Graph Data Query Partitioning and Layering
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Solution Overview
Problem
Graph data query efficiency is low due to the need to search through the entire dataset, even after partitioning, especially for queries requiring distance-related information, as existing solutions do not effectively utilize partitioning to improve query accuracy and speed.
Innovation Solution
A method and apparatus that perform partitioning and layering on graph data, where the partition number and layer number of a vertex are used to determine candidate vertices for querying, forming a candidate set for efficient data retrieval based on query conditions, and performing queries within this reduced dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If partitioning is performed on graph data, then storage and calculation are separated into multiple partitions, but query efficiency remains low for distance-related queries as the entire graph data set must still be searched
Solution Approach 1:
The patent divides the graph data set into multiple partitions based on vertex connectivity and distance metrics. Each partition contains vertices at specific distance ranges from query vertices, allowing the system to segment the search space and only query relevant partitions rather than the entire data set, thereby improving query efficiency while reducing the volume of data that must be searched.
2Measurement precision
If graph data is queried based on all graph data in the whole set, then query accuracy is maintained, but query efficiency decreases as data volume grows
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing distance metrics and partition assignments for all vertices in the graph data set. This preliminary organization allows the query system to quickly identify and access only the relevant partitions containing vertices within the specified distance range, maintaining query accuracy while significantly reducing the time required to execute queries as the data set grows.
Data Source
AI summary
A graph data query method and apparatus are disclosed, where the method includes: acquiring a partition number and a layer number of a query vertex; determining, based on the partition number and the layer number of the query vertex, a partition number and a layer number of a candidate vertex indicated by a query condition, and using the partition number and the layer number of the candidate vertex respectively as a candidate partition number and a candidate layer number; forming a candidate set using a vertex whose partition number and layer number satisfy any group of a candidate partition number and a candidate layer number; and performing graph data query in the candidate set according to the query condition.