Graph Database Storage Segmentation for Memory-Efficient Queries
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Solution Overview
Problem
Graph databases face limitations in handling large-scale data analysis due to memory storage constraints, leading to inefficient disk access during query processes.
Innovation Solution
Store attribute information in a disk and retrieval information in memory, allowing queries to be performed directly from memory using retrieval information, reducing disk access and optimizing data size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If all graph data is stored in memory, then query efficiency is improved, but storage space is insufficient for large-scale data
Solution Approach 1:
The patent divides graph data into two segments: retrieval information (nodes, edges, relationships) is stored in memory for fast access, while attribute information (detailed data) is stored on disk. This segmentation allows the system to maintain fast query performance for structural data while having unlimited storage capacity for detailed attributes.
Solution Approach 2:
The patent extracts the attribute information from the graph data structure and separates it from the retrieval information. By taking out the large-volume attribute data and storing it externally on disk, the system resolves the memory storage limitation while preserving the ability to efficiently query graph structures through memory-resident retrieval information.
2Quantity of substance
If all graph data is stored on disk, then storage space is sufficient, but query efficiency deteriorates due to frequent disk access
Solution Approach 1:
The patent segments data access by keeping frequently accessed retrieval information in memory and only accessing disk when attribute information is needed. This reduces I/O operations during typical graph queries while still supporting large-scale data storage.
Solution Approach 2:
The patent performs preliminary action by pre-loading retrieval information into memory during system initialization or data loading phases. This ensures that when queries are executed, the critical graph structure data is already available in memory, eliminating the need for frequent disk access during query operations.
3Productivity
If retrieval information and attribute information are both stored in memory, then query performance is optimized, but memory consumption increases
Solution Approach 1:
The patent extracts attribute information from memory storage requirements and relocates it to disk storage. This extraction allows the system to maintain only the essential retrieval information in memory, significantly reducing memory consumption while preserving query performance for graph structure operations.
Data Source
AI summary
The specification provides computer-implemented methods, apparatuses and computer-readable media for data storage in a database. An example computer-implemented method includes: obtaining graph data to be stored, where the graph data include retrieval information and attribute information corresponding to a graph data object, and the retrieval information is used to perform retrieval query on the graph data; storing the attribute information into a disk managed by a storage engine corresponding to the graph database, and obtaining a storage location of the attribute information on the disk; and further storing the retrieval information corresponding to the graph data object and the storage location into a memory managed by the storage engine.