📸 Performance Graph Snapshot System

[peter7775]

Issue: Performance Graph Snapshot System

📸 Feature Request: Live Performance Graph Snapshots

Problem Statement

The real-time performance monitoring system generates a constantly changing graph with live metrics as Neo4j relationships (QUERIES_PER_SEC, AVG_LATENCY_MS, etc.). Currently, there's no way to capture and analyze specific moments in time when performance issues occur, making it difficult to:

• Debug bottlenecks that occurred at specific timestamps
• Compare performance states before/during/after incidents
• Create historical reports of system performance
• Share specific graph states with team members
• Analyze performance patterns over time

Proposed Solution

Implement a Performance Graph Snapshot System that allows:

1. Manual Snapshots: On-demand capture of current graph state
2. Automated Snapshots: Time-based or trigger-based captures
3. Incident Snapshots: Auto-capture when bottlenecks are detected
4. Comparative Analysis: Side-by-side snapshot comparison
5. Export/Import: Save and load snapshots for analysis

Technical Requirements

1. Snapshot Data Structure

{
  "snapshot_id": "snap_20250106_142305_bottleneck",
  "timestamp": "2025-01-06T14:23:05Z",
  "trigger_type": "manual|scheduled|threshold|incident",
  "trigger_reason": "High latency detected on users->products",
  "graph_state": {
    "nodes": [
      {
        "id": "users:Table",
        "type": "Table", 
        "properties": {
          "name": "users",
          "hotspot_score": 89.5,
          "total_queries": 15420
        }
      }
    ],
    "relationships": [
      {
        "id": "users_products_qps",
        "source": "users:Table",
        "target": "products:Table", 
        "type": "QUERIES_PER_SEC",
        "properties": {
          "value": 89.2,
          "timestamp": "2025-01-06T14:23:05Z",
          "trend": "increasing"
        }
      }
    ]
  },
  "performance_summary": {
    "total_qps": 1247.5,
    "avg_latency": 156.2,
    "bottlenecks_count": 3,
    "critical_paths": ["users->products->inventory"]
  },
  "metadata": {
    "database_type": "postgresql",
    "database_name": "chinook",
    "capture_duration_ms": 234,
    "graph_complexity": "high"
  }
}

2. Snapshot Triggers

Manual Triggers:

• Dashboard "📸 Capture Snapshot" button
• API endpoint: POST /api/performance/snapshots
• Keyboard shortcut: Ctrl+Shift+S

Automated Triggers:

snapshot_triggers:
  scheduled:
    - interval: "5m"
      condition: "business_hours"
    - interval: "30m" 
      condition: "off_hours"
      
  threshold_based:
    - metric: "avg_latency"
      threshold: "> 1000ms"
      duration: "30s"
    - metric: "queries_per_sec"
      threshold: "> 500"
      
  incident_based:
    - bottleneck_detected: true
      severity: "critical|high"
    - deadlock_count: "> 5"
    - error_rate: "> 5%"

Smart Triggers:

• Performance degradation detection
• Unusual traffic patterns
• Before/after major deployments
• Database maintenance windows

3. Snapshot Management

Storage Options:

• File System: JSON files with timestamp naming
• Neo4j Database: Separate snapshot graph database
• Time-series DB: InfluxDB/TimescaleDB for efficient querying
• Cloud Storage: S3/GCS for archival

Retention Policies:

retention_policies:
  manual_snapshots: "90d"
  scheduled_snapshots: "30d" 
  incident_snapshots: "1y"
  high_severity: "permanent"

4. Snapshot Visualization

Snapshot Viewer:

• Static graph visualization of captured state
• Timeline scrubber for browsing snapshots
• Filtering by trigger type, severity, date range
• Search by snapshot ID or description

Comparative Analysis:

• Side-by-side snapshot comparison
• Diff visualization showing changes between snapshots
• Performance trend analysis across snapshots
• Bottleneck progression tracking

5. API Endpoints

// Create snapshot
POST /api/performance/snapshots
{
  "trigger_type": "manual",
  "description": "Before deployment analysis",
  "include_historical": true
}

// List snapshots
GET /api/performance/snapshots?limit=50&trigger=incident&since=2025-01-01

// Get snapshot
GET /api/performance/snapshots/{snapshot_id}

// Compare snapshots
GET /api/performance/snapshots/compare?baseline={id1}&target={id2}

// Delete snapshot
DELETE /api/performance/snapshots/{snapshot_id}

// Export snapshot
GET /api/performance/snapshots/{snapshot_id}/export?format=json|cypher|csv

