14. Chapter 14: Performance Optimization and Caching

14.1. Learning Objectives

By the end of this chapter, you will understand:

• Performance optimization strategies for Web GIS applications

• Implementing effective caching mechanisms for geospatial data

• Frontend optimization techniques for map rendering and interactions

• Backend optimization for spatial queries and data processing

• CDN configuration and edge computing for global performance

• Performance monitoring and bottleneck identification

14.2. Performance Challenges in Web GIS

Web GIS applications face unique performance challenges compared to traditional web applications. Geospatial data is inherently large and complex, map rendering requires intensive graphics processing, and users expect real-time interactions with massive datasets. Understanding these challenges is essential for building responsive and scalable Web GIS applications.

14.2.1. Understanding Geospatial Performance Bottlenecks

Large Dataset Handling: Geospatial datasets often contain millions of features with complex geometries. A single shapefile can contain detailed administrative boundaries with thousands of vertices per polygon, creating significant challenges for data transfer, storage, and rendering. Traditional web optimization techniques must be adapted to handle the unique characteristics of spatial data.

Rendering Performance: Map rendering involves complex operations including coordinate transformations, geometric simplification, and graphics rendering. Each zoom level may require different levels of detail, and smooth pan and zoom interactions demand consistent frame rates. Modern mapping libraries use techniques like tile caching, vector tiles, and WebGL acceleration to address these challenges.

Real-time Data Processing: Many Web GIS applications require real-time or near-real-time data processing for applications like vehicle tracking, environmental monitoring, or emergency response. This demands efficient data pipelines, optimized database queries, and effective caching strategies to maintain responsiveness.

Network Latency and Bandwidth: Geospatial applications often serve global audiences with varying network conditions. Large tile downloads, complex vector datasets, and frequent API calls can create poor user experiences for users with limited bandwidth or high latency connections.

Memory Management: Client-side applications must manage memory efficiently when handling large datasets, multiple map layers, and complex visualizations. Memory leaks can quickly degrade performance, particularly in applications that allow users to load and interact with multiple datasets simultaneously.

14.2.2. Performance Optimization Strategy

Layered Optimization Approach: Effective Web GIS performance optimization requires a comprehensive strategy addressing multiple layers of the application stack. This includes frontend optimizations for rendering and user interactions, backend optimizations for data processing and query performance, network optimizations for data transfer, and infrastructure optimizations for scalability and reliability.

Data-Driven Optimization: Performance optimization should be guided by real-world usage patterns and performance metrics. Understanding how users interact with your application, which features are accessed most frequently, and where performance bottlenecks occur enables targeted optimization efforts that provide the greatest impact.

Progressive Enhancement: Applications should provide a baseline experience that works across all devices and network conditions, with enhanced features for users with better hardware and connectivity. This approach ensures accessibility while taking advantage of modern capabilities where available.

14.3. Frontend Performance Optimization

14.3.1. Map Rendering Optimization

// client/src/services/mapOptimizationService.ts
interface MapPerformanceConfig {
  maxFeatures: number;
  simplificationTolerance: number;
  clusterThreshold: number;
  tileSize: number;
  maxZoom: number;
  vectorTileUrl: string;
  rasterFallbackUrl: string;
}

interface PerformanceMetrics {
  renderTime: number;
  featureCount: number;
  memoryUsage: number;
  frameRate: number;
  loadTime: number;
}

export class MapOptimizationService {
  private config: MapPerformanceConfig;
  private metrics: PerformanceMetrics[] = [];
  private renderingQueue: Array<() => void> = [];
  private isProcessing = false;

  constructor(config: MapPerformanceConfig) {
    this.config = config;
    this.startPerformanceMonitoring();
  }

  // Implement level-of-detail rendering
  optimizeLayerForZoom(layer: any, zoom: number): any {
    const startTime = performance.now();

    // Determine appropriate level of detail based on zoom
    const detailLevel = this.calculateDetailLevel(zoom);
    
    // Apply geometric simplification
    const simplifiedLayer = this.simplifyGeometry(layer, detailLevel);
    
    // Apply feature filtering based on importance
    const filteredLayer = this.filterFeaturesByImportance(simplifiedLayer, detailLevel);
    
    // Apply clustering for point data at low zoom levels
    const optimizedLayer = zoom < 10 && layer.type === 'point' 
      ? this.clusterFeatures(filteredLayer)
      : filteredLayer;

    const renderTime = performance.now() - startTime;
    this.recordMetrics({
      renderTime,
      featureCount: optimizedLayer.features?.length || 0,
      memoryUsage: this.estimateMemoryUsage(optimizedLayer),
      frameRate: 0, // Will be updated by animation frame monitoring
      loadTime: 0
    });

    return optimizedLayer;
  }

  // Implement progressive loading
  async loadDataProgressively(
    bounds: [number, number, number, number],
    zoom: number,
    onProgress: (loaded: number, total: number) => void
  ): Promise<any[]> {
    const tiles = this.calculateRequiredTiles(bounds, zoom);
    const results: any[] = [];
    
