Adaptive Database Compression for Storage and Processing Balance
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Solution Overview
Problem
Existing compression techniques in distributed database systems face a trade-off between storage space savings and computational overhead, leading to inefficient resource utilization and increased processing time, especially during data transfer.
Innovation Solution
An adaptive compression/decompression method that analyzes data characteristics, dynamically adjusts compression processes and levels based on system resources, storage savings, and performance requirements, and continuously monitors performance to optimize resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If compression techniques are applied to reduce storage space, then storage space is saved, but CPU utilization and processing time increase
Solution Approach 1:
The patent implements dynamic compression level adjustment based on real-time system conditions. The compression level is not fixed but adapts dynamically according to available CPU resources, data characteristics, and workload conditions. This resolves the contradiction by allowing the system to achieve high compression ratios when resources are abundant while maintaining fast processing when resources are constrained.
Solution Approach 2:
The system changes compression parameters (compression level, algorithm selection) based on data characteristics and system state. By analyzing data compressibility and adjusting parameters accordingly, the system optimizes the balance between storage savings and processing overhead, avoiding unnecessary compression for already-compressed or random data while applying aggressive compression where beneficial.
2Quantity of substance
If compression operations are performed during data transfer, then data transmission volume is reduced, but additional processing overhead is introduced
Solution Approach 1:
The system performs compression analysis and preparation before actual data transfer. It pre-evaluates data characteristics and determines optimal compression strategies in advance, so that during transfer, the compression operations are already optimized and executed efficiently, minimizing additional processing overhead during the critical transfer window.
Solution Approach 2:
The compression system monitors its own performance and automatically adjusts parameters to optimize the balance between reduction ratio and processing overhead. It self-regulates the compression intensity based on observed system load and transfer performance, eliminating the need for external intervention and reducing overall system complexity.
3Quantity of substance
If higher compression levels are used, then storage savings increase, but system resources are overutilized
Solution Approach 1:
The patent implements continuous monitoring of system resource utilization during compression operations. The system measures CPU usage, memory consumption, and other relevant metrics, then uses this feedback to dynamically adjust compression levels. When resources approach threshold levels, the system automatically reduces compression intensity, preventing resource overutilization while maintaining acceptable storage savings.
Solution Approach 2:
Compression levels transition from static to dynamic, adapting in real-time to system resource conditions. The system can scale compression intensity up or down based on current workload and available resources, ensuring that storage optimization goals are met without consistently overloading the system.
Data Source
AI summary
In several aspects, a computing device analyzes data to determine its characteristics. The computing device selects at least one compression process based on the characteristics. A compression switch and a compression level are dynamically adjusted based on multiple factors including available system resources, desired storage savings and performance requirement. The compression level dynamically varies depending on the data being processed and a workload on a system. Performance of compression and decompression operations are continuously monitored for dynamically adjusting compression parameters to optimize performance.