SQL Performance Benchmark Integration with Graph Load Visualization

[peter7775]

SQL Performance Benchmark Integration with Graph Load Visualization

Problem Description

Database performance analysis typically involves examining query execution plans, table statistics, and index usage separately from the actual data structure. This fragmented approach makes it difficult to understand the relationship between data architecture and performance bottlenecks.

Traditional performance monitoring tools show metrics in isolation, but don't provide a visual representation of how database load flows through table relationships, join operations, and data dependencies. Understanding the performance impact of specific relationships and identifying bottlenecks in complex database schemas remains challenging.

Proposed Solution

Implement a comprehensive performance benchmark integration system that:

1. Integrates with SQL benchmark tools to collect performance metrics
2. Maps performance data to graph relationships showing load flows
3. Visualizes performance bottlenecks as directional graph edges with weight/color coding
4. Provides interactive performance analysis with drill-down capabilities
5. Generates performance optimization recommendations based on graph analysis

Key Features

1. Benchmark Integration Engine

performance_benchmarking:
  enabled: true
  
  # Benchmark tool integrations
  benchmark_tools:
    mysql_workbench:
      enabled: true
      connection_string: "mysql://benchmark_user:password@localhost:3306/testdb"
      benchmark_duration: "30m"
      
    sysbench:
      enabled: true
      test_types: ["oltp_read_write", "oltp_read_only", "select_random_points"]
      threads: [1, 4, 8, 16, 32]
      table_size: 100000
      
    custom_queries:
      enabled: true
      query_sets:
        - name: "frequent_operations"
          queries:
            - "SELECT * FROM users u JOIN orders o ON u.id = o.user_id WHERE u.active = 1"
            - "SELECT COUNT(*) FROM products p JOIN categories c ON p.category_id = c.id"
            - "UPDATE inventory SET quantity = quantity - 1 WHERE product_id = ?"
        
        - name: "reporting_queries"
          queries:
            - "SELECT u.name, COUNT(o.id) FROM users u LEFT JOIN orders o ON u.id = o.user_id GROUP BY u.id"
            - "SELECT SUM(oi.price * oi.quantity) FROM order_items oi JOIN orders o ON oi.order_id = o.id"

  # Performance data collection
  metrics_collection:
    query_execution_time: true
    rows_examined: true
    rows_returned: true
    index_usage: true
    temporary_tables: true
    filesorts: true
    join_operations: true
    lock_contention: true
    
  # Real-time monitoring
  live_monitoring:
    enabled: true
    sampling_interval: "5s"
    slow_query_threshold: "1s"
    capture_query_plans: true

# Graph performance visualization
performance_visualization:
  # Edge weight mapping (relationship load visualization)
  edge_weight_metrics:
    primary_metric: "query_frequency"  # queries_per_second, avg_execution_time, total_rows_examined
    secondary_metric: "avg_execution_time"
    
  # Visual encoding
  visual_encoding:
    edge_thickness:
      metric: "query_frequency"
      scale: "logarithmic"
      min_thickness: 1
      max_thickness: 10
      
    edge_color:
      metric: "avg_execution_time"
      color_scale: "green_yellow_red"  # green (fast) -> red (slow)
      thresholds:
        fast: "< 100ms"
        medium: "100ms - 1s"
        slow: "> 1s"
    
    edge_animation:
      show_data_flow: true
      animation_speed_based_on: "query_frequency"
      
  # Node performance indicators
  node_metrics:
    size_based_on: "total_queries_involved"
    color_based_on: "avg_response_time"
    show_hotspots: true
    
# Performance analysis features
analysis_features:
  bottleneck_detection:
    enabled: true
    algorithms:
      - "critical_path_analysis"
      - "hotspot_identification" 
      - "cascade_impact_analysis"
      
  optimization_suggestions:
    enabled: true
    suggestion_types:
      - "missing_indexes"
      - "inefficient_joins"
      - "denormalization_opportunities"
      - "query_rewrite_suggestions"
      - "partitioning_recommendations"
      
  performance_trends:
    enabled: true
    historical_data_retention: "30d"
    trend_analysis: true
    performance_regression_detection: true

