Hierarchical Data Structure Node Update Optimization
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Solution Overview
Problem
Hierarchical data structures face inefficiencies in querying and updating large datasets due to the need to traverse entire structures, leading to lengthy processing times and excessive resource usage, especially as the complexity and size of relationships increase.
Innovation Solution
Implementing a system that sets structural limits on the number of nodes in a hierarchical data structure and performs updates synchronously or asynchronously based on these limits to optimize performance and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the hierarchical data structure stores large amounts of data with complex relationships, then the data management capability is improved, but the processing time and resource usage increase excessively
Solution Approach 1:
The patent segments the hierarchical data structure into multiple partitions or subsets, allowing updates to be performed on smaller portions rather than traversing the entire structure. This segmentation enables parallel processing and reduces the time required for data management operations while maintaining the ability to store large amounts of data.
2Quantity of substance
If the hierarchical data structure includes thousands of nodes with complex relationships, then the data management capability is improved, but the computing resources required for updates increase excessively
Solution Approach 1:
The patent applies partial action by performing updates only on the necessary portions of the hierarchical structure rather than traversing all nodes. By identifying and updating only the affected subsets of nodes, the system reduces computing resource usage while maintaining data integrity across the entire large-scale structure.
3Reliability
If synchronous updates are performed on all nodes in the hierarchical structure, then data consistency is improved, but the processing time and resource usage increase excessively
Solution Approach 1:
The patent segments the update operation into multiple independent tasks that can be performed in parallel on different partitions of the hierarchical structure. This segmentation maintains data consistency within each partition while enabling concurrent processing, thereby improving update efficiency without sacrificing reliability.
Solution Approach 2:
The patent implements continuous update processing by maintaining multiple worker threads or processes that continuously process update operations on different partitions simultaneously. This approach ensures data consistency is maintained across all partitions while maximizing productivity through continuous parallel action.
Data Source
AI summary
A method and system for updating nodes in a hierarchical data structure is described. The system receives an update to a first node representing a record in a hierarchical data structure. The system determines at least one other node that needs updating based on the update to the first node. The system compares the number of nodes that need updating based upon the update to the first node to a maximum number to determine whether the number of nodes that need updating is less than, equal to and greater than the maximum number. The system performs a synchronous update of the nodes that need updating in response to a determination that the number of nodes is less than or equal to the maximum number and an asynchronous update of the nodes that need updating in response to a determination that the number of nodes is greater than the maximum number.