Intelligent Pagination Mechanism for Large Data Sets
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Solution Overview
Problem
Existing pagination techniques in data storage systems fail to achieve both efficiency and accuracy, particularly when handling large data sets, often resulting in missing or duplicate data and performance degradation.
Innovation Solution
The implementation of an intelligent pagination method that dynamically chooses between offset pagination and snapshot pagination based on query conditions, using snapshot queries to efficiently extract and store data through a snapshot storage system, and managing snapshot storage to optimize data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If offset pagination is used to extract data, then the implementation is simple, but data accuracy deteriorates with large data sets resulting in missing or duplicate data
Solution Approach 1:
The system dynamically selects between offset pagination and snapshot pagination based on query conditions and data set characteristics. For large data sets, snapshot pagination is automatically chosen to maintain data accuracy, while offset pagination is used for smaller data sets where simplicity is sufficient.
Solution Approach 2:
The invention changes the pagination parameter from simple offset-based positioning to snapshot-based positioning with business keys. This parameter change enables accurate data retrieval for large data sets by using stable business keys instead of relative offsets that fail with large volumes of data.
2Ease of manufacture
If offset pagination is used for large data sets, then the implementation remains simple, but productivity deteriorates due to slow data extraction
Solution Approach 1:
The system dynamically adjusts the pagination strategy based on the size and characteristics of the data set. When large data sets are detected, the system switches to snapshot pagination which pre-positions snapshots at optimal intervals, dramatically improving data extraction speed compared to sequential offset pagination.
Solution Approach 2:
Snapshot pagination performs preliminary actions by creating and storing snapshots at strategic positions within the data set before actual data extraction. This preliminary positioning allows the system to jump directly to relevant data sections, avoiding the slow sequential scanning inherent in offset pagination for large data sets.
3Manufacturing precision
If snapshot pagination is used for large data sets, then data accuracy and efficiency improve, but device complexity increases due to snapshot storage management
Solution Approach 1:
The snapshot storage system automatically manages snapshot creation, storage, and retrieval based on query patterns. The system self-adjusts by identifying frequently accessed data ranges and automatically generating appropriate snapshots, reducing the need for manual configuration and management while maintaining high data accuracy.
Solution Approach 2:
The snapshot storage mechanism serves multiple functions: it acts as a cache for frequently accessed data, a positioning mechanism for large data sets, and a performance optimization layer. This multi-functionality justifies the added complexity by delivering comprehensive benefits across different query scenarios.
4Device complexity
If traditional pagination methods are used, then the system structure remains simple, but loss of time increases due to repeated data extraction
Solution Approach 1:
The system performs preliminary actions by creating snapshots of data at strategic positions before actual queries are executed. These pre-positioned snapshots enable rapid data retrieval by allowing the system to jump directly to relevant sections without sequential scanning, dramatically reducing data retrieval time.
Solution Approach 2:
Instead of repeatedly extracting and processing the same large data sets, the system creates snapshot copies at optimal positions. These copies serve as efficient access points for subsequent queries, eliminating the need for repeated full data extractions and significantly reducing time loss.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for processing a data query from a user device. In an embodiment, a data storage operator receives the data query from the user device. The data storage operator determines that the data query is a snapshot query and the snapshot query does not have a skiptoken. The data storage operator then determines that the snapshot query satisfies snapshot conditions and takes a snapshot based on the snapshot query. The data storage operator stores the snapshot into the snapshot storage and extracts data from a data storage based on the snapshot. The disclosed system enables an intelligent data query by determining whether or not to perform snapshot query. When performing snapshot query, the snapshot is stored in the snapshot storage for future use. In such a case, the data query is efficient and accurate.