2. Benchmark Tool Integrations

Sysbench Integration:

sysbench_integration:
  test_scenarios:
    - name: "oltp_mixed_workload"
      test_type: "oltp_read_write"
      tables: 4
      table_size: 100000
      threads: [1, 2, 4, 8, 16]
      time: 60
      warmup_time: 10
      
    - name: "read_heavy_workload"  
      test_type: "oltp_read_only"
      tables: 8
      table_size: 500000
      threads: [4, 8, 16, 32]
      time: 120
      
  metrics_mapping:
    relationships:
      - source_table: "sbtest1"
        target_table: "sbtest2"
        join_frequency: "queries_per_second"
        join_performance: "avg_latency"

MySQL Performance Schema Integration:

performance_schema_integration:
  enabled: true
  tables_to_monitor:
    - "events_statements_summary_by_digest"
    - "table_io_waits_summary_by_table"
    - "events_waits_summary_by_instance"
    - "file_summary_by_instance"
    
  query_analysis:
    capture_slow_queries: true
    analyze_join_patterns: true
    track_index_usage: true
    monitor_lock_contention: true

Custom Benchmark Scripts:

custom_benchmarks:
  load_test_scenarios:
    - name: "user_registration_flow"
      description: "Simulate user registration and initial activity"
      queries:
        - weight: 10
          query: "INSERT INTO users (email, password_hash, created_at) VALUES (?, ?, NOW())"
        - weight: 5  
          query: "INSERT INTO user_profiles (user_id, first_name, last_name) VALUES (?, ?, ?)"
        - weight: 3
          query: "SELECT * FROM users WHERE email = ?"
          
    - name: "order_processing_flow"
      description: "E-commerce order processing simulation"
      queries:
        - weight: 8
          query: "INSERT INTO orders (user_id, status, total_amount) VALUES (?, 'pending', ?)"
        - weight: 12
          query: "INSERT INTO order_items (order_id, product_id, quantity, price) VALUES (?, ?, ?, ?)"
        - weight: 6
          query: "UPDATE inventory SET quantity = quantity - ? WHERE product_id = ?"
        - weight: 4
          query: "SELECT SUM(price * quantity) FROM order_items WHERE order_id = ?"

Technical Implementation

1. Performance Data Collection Engine

New service: internal/application/services/performance/

type PerformanceBenchmarkService struct {
    mysqlRepo     ports.MySQLPort
    neo4jRepo     ports.Neo4jPort
    benchmarkTools map[string]BenchmarkTool
    metricsStore  *MetricsStore
    analyzer      *PerformanceAnalyzer
}

// Benchmark execution
func (p *PerformanceBenchmarkService) RunBenchmarkSuite(suite BenchmarkSuite) (*BenchmarkResults, error)
func (p *PerformanceBenchmarkService) ExecuteCustomQueries(queries []QueryDefinition) (*QueryResults, error)
func (p *PerformanceBenchmarkService) CollectRealTimeMetrics() (*LiveMetrics, error)

// Performance analysis
func (p *PerformanceBenchmarkService) AnalyzeQueryPatterns(results *BenchmarkResults) (*QueryPatternAnalysis, error)
func (p *PerformanceBenchmarkService) IdentifyBottlenecks() ([]PerformanceBottleneck, error)
func (p *PerformanceBenchmarkService) GenerateOptimizationSuggestions() ([]OptimizationSuggestion, error)

2. Graph Performance Mapping

type PerformanceGraphMapper struct {
    graphRepo    ports.Neo4jPort
    metricsStore *MetricsStore
}

type PerformanceEdge struct {
    SourceTable      string
    TargetTable      string
    QueryFrequency   float64    // queries per second
    AvgExecutionTime time.Duration
    TotalRowsExamined int64
    IndexUsageRatio  float64
    JoinType         string
    Performance      PerformanceRating
}

type PerformanceNode struct {
    TableName        string
    QueryLoad        float64    // total queries involving this table
    AvgResponseTime  time.Duration
    HotspotScore     float64    // calculated bottleneck score
    IndexEfficiency  float64
    LockContentionLevel float64
}

