Database Performance Statistical Comparison via Time Normalization
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Solution Overview
Problem
Current methods for diagnosing and optimizing database performance are hindered by the difficulty in accurately isolating key statistical differences between periods of varying lengths and loads, requiring manual comparison of lengthy reports generated from snapshots.
Innovation Solution
A method for automatically generating a report that normalizes performance statistics differences between two periods by using database time, allowing for comparison of disparate time periods and loads, and generating a report that highlights key statistical changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual comparison of performance statistics reports is used, then detailed analysis of statistical differences is possible, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs self-comparison of performance statistics by automatically normalizing and comparing metrics between different time periods. The database system itself generates the comparison reports without requiring manual intervention, allowing the system to diagnose its own performance changes automatically.
Solution Approach 2:
The system changes the parameter of time normalization by adjusting performance statistics to account for varying database time periods. This normalization process transforms raw statistics into comparable metrics that account for different operational durations, enabling accurate comparisons between periods of unequal length.
2Adaptability or versatility
If performance statistics are collected over varying time periods with different loads, then comprehensive performance data is captured, but accurate comparison between periods becomes difficult
Solution Approach 1:
The system applies parameter changes by normalizing performance statistics based on database time rather than fixed time intervals. This adjustment accounts for varying operational loads and durations, transforming statistics from different periods into comparable formats that maintain measurement precision despite flexible collection periods.
Solution Approach 2:
The system introduces an intermediary normalization layer that mediates between raw performance statistics and final comparisons. This intermediate processing step adjusts and standardizes the statistics, enabling accurate comparisons between periods with different loads and durations.
3Loss of information
If comprehensive performance statistics are gathered for detailed analysis, then complete performance picture is obtained, but report length and complexity increase
Solution Approach 1:
The system extracts and highlights only the key statistical differences and significant performance changes from the comprehensive data. By identifying and presenting only the most relevant metrics that show meaningful variations between periods, the system reduces report complexity while maintaining essential information.
Solution Approach 2:
The system segments the comprehensive performance statistics into meaningful categories and groups related metrics together. This segmentation organizes the data into manageable sections that are easier to analyze, reducing the perceived complexity while preserving complete performance information through structured presentation.
Data Source
AI summary
A technique for automatically generating a report comprising normalized differences in performance statistics between two separate periods. In one embodiment of the invention, database performance statistics are collected on a periodic basis over various time periods. In order to accurately compare database system performance between two discrete periods of time, the difference in the performance statistics of each period are normalized prior to comparing the two periods with each other. By normalizing the statistical differences in each period prior to comparing the differences, periods of different lengths of time as well as different levels of database system load may be compared. In one embodiment, a report is automatically generated which lists the performance statistics being evaluated, the difference in the statistic between each period, the value of each statistical difference as normalized by database time, and the difference between the normalized values.