Selective Database Backup Compression via Table Space Segmentation
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Solution Overview
Problem
Current database management systems face inefficiencies in backup operations due to the all-or-nothing approach to data compression, which can result in wasted processor cycles and increased storage requirements, especially when compressing table spaces with less compressible data, leading to uncertain or insignificant space savings.
Innovation Solution
Implementing a selective data transformation method during database backups, where specific data subsets can be compressed or encrypted based on characteristics like size, type, and compressibility, allowing for a single backup image to contain both transformed and untransformed data, optimizing processing resources and storage usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of stationary object
If compression is applied to all data subsets during backup, then storage space is reduced, but processor time and computational resources are excessively consumed
Solution Approach 1:
The backup image is segmented into multiple compressed volumes, allowing selective compression of data subsets. The system divides the database into multiple table spaces and further segments them into data subsets that can be independently compressed and stored in separate volumes, enabling parallel processing and reducing overall backup time while maintaining storage efficiency.
Solution Approach 2:
The system applies compression selectively to only those data subsets that meet specific criteria (such as compressibility thresholds), rather than compressing all data. This partial action approach avoids wasting processor time on data that would not benefit from compression, while still achieving significant storage space reduction for suitable data subsets.
2Volume of stationary object
If compression is applied to all data subsets during backup, then storage space is reduced, but the backup process becomes excessively complex
Solution Approach 1:
The system automatically evaluates each data subset to determine its compressibility characteristics and makes compression decisions without requiring manual intervention. The backup utility autonomously identifies suitable data subsets, applies appropriate compression algorithms, and manages the segmented volume creation, simplifying the overall process while achieving storage optimization.
Solution Approach 2:
The system dynamically adjusts compression parameters and algorithms based on the characteristics of each data subset. By evaluating data properties and selecting appropriate compression settings, the system optimizes the balance between storage space reduction and processing complexity, applying compression only where beneficial.
3Loss of time
If selective compression is applied to only some data subsets, then processor time is saved, but the backup system complexity increases
Solution Approach 1:
The backup utility automatically evaluates each data subset's compressibility and makes intelligent decisions about which subsets to compress, eliminating the need for manual configuration. This self-service approach saves processor time by avoiding compression of unsuitable data while keeping the system relatively simple through automated decision-making.
Solution Approach 2:
The system dynamically changes compression parameters based on data subset characteristics, automatically adjusting which data receives compression treatment. This parameter adaptation allows the system to optimize processor time usage while managing complexity through rule-based automated selection rather than manual intervention.
Data Source
AI summary
Differential or selective elective data transformation, which can include compression and/or encryption, is applied to selected data subsets, such as selected table spaces, of a database during a single database operation. In response to a received backup command, a backup utility of a database management system obtains data from a number of data subsets of a source database that are specified for inclusion in a backup image. At least one of the data subsets is specified for data transformation while subsets are not. The data from the specified data subsets is identified in the obtained data, and transformed prior to writing a single backup image to archive media. The backup image therefore contains both transformed and untransformed data. The selection of data subsets for transformation can be made automatically without requiring user specification according to predefined data characteristics including subset size, data type, compressibility, or encryption.