Mapping Engine for Relational to Graph Database Conversion
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Solution Overview
Problem
Traditional relational databases are not ideal for handling large and complex data sets, such as big data, and face challenges when converting or copying data between relational and graph databases due to structural differences, making it difficult to scale and manage data effectively.
Innovation Solution
A mapping engine is used to convert data between relational and graph databases by processing columns from relational databases into typed, directed property graphs and vice versa, with a synchronization engine to manage updates and ensure database consistency constraints are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in traditional relational databases, then data can be stored in structured formats, but the system cannot efficiently handle large and complex data sets and faces difficulties when converting to graph databases
Solution Approach 1:
The patent introduces a mapping engine as an intermediary component that facilitates conversion between relational and graph database structures. This mapping engine processes queries, transforms data models, and synchronizes changes between the two database types, thereby resolving the complexity of direct conversion while maintaining adaptability to handle large and complex data sets effectively
2Adaptability or versatility
If a mapping engine is introduced to convert data between relational and graph databases, then data conversion capability is improved, but system complexity increases
Solution Approach 1:
The mapping engine is designed with multi-functionality, handling multiple operations including data conversion, query processing, change synchronization, and constraint validation within a single integrated component. This universal approach improves data conversion capability while managing system complexity by consolidating multiple functions rather than requiring separate components for each operation
3Reliability
If data synchronization is implemented to maintain consistency between relational and graph databases, then data integrity is improved, but processing time and complexity increase
Solution Approach 1:
The mapping engine performs preliminary validation of database constraints and pre-processes synchronization operations before actual data conversion occurs. By validating constraints upfront and preparing transformation rules in advance, the system maintains data integrity while reducing real-time processing time during synchronization operations
Data Source
AI summary
Examples for mapping a relational database to a graph database include a mapping engine to execute an arbitrary query on a relational database, identify a result column tag based on a tag of an underlying base table, process the result column into a typed, directed property graph based on the result column tag, and output the typed, directed property graph to a graph database. Examples for mapping a graph database to a relational database include processing a graph transaction by updating a mapping layer with a surrogate describing a change to a database object, determining, for an object in the mapping layer, if a database constraint defined on the object is satisfied, collecting database changes defined by the surrogate into a database change request, submitting the change request to a relational database as a transaction, and deleting the surrogate for the object in the mapping layer.