Database Storage Throughput Analyzer Granular Monitoring

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Solution Overview

Problem

Current database monitoring tools fail to accurately identify the cause of overloaded or throttled databases due to lack of granular storage throughput data, making it difficult to determine which part of the database is causing the issue and when service level objectives (SLOs) are exceeded.

Innovation Solution

The Database Storage Throughput Analyzer (DSTA) tool provides detailed analysis of storage throughput at various granularities, generating queries to output graphical data that helps administrators pinpoint issues by segmenting throughput data into smaller intervals and organizing it by database components, and sends alerts when SLOs are exceeded.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If storage throughput data is monitored at coarse intervals, then monitoring simplicity is maintained, but troubleshooting accuracy deteriorates due to inability to identify specific problem causes

Engineering Contradiction:

Improvestorage throughput measurement precisionVSAvoidmonitoring system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments storage throughput monitoring into multiple granularities (coarse and fine intervals) and allows dynamic switching between them. The system divides monitoring resources allocatively - using coarse intervals for normal operation and fine intervals only when problems are detected, thus achieving high measurement precision without proportionally increasing overall system complexity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The monitoring system dynamically adjusts the monitoring interval based on system state. During normal operation, coarse intervals are used to maintain simplicity. When SLO violations or anomalies are detected, the system automatically switches to fine intervals for precise troubleshooting, making the complexity adaptive rather than static.

Inventive Principle:

Principle #15Dynamics

2Measurement precision

If fine-grained storage throughput data is collected continuously, then troubleshooting accuracy improves, but data processing overhead and system resource consumption increase

Engineering Contradiction:

Improvestorage throughput measurement precisionVSAvoiddata processing energy consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent segments data collection into two tiers: coarse-grained continuous monitoring for all systems, and fine-grained monitoring only for problematic segments. This segmentation ensures high measurement precision is achieved only where needed, minimizing overall data processing energy consumption while maintaining the capability for detailed analysis when required.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Instead of applying fine-grained monitoring universally (excessive action), the system applies it partially only to specific database segments or time periods where problems are detected. This partial action achieves the necessary measurement precision for troubleshooting without the prohibitive energy cost of continuous fine-grained monitoring across the entire system.

Inventive Principle:

Principle #16Partial or excessive action

3Ease of operation

If storage monitoring data is aggregated at higher levels, then system simplicity is maintained, but ability to identify specific database components causing issues deteriorates

Engineering Contradiction:

Improvemonitoring operation easeVSAvoiddatabase component identification information

Core Design Contradiction:

Ease of operationVSLoss of information

Solution Approach 1:

The patent implements a segmented monitoring architecture where data is collected at multiple levels of granularity. High-level aggregated views provide ease of operation for overall system monitoring, while underlying fine-grained segment data preserves component identification information. Users can drill down from aggregated views to specific database segments as needed.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system adds a temporal and hierarchical dimension to monitoring data organization. Data is structured with both aggregated summaries (higher dimension) and detailed segment records (lower dimension). This multi-dimensional organization allows easy access to high-level trends while preserving the ability to access specific component information when required, without losing critical identification data.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS10459895B2Database storage monitoring equipment

Publication Date: 2019.10.29 BANK OF AMERICA CORP

AI summary

Equipment, tools, systems, storage media, and methods that allow a user to monitor database storage at various granularities and set thresholds for generating alerts are described. In one aspect, a system includes a client device configured to execute an application, a database configured to support the client device, a storage array configured to provide information to the database, and a computing device. The computing device may execute a Database Storage Throughput Analyzer (DSTA) tool, specify an interval, specify a particular database cluster of the database, specify a service level objective for controlling communications between the database and the storage array, generate a query, receive storage throughput measurements for the database, and apply the query to the storage throughput measurements to aid users in identifying database storage issues.