    // Sort tiles by importance (center tiles first)
    const sortedTiles = this.prioritizeTiles(tiles, bounds);
    
    // Load tiles in batches to avoid overwhelming the network
    const batchSize = 4;
    for (let i = 0; i < sortedTiles.length; i += batchSize) {
      const batch = sortedTiles.slice(i, i + batchSize);
      
      const batchPromises = batch.map(async (tile) => {
        try {
          const data = await this.loadTile(tile);
          return this.optimizeLayerForZoom(data, zoom);
        } catch (error) {
          console.warn(`Failed to load tile ${tile.x},${tile.y}:`, error);
          return null;
        }
      });

      const batchResults = await Promise.allSettled(batchPromises);
      
      batchResults.forEach((result) => {
        if (result.status === 'fulfilled' && result.value) {
          results.push(result.value);
        }
      });

      onProgress(results.length, sortedTiles.length);
      
      // Allow browser to process other tasks between batches
      await this.yieldToMainThread();
    }

    return results;
  }

  // Implement efficient feature clustering
  clusterFeatures(layer: any): any {
    if (!layer.features || layer.features.length < this.config.clusterThreshold) {
      return layer;
    }

    const clusters = new Map<string, any[]>();
    const gridSize = this.calculateClusterGridSize();

    // Group features by grid cells
    layer.features.forEach((feature: any) => {
      const point = this.getFeatureCenter(feature);
      const gridKey = this.getGridKey(point, gridSize);
      
      if (!clusters.has(gridKey)) {
        clusters.set(gridKey, []);
      }
      clusters.get(gridKey)!.push(feature);
    });

    // Create cluster features
    const clusteredFeatures = Array.from(clusters.entries()).map(([gridKey, features]) => {
      if (features.length === 1) {
        return features[0];
      }

      const center = this.calculateClusterCenter(features);
      return {
        type: 'Feature',
        properties: {
          cluster: true,
          point_count: features.length,
          clusterId: gridKey,
          features: features.slice(0, 10) // Store sample of features
        },
        geometry: {
          type: 'Point',
          coordinates: center
        }
      };
    });

    return {
      ...layer,
      features: clusteredFeatures
    };
  }

  // Implement geometry simplification
  simplifyGeometry(layer: any, tolerance: number): any {
    if (!layer.features) return layer;

    const simplifiedFeatures = layer.features.map((feature: any) => {
      if (!feature.geometry || feature.geometry.type === 'Point') {
        return feature;
      }

      try {
        // Use simplified version of Douglas-Peucker algorithm
        const simplifiedGeometry = this.douglasPeucker(feature.geometry, tolerance);
        
        return {
          ...feature,
          geometry: simplifiedGeometry
        };
      } catch (error) {
        console.warn('Failed to simplify geometry:', error);
        return feature;
      }
    });

    return {
      ...layer,
      features: simplifiedFeatures
    };
  }

  // Implement request batching
  private batchRequests<T>(
    requests: Array<() => Promise<T>>,
    batchSize: number = 5,
    delayMs: number = 100
  ): Promise<T[]> {
    return new Promise((resolve) => {
      const results: T[] = [];
      let currentBatch = 0;

      const processBatch = async () => {
        const start = currentBatch * batchSize;
        const end = Math.min(start + batchSize, requests.length);
        const batch = requests.slice(start, end);

        try {
          const batchResults = await Promise.allSettled(
            batch.map(request => request())
          );

          batchResults.forEach((result) => {
            if (result.status === 'fulfilled') {
              results.push(result.value);
            }
          });
        } catch (error) {
          console.error('Batch processing error:', error);
        }

        currentBatch++;

        if (currentBatch * batchSize < requests.length) {
          setTimeout(processBatch, delayMs);
        } else {
          resolve(results);
        }
      };

      processBatch();
    });
  }

  // Memory management utilities
  cleanupUnusedResources(): void {
    // Remove cached data outside current viewport
    this.cleanupViewportCache();
    
    // Force garbage collection if available (development)
    if (typeof window !== 'undefined' && window.gc) {
      window.gc();
    }

    // Clear old performance metrics
    if (this.metrics.length > 1000) {
      this.metrics = this.metrics.slice(-500);
    }
  }

