Time-Based Partitioning for Data Set Copies
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Solution Overview
Problem
Existing data management systems face inefficiencies in replicating changes from source data to separate copies, particularly in scenarios where costly in-place updates are required, leading to high computational resource utilization and storage waste.
Innovation Solution
Implementing time-based partitioning to avoid in-place updates by generating delta data set partitions corresponding to different time periods, allowing for efficient replication and access to the latest data set version without scanning the entire data set, and periodically purging obsolete partitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If in-place updates are used to replicate changes from source data to copies, then data consistency is maintained, but computational resource utilization increases and storage efficiency decreases
Solution Approach 1:
The patent segments the data set into multiple partitions organized by time periods. Instead of performing in-place updates across the entire data set, changes are replicated to specific partitions based on their time period. This segmentation allows parallel processing and reduces the computational overhead of maintaining data consistency across copies.
Solution Approach 2:
The patent pre-organizes data into time-based partitions before replication occurs. By having partitions ready in advance with their time period assignments, the system can efficiently replicate changes without needing to scan or reorganize data during the update process, thereby reducing computational resource utilization while maintaining consistency.
2Reliability
If in-place updates are used to replicate changes from source data to copies, then data consistency is maintained, but storage efficiency decreases
Solution Approach 1:
By segmenting data into time-based partitions, the system can efficiently manage storage resources. When replicating changes, only the necessary partitions are updated, avoiding redundant storage of entire data sets. This segmentation enables better storage efficiency while maintaining data consistency through targeted replication.
Solution Approach 2:
The patent implements periodic purging of obsolete partitions after data has been successfully replicated. By discarding old partitions that are no longer needed for consistency, the system recovers storage space and eliminates waste, while the time-based partitioning ensures that data consistency is maintained for the required retention period.
3Productivity
If time-based partitioning is implemented, then computational efficiency improves and storage efficiency improves, but system complexity increases
Solution Approach 1:
The patent introduces time-based partitioning as a systematic way to segment data. While this adds some complexity in terms of partition management, it dramatically improves computational efficiency by enabling parallel processing and targeted replication. The segmentation creates a structured approach that simplifies the replication process compared to unstructured in-place updates.
Solution Approach 2:
By pre-organizing data into time-based partitions, the system eliminates the need for complex runtime decision-making during replication. The preliminary organization of data by time period provides a clear framework for replication operations, reducing the complexity of managing updates while improving computational efficiency through structured processing.
4Measurement precision
If entire data set scanning is performed to access latest version, then data accuracy is ensured, but access time increases
Solution Approach 1:
The patent segments the data set into time-based partitions, allowing the system to access the latest version by simply identifying the most recent partition rather than scanning the entire data set. This segmentation maintains data accuracy by preserving the complete partition structure while dramatically reducing access time through targeted retrieval of the latest partition.
Solution Approach 2:
The system pre-organizes data into time-based partitions with clear temporal boundaries. This preliminary organization enables direct access to the latest data version by referencing the most recent partition identifier, eliminating the need for full data set scanning while ensuring data accuracy through the structured partition hierarchy.
Data Source
AI summary
Time-based partitioning of a data set is applied to capture updates to the data set in a copy of the data set. Items that have been updated in a data set with in a time period are identified. Partitions of the data set that include the updated items are created according to a partitioning scheme. The created partitions are grouped in a storage location for the time period in a file structure that stores a copy of the database. A latest version of the copy of the data set may be accessed according to latest partitions of the data set stored in the different locations of the file structure.