Use Cases

1. Incident Analysis

# Automatically capture when bottleneck detected
"Critical bottleneck in users->products relationship"
→ Auto-snapshot: snap_20250106_142305_bottleneck
→ Analysis: Latency spike from 45ms to 1200ms
→ Root cause: Missing index on user.category_id

2. Performance Regression

# Compare before/after deployment
Baseline: snap_20250106_090000_pre_deploy
Current:  snap_20250106_100000_post_deploy
→ Diff shows: 40% increase in payment->inventory latency
→ Action: Rollback deployment, investigate payment service

3. Capacity Planning

# Historical analysis for scaling decisions
Weekly snapshots over 3 months:
→ Trend: 15% monthly increase in user activity
→ Prediction: Need database scaling by Q2
→ Recommendation: Implement read replicas

4. Performance Baseline

# Establish healthy system baseline
Daily snapshots during stable period:
→ Baseline QPS: 800-1200
→ Baseline latency: 15-45ms
→ Normal patterns: 3x load during peak hours

Implementation Priority

Phase 1: Core Functionality

• Basic snapshot data structure
• Manual snapshot capture
• File-based storage
• Simple snapshot viewer

Phase 2: Automation

• Scheduled snapshots
• Threshold-based triggers
• Retention policies
• Snapshot management API

Phase 3: Analysis

• Snapshot comparison
• Trend analysis
• Performance regression detection
• Smart alerting integration

Phase 4: Advanced Features

• Predictive snapshots
• ML-based anomaly detection
• Custom snapshot templates
• Integration with monitoring tools

Configuration Example

performance:
  snapshots:
    enabled: true
    storage:
      type: "filesystem"  # filesystem|neo4j|timeseries
      path: "./snapshots"
      compression: true
      
    auto_capture:
      enabled: true
      triggers:
        scheduled:
          - "0 */5 * * * *"  # Every 5 minutes
        thresholds:
          avg_latency: "> 1000ms"
          error_rate: "> 2%"
          bottleneck_score: "> 80"
          
    retention:
      manual_snapshots: "90d"
      auto_snapshots: "30d"
      incident_snapshots: "1y"
      max_snapshots: 10000
      
    export:
      formats: ["json", "cypher", "csv"]
      compression: true
      include_metadata: true

Expected Benefits

1. 🔍 Root Cause Analysis: Quick identification of performance issues
2. 📊 Historical Tracking: Long-term performance trend analysis
3. ⚡ Faster Debugging: Instant access to problematic graph states
4. 📈 Predictive Insights: Pattern recognition for proactive optimization
5. 🤝 Team Collaboration: Shareable performance states for discussion
6. 📋 Compliance: Performance audit trails for reporting

Success Metrics

• Reduced MTTR (Mean Time To Recovery) for performance issues
• Increased proactive issue detection before user impact
• Improved deployment confidence through before/after comparison
• Enhanced performance optimization accuracy through historical data

---

Priority: High
Complexity: Medium
Estimated Effort: 2-3 weeks

Dependencies

Critical Prerequisites (Must be completed first):

• ✅ Performance monitoring system foundation (SQL Performance Benchmark Integration with Graph Load Visualization #12)
• 🔄 Real-time performance metrics as Neo4j relationships (current work-in-progress)
  • Live performance data collection from database
  • Performance metrics stored as named relationships (QUERIES_PER_SEC, AVG_LATENCY_MS, etc.)
  • Real-time graph updates with bottleneck detection
  • WebSocket streaming for live metrics updates
• ✅ Neo4j graph structure with performance data integration
• 🔄 Interactive graph visualization with live performance overlay (current work-in-progress)
  • Visual indicators for bottlenecks (color coding, thickness, animations)
  • Real-time graph rendering of performance states

Technical Dependencies:

• Neo4j driver and graph operations
• Performance data collection infrastructure
• WebSocket real-time streaming
• Graph visualization frontend

Note: This snapshot system cannot be implemented until the real-time performance monitoring with live Neo4j relationship updates is fully functional. The snapshots need actual live performance data to capture.

Related Issues

• SQL Performance Benchmark Integration with Graph Load Visualization #12: Performance monitoring integration ✅
• Current Work: Real-time performance metrics as Neo4j relationships 🔄
• Current Work: Live graph visualization with bottleneck detection 🔄
• Future: Performance alerting system integration
• Future: ML-based anomaly detection