  // Performance monitoring
  private startPerformanceMonitoring(): void {
    // Monitor frame rate
    let lastFrameTime = performance.now();
    let frameCount = 0;

    const measureFrameRate = () => {
      const currentTime = performance.now();
      frameCount++;

      if (currentTime - lastFrameTime >= 1000) {
        const fps = frameCount;
        frameCount = 0;
        lastFrameTime = currentTime;

        // Update latest metrics with frame rate
        if (this.metrics.length > 0) {
          this.metrics[this.metrics.length - 1].frameRate = fps;
        }
      }

      requestAnimationFrame(measureFrameRate);
    };

    requestAnimationFrame(measureFrameRate);

    // Monitor memory usage
    setInterval(() => {
      if ('memory' in performance) {
        const memory = (performance as any).memory;
        console.debug('Memory usage:', {
          used: Math.round(memory.usedJSHeapSize / 1024 / 1024) + 'MB',
          total: Math.round(memory.totalJSHeapSize / 1024 / 1024) + 'MB',
          limit: Math.round(memory.jsHeapSizeLimit / 1024 / 1024) + 'MB'
        });
      }
    }, 10000);
  }

  // Utility methods
  private calculateDetailLevel(zoom: number): number {
    if (zoom > 15) return 1.0;
    if (zoom > 10) return 0.1;
    if (zoom > 5) return 0.01;
    return 0.001;
  }

  private simplificationTolerance(zoom: number): number {
    return Math.max(0.0001, this.config.simplificationTolerance / Math.pow(2, zoom));
  }

  private calculateRequiredTiles(
    bounds: [number, number, number, number],
    zoom: number
  ): Array<{x: number, y: number, z: number}> {
    const [west, south, east, north] = bounds;
    const tiles: Array<{x: number, y: number, z: number}> = [];

    const minTileX = Math.floor((west + 180) / 360 * Math.pow(2, zoom));
    const maxTileX = Math.floor((east + 180) / 360 * Math.pow(2, zoom));
    const minTileY = Math.floor((1 - Math.log(Math.tan(north * Math.PI / 180) + 1 / Math.cos(north * Math.PI / 180)) / Math.PI) / 2 * Math.pow(2, zoom));
    const maxTileY = Math.floor((1 - Math.log(Math.tan(south * Math.PI / 180) + 1 / Math.cos(south * Math.PI / 180)) / Math.PI) / 2 * Math.pow(2, zoom));

    for (let x = minTileX; x <= maxTileX; x++) {
      for (let y = minTileY; y <= maxTileY; y++) {
        tiles.push({ x, y, z: zoom });
      }
    }

    return tiles;
  }

  private prioritizeTiles(
    tiles: Array<{x: number, y: number, z: number}>,
    bounds: [number, number, number, number]
  ): Array<{x: number, y: number, z: number}> {
    const [west, south, east, north] = bounds;
    const centerX = (west + east) / 2;
    const centerY = (south + north) / 2;

    return tiles.sort((a, b) => {
      const aDistance = this.tileDistanceFromCenter(a, centerX, centerY);
      const bDistance = this.tileDistanceFromCenter(b, centerX, centerY);
      return aDistance - bDistance;
    });
  }

  private tileDistanceFromCenter(
    tile: {x: number, y: number, z: number},
    centerX: number,
    centerY: number
  ): number {
    const tileSize = 360 / Math.pow(2, tile.z);
    const tileCenterX = tile.x * tileSize - 180 + tileSize / 2;
    const tileCenterY = 85.0511 - tile.y * tileSize * 170.1022 / Math.pow(2, tile.z);
    
    return Math.sqrt(
      Math.pow(tileCenterX - centerX, 2) + Math.pow(tileCenterY - centerY, 2)
    );
  }

  private async loadTile(tile: {x: number, y: number, z: number}): Promise<any> {
    const url = this.config.vectorTileUrl
      .replace('{z}', tile.z.toString())
      .replace('{x}', tile.x.toString())
      .replace('{y}', tile.y.toString());

    const response = await fetch(url);
    if (!response.ok) {
      throw new Error(`Failed to load tile: ${response.statusText}`);
    }

    return response.json();
  }

  private async yieldToMainThread(): Promise<void> {
    return new Promise(resolve => setTimeout(resolve, 0));
  }

  private getFeatureCenter(feature: any): [number, number] {
    if (feature.geometry.type === 'Point') {
      return feature.geometry.coordinates;
    }
    
