Dynamic Database Configuration Using Application Performance Metrics

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Solution Overview

Problem

Conventional database management techniques fail to adapt to rapid changes in database usage, leading to issues like over-provisioning, under-provisioning, and inefficient query execution plans, as they rely solely on historical statistics and do not consider real-time application-level metrics.

Innovation Solution

An adaptive database system that integrates application-level metrics with database performance data to dynamically adjust configurations, such as query execution plans and resource allocation, using machine learning to predict and mitigate performance issues in real-time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If conventional database management techniques rely on historical statistics and query execution plans, then database configuration stability is maintained, but the system cannot adapt to rapid changes in database usage, leading to over-provisioning or under-provisioning

Engineering Contradiction:

Improveadaptability to changing database usageVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements dynamic database configuration by continuously monitoring real-time database metrics (CPU usage, memory consumption, I/O operations, query execution time) and automatically adjusting configuration parameters based on observed workload patterns. This transforms the static, historical-statistics-based configuration approach into a dynamic system that adapts to changing usage conditions, resolving the contradiction between adaptability and system complexity.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system establishes a feedback loop where database performance metrics are continuously collected, analyzed, and used to adjust configuration parameters. The monitoring component gathers real-time data, the analysis component identifies optimization opportunities, and the configuration adjustment component implements changes, creating a closed-loop control system that enables adaptive optimization without manual intervention.

Inventive Principle:

Principle #23Feedback

2Productivity

If database configuration is statically optimized based on historical data, then implementation simplicity is maintained, but performance efficiency deteriorates under varying workload conditions

Engineering Contradiction:

Improvedatabase performance efficiencyVSAvoidconfiguration management simplicity

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The patent implements self-service optimization where the database system automatically monitors its own performance metrics, analyzes configuration opportunities, and adjusts its own configuration parameters without external intervention. The system autonomously identifies bottlenecks (such as suboptimal query execution plans or insufficient buffer cache size) and applies corrections, eliminating the need for manual performance tuning while maintaining high performance efficiency under varying workloads.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary analysis of database workload patterns and configuration needs before performance degradation occurs. By continuously monitoring metrics and predicting future resource requirements, the system proactively adjusts configuration parameters in advance, preventing performance issues rather than reacting to them after they manifest.

Inventive Principle:

Principle #10Preliminary action

3Speed

If real-time monitoring and dynamic adjustment mechanisms are implemented, then database responsiveness to workload changes improves, but system complexity and resource consumption increase

Engineering Contradiction:

Improveresponse time to workload changesVSAvoidcomputational resource consumption

Core Design Contradiction:

SpeedVSUse of energy by moving object

Solution Approach 1:

The patent implements selective monitoring and adjustment by focusing computational resources on the most critical database metrics and configuration parameters. Rather than continuously analyzing all possible database operations, the system identifies key performance indicators (such as query execution time, buffer cache hit ratio, and CPU utilization) and concentrates monitoring and optimization efforts on these high-impact areas, achieving effective real-time responsiveness with reduced computational overhead.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12561293B2Dynamic database configuration based on application performance metrics

Publication Date: 2026.02.24 SALESFORCE INC

AI summary

One or more observed database metric values characterizing observed database performance information about a database system implemented in a computing services environment may be determined. One or more observed application metric values characterizing observed performance information about a network-accessible application implemented on an application server in the computing services environment may be determined. The application server may store information in the database system generated while providing computing services via the computing services environment. An updated database system configuration setting for the database system may be determined by applying a machine learning prediction model to a dataset including the one or more observed database metric values and the one or more observed application metric values. An instruction to update the database system may be transmitted based on the updated database system configuration setting.