Delta-Based Graph Storage for Cloud Asset Change History
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Solution Overview
Problem
Current approaches to tracking change histories of cloud assets in cloud environments result in inefficient storage and data management due to the frequent creation of new graphs for every change, leading to a large number of stored graphs and increased complexity.
Innovation Solution
Implement a delta-based graph storage optimization system that maintains a single graph by creating new asset property nodes to represent changes, rather than rebuilding the entire graph for each update, and includes start and end timestamps for relationship validity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a new graph is created every time a change occurs to track asset history, then the completeness of change history tracking is improved, but storage overhead and data management complexity increase significantly
Solution Approach 1:
The patent segments the graph data into immutable graph snapshots (stored in object storage) and mutable graph metadata (stored in the graph database). This segmentation allows the system to maintain complete change history through snapshots while keeping the active graph lightweight and manageable in the database.
Solution Approach 2:
The patent extracts the historical graph data into separate immutable snapshots stored in object storage, removing the burden of storing complete historical graphs in the graph database. This extraction reduces storage overhead in the database while preserving complete change history in the extracted snapshots.
2Measurement precision
If a new graph is created every time a change occurs to track asset history, then the accuracy of point-in-time views is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by creating immutable graph snapshots at each change point before proceeding with updates. These pre-created snapshots serve as accurate point-in-time views that can be retrieved without recomputation, eliminating the need to recreate graphs when querying historical states.
Solution Approach 2:
The patent creates copies of the graph state at each change point and stores them as immutable snapshots. These copies enable accurate point-in-time queries without requiring the system to recreate or recompute the graph state, significantly reducing processing time for historical queries.
3Reliability
If configuration snapshots are received every 5 to 10 minutes, then the freshness of configuration data is improved, but the frequency of graph updates increases leading to storage inefficiency
Solution Approach 1:
The patent extracts only the changed configuration data from frequent snapshots and applies these deltas to the graph, rather than processing complete graphs. This extraction approach maintains data freshness by frequently updating the graph with minimal changes, while avoiding the storage inefficiency of creating complete graph snapshots for every configuration update.
Solution Approach 2:
The patent changes the parameter of graph storage from storing complete graphs to storing only incremental changes (deltas). This parameter change allows the system to maintain high configuration data freshness through frequent updates while dramatically improving storage efficiency by storing only the minimal necessary information.
4Stability of the object's composition
If the entire graph is rebuilt for each configuration update, then the consistency of the graph database is improved, but the processing complexity and computational overhead increase
Solution Approach 1:
The patent segments graph data into immutable snapshots (for consistency) and mutable metadata (for efficient updates). This segmentation allows the system to maintain graph consistency through the immutable snapshot chain while reducing processing complexity by updating only the necessary metadata portions rather than rebuilding entire graphs.
Solution Approach 2:
The patent applies partial action by updating only the necessary portions of the graph (metadata and deltas) rather than performing excessive full graph rebuilds. This approach maintains graph consistency through selective updates while significantly reducing processing complexity and computational overhead.
Data Source
AI summary
Systems and methods are disclosed for implementing a process for graph database storage optimization, applicable to delta-based cloud asset tracking. In certain embodiments, a method may comprise implementing a delta-based graph storage optimization system for asset tracking in a cloud environment, including storing a graph database representing a configuration of a cloud environment, obtaining configuration settings representing a current state of the cloud environment from a cloud platform, and identifying a delta based on changes between the configuration from the graph database and the configuration settings from the cloud platform. The method may further comprise creating an asset property node based on the delta, and adding the asset property node to the graph database without creating a new graph based on the configuration settings.