Graph Data Structures for Fast Relational Queries on NoSQL Data
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Solution Overview
Problem
Existing database systems face challenges in balancing response time, querying flexibility, and data insertion speed, particularly in complex computing infrastructures with limited resources, where relational and non-relational storage techniques often result in trade-offs and inefficiencies.
Innovation Solution
A computer-implemented database system that utilizes a graph data structure to combine relational and non-relational querying capabilities by pre-building graph data structures within non-relational databases, enabling efficient traversal and retrieval of data elements based on their relationships, thereby supporting near-real-time querying and improved insertion speeds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If non-relational database techniques are used for storage, then insertion speed and scalability are improved, but querying flexibility deteriorates
Solution Approach 1:
The system performs preliminary action by pre-building a graph data structure that maps relational relationships between non-relational database documents before queries are executed. This graph structure is constructed in advance using metadata and relationships extracted from the documents, enabling fast relational queries without requiring complex real-time processing during query execution.
Solution Approach 2:
The graph data structure serves as an intermediary layer between the non-relational database storage and relational query processing. It mediates between the document-oriented storage model and the relational query model, allowing relational queries to be answered efficiently by traversing the pre-computed graph relationships rather than performing complex joins on the original non-relational data.
2Adaptability or versatility
If relational storage techniques are used for storage, then querying flexibility is improved, but bulk insertion and updating speed deteriorates
Solution Approach 1:
The system segments the data storage and querying functions into distinct components: non-relational database for bulk insertion and document storage, and graph data structure for relational query processing. This segmentation allows each component to be optimized for its specific function - the non-relational database handles high-speed bulk insertion while the graph structure handles flexible relational querying.
Solution Approach 2:
The graph data structure is built in advance from the non-relational database documents, capturing relational relationships before they are needed for queries. This preliminary construction allows the system to maintain the speed benefits of non-relational storage while enabling flexible relational querying through the pre-computed graph structure.
3Adaptability or versatility
If relational queries are executed on non-relational databases, then querying flexibility is improved, but response time deteriorates
Solution Approach 1:
The system performs preliminary action by pre-computing and storing graph data structures that represent relational relationships between documents. When a relational query is received, the system traverses the pre-built graph structure rather than performing complex joins on the original documents, dramatically reducing query response time while maintaining full relational querying flexibility.
Solution Approach 2:
The graph data structure acts as an intermediary that enables efficient relational query processing on top of non-relational database storage. It provides a fast lookup mechanism for relational relationships, eliminating the need for slow full-document traversal and complex join operations that would otherwise be required.
4Adaptability or versatility
If complex computing infrastructures are used for processing, then querying capability is improved, but processing constraints and response time deteriorates
Solution Approach 1:
The system extracts the relational querying capability from the complex computing infrastructure by using a simple graph data structure that can be built and traversed efficiently. Instead of relying on complex relational database engines, the patent extracts the essential relational relationships and represents them as simple graph structures with nodes and edges, enabling fast querying with minimal computing overhead.
Data Source
AI summary
An approach and corresponding computer system adapted for combining relational and non-relational data storage and indexing techniques is proposed. The approach utilizes graph data structures that are specifically configured for supporting performance improvements in relational querying while maintaining the advantages from a non-relational database. The graph data structures can be used for graph traversals and graph operations, among others, which can improve an overall computational complexity level associated with various operations at the cost of storage.