Graph Table Orchestration via Columnar Storage
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Solution Overview
Problem
Conventional graph and table operation solutions, such as GraphLab and SAP HANA, rely on proprietary platforms that limit flexibility and hinder orchestration, as they do not support seamless interaction between graph and table data storage and handling, especially in Big Data environments where efficient data processing and user experience are critical.
Innovation Solution
The proposed solution involves a graph/table orchestration system that uses columnar stores to store vertices and edges as collections of tables partitioned by type, implementing an orchestration layer that supports graph operations on multiple distributed execution engines, allowing for seamless interaction between graph and table operations without proprietary constraints, and employing metadata repositories to track graph attributes and storage characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If proprietary platforms (GraphLab, SAP HANA) are used for graph and table operations, then operational stability is improved, but system flexibility and adaptability deteriorate
Solution Approach 1:
The system segments graph operations and table operations into separate modular components that can independently interact through standardized interfaces. This allows each component to maintain its own stability while the overall system gains flexibility through reconfigurable connections between segments.
Solution Approach 2:
The patent implements a universal orchestration layer that can handle both graph operations and table operations through common interfaces and data structures. This multi-functional approach enables a single system to perform diverse operations without requiring proprietary platform-specific implementations.
2Adaptability or versatility
If seamless interaction between graph and table operations is implemented, then adaptability is improved, but system complexity increases
Solution Approach 1:
The patent introduces an orchestration layer as an intermediary between graph operations and table operations. This mediator handles the complexity of coordinating between different operation types, allowing seamless interaction while shielding users from the underlying system complexity through abstracted interfaces.
3Adaptability or versatility
If columnar stores with different storage characteristics are supported, then adaptability is improved, but manufacturing precision and data consistency become more difficult to maintain
Solution Approach 1:
The system dynamically adjusts storage parameters and data organization strategies based on the specific characteristics of each columnar store being used. By modifying parameters such as compression schemes, partitioning strategies, and data formatting according to the target storage system's capabilities, the patent maintains data consistency across diverse storage platforms while preserving adaptability.
Data Source
AI summary
A system and method for orchestrating a table operation of data with a graph operation of the data using columnar stores. The orchestration includes storing vertices and edges as collections of tables by type, and supporting the columnar stores with different storage characteristics. The techniques may also include a graph query optimizer that combines chained operators of a graph query; and/or the graph query executed via an in-memory distributed query execution engine.