    // Calculate centroid for other geometry types
    const bbox = this.calculateBoundingBox(feature.geometry);
    return [
      (bbox[0] + bbox[2]) / 2,
      (bbox[1] + bbox[3]) / 2
    ];
  }

  private calculateBoundingBox(geometry: any): [number, number, number, number] {
    // Simplified bounding box calculation
    let minX = Infinity, minY = Infinity, maxX = -Infinity, maxY = -Infinity;

    const processCoordinate = (coord: number[]) => {
      minX = Math.min(minX, coord[0]);
      maxX = Math.max(maxX, coord[0]);
      minY = Math.min(minY, coord[1]);
      maxY = Math.max(maxY, coord[1]);
    };

    const processCoordinates = (coords: any) => {
      if (Array.isArray(coords[0])) {
        coords.forEach((coord: any) => processCoordinates(coord));
      } else {
        processCoordinate(coords);
      }
    };

    processCoordinates(geometry.coordinates);
    return [minX, minY, maxX, maxY];
  }

  private getGridKey(point: [number, number], gridSize: number): string {
    const x = Math.floor(point[0] / gridSize);
    const y = Math.floor(point[1] / gridSize);
    return `${x},${y}`;
  }

  private calculateClusterGridSize(): number {
    // Grid size should be based on current zoom level and screen size
    return 0.01; // Simplified implementation
  }

  private calculateClusterCenter(features: any[]): [number, number] {
    const sum = features.reduce(
      (acc, feature) => {
        const center = this.getFeatureCenter(feature);
        return [acc[0] + center[0], acc[1] + center[1]];
      },
      [0, 0]
    );

    return [sum[0] / features.length, sum[1] / features.length];
  }

  private douglasPeucker(geometry: any, tolerance: number): any {
    // Simplified Douglas-Peucker implementation
    if (!geometry.coordinates || geometry.type === 'Point') {
      return geometry;
    }

    const simplifyLineString = (coords: number[][]): number[][] => {
      if (coords.length <= 2) return coords;
      
      // Find the point with maximum distance from line segment
      let maxDistance = 0;
      let maxIndex = 0;
      
      for (let i = 1; i < coords.length - 1; i++) {
        const distance = this.pointLineDistance(
          coords[i],
          coords[0],
          coords[coords.length - 1]
        );
        
        if (distance > maxDistance) {
          maxDistance = distance;
          maxIndex = i;
        }
      }

      // If max distance is greater than tolerance, recursively simplify
      if (maxDistance > tolerance) {
        const left = simplifyLineString(coords.slice(0, maxIndex + 1));
        const right = simplifyLineString(coords.slice(maxIndex));
        
        return left.slice(0, -1).concat(right);
      } else {
        return [coords[0], coords[coords.length - 1]];
      }
    };

    switch (geometry.type) {
      case 'LineString':
        return {
          ...geometry,
          coordinates: simplifyLineString(geometry.coordinates)
        };
      case 'Polygon':
        return {
          ...geometry,
          coordinates: geometry.coordinates.map((ring: number[][]) => 
            simplifyLineString(ring)
          )
        };
      default:
        return geometry;
    }
  }

  private pointLineDistance(
    point: number[],
    lineStart: number[],
    lineEnd: number[]
  ): number {
    const A = point[0] - lineStart[0];
    const B = point[1] - lineStart[1];
    const C = lineEnd[0] - lineStart[0];
    const D = lineEnd[1] - lineStart[1];

    const dot = A * C + B * D;
    const lenSq = C * C + D * D;
    
    if (lenSq === 0) return Math.sqrt(A * A + B * B);

    let param = dot / lenSq;
    
    if (param < 0) {
      return Math.sqrt(A * A + B * B);
    } else if (param > 1) {
      const E = point[0] - lineEnd[0];
      const F = point[1] - lineEnd[1];
      return Math.sqrt(E * E + F * F);
    } else {
      const x = lineStart[0] + param * C;
      const y = lineStart[1] + param * D;
      const dx = point[0] - x;
      const dy = point[1] - y;
      return Math.sqrt(dx * dx + dy * dy);
    }
  }

  private filterFeaturesByImportance(layer: any, detailLevel: number): any {
    if (!layer.features || detailLevel >= 1.0) return layer;

    // Sort features by importance (area, population, etc.)
    const sortedFeatures = layer.features.sort((a: any, b: any) => {
      const aImportance = this.calculateFeatureImportance(a);
      const bImportance = this.calculateFeatureImportance(b);
      return bImportance - aImportance;
    });

    // Keep only the most important features based on detail level
    const keepCount = Math.ceil(sortedFeatures.length * detailLevel);
    
    return {
      ...layer,
      features: sortedFeatures.slice(0, keepCount)
    };
  }

  private calculateFeatureImportance(feature: any): number {
    // Calculate importance based on properties and geometry
    let importance = 1;

    // Increase importance based on population, area, etc.
    if (feature.properties?.population) {
      importance += Math.log10(feature.properties.population);
    }

    if (feature.properties?.area) {
      importance += Math.log10(feature.properties.area);
    }

