Graph Analytics Engine Querying for Scalable Relationship Traversal
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Solution Overview
Problem
Existing data querying technologies face challenges in query optimization, scalability, and complexity when dealing with large-scale graph-based data, particularly in balancing trade-offs between query complexity and computational efficiency, and require efficient graph processing and integration into expandable databases.
Innovation Solution
Implementing a graph analytics engine that allows querying of databases without the need for an 'Extract, Transform, and Load' process, supports auto-scaling, achieves low latency for complex queries, is compatible with existing query languages and data lakes, and maintains data control within cloud-native infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional data querying methods are used on large-scale databases, then query capabilities are limited, but query latency increases and scalability is reduced
Solution Approach 1:
The patent segments the database system into multiple graph analytics engines that can independently process different portions of graph data. Each engine handles specific graph traversal tasks, enabling parallel processing and reducing overall query latency while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The patent introduces graph analytics engines as intermediary components between the query interface and the underlying database storage. These engines specialize in graph traversal operations, acting as mediators that optimize query processing by handling complex graph relationships efficiently without requiring changes to the core database infrastructure
2Adaptability or versatility
If graph-based data querying is implemented, then query power is enhanced, but computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-computing and caching graph traversal results, graph schemas, and intermediate computation states. This allows the system to handle complex graph queries more efficiently by reusing previously computed information, reducing the computational burden on each individual query while enhancing overall query capability
Solution Approach 2:
The patent implements dynamic resource allocation and adaptive query execution plans that adjust computational complexity based on the specific query requirements and available resources. The graph analytics engines can dynamically scale their processing power and optimize their traversal strategies, allowing high query versatility without permanently high computational complexity
3Quantity of substance
If data is stored in tabular format, then storage efficiency is maintained, but graph relationship querying becomes complex
Solution Approach 1:
The patent uses graph analytics engines as intermediaries that translate between tabular storage formats and graph relationship representations. These engines maintain storage efficiency by working with the existing tabular database structure while providing efficient graph relationship querying capabilities through specialized traversal algorithms and in-memory graph representations
Solution Approach 2:
The patent creates lightweight in-memory copies of graph data structures from the tabular database for query processing. These copies maintain the essential graph relationships needed for efficient traversal while allowing the original tabular storage to remain unchanged, thus preserving storage efficiency while enabling complex graph querying
Data Source
AI summary
This application is directed to data query. A data query method includes receiving a query that defines a graph relationship between target entities within a to-be-queried database. The data query method further includes traversing the to-be-queried database using the query through a graph analytics engine to obtain output entries. Each output entry includes data items matching the graph relationship defined by the query. The graph analytics engine includes an auxiliary component for the query. The auxiliary component further includes vertices and edges associated with the to-be-queried database, and each edge links two vertices. The data query method further includes generating a graph-based representation of output entries.