Query Execution Across Multiple Graphs Using Cross-Collection References
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Solution Overview
Problem
As the amount of information stored in graphs increases, it becomes difficult to retain all of it in memory, leading to challenges in efficiently accessing and querying across multiple sub-graphs, especially when relevant resources and relationships are split across multiple computing devices.
Innovation Solution
The system splits a graph or isolated collection into multiple subparts, using cross-collection or cross-graph reference resources to traverse and access information across these subparts, allowing query execution to be paused and transferred between subparts until completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the graph is split into multiple subparts across multiple computing devices, then the storage capacity is improved, but the query execution efficiency deteriorates
Solution Approach 1:
The graph is divided into multiple isolated collection subparts distributed across different computing devices, allowing the system to store larger datasets than a single device could handle. Each subpart contains a portion of the graph data, enabling scalable storage while maintaining the ability to query across all subparts through cross-collection reference resources.
2Ease of operation
If cross-collection reference resources are used to traverse multiple subparts, then the data accessibility is improved, but the system complexity increases
Solution Approach 1:
Cross-collection reference resources serve as intermediaries that enable traversal between isolated collection subparts. These reference resources contain information about other subparts, allowing the system to access data across multiple computing devices without requiring complex direct connections between all components. The intermediary simplifies the traversal process while maintaining data accessibility.
3Loss of information
If query execution is transferred between multiple subparts, then the query completeness is improved, but the execution time increases
Solution Approach 1:
The system performs preliminary actions by identifying and pausing query execution at appropriate points in each subpart, then transferring the execution context to the next relevant subpart. This preliminary organization of query execution across subparts ensures that all necessary data is accessed while minimizing redundant operations and optimizing the overall execution time through structured transfer mechanisms.
Data Source
AI summary
Examples of the present disclosure describe systems and methods for query execution across multiple graphs. In an example, a graph or isolated collection may be split into multiple subparts, such that each subpart may store information of the isolated collection. Cross-collection reference resources may be used to reference resources that are stored by other isolated collection subparts. A breadth-first search of an isolated collection subpart may be performed in order to identify matches or potential matches in an isolated collection subpart. In an example, a potential match may comprise a cross-collection reference resource, which may reference a resource in another isolated collection subpart. Once query execution has completed in the isolated collection subpart, query execution may be paused and transferred to another isolated collection subpart that comprises a resource referenced by a cross-collection resource reference. Accordingly, query execution may resume in the subsequent isolated collection subpart.