    // Increase importance for administrative boundaries
    if (feature.properties?.admin_level) {
      importance += (10 - feature.properties.admin_level);
    }

    return importance;
  }

  private estimateMemoryUsage(layer: any): number {
    // Rough estimation of memory usage in bytes
    return JSON.stringify(layer).length * 2; // UTF-16 characters
  }

  private cleanupViewportCache(): void {
    // Implementation would depend on specific caching strategy
    console.debug('Cleaning up viewport cache');
  }

  private recordMetrics(metrics: PerformanceMetrics): void {
    this.metrics.push({
      ...metrics,
      timestamp: Date.now()
    } as any);

    // Keep only recent metrics
    if (this.metrics.length > 100) {
      this.metrics.shift();
    }
  }

  getPerformanceReport(): any {
    if (this.metrics.length === 0) return null;

    const recent = this.metrics.slice(-10);
    
    return {
      averageRenderTime: recent.reduce((sum, m) => sum + m.renderTime, 0) / recent.length,
      averageFrameRate: recent.reduce((sum, m) => sum + m.frameRate, 0) / recent.length,
      averageFeatureCount: recent.reduce((sum, m) => sum + m.featureCount, 0) / recent.length,
      totalMemoryUsage: recent[recent.length - 1]?.memoryUsage || 0,
      metricsCount: this.metrics.length
    };
  }
}

14.3.2. Component-Level Optimization

// client/src/components/optimized/OptimizedMapComponent.tsx
import React, { useMemo, useCallback, memo, useRef, useEffect } from 'react';
import { Map, Source, Layer } from 'react-map-gl';
import { MapOptimizationService } from '../../services/mapOptimizationService';

interface OptimizedMapProps {
  data: any[];
  viewport: {
    longitude: number;
    latitude: number;
    zoom: number;
  };
  onViewportChange: (viewport: any) => void;
  config?: any;
}

// Memoize expensive calculations
const useOptimizedMapData = (data: any[], zoom: number) => {
  return useMemo(() => {
    const optimizer = new MapOptimizationService({
      maxFeatures: 10000,
      simplificationTolerance: 0.001,
      clusterThreshold: 100,
      tileSize: 512,
      maxZoom: 18,
      vectorTileUrl: '',
      rasterFallbackUrl: ''
    });

    return data.map(layer => optimizer.optimizeLayerForZoom(layer, zoom));
  }, [data, zoom]);
};

// Throttled viewport change handler
const useThrottledViewportChange = (
  onViewportChange: (viewport: any) => void,
  delay: number = 100
) => {
  const timeoutRef = useRef<NodeJS.Timeout>();

  return useCallback((viewport: any) => {
    if (timeoutRef.current) {
      clearTimeout(timeoutRef.current);
    }

    timeoutRef.current = setTimeout(() => {
      onViewportChange(viewport);
    }, delay);
  }, [onViewportChange, delay]);
};

// Virtualized layer rendering
const VirtualizedLayers: React.FC<{
  layers: any[];
  viewport: any;
  maxLayers: number;
}> = memo(({ layers, viewport, maxLayers }) => {
  // Only render layers visible in viewport and limit total number
  const visibleLayers = useMemo(() => {
    const sorted = layers
      .filter(layer => isLayerVisible(layer, viewport))
      .sort((a, b) => getLayerPriority(b, viewport) - getLayerPriority(a, viewport));
    
    return sorted.slice(0, maxLayers);
  }, [layers, viewport, maxLayers]);

  return (
    <>
      {visibleLayers.map((layer, index) => (
        <Source key={layer.id} id={layer.id} type="geojson" data={layer.data}>
          <Layer {...layer.style} />
        </Source>
      ))}
    </>
  );
});

// Main optimized map component
export const OptimizedMapComponent: React.FC<OptimizedMapProps> = memo(({
  data,
  viewport,
  onViewportChange,
  config = {}
}) => {
  const mapRef = useRef<any>();
  const optimizationService = useRef<MapOptimizationService>();

  // Initialize optimization service
  useEffect(() => {
    optimizationService.current = new MapOptimizationService({
      maxFeatures: 10000,
      simplificationTolerance: 0.001,
      clusterThreshold: 100,
      tileSize: 512,
      maxZoom: 18,
      vectorTileUrl: config.vectorTileUrl || '',
      rasterFallbackUrl: config.rasterFallbackUrl || ''
    });

    return () => {
      optimizationService.current?.cleanupUnusedResources();
    };
  }, [config]);

  // Optimize data based on zoom level
  const optimizedData = useOptimizedMapData(data, viewport.zoom);
  
