Hierarchical Versioning for Distributed Unstructured Data Consistency
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Solution Overview
Problem
Large centralized data stores managing unstructured data face challenges in scalability and maintaining data integrity and consistency due to the unstructured nature of the data and distributed access paradigms, making it difficult for organizations to efficiently manage and maintain data consistency across multiple consumers with varying tolerance levels.
Innovation Solution
Implementing hierarchical conjoint versioning techniques in a distributed storage system, where a core data object is maintained at a root node, and additional data is stored in child nodes, with version identifiers and update criteria ensuring data consistency across nodes, allowing consumers to aggregate data from selected nodes based on version identifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored in a centralized data store, then data consistency can be maintained, but scalability is limited and resource bottlenecks occur
Solution Approach 1:
The patent segments the centralized data store into multiple distributed nodes, where each node stores portions of the data object. This segmentation allows the system to scale horizontally by adding more nodes while maintaining data consistency through version identifiers and selective updates based on consumer tolerance levels.
2Productivity
If data is decentralized across multiple nodes, then scalability is improved, but maintaining data integrity and consistency becomes difficult
Solution Approach 1:
The patent implements a feedback mechanism where version identifiers are propagated across distributed nodes. When data is updated at one node, the version identifier change is communicated to other nodes, enabling them to selectively update their copies based on their tolerance levels and consumer requirements, thus maintaining consistency in a decentralized manner.
3Reliability
If version identifiers are propagated to all nodes, then data consistency is maintained, but network traffic and processing overhead increase
Solution Approach 1:
The patent applies local quality by allowing different nodes to have different update behaviors based on their specific characteristics and consumer tolerance levels. Not all nodes receive or process every version identifier propagation, enabling energy-efficient selective updates where only relevant nodes perform synchronization operations.
4Measurement precision
If all consumers require the latest data version, then data freshness is improved, but resource bottlenecks and processing overhead increase
Solution Approach 1:
The patent introduces dynamic update criteria where consumers and nodes can specify their tolerance levels for data staleness. This dynamic approach allows the system to adapt update frequency and propagation scope based on real-time requirements, balancing data freshness with processing overhead by updating only when necessary for each consumer-node pair.
Data Source
AI summary
In various example embodiments, a system and method for versioning data in an unstructured data store are presented. The method includes generating a first record in the unstructured data store based on a version of a second record in the unstructured data store. The method further includes initializing a parent version identifier of the first record based on the version of the second record. The method further includes comparing the parent version identifier of the first record to a version identifier of the second record. The method further includes determining whether the first record is consistent based on the comparison.