Unified Graph Interface for Relational Data Querying
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Solution Overview
Problem
Large-scale multi-user applications face challenges in managing highly relational data due to siloed data management and bespoke APIs, leading to complexity, inefficiency, and fragmentation, which hinders development and collaboration.
Innovation Solution
Implementing a graph serving layer with a global look-aside index and managed online service component to streamline data management, enabling efficient querying and hosting of new entities, reducing development overhead and enhancing agility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If data is managed in siloed structures with bespoke APIs, then data storage and management are organized by domain, but querying and integrating data across different domains becomes complex and inefficient
Solution Approach 1:
The patent merges siloed data domains into a unified graph structure where all data entities (users, organizations, jobs, shares, etc.) are interconnected through a common graph interface. This allows data to be organized by domain while maintaining efficient cross-domain querying through graph traversal operations, eliminating the complexity of integrating multiple bespoke APIs.
Solution Approach 2:
The patent introduces a graph serving layer as an intermediary between data storage and access operations. This layer provides a standardized graph interface that mediates between the underlying siloed data structures and the querying operations, enabling efficient data integration without requiring changes to the original domain-specific storage organization.
2Adaptability or versatility
If bespoke APIs are used for each data entity, then specific data access requirements are met, but development overhead and fragmentation increase
Solution Approach 1:
The patent implements a universal graph interface that can handle diverse data access requirements through a single standardized API. The graph structure supports multiple query patterns (by entity type, by relationship, by path) and can access any data entity through the same interface, eliminating the need for multiple bespoke APIs while maintaining adaptability to specific access needs.
Solution Approach 2:
The patent segments the monolithic API landscape into modular graph operations that can be composed to achieve specific data access patterns. The graph interface is divided into fundamental operations (node access, edge traversal, path finding) that can be combined in various ways to meet different data access requirements without creating additional bespoke APIs.
3Reliability
If traditional data management approaches are used, then existing systems are maintained, but onboarding new data entities is time-consuming and slow
Solution Approach 1:
The patent establishes a pre-configured graph schema and serving infrastructure in advance that can accommodate new data entities without requiring system reconfiguration. The graph structure is designed with extensibility built-in, allowing new entity types and relationships to be added by simply defining new graph nodes and edges rather than creating entirely new data management systems.
Solution Approach 2:
The patent implements a dynamic graph schema that can adapt to new data entities through programmatic definition rather than rigid pre-definition. The graph serving layer dynamically interprets and processes queries for any entity type that conforms to the graph interface, enabling rapid onboarding of new data entities while maintaining system stability through consistent interface contracts.
Data Source
AI summary
There is difficulty in managing and querying highly relational data in large-scale multi-user applications. The interconnected nature of the data entities poses challenges in efficiently storing, retrieving, and manipulating the data. Disclosed are solutions that involve establishing a graph serving layer within the API schema of the application. This layer defines a structured vocabulary for describing the data entities and their interrelations. The data is stored in accordance with this structure, and a global look-aside index is generated to enable efficient querying. The index supports filter and join operations, allowing for targeted retrieval of data based on specific criteria. Queries are received via the API schema, and the relevant data is retrieved using the global look-aside index. The retrieved data is then returned to the requesting application or user via the API schema.