Dynamic Graph Loading for Real-Time Queries and Fast Traversal
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Solution Overview
Problem
Traditional graph databases suffer from slow data writing processes that hinder real-time data availability and lack effective graph traversal algorithms, and existing traversal algorithms lack the ability to leverage the structure of the graph database to accelerate traversal.
Innovation Solution
A dynamically loading graph database that does not use persistent storage, dynamically discovers nodes and relationships, and signals hints of the underlying data structure at query time, implementing a less generic traversal algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional graph databases use persistent storage to store database objects and graph structure, then data persistence beyond individual query executions is achieved, but write speed becomes too slow to enable real-time data availability
Solution Approach 1:
The patent implements a dynamic graph database that transitions from static persistent storage to a dynamic in-memory structure. The graph database is rebuilt from scratch for each query based on current data from external sources, allowing the system to adapt to real-time data changes without being constrained by traditional persistent storage limitations. This dynamic approach enables fast write speeds while maintaining data persistence through the query-processing mechanism.
Solution Approach 2:
The patent performs preliminary data fetching and graph reconstruction before query execution. By proactively retrieving data from external sources and rebuilding the graph structure in advance, the system prepares all necessary data in memory, eliminating the need for slow persistent storage operations during query execution and enabling real-time data availability.
2Adaptability or versatility
If traditional graph databases use generic graph traversal algorithms, then broad applicability is achieved, but the ability to leverage graph structure properties to accelerate traversal is limited
Solution Approach 1:
The patent implements specialized graph traversal algorithms that are optimized for specific graph structures and query patterns. Instead of using a single generic traversal algorithm, the system adapts the traversal strategy based on the specific graph structure properties and query requirements, enabling accelerated traversal by leveraging local graph characteristics such as node degrees, edge weights, and graph topology.
Solution Approach 2:
The patent dynamically adjusts traversal parameters based on graph structure properties. By changing traversal parameters such as maximum depth, breadth-first versus depth-first search, and edge selection criteria according to the specific graph characteristics, the system optimizes traversal speed for different graph structures while maintaining broad applicability.
3Speed
If traditional graph databases encode internal optimization at query time, then query performance is improved, but the writing data process remains slow and does not enable real-time data availability
Solution Approach 1:
The patent inverts the traditional query-processing approach by rebuilding the entire graph database from external sources for each query rather than querying an existing persistent graph structure. This inversion of the conventional workflow—where data is fetched and graph is constructed before query execution—enables both fast query performance through in-memory operations and real-time data availability by eliminating slow write operations to persistent storage.
Data Source
AI summary
A computer-implemented method for dynamically loading a graph database. The method includes providing a query interface to one or more web clients over a communications network. The method includes receiving, from at least one of the one or more web clients, a query via the query interface. Based on the query, executing one or more external calls to one or more data sources. The method includes implementing one or more nodes-related methods to process node data from the one or more data sources. The method includes implementing one or more relationships-related methods to process relationship data from the one or more data sources. The method includes performing Create, Read, Update, Delete (CRUD) operation on data from the one or more data sources and constructing a graph database based on the results of the at least one CRUD operation.