Distributed Graph Query Processing With Half-Edge Storage
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Solution Overview
Problem
Graph databases and processing systems face limitations in scalability and efficiency, particularly in handling complex computational tasks and maintaining historical data states.
Innovation Solution
A node-centric architecture with asynchronous message-passing and half-edge representation, combined with scalable storage and query mechanisms, allows for efficient processing and historical state retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If graph databases use traditional relational or document storage architectures, then data storage and retrieval are straightforward, but the ability to model complex relationships and perform semantic queries is limited
Solution Approach 1:
The patent segments the graph into nodes and edges as distinct fundamental units, allowing independent storage and processing. Each node contains properties and references to edges, while edges connect nodes through standardized interfaces. This segmentation enables flexible modeling of complex relationships without requiring complex storage structures.
Solution Approach 2:
The patent implements a universal graph data model where the same node-edge structure can represent various types of relationships and data entities. The standardized interfaces and generic query processing mechanism allow the system to handle diverse query types and data models without requiring architecture changes, achieving both versatility and simplicity.
2Productivity
If graph processing systems implement comprehensive query processing capabilities, then semantic query performance improves, but system scalability is limited
Solution Approach 1:
The patent divides the graph processing system into independent node processors, each handling queries for its local nodes. This segmentation allows the system to scale horizontally by adding more node processors without increasing the complexity of individual processing units. Each node processor maintains local state and processes queries independently, enabling parallel processing and improved scalability.
Solution Approach 2:
The patent introduces a temporal dimension to the graph model by maintaining historical states of nodes and edges. This allows the system to answer queries about past states without requiring separate historical graph copies, enabling temporal queries while maintaining scalability through efficient state management and compression techniques.
3Measurement precision
If graph databases maintain complete historical data states, then historical state queries are accurate, but data storage requirements and system complexity increase
Solution Approach 1:
The patent extracts only the necessary historical state information needed for queries, rather than storing complete historical records. By maintaining only the state changes and relevant properties at each temporal point, the system reduces storage requirements while preserving the ability to answer historical queries accurately through selective data retrieval.
Solution Approach 2:
The patent changes the storage parameter from complete historical records to state transitions and key property changes. This parameter transformation allows the system to store minimal data while maintaining query accuracy, as the system can reconstruct historical states by applying state transitions in chronological order rather than storing every historical snapshot in detail.
4Reliability
If graph processing systems implement fine-grained consistency control, then data consistency is improved, but system complexity and performance overhead increase
Solution Approach 1:
The patent segments consistency control into individual node-level operations, where each node processor manages its own state consistency independently. This eliminates the need for complex global consistency mechanisms while maintaining fine-grained consistency through local state management and transaction isolation at the node level.
Solution Approach 2:
The patent implements dynamic consistency control that adapts to different operational contexts. The system can switch between different consistency models (e.g., strong consistency for critical operations, eventual consistency for less critical ones) based on query requirements, providing fine-grained consistency control without requiring a single complex global consistency mechanism.
Data Source
AI summary
A graph comprising nodes and edges is stored by a distributed system as a collection of nodes and half-edges stored with their respective nodes. A message processor is associated with a node as needed to process messages passed between nodes, such that a given node has zero or one message processor assigned to it at a given time. Queries of the graph are resolved by processing a first portion of the query at a first node, and forwarding the results with the remaining portions of the query to a node linked by an edge to the present node.