  // Throttle viewport changes to reduce re-renders
  const throttledViewportChange = useThrottledViewportChange(onViewportChange);

  // Memoize map style to prevent unnecessary re-renders
  const mapStyle = useMemo(() => ({
    version: 8,
    sources: {},
    layers: []
  }), []);

  // Handle viewport changes with performance monitoring
  const handleViewportChange = useCallback((newViewport: any) => {
    const startTime = performance.now();
    
    throttledViewportChange(newViewport);
    
    // Log performance metrics
    const duration = performance.now() - startTime;
    console.debug('Viewport change duration:', duration);
  }, [throttledViewportChange]);

  // Cleanup on unmount
  useEffect(() => {
    return () => {
      optimizationService.current?.cleanupUnusedResources();
    };
  }, []);

  return (
    <div style={{ width: '100%', height: '100%', position: 'relative' }}>
      <Map
        ref={mapRef}
        {...viewport}
        onMove={evt => handleViewportChange(evt.viewState)}
        mapStyle={mapStyle}
        style={{ width: '100%', height: '100%' }}
        maxZoom={18}
        minZoom={1}
        // Performance optimizations
        preserveDrawingBuffer={false}
        antialias={false}
        powerPreference="high-performance"
      >
        <VirtualizedLayers
          layers={optimizedData}
          viewport={viewport}
          maxLayers={config.maxLayers || 20}
        />
      </Map>

      {/* Performance indicator */}
      <PerformanceIndicator service={optimizationService.current} />
    </div>
  );
});

// Performance monitoring component
const PerformanceIndicator: React.FC<{
  service?: MapOptimizationService;
}> = ({ service }) => {
  const [report, setReport] = React.useState<any>(null);

  useEffect(() => {
    if (!service) return;

    const interval = setInterval(() => {
      const performanceReport = service.getPerformanceReport();
      setReport(performanceReport);
    }, 1000);

    return () => clearInterval(interval);
  }, [service]);

  if (!report || process.env.NODE_ENV === 'production') {
    return null;
  }

  return (
    <div style={{
      position: 'absolute',
      top: 10,
      right: 10,
      background: 'rgba(0,0,0,0.7)',
      color: 'white',
      padding: '8px',
      borderRadius: '4px',
      fontSize: '12px',
      fontFamily: 'monospace'
    }}>
      <div>FPS: {Math.round(report.averageFrameRate)}</div>
      <div>Render: {Math.round(report.averageRenderTime)}ms</div>
      <div>Features: {report.averageFeatureCount}</div>
      <div>Memory: {Math.round(report.totalMemoryUsage / 1024)}KB</div>
    </div>
  );
};

// Utility functions
function isLayerVisible(layer: any, viewport: any): boolean {
  if (!layer.bounds) return true;
  
  const [west, south, east, north] = layer.bounds;
  const padding = 0.1; // 10% padding for smooth transitions
  
  return !(
    viewport.longitude + padding < west ||
    viewport.longitude - padding > east ||
    viewport.latitude + padding < south ||
    viewport.latitude - padding > north
  );
}

function getLayerPriority(layer: any, viewport: any): number {
  let priority = layer.priority || 0;
  
  // Increase priority for layers closer to viewport center
  if (layer.bounds) {
    const [west, south, east, north] = layer.bounds;
    const layerCenterX = (west + east) / 2;
    const layerCenterY = (south + north) / 2;
    const distance = Math.sqrt(
      Math.pow(layerCenterX - viewport.longitude, 2) +
      Math.pow(layerCenterY - viewport.latitude, 2)
    );
    priority += Math.max(0, 10 - distance);
  }
  
  return priority;
}

OptimizedMapComponent.displayName = 'OptimizedMapComponent';

14.4. Backend Performance Optimization

14.4.1. Database Query Optimization

-- Spatial indexing and query optimization
-- Create spatial indexes for geometry columns
CREATE INDEX idx_features_geom ON features USING GIST(geom);
CREATE INDEX idx_features_bounds ON features USING GIST(ST_Envelope(geom));

-- Create composite indexes for common query patterns
CREATE INDEX idx_features_type_geom ON features (feature_type) INCLUDE (geom);
CREATE INDEX idx_features_updated_geom ON features (updated_at DESC) INCLUDE (geom);

-- Partitioning for large datasets
CREATE TABLE features_partitioned (
    id SERIAL PRIMARY KEY,
    feature_type VARCHAR(50) NOT NULL,
    geom GEOMETRY(GEOMETRY, 4326),
    properties JSONB,
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
) PARTITION BY HASH (id);