// Graph enhancement methods
func (m *PerformanceGraphMapper) EnhanceGraphWithPerformanceData(graph *GraphAggregate) (*PerformanceGraph, error)
func (m *PerformanceGraphMapper) CalculateEdgeWeights(queryResults *QueryResults) (map[string]float64, error)
func (m *PerformanceGraphMapper) IdentifyPerformanceHotspots(graph *PerformanceGraph) ([]HotspotNode, error)

3. Benchmark Tool Adapters

Sysbench Adapter:

type SysbenchAdapter struct {
    config SysbenchConfig
}

func (s *SysbenchAdapter) RunBenchmark(testType string, params SysbenchParams) (*SysbenchResults, error)
func (s *SysbenchAdapter) ParseResults(output string) (*BenchmarkMetrics, error)
func (s *SysbenchAdapter) MapResultsToGraphRelationships(results *SysbenchResults) ([]PerformanceEdge, error)

Performance Schema Adapter:

type PerformanceSchemaAdapter struct {
    db *sql.DB
}

func (p *PerformanceSchemaAdapter) CollectQueryStatistics() (*QueryStatistics, error)
func (p *PerformanceSchemaAdapter) GetTableIOStatistics() (*TableIOStats, error)
func (p *PerformanceSchemaAdapter) AnalyzeSlowQueries() ([]SlowQuery, error)

4. Performance Visualization Engine

Enhanced visualization: internal/interfaces/web/performance/

// Performance visualization endpoints
func HandlePerformanceDashboard(w http.ResponseWriter, r *http.Request)
func HandleBenchmarkExecution(w http.ResponseWriter, r *http.Request)
func HandlePerformanceGraph(w http.ResponseWriter, r *http.Request)
func HandleBottleneckAnalysis(w http.ResponseWriter, r *http.Request)
func HandleOptimizationSuggestions(w http.ResponseWriter, r *http.Request)

// API endpoints
GET  /api/performance/benchmark/run
GET  /api/performance/metrics/live
GET  /api/performance/graph/load-analysis
GET  /api/performance/bottlenecks
POST /api/performance/custom-benchmark

5. Real-time Performance Monitoring

type LivePerformanceMonitor struct {
    db           *sql.DB
    neo4jSession neo4j.Session
    updateInterval time.Duration
    metricsBuffer *CircularBuffer
}

func (m *LivePerformanceMonitor) StartMonitoring() error
func (m *LivePerformanceMonitor) UpdateGraphMetrics() error
func (m *LivePerformanceMonitor) DetectPerformanceAnomalies() ([]Anomaly, error)
func (m *LivePerformanceMonitor) TriggerAlerts(anomalies []Anomaly) error

Acceptance Criteria

Benchmark Integration

• System integrates with Sysbench for standardized benchmarking
• MySQL Performance Schema data is collected and analyzed
• Custom query benchmarks can be defined and executed
• Real-time performance metrics are captured during operation
• Benchmark results are stored with timestamps for trend analysis

Graph Performance Visualization

• Graph edges show load/performance as thickness and color
• Nodes are sized and colored based on performance metrics
• Interactive performance heatmap overlays on normal graph
• Animation shows data flow patterns based on query frequency
• Performance bottlenecks are visually highlighted

Performance Analysis

• Critical path analysis identifies performance bottlenecks
• Hotspot detection finds tables/relationships under stress
• Query pattern analysis reveals inefficient access patterns
• Performance regression detection compares historical data
• Optimization suggestions are generated based on analysis

Reporting and Alerting

• Performance reports with graphs and recommendations
• Real-time alerts for performance degradation
• Trend analysis showing performance changes over time
• Export capabilities for performance data and visualizations
• Integration with monitoring systems for alerting

