Relational Data Retention via HBase for Scalable Database Access
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Relational database systems face scalability limitations when managing large volumes of data, making them less effective in environments requiring flexible and scalable data management, such as on-demand services and multitenant databases.
Innovation Solution
Data is selectively retained on a non-relational database like Apache HBase, with transfer occurring in response to predefined conditions, and SQL queries are translated into parallel scans on the non-relational database using a JDBC API, allowing seamless access and management of data across relational and non-relational environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If relational database systems are used to store large amounts of data, then data management functionality is provided, but scalability is limited and the system becomes less effective
Solution Approach 1:
The patent divides the database system into two separate systems: a relational database management system (RDBMS) for structured data and transactional operations, and a non-relational database (HBase) for unstructured data and scalable storage. This segmentation allows each system to operate in its optimal performance zone, with the RDBMS handling ACID-compliant operations and HBase providing horizontal scalability for large-scale data retention.
Solution Approach 2:
The patent introduces an intermediary layer that translates between SQL queries from the RDBMS and the native query interface of HBase. This intermediary enables seamless communication between the two database systems, allowing the RDBMS to leverage HBase's scalable storage capabilities while maintaining SQL compatibility for applications.
2Quantity of substance
If data volume increases in relational database systems, then more data can be stored, but access performance and query efficiency deteriorate
Solution Approach 1:
The patent extracts large volumes of unstructured or semi-structured data from the relational database and stores them in the HBase non-relational database. This extraction relieves the RDBMS from handling excessive data volumes, allowing it to maintain high access performance for critical structured data while HBase handles the bulk storage of large datasets with efficient columnar storage and distributed architecture.
3Adaptability or versatility
If relational database systems are used for flexible data management, then data integrity is maintained, but system complexity increases and scalability is reduced
Solution Approach 1:
The patent implements a dynamic architecture where the system automatically routes different types of data to appropriate storage systems based on their characteristics. Structured data requiring strong consistency and transactions remains in the RDBMS, while unstructured data benefiting from horizontal scaling is stored in HBase. This dynamic data classification and routing mechanism provides flexibility without requiring manual intervention or increasing overall system complexity.
Data Source
AI summary
Systems and techniques for managing data in a relational database environment and a non-relational database environment. Data in the relational database environment that is static and to be maintained beyond a preselected threshold length of time is identified. The data is copied from the relational database and stored in the data the non-relational database. Access to the data is provided from the non-relational database via a user interface that accesses both the relational database and the non-relational database.