Graph Database Record Placement Defragmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Graph databases experience data fragmentation due to non-contiguous storage of related records, leading to inefficient traversal and performance issues during search operations, especially when accessed concurrently by multiple users.
Innovation Solution
Implementing a dynamic ruleset for optimized record allocation and defragmentation in graph databases, which involves estimating and allocating records based on access patterns, system configurations, and policies to ensure contiguous storage of related nodes and edges, and relocating fragmented records to resolve fragmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If records are stored non-contiguously in graph database, then memory allocation is simpler, but data traversal efficiency deteriorates
Solution Approach 1:
The system performs preliminary actions by allocating contiguous memory blocks for related records before data traversal operations. When records are inserted into the graph database, the patent pre-allocates contiguous memory regions for nodes and their related edges, ensuring that related data is physically adjacent in memory before any traversal occurs. This eliminates the need for complex pointer chasing during traversal operations.
Solution Approach 2:
The patent segments the graph database storage into distinct contiguous regions based on relationship types. Related nodes and edges are allocated in separate contiguous memory segments, allowing efficient traversal within each segment while maintaining overall database flexibility. This segmentation enables the system to maintain simple allocation strategies while achieving efficient traversal through localized contiguous storage.
2Stability of the object's composition
If dynamic ruleset is implemented for record allocation, then data fragmentation is reduced, but system complexity increases
Solution Approach 1:
The system changes parameters dynamically by adjusting record allocation strategies based on access patterns and database state. The patent implements a dynamic ruleset that modifies allocation parameters such as contiguous block sizes, memory region assignments, and defragmentation thresholds based on observed data access patterns. This allows the system to adapt to changing workloads while maintaining reduced fragmentation without requiring complete architectural complexity.
Solution Approach 2:
The dynamic ruleset implements self-service mechanisms where the system automatically monitors its own fragmentation levels and access patterns, then autonomously adjusts allocation strategies. The patent includes automated defragmentation routines and adaptive allocation algorithms that self-regulate based on database state, reducing the need for external management complexity while maintaining optimal data organization.
3Adaptability or versatility
If multiple read operations are required for fragmented data, then data storage flexibility is improved, but query performance deteriorates
Solution Approach 1:
The patent merges related records into contiguous memory blocks, combining nodes and their associated edges into single continuous storage regions. This merging strategy allows the system to maintain storage flexibility through logical record organization while achieving physical contiguity that enables single-read operations to retrieve complete related data sets, thereby improving query performance without sacrificing adaptability.
Data Source
AI summary
Methods and systems are disclosed for optimizing record placement in defragmenting a graph database. Issues with fragmented data within a graph database are addressed on the record level by placing data that is frequently accessed together contiguously within memory. For example, a dynamic rule set may be developed based on dynamically analyzing access patterns of the graph database, policies, system characteristics and/or other heuristics. Based on statistics regarding normal query patterns, the systems and methods may identify an optimal position for certain types of edges that are often traversed with respect to particular types of nodes.