-- Create partitions
CREATE TABLE features_part_0 PARTITION OF features_partitioned
    FOR VALUES WITH (MODULUS 4, REMAINDER 0);
CREATE TABLE features_part_1 PARTITION OF features_partitioned  
    FOR VALUES WITH (MODULUS 4, REMAINDER 1);
CREATE TABLE features_part_2 PARTITION OF features_partitioned
    FOR VALUES WITH (MODULUS 4, REMAINDER 2);
CREATE TABLE features_part_3 PARTITION OF features_partitioned
    FOR VALUES WITH (MODULUS 4, REMAINDER 3);

-- Spatial indexes on partitions
CREATE INDEX idx_features_part_0_geom ON features_part_0 USING GIST(geom);
CREATE INDEX idx_features_part_1_geom ON features_part_1 USING GIST(geom);
CREATE INDEX idx_features_part_2_geom ON features_part_2 USING GIST(geom);
CREATE INDEX idx_features_part_3_geom ON features_part_3 USING GIST(geom);

-- Materialized views for common aggregations
CREATE MATERIALIZED VIEW feature_summary_by_type AS
SELECT 
    feature_type,
    COUNT(*) as feature_count,
    ST_Extent(geom) as bounds,
    AVG(ST_Area(geom)) as avg_area,
    MIN(created_at) as first_created,
    MAX(updated_at) as last_updated
FROM features
GROUP BY feature_type;

CREATE INDEX idx_feature_summary_type ON feature_summary_by_type (feature_type);

-- Refresh materialized view periodically
CREATE OR REPLACE FUNCTION refresh_feature_summary()
RETURNS void AS $$
BEGIN
    REFRESH MATERIALIZED VIEW CONCURRENTLY feature_summary_by_type;
END;
$$ LANGUAGE plpgsql;

-- Optimized spatial queries with proper indexing
-- Bounding box query with spatial index
EXPLAIN (ANALYZE, BUFFERS)
SELECT id, feature_type, ST_AsGeoJSON(geom) as geometry, properties
FROM features
WHERE geom && ST_MakeEnvelope($1, $2, $3, $4, 4326)
  AND ST_Intersects(geom, ST_MakeEnvelope($1, $2, $3, $4, 4326))
LIMIT 1000;

-- Optimized nearest neighbor query
EXPLAIN (ANALYZE, BUFFERS)
SELECT id, feature_type, ST_AsGeoJSON(geom) as geometry, properties,
       ST_Distance(geom, ST_SetSRID(ST_Point($1, $2), 4326)) as distance
FROM features
WHERE geom && ST_DWithin(ST_SetSRID(ST_Point($1, $2), 4326), 1000)
ORDER BY geom <-> ST_SetSRID(ST_Point($1, $2), 4326)
LIMIT 10;

-- Clustering query for map visualization
CREATE OR REPLACE FUNCTION cluster_points(
    bbox GEOMETRY,
    zoom_level INTEGER,
    cluster_distance FLOAT DEFAULT 50
)
RETURNS TABLE(
    cluster_id INTEGER,
    point_count INTEGER,
    geom GEOMETRY,
    sample_properties JSONB
) AS $$
BEGIN
    RETURN QUERY
    WITH clustered AS (
        SELECT 
            ST_ClusterKMeans(geom, 
                CASE 
                    WHEN COUNT(*) OVER() > 1000 THEN 100
                    WHEN COUNT(*) OVER() > 100 THEN 50
                    ELSE 10
                END
            ) OVER() as cluster_id,
            geom,
            properties
        FROM features
        WHERE geom && bbox
    )
    SELECT 
        c.cluster_id::INTEGER,
        COUNT(*)::INTEGER as point_count,
        ST_Centroid(ST_Collect(c.geom)) as geom,
        (array_agg(c.properties))[1] as sample_properties
    FROM clustered c
    GROUP BY c.cluster_id;
END;
$$ LANGUAGE plpgsql;

14.4.2. Caching Strategies

// server/src/services/cacheService.ts
import Redis from 'ioredis';
import { LRUCache } from 'lru-cache';

interface CacheOptions {
  ttl: number;
  maxSize?: number;
  compressionThreshold?: number;
}

interface SpatialCacheKey {
  type: 'bbox' | 'tile' | 'feature' | 'query';
  bounds?: [number, number, number, number];
  zoom?: number;
  x?: number;
  y?: number;
  z?: number;
  featureId?: string;
  queryHash?: string;
}

export class SpatialCacheService {
  private redis: Redis;
  private memoryCache: LRUCache<string, any>;
  private compressionThreshold: number;

  constructor(redisUrl: string, options: CacheOptions = { ttl: 3600 }) {
    this.redis = new Redis(redisUrl);
    this.compressionThreshold = options.compressionThreshold || 1024; // 1KB

