Database Cache Fragmentation Optimization via Dynamic Sizing
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Solution Overview
Problem
Current database systems face challenges in optimizing cache memory sizes and fragmentation, leading to inefficient data throughput due to fixed cache sizes and manual adjustment requirements, which hinder automatic and continuous optimization.
Innovation Solution
A method for continuous automatic background optimization of cache memory, involving measurement of processing times and cache hit ratios, computation of approximation functions to predict optimal cache fragmentation, and dynamic adjustment of cache sizes and fragmentation to minimize processing times and maximize throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cache memory size and fragmentation are manually adjusted, then initial cache performance can be optimized, but the cache configuration becomes fixed and cannot adapt to changing database usage patterns
Solution Approach 1:
The patent implements dynamic cache optimization by continuously monitoring database access patterns and automatically adjusting cache fragment sizes in real-time. The system transitions from static manual configuration to dynamic adaptive configuration, where cache parameters are continuously optimized based on actual usage patterns without requiring manual intervention.
Solution Approach 2:
The system establishes a feedback loop that continuously monitors database query performance and cache hit ratios, uses this information to compute optimal cache configurations, and automatically applies adjustments. This closed-loop feedback mechanism enables the cache to adapt to changing workload patterns automatically.
2Quantity of substance
If cache fragments are made larger to reduce fragmentation overhead, then caching capacity increases, but processing time for managing and accessing fragmented data may increase
Solution Approach 1:
The system dynamically adjusts cache fragment sizes based on actual database access patterns rather than using fixed large fragments. By continuously monitoring which data objects are frequently accessed together, the system optimizes fragment sizes in real-time, ensuring that cache capacity is effectively utilized without excessive fragmentation overhead.
Solution Approach 2:
The patent changes the parameters of cache fragmentation dynamically by computing optimal fragment sizes based on measured database access patterns. The system adjusts fragmentation parameters (number of fragments, size distribution) to find the optimal balance between cache capacity utilization and access efficiency.
3Productivity
If automatic optimization of cache fragmentation is implemented, then continuous performance improvement is achieved, but system complexity increases due to additional monitoring and computation requirements
Solution Approach 1:
The system implements self-service automatic optimization where the database system monitors its own cache performance and automatically adjusts cache fragment configurations without external intervention. The optimization process is integrated into the database management system itself, allowing it to self-optimize based on its own operational characteristics.
Solution Approach 2:
The patent integrates multiple functions into a unified cache optimization system that simultaneously performs monitoring, analysis, computation of optimal configurations, and automatic adjustment. This multi-functional approach consolidates complexity into a single integrated mechanism rather than requiring separate systems for each function.
4Productivity
If cache fragments are dynamically adjusted based on access patterns, then cache efficiency improves, but the system requires continuous monitoring and computation which consumes additional resources
Solution Approach 1:
The system applies partial optimization by focusing computational resources on the most significant cache fragmentation issues rather than continuously optimizing all parameters at maximum detail. The optimization process adjusts fragment sizes incrementally based on measured performance impacts, applying changes only where they provide measurable benefit.
Data Source
AI summary
A system and method are disclosed to improve processing time of a database system by continuous automatic background optimization of a cache memory that is fragmented into a plurality of cache fragments. The system and method include collecting indicators about efficiency of individual cache fragments by at least one of measuring a cache hit ratio of each cache fragment, measuring a processing time that a CPU of the database system needs to prepare data in the individual cache fragments, and measuring execution time the CPU needs to process the data in accordance with a SQL query. The system and method include calculating and revising approximation curves for measured values of each cache fragment to find a combination of cache fragment sizes with a highest system throughput. The system and method include changing the sizes of the cache fragments to receive highest system throughput.