Shared Memory Connector for Relational Graph Data Processing
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Solution Overview
Problem
The existing systems face challenges in efficiently converting and moving data from relational databases to graph engines, as the conversion process is time-consuming and resource-intensive, hindering the efficient processing of data in graph engines.
Innovation Solution
A connector system that uses shared memory and convertors to directly transform data between relational and graph formats, enabling high-performance data movement and processing by accessing data through memory location identifiers, allowing for efficient computation offloading between relational and graph engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is converted from relational format to graph format using traditional methods, then data can be processed in graph engine, but the conversion process is time-consuming and resource-intensive
Solution Approach 1:
The patent merges the relational database and graph engine into a unified system where they share common memory space and can access each other's data structures directly. This integration eliminates the need for complete data conversion by allowing the graph engine to query relational data directly through shared memory pointers, thereby reducing conversion time and resource usage while maintaining high processing efficiency
Solution Approach 2:
The patent introduces a connector as an intermediary component that bridges the relational database and graph engine. This connector provides efficient data access mechanisms including shared memory buffers and pointer-based references, allowing both systems to access the same data without full conversion, thus resolving the time-consuming conversion problem while enabling graph processing capabilities
2Productivity
If data is converted from relational format to graph format, then specialized graph processing can be performed, but resource usage increases
Solution Approach 1:
The patent creates a universal data access layer that serves both relational and graph processing needs. The shared memory structure and connector provide a multi-functional interface that allows the same data to be accessed by both relational queries and graph algorithms without requiring separate data copies or extensive conversion, thereby reducing resource usage while maintaining both processing capabilities
Solution Approach 2:
The system enables self-service data access where the graph engine can directly access relational data through shared memory without requiring complete conversion to graph format. The connector automatically manages memory allocation and data access protocols, allowing efficient graph processing on relational data structures without additional resource overhead for full data transformation
Data Source
AI summary
A non-transitory computer readable medium can store machine readable instructions that when accessed and executed by a processing resource cause a computing device to perform operations. The operations can include establishing a connection between data stores (such as a relational data store and a graph engine), wherein the connection includes a shared memory buffer storing data in a data format according to internal structures of the graph engine. The connection between the data stores is bi-directional. The connection enables data that is stored in the shared memory to be processed by either of the graph engine and the relational database. Upon receiving a query, the graph engine or the relational database can be selected to process the data based on a query. The data can be processed by the selected one of the graph engine or the relational database.