Usability and Performance

• Benchmark execution doesn't significantly impact production
• Large result sets are handled efficiently
• UI remains responsive during long-running benchmarks
• Performance data collection has minimal overhead
• Clear documentation and examples for custom benchmarks

Usage Examples

Example 1: E-commerce Performance Analysis

performance_benchmarking:
  enabled: true
  
  custom_queries:
    frequent_operations:
      - query: |
          SELECT u.*, p.name as profile_name 
          FROM users u 
          JOIN user_profiles p ON u.id = p.user_id 
          WHERE u.active = 1
        weight: 20
        description: "User profile lookup"
        
      - query: |
          SELECT o.*, oi.product_id, oi.quantity 
          FROM orders o 
          JOIN order_items oi ON o.id = oi.order_id 
          WHERE o.user_id = ? AND o.status = 'active'
        weight: 15
        description: "Active orders with items"
        
      - query: |
          UPDATE inventory 
          SET quantity = quantity - ?, last_updated = NOW() 
          WHERE product_id = ? AND quantity >= ?
        weight: 10
        description: "Inventory update"

performance_visualization:
  edge_weight_metrics:
    primary_metric: "query_frequency"
    
  visual_encoding:
    edge_thickness:
      metric: "query_frequency" 
      scale: "linear"
    edge_color:
      metric: "avg_execution_time"
      thresholds:
        fast: "< 50ms"    # green
        medium: "50-200ms" # yellow  
        slow: "> 200ms"   # red

Example 2: Analytical Workload Performance

analytical_benchmarks:
  reporting_queries:
    - query: |
        SELECT 
          DATE(o.created_at) as order_date,
          COUNT(*) as order_count,
          SUM(o.total_amount) as revenue
        FROM orders o
        JOIN users u ON o.user_id = u.id
        WHERE o.created_at >= DATE_SUB(NOW(), INTERVAL 30 DAY)
        GROUP BY DATE(o.created_at)
        ORDER BY order_date
      weight: 5
      description: "Daily revenue report"
      
    - query: |
        SELECT 
          p.category_id,
          c.name as category_name,
          COUNT(oi.id) as items_sold,
          SUM(oi.price * oi.quantity) as revenue
        FROM products p
        JOIN categories c ON p.category_id = c.id
        JOIN order_items oi ON p.id = oi.product_id
        JOIN orders o ON oi.order_id = o.id
        WHERE o.status = 'completed'
        GROUP BY p.category_id, c.name
        ORDER BY revenue DESC
      weight: 3
      description: "Category performance analysis"

performance_analysis:
  focus_areas:
    - "joins_performance"
    - "aggregation_efficiency" 
    - "index_utilization"

Example 3: High-Frequency Trading Simulation

high_frequency_benchmark:
  test_scenarios:
    - name: "order_book_updates"
      queries:
        - query: "INSERT INTO orders (symbol, price, quantity, type) VALUES (?, ?, ?, 'buy')"
          weight: 40
          target_qps: 1000
          
        - query: "UPDATE orders SET quantity = quantity - ? WHERE id = ? AND quantity >= ?"
          weight: 30
          target_qps: 800
          
        - query: "SELECT * FROM orders WHERE symbol = ? AND type = 'sell' ORDER BY price ASC LIMIT 10"
          weight: 25
          target_qps: 1200
          
        - query: "DELETE FROM orders WHERE quantity = 0"
          weight: 5
          target_qps: 100

  performance_targets:
    max_latency: "5ms"
    avg_latency: "1ms" 
    throughput: "2000 qps"
    
  stress_testing:
    duration: "10m"
    ramp_up_time: "2m"
    concurrent_connections: 50

Visualization Features

Performance Dashboard

• Real-time Metrics: Live QPS, latency, and throughput displays
• Performance Heatmap: Color-coded table/relationship performance
• Bottleneck Timeline: Historical view of performance issues
• Load Distribution: Where database load is concentrated

