Graph Operations Engine for Scalable Multi-Tenant Data Configuration
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Solution Overview
Problem
Conventional multi-tenant systems lack a comprehensive and integrated solution for managing tenants in cloud computing platforms, leading to inefficiencies in tenant data configuration, access, and security policies, with developers relying on custom-built tools that require tedious coding, limited interoperability, and a lack of consistent user interfaces.
Innovation Solution
Implementing graph operations using a graph operations engine that executes graph language requests as a tree of data operations, comprising leaf and root nodes, to manage tenant data efficiently, supporting composition, transformation, aggregation, and sequencing of operations across a network of computing nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If custom-built tools are used for tenant management, then developers can implement specific tenant management scenarios, but the system complexity increases due to tedious coding and maintenance of custom APIs
Solution Approach 1:
The patent implements a universal graph operations engine that handles multiple tenant management scenarios through a single unified system. The graph language and operation framework provide multi-functional capabilities for tenant data configuration, access policy management, and security policy enforcement across diverse workloads, eliminating the need for separate custom-built tools for each scenario.
Solution Approach 2:
The patent introduces a graph operations engine as an intermediary layer between the user interface and the backend tenant management system. This mediator translates high-level graph language requests into executable operations, simplifying the interface while maintaining comprehensive functionality for tenant management across different scenarios.
2Adaptability or versatility
If custom APIs are developed for different tenant management scenarios, then specific operational needs can be met, but interoperability is limited and errors increase
Solution Approach 1:
The graph operations engine provides a universal framework that handles diverse tenant management operations through standardized graph language constructs. This unified approach improves reliability by eliminating the errors associated with multiple custom APIs while maintaining the ability to address different operational needs through flexible graph operation definitions.
3Adaptability or versatility
If manual aggregation of data operations is performed, then flexibility in processing is maintained, but computational overhead and processing time increase
Solution Approach 1:
The graph operations engine implements self-service automation where the system automatically composes, transforms, and aggregates data operations based on graph language definitions. The engine autonomously executes the tree of data operations across computing nodes, eliminating manual aggregation while maintaining processing flexibility through configurable graph operation parameters.
Solution Approach 2:
The patent implements continuous automated processing of tenant management operations through the graph operations engine. The system continuously executes data operations in a streamlined pipeline, maintaining flexibility through configurable graph language while eliminating the interruptions and inefficiencies of manual aggregation, thereby improving overall processing efficiency.
4Stability of the object's composition
If a comprehensive tenant management solution is implemented, then consistency across tenants is improved, but the initial development effort and complexity increase
Solution Approach 1:
The graph operations engine serves as a universal platform that enforces consistent tenant management across diverse workloads through standardized operations. The unified graph language and execution framework ensure consistent application of access and security policies across all tenants, achieving stability without requiring separate development efforts for each tenant scenario.
Data Source
AI summary
Methods, systems, and computer storage media for providing a multi-tenant system that executes graph language requests using graph operations of a graph language. A graph language request—that configures tenant data for tenants in a multi-tenant system—is executed using a graph operations engine. The graph operations engine receives and parses a graph language request that includes a list of tenants and a definition of data operations. The set of data operations of the definition are executed on a tree of data operation nodes comprising a plurality of leaf nodes and a root node. Executing the data operations is based on graph language actions (e.g., composition, transformation, and aggregation) that support asynchronously returning results data associated with configuring the Tenant data. Executing the data operations of the definition causes generation of results data (e.g., root node results or leaf node results) configuration of the tenant data in the multi-tenant system.