Strong References Between Catalog Entries in Non-Relational Databases
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Solution Overview
Problem
Conventional data storage and processing systems face challenges in managing multiple versions of data, leading to arbitrary deletion methods that ignore relationships between data elements, resulting in broken dependencies and instability in restored data sets.
Innovation Solution
Implementing context-sensitive references between data element versions to ensure that dependent elements are not removed from storage before their parent elements, maintaining integrity by allowing them to expire together, using directed relationships and strong references in a non-relational database environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If arbitrary deletion methods are used to clean up storage by removing oldest or expired versions, then storage space is reclaimed and data volume is reduced, but data element relationships are broken and dependent objects become unstable
Solution Approach 1:
The system performs preliminary identification of dependent relationships between data elements before deletion operations. By establishing dependency graphs and identifying parent-child relationships in advance, the system prevents harmful deletion actions that would break data integrity, while still allowing safe deletions to reclaim storage space.
Solution Approach 2:
The system continuously monitors and updates dependency relationships between data elements. Before executing deletion operations, the system checks feedback from the dependency graph to determine whether deleting a particular version would break relationships with dependent objects, thereby making informed decisions about which data can safely be removed.
2Adaptability or versatility
If data elements with different expiration times are stored separately, then storage flexibility is improved and space is optimized, but dependent relationships between elements are lost and restored data sets become broken
Solution Approach 1:
The system performs preliminary identification and marking of data elements that have dependent relationships before expiration or deletion operations. By pre-processing the dependency graph and identifying parent-child relationships, the system ensures that dependent elements are preserved together even when they have different individual expiration times, maintaining data set stability while allowing storage flexibility.
3Productivity
If conventional arbitrary deletion is performed on expired data versions, then storage capacity is increased and old data is removed, but parent-child relationships are destroyed and remaining data becomes unstable
Solution Approach 1:
The system segments the data storage management process into distinct phases: dependency identification, relationship analysis, and conditional deletion. By dividing the deletion process and analyzing relationships separately, the system can efficiently manage storage while preserving critical parent-child dependencies, achieving both storage efficiency and data stability.
Data Source
AI summary
In one example, a method for managing data includes identifying a dependent relation between a first version and a second version of a data element, wherein the first version is dependent upon the second version, and inserting a strong reference between the first version of the data element and the second version of the data element. Next, respective expiration times are determined for each of the first version and the second version, and a determination made as to whether the first version expires prior to the second version. When the second version has an expiration time prior to an expiration time of the first version, expiration of the second version is suspended.