Interactive Performance Graph

• Edge Animations: Data flow visualization with speed based on QPS
• Thickness Encoding: Relationship load shown as edge thickness
• Color Temperature: Performance "heat" from green (fast) to red (slow)
• Hotspot Highlighting: Automatic identification of stressed nodes
• Performance Layers: Toggle between different metric visualizations

Analysis Views

• Critical Path View: Shows the slowest execution paths
• Bottleneck Cascade: How performance issues propagate
• Index Efficiency Map: Visual representation of index usage
• Query Pattern Analysis: Common access patterns and their performance

Performance Reports

• Executive Summary: High-level performance overview
• Technical Deep-dive: Detailed analysis with query plans
• Optimization Roadmap: Prioritized list of performance improvements
• Before/After Comparisons: Performance impact of changes

Files to Modify/Create

internal/
├── application/
│   ├── ports/
│   │   ├── benchmark_tool_port.go              # New: benchmark tool interface
│   │   └── performance_analyzer_port.go        # New: performance analysis interface
│   └── services/
│       ├── performance/                        # New: performance services
│       │   ├── benchmark_service.go
│       │   ├── performance_analyzer.go
│       │   ├── metrics_collector.go
│       │   ├── sysbench_adapter.go
│       │   ├── performance_schema_adapter.go
│       │   └── live_monitor.go
│       └── graph/
│           └── performance_graph_service.go    # Extension: performance-enhanced graphs
├── domain/
│   ├── models/
│   │   ├── benchmark_result.go                 # New: benchmark data models
│   │   ├── performance_metrics.go              # New: performance metrics models
│   │   └── performance_graph.go                # New: performance graph models
│   └── valueobjects/
│       └── performance/                        # New: performance value objects
├── interfaces/
│   ├── web/
│   │   ├── performance/                        # New: performance web interface
│   │   │   ├── dashboard.html
│   │   │   ├── benchmark_runner.html
│   │   │   ├── performance_graph.html
│   │   │   └── analysis_reports.html
│   │   └── static/
│   │       ├── css/performance.css
│   │       └── js/performance_visualization.js
│   └── api/
│       └── performance_handlers.go             # New: performance API endpoints
└── config/
    ├── performance/                            # New: performance configurations
    │   ├── benchmark_suites.yml
    │   ├── sysbench_configs.yml
    │   └── custom_benchmarks.yml
    └── examples/
        └── performance_config.yml

tools/
├── benchmarks/                                 # New: benchmark scripts and tools
│   ├── sysbench_runner.sh
│   ├── custom_benchmark_generator.py
│   └── performance_test_data_generator.sql
└── monitoring/
    ├── performance_alerts.yml
    └── grafana_dashboards/
        └── mysql_graph_performance.json

docs/
├── performance/                                # New: performance documentation
│   ├── README.md
│   ├── benchmark_setup.md
│   ├── performance_analysis_guide.md
│   └── optimization_cookbook.md
└── screenshots/
    ├── performance_dashboard.png
    └── load_visualization.png

Effort Estimate

Time Estimate: 10-14 days

Breakdown:

• Benchmark tool integrations (Sysbench, Performance Schema): 3 days
• Performance data collection and analysis engine: 3 days
• Graph performance mapping and visualization: 3 days
• Real-time monitoring and alerting: 2 days
• Dashboard and reporting system: 2 days
• Testing, documentation, and optimization: 1-2 days

Priority

High - Performance optimization is crucial for database-driven applications. This feature provides unique value by combining structural analysis with performance data, offering insights that traditional monitoring tools cannot provide.

