Versioned Data Table Indexing for Streaming History
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Solution Overview
Problem
Traditional streaming data storage solutions fail to preserve historical data versions, leading to difficulties in searching and retrieving data from different versions, which hampers user experience and increases storage resource usage.
Innovation Solution
A method that involves acquiring and storing data from a first moment in streaming data in a data table, alongside storing data from a previous moment, and updating an index related to the object being processed, allowing for efficient look-up and management of historical data without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional streaming data storage solutions are used, then storage resource usage is reduced, but the ability to search and retrieve historical data versions is lost
Solution Approach 1:
The patent segments historical data storage by organizing data into versioned entries with unique identifiers. Each version of streaming data is stored as a separate entry in the data table, allowing individual version retrieval while maintaining efficient storage through structured organization of data versions, timestamps, and identifiers.
2Ease of operation
If historical data versions are preserved, then search and retrieval capability is improved, but storage resource usage increases
Solution Approach 1:
The patent implements feedback mechanisms through index structures that automatically update when new data versions are stored. The index maintains mappings between data identifiers, timestamps, and version information, enabling efficient search and retrieval operations without requiring full data table scans, thus balancing accessibility with storage efficiency.
3Extent of automation
If data is stored chronologically without user intervention, then ease of operation is improved, but automated index management complexity increases
Solution Approach 1:
The system performs self-service through automated index update mechanisms that trigger whenever new streaming data is stored. The index structure automatically recalculates and updates its mappings based on new data entries, eliminating the need for manual index management while maintaining chronological organization and version tracking of streaming data.
Data Source
AI summary
Embodiments of the present disclosure relate to processing data. An example method includes acquiring data related to a first moment in streaming data of an object to be processed. The method further includes storing the data in a first entry of a data table based on an identification of the object to be processed, wherein the data table further includes a second entry before the first entry, and the second entry stores data related to a second moment before the first moment in the streaming data. The method further includes updating an index related to the object to be processed based on the first entry. Thus, a solution to the problem of performing search in data at different moments is provided, and it is unnecessary for a user to participate in the solution, thus improving the user experience and reducing the use of storage resources.