Partitioned RDBMS Schema for Scalable SQL Data Management
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Solution Overview
Problem
Relational database management systems (RDBMS) face limitations in storing large quantities of data and scalability, while NoSQL databases lack SQL query capabilities and fixed schema, making it difficult to manage and manipulate data across enterprises.
Innovation Solution
The system divides the schema of tables in RDBMS into disjunctive partitions, allowing valid SQL queries with join operations between tables within the same partition to be executed, while preventing invalid queries that span across different partitions, and dynamically manages storage by acquiring additional RDBMS as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in a traditional RDBMS, then SQL query functionality and data accessibility are maintained, but the system cannot store huge quantities of data and is not easily scalable
Solution Approach 1:
The patent divides the RDBMS into multiple partitions, each capable of storing a portion of the total data. This segmentation allows the system to scale horizontally by adding more partitions while maintaining SQL query functionality within each partition. The schema is divided into disjunctive partitions that can be independently managed and scaled.
2Quantity of substance
If NoSQL database is used to store large quantities of data, then scalability is improved, but the system lacks fixed schema and SQL query capabilities making data manipulation difficult
Solution Approach 1:
Each partition in the system maintains a fixed schema similar to traditional RDBMS, allowing SQL queries to be executed locally within each partition. This local quality preservation enables data manipulation and querying to remain easy and structured, while the distributed architecture across multiple partitions provides the necessary scalability and large storage capacity.
3Adaptability or versatility
If the schema of tables is divided into disjunctive partitions, then scalability and storage capacity are improved, but queries spanning multiple partitions become invalid and more complex
Solution Approach 1:
The patent extracts the complexity of cross-partition queries by preventing them entirely. The system design ensures that queries are executed within a single partition, and the schema is organized such that related data resides in the same partition. This extraction of cross-partition complexity simplifies query execution while maintaining scalability through the partitioned architecture.
Data Source
AI summary
A mechanism for managing data in relational database management systems (RDBMS). The method includes receiving a structured query language (SQL) query to be executed on a relational database management system (RDBMS). The RDBMS includes a schema of tables divided into a plurality of partitions and the SQL query includes an operation of data to be executed on the RDBMS. The method also includes determining whether the SQL query is a valid SQLT query. The SQL query is a valid SQLT query when the SQL query includes a join operation applied to data from tables in a same partition of the plurality of partitions.