Business Value

For Database Administrators

• Visual Performance Bottleneck Identification: Quickly spot performance issues in complex schemas
• Optimization Priority: Understand which relationships need attention first
• Impact Analysis: See how performance changes affect the entire system

for Performance Engineers

• Data-driven Optimization: Base optimization decisions on actual load patterns
• Regression Testing: Visual comparison of performance before/after changes
• Capacity Planning: Understand load distribution for scaling decisions

For Development Teams

• Query Optimization: Identify inefficient data access patterns
• Architecture Decisions: Understand performance implications of schema changes
• Monitoring Integration: Continuous performance awareness during development

Future Enhancements

Advanced Analytics

• Machine Learning: Predict performance bottlenecks before they occur
• Automated Optimization: Suggest and apply index/query optimizations
• Performance Forecasting: Project performance under different load scenarios

Extended Tool Support

• PostgreSQL Support: Extend to other database systems
• Cloud Integration: AWS RDS, Google Cloud SQL performance metrics
• Application Monitoring: Integrate with APM tools (New Relic, DataDog)

Enterprise Features

• Multi-environment Comparison: Compare performance across dev/staging/prod
• Performance SLAs: Set and monitor performance targets
• Cost Analysis: Correlate performance with infrastructure costs

This feature would establish mysql-graph-visualizer as a premier database performance analysis platform, combining the power of graph visualization with deep performance insights.

[peter7775]

Starting Implementation

I'm taking on this issue and will implement it in phases:

📋 Implementation Plan

Phase 1: Foundation (Week 1)

• Create feature branch: feature/issue-12-performance-benchmark
• Implement core performance service interfaces
• Create basic Sysbench adapter
• Set up performance metrics data models

Phase 2: Core Features (Week 2)

• Performance Schema integration
• Graph performance mapping
• Basic performance visualization

Phase 3: Advanced Features (Week 3-4)

• Real-time monitoring
• Bottleneck detection
• Performance analytics dashboard

Approach

Starting with MVP implementation focusing on:

1. Sysbench integration as the primary benchmark tool
2. Basic graph performance overlays (edge thickness/color)
3. Performance data collection and storage

This will establish the foundation for the full-featured performance analysis platform described in the issue.

---

Status: In Progress
Branch: feature/issue-12-performance-benchmark
ETA: 3-4 weeks for full implementation

[peter7775]

Phase 1 Complete: Foundation Implementation

Implemented Components

Core Interfaces & Ports:

• BenchmarkToolPort - Generic interface for all benchmark tools
• PerformanceAnalyzerPort - Advanced analysis capabilities interface

Service Layer:

• BenchmarkService - Core orchestration service with async execution
• SysbenchAdapter - Complete Sysbench integration

Key Features Delivered:

• Async Benchmark Execution - Non-blocking with progress tracking
• Performance Metrics Collection - QPS, latency, percentiles, errors
• Query Performance Analysis - Individual query pattern analysis
• Graph Data Mapping - Performance data → graph nodes/edges
• Configurable Scenarios - Predefined test configurations
• Thread-Safe Operations - Concurrent execution management
• Resource Management - Automatic cleanup and lifecycle management

🧪 Sysbench Integration

Supported Test Types:

• oltp_read_write, oltp_read_only, oltp_write_only
• oltp_point_select, oltp_insert, oltp_update_*
• select_random_points, bulk_insert, etc.

Database Support:

• MySQL with InnoDB engine optimization
• PostgreSQL with schema configuration
• Database URL parsing and connection management

Performance Analysis:

• Real-time metrics parsing from sysbench output
• Synthetic query performance data generation
• Performance impact classification (LOW/MEDIUM/HIGH)
• Index efficiency calculations

Performance Graph Integration

Graph Enhancement:

• PerformanceEnhancedGraph - Graphs with performance overlays
• PerformanceNode - Tables with hotspot scoring
• PerformanceEdge - Relationships with load factors
• Performance ranking system (FAST/MEDIUM/SLOW)

Architecture Highlights

Clean Architecture:

• Port-Adapter pattern for external tool integration
• Service orchestration with dependency injection
• Domain-driven design principles maintained

Extensibility:

• Easy addition of new benchmark tools
• Configurable test scenarios
• Plugin-ready architecture

Production Ready:

• Comprehensive error handling
• Structured logging with context
• Configuration validation
• Resource limits and safety guards

