Compression Rule Recommendation for Low-Cost Database Data Compression
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Solution Overview
Problem
Current data compression technologies in relational databases face challenges in efficiently selecting and applying optimal compression coding rules, leading to high computation costs and resource consumption, especially when dealing with large datasets and varying data types, which hinders rapid database development and storage efficiency.
Innovation Solution
A data compression method that utilizes a recommendation record to quickly identify and apply existing compression coding rules for similar historical objects, or initiates a regular process to estimate and select optimal rules if no match is found, thereby reducing the need for repeated computations and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If regular compression coding process is used to select optimal compression coding rule, then compression efficiency is improved, but computation cost increases
Solution Approach 1:
The system pre-computes and stores compression rate information for multiple compression coding rules on historical compressed objects in a recommendation record. When compressing a to-be-compressed object, the system first searches the recommendation record for a pre-computed rule that meets the compression rate condition, avoiding the need to perform regular compression coding process and thereby reducing computation cost while maintaining compression efficiency.
2Use of energy by moving object
If recommendation record is searched for existing compression rule, then computation cost is reduced, but compression efficiency may deteriorate
Solution Approach 1:
The system searches the recommendation record for a recommended compression coding rule that meets the compression rate condition. If such a rule is found, it is used for compression; if not found, the system falls back to the regular compression coding process to obtain estimated compression rates and select an optimal rule. This feedback mechanism ensures that computation cost is reduced when possible while maintaining compression efficiency through fallback to optimal rule selection.
Data Source
AI summary
A data compression method includes: obtaining a to-be-compressed object; searching a recommendation record for a recommended compression coding rule that meets a compression rate condition, the recommendation record being configured to record a compression coding rule of a historical compressed object and corresponding compression rate information, and the historical compressed object being of a same type as the to-be-compressed object; and if the recommended compression coding rule that meets the compression rate condition is found, compressing the to-be-compressed object by using the recommended compression coding rule; and if the recommended compression coding rule that meets the compression rate condition is not found, starting a regular compression coding process to obtain estimated compression rates of a plurality of compression coding rules for the to-be-compressed object, selecting a target compression coding rule based on at least the estimated compression rates, and compressing the to-be-compressed object by using the target compression coding rule.