Query Response Time Prediction Using Cache Miss Factors
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Solution Overview
Problem
Existing methods for predicting query elapsed response time during application development stages are inadequate, particularly in handling varying database sizes and data volume growth, and often rely on past query history or Machine Learning approaches that are not suitable for this stage and may increase error possibilities.
Innovation Solution
A system and method that classify queries based on database access patterns, determine cache miss factors using database statistics, and calculate time-related values to predict query elapsed response time, specifically designed for the application development stage and capable of handling varying database sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods use past query history or Machine Learning approaches to predict query response time, then prediction capability is provided, but accuracy deteriorates during application development stage and error possibility increases
Solution Approach 1:
The patent changes the parameters used for prediction from past query history and ML models to database statistics including data distribution, access patterns, and cache miss factors. This parameter transformation enables accurate predictions during application development stage by using fundamental database characteristics rather than historical data that may not represent future behavior.
2Quantity of substance
If database size increases to handle growing data volume, then data storage capability is improved, but query response time deteriorates due to increased data access complexity
Solution Approach 1:
The patent introduces cache miss factors as an intermediary parameter that mediates between database size and query response time. By calculating cache miss factors based on database statistics and using them in the prediction formula, the system accounts for cache behavior in larger databases without requiring actual changes to database size or cache configuration.
3Loss of time
If cache configuration is modified to improve query performance, then response time is reduced, but system complexity increases due to DB server internal changes
Solution Approach 1:
The patent creates a virtual copy of cache behavior through mathematical modeling. Instead of actually modifying DB server cache configurations, the system calculates cache miss factors that replicate cache behavior effects in the prediction formula. This allows performance prediction without implementing complex cache configuration changes in the actual database system.
Data Source
AI summary
System(s) and method(s) for predicting effect of database cache on query elapsed response time during an application development stage are disclosed. Query executed on a database is classified and a query type is obtained. Database statistics are collected to further determine one or more cache miss factors with respect to the query type. One or more time components are calculated due to the one or more cache miss factors with respect to the query type. The one or more time components are used to predict the query elapsed response time for varying size of the database.