---

Next Steps: Phase 2

Upcoming in Phase 2:

1. Performance Schema Integration - MySQL P_S data collection
2. Graph Visualization Enhancement - Interactive performance overlays
3. Real-time Monitoring - Live performance tracking
4. Bottleneck Detection - Automated performance issue identification

Commit: 1b8b9ec - feat: Implement Phase 1 of Performance Benchmark Integration

The foundation is solid and ready for advanced features! 💪

[peter7775]

🚀 Phase 2A Complete: Advanced Analysis Components

✅ Implemented Components

MySQL Performance Schema Adapter:

• 📊 Real-time Data Collection - Live monitoring of MySQL Performance Schema
• 🔍 Comprehensive Metrics - Statement stats, table I/O, index usage, connections
• ⚙️ Configurable Collection - Selective data gathering with filtering options
• 🎯 Smart Query Analysis - SQL parsing and table relationship extraction
• 📈 Performance Schema Validation - Automatic availability detection
• 🛡️ Production Safety - Query caching, prepared statements, resource limits

Advanced Performance Analyzer:

• 🔴 Bottleneck Detection - Sophisticated algorithms for performance issue identification
• 📊 Performance Scoring - 0-100 scoring system with component breakdown
• 🛤️ Critical Path Analysis - Graph-based slowest execution path detection
• 🔥 Hotspot Detection - Weighted scoring for resource-intensive operations
• 🔍 Query Pattern Analysis - Pattern grouping and anti-pattern detection
• 📉 Regression Detection - Automatic performance degradation identification
• 💡 Optimization Suggestions - AI-driven recommendations for improvements
• 📈 Trend Analysis - Time-series analysis with anomaly detection

🧠 Advanced Algorithms Implemented

Bottleneck Classification:

• Multi-factor severity analysis (Critical/High/Medium/Low)
• Confidence scoring for recommendation accuracy
• Business impact assessment
• Resource usage quantification

Performance Scoring System:

• Latency Factor (30% weight) - Response time analysis
• Throughput Factor (25% weight) - QPS and TPS evaluation
• Error Rate Factor (25% weight) - Reliability assessment
• Resource Efficiency (20% weight) - Resource utilization analysis

Regression Detection:

• Latency degradation analysis (>10% threshold)
• Throughput decline detection (>5% threshold)
• Error rate spike identification (>20% threshold)
• Automated cause inference

Query Analysis Features:

• SQL pattern extraction and table identification
• Relationship type detection (JOIN, FILTER, SINGLE_TABLE)
• Index usage analysis and efficiency scoring
• Performance impact classification (LOW/MEDIUM/HIGH)

🏗️ Architecture Highlights

Configurable Analysis:

• Customizable thresholds for all detection algorithms
• Adjustable weights for scoring components
• Flexible filtering and data collection parameters

Production-Ready:

• Thread-safe operations with proper locking
• Comprehensive error handling and logging
• Resource management with automatic cleanup
• Query caching for performance optimization

Extensible Design:

• Plugin architecture for additional analyzers
• Modular algorithm implementation
• Easy integration with existing graph services

📊 Key Capabilities Unlocked

1. Real-time Performance Monitoring - Live MySQL P_S data collection
2. Intelligent Bottleneck Detection - Automated performance issue identification
3. Performance Benchmarking - Comprehensive scoring and comparison
4. Trend Analysis - Historical performance pattern recognition
5. Optimization Guidance - Data-driven improvement recommendations

---

🔜 Next: Phase 2B

Coming up in Phase 2B:

• 🗺️ Graph Performance Mapper - Visual performance overlay system
• ⚡ Real-time Performance Monitor - WebSocket-based live updates
• 🎛️ Performance Dashboard APIs - REST endpoints for data access
• 🎨 Graph Visualization Enhancements - Interactive performance layers

Commit: 477e2f1 - Advanced analysis foundation is now solid!

The performance analysis capabilities are now enterprise-grade and ready for sophisticated database optimization workflows! 💪