Dynamic Data Model Synchronization in Relational Databases
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Solution Overview
Problem
Existing relational database management systems (RDBMS) are limited in supporting dynamic changes to data models during application runtime, as changes to the database schema become incompatible with the application model, preventing dynamic updates and limiting flexibility in data modeling.
Innovation Solution
A system that automatically propagates updates to the data model by executing SQL queries corresponding to changes in the data model, allowing for dynamic creation, alteration, or deletion of fields and tables in the relational database, using a data-processing framework that generates and executes queries to maintain schema consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If static mapping between application objects and database tables is used, then schema stability is maintained, but adaptability to dynamic changes during runtime is lost
Solution Approach 1:
The patent implements dynamic data modeling by allowing the data model to change during application runtime. The system automatically executes DDL statements (CREATE, DROP, ALTER, ADD) against the relational database based on changes detected in the data model, enabling the schema to adapt dynamically while maintaining consistency with the application model.
2Adaptability or versatility
If database schema changes are made during runtime, then adaptability is improved, but compatibility between application model and database schema is compromised
Solution Approach 1:
The system implements a feedback mechanism that continuously monitors changes to the data model during runtime. When changes are detected, the system automatically generates and executes corresponding DDL statements against the database, then verifies the changes maintain model-schema compatibility. This closed-loop feedback ensures adaptability while preserving reliability.
Solution Approach 2:
The system performs self-service by automatically detecting data model changes and propagating them to the database without requiring manual intervention. The framework autonomously generates appropriate DDL statements and executes them, enabling the system to self-adjust and maintain consistency between the application model and database schema dynamically.
3Ease of operation
If manual schema updates are performed, then control over changes is maintained, but productivity and speed of implementation are reduced
Solution Approach 1:
The system automates the schema update process by automatically detecting changes in the data model and generating corresponding DDL statements. This self-service capability eliminates manual intervention while maintaining control through the automated change propagation mechanism, significantly improving productivity and implementation speed.
Solution Approach 2:
The system performs preliminary actions by automatically generating DDL statements before executing them against the database. This preliminary generation and validation of change scripts ensures control over the changes while automating the implementation process, improving both productivity and reliability.
Data Source
AI summary
The disclosed embodiments provide a system that facilitates making updates to a data model, wherein the data model is associated with data items stored in a relational database system. In particular, updates to the data model are automatically propagated to the relational database system so that the structure of the relational database system remains consistent with the data model. This is accomplished by automatically executing queries corresponding to the updates against the relational database system. For example, changes to fields and/or tables in the data model can be automatically propagated to a relational database system by executing “Create,”“Drop,”“Alter,” and/or “Add” statements against corresponding fields and/or tables in the relational database system.