Data Model Neutral Layer for Distributed Database Architecture
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional database architectures face inefficiencies in data access and storage due to limitations in handling data across different data models, leading to inconsistencies and replication challenges when dealing with relational and hierarchical databases.
Innovation Solution
A data model neutral data layer translates data requests across various database interfaces, allowing consistent data management across different data models by using a common data storage system that maintains metadata descriptions of each data model type, enabling data replication and synchronization without requiring separate transactional and relational database instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate transactional and relational database instances are maintained to handle different data models, then data consistency and replication difficulties are addressed, but device complexity and operational overhead increase
Solution Approach 1:
A data model neutral layer is introduced as an intermediary between multiple database interfaces and the underlying data storage. This neutral layer translates data requests from different data models (relational, hierarchical, etc.) into a unified internal representation, eliminating the need for separate transactional and relational database instances while maintaining data consistency across all interfaces.
Solution Approach 2:
The data model neutral layer provides universal access to data stored in any format. Instead of maintaining separate specialized database instances for different data models, the system uses a single universal layer that can handle multiple data model types through translation, reducing overall system complexity while preserving reliability.
2Adaptability or versatility
If hierarchical database commands are translated to relational database language, then data access is enabled across different data models, but processing time and translation overhead increase
Solution Approach 1:
The data model neutral layer performs preliminary translation of data requests into a unified internal representation before data retrieval. By preparing and normalizing data requests in advance at the neutral layer, the system eliminates repeated translation overhead during data access operations, improving performance while maintaining multi-model compatibility.
3Productivity
If data is stored in separate memories or memory partitions, then data distribution and scalability are improved, but data access efficiency and consistency deteriorate
Solution Approach 1:
The data model neutral layer implements feedback mechanisms to track data changes across distributed memory partitions. When data is modified in one partition, the neutral layer receives feedback about this change and propagates updates to other partitions, maintaining data consistency without requiring synchronous access to all partitions, thus preserving both distribution efficiency and access speed.
Data Source
AI summary
Systems and methods for managing distributed data using any of a plurality of data models are disclosed. One method includes receiving a data request from one of a plurality of database interfaces, each database interface associated with a different data model type. The method further includes translating the data request to a second data request based at least in part on a data model neutral description of a data model in the data store that is associated with data and the database interface, wherein the data store maintains descriptions of each of a plurality of different data models corresponding to the different data model types. The method also includes executing the second data request, thereby reflecting the data request in data storage such that data is managed consistently across each of the plurality of database interfaces.