema of the ideal database is optimally structured based on the input data. An ideal database schema is automatically adjusted based on the input data using a processor and a memory.