    // Memory cache for frequently accessed small items
    this.memoryCache = new LRUCache({
      max: options.maxSize || 1000,
      ttl: options.ttl * 1000, // Convert to milliseconds
      updateAgeOnGet: true,
      allowStale: false
    });
  }

  // Generate cache keys for spatial data
  generateSpatialKey(keyData: SpatialCacheKey): string {
    switch (keyData.type) {
      case 'bbox':
        const [west, south, east, north] = keyData.bounds!;
        return `bbox:${west.toFixed(6)},${south.toFixed(6)},${east.toFixed(6)},${north.toFixed(6)}:${keyData.zoom}`;
      
      case 'tile':
        return `tile:${keyData.z}/${keyData.x}/${keyData.y}`;
      
      case 'feature':
        return `feature:${keyData.featureId}`;
      
      case 'query':
        return `query:${keyData.queryHash}`;
      
      default:
        throw new Error(`Unknown cache key type: ${keyData.type}`);
    }
  }

  // Multi-level caching: memory -> Redis -> database
  async get<T>(key: string): Promise<T | null> {
    try {
      // Check memory cache first
      const memoryResult = this.memoryCache.get(key);
      if (memoryResult !== undefined) {
        return memoryResult as T;
      }

      // Check Redis cache
      const redisResult = await this.redis.get(key);
      if (redisResult) {
        const data = await this.decompress(redisResult);
        const parsed = JSON.parse(data);
        
        // Store in memory cache for faster future access
        this.memoryCache.set(key, parsed);
        
        return parsed as T;
      }

      return null;
    } catch (error) {
      console.error('Cache get error:', error);
      return null;
    }
  }

  async set<T>(key: string, value: T, ttl?: number): Promise<void> {
    try {
      const serialized = JSON.stringify(value);
      const compressed = await this.compress(serialized);
      
      // Store in both memory and Redis
      this.memoryCache.set(key, value);
      
      if (ttl) {
        await this.redis.setex(key, ttl, compressed);
      } else {
        await this.redis.set(key, compressed);
      }
    } catch (error) {
      console.error('Cache set error:', error);
    }
  }

  // Spatial-aware cache invalidation
  async invalidateSpatialRegion(bounds: [number, number, number, number]): Promise<void> {
    try {
      const [west, south, east, north] = bounds;
      
      // Find all cache keys that intersect with this region
      const pattern = 'bbox:*';
      const keys = await this.redis.keys(pattern);
      
      const keysToInvalidate: string[] = [];
      
      for (const key of keys) {
        const keyBounds = this.extractBoundsFromKey(key);
        if (keyBounds && this.boundsIntersect(bounds, keyBounds)) {
          keysToInvalidate.push(key);
        }
      }

      // Batch delete
      if (keysToInvalidate.length > 0) {
        await this.redis.del(...keysToInvalidate);
        
        // Remove from memory cache
        keysToInvalidate.forEach(key => {
          this.memoryCache.delete(key);
        });
      }
    } catch (error) {
      console.error('Spatial cache invalidation error:', error);
    }
  }

  // Tile-based caching for map data
  async getTile(x: number, y: number, z: number): Promise<Buffer | null> {
    const key = this.generateSpatialKey({ type: 'tile', x, y, z });
    
    try {
      const cached = await this.redis.getBuffer(key);
      return cached;
    } catch (error) {
      console.error('Tile cache get error:', error);
      return null;
    }
  }

  async setTile(x: number, y: number, z: number, data: Buffer, ttl: number = 3600): Promise<void> {
    const key = this.generateSpatialKey({ type: 'tile', x, y, z });
    
    try {
      await this.redis.setex(key, ttl, data);
    } catch (error) {
      console.error('Tile cache set error:', error);
    }
  }

  // Query result caching with automatic invalidation
  async getCachedQuery<T>(
    query: string,
    params: any[],
    ttl: number = 300
  ): Promise<T | null> {
    const queryHash = this.hashQuery(query, params);
    const key = this.generateSpatialKey({ type: 'query', queryHash });
    
    return this.get<T>(key);
  }

  async setCachedQuery<T>(
    query: string,
    params: any[],
    result: T,
    ttl: number = 300
  ): Promise<void> {
    const queryHash = this.hashQuery(query, params);
    const key = this.generateSpatialKey({ type: 'query', queryHash });
    
    await this.set(key, result, ttl);
  }

  // Batch operations for improved performance
  async mget<T>(keys: string[]): Promise<(T | null)[]> {
    try {
      // Check memory cache first
      const results: (T | null)[] = new Array(keys.length);
      const redisKeys: string[] = [];
      const redisIndexes: number[] = [];

      for (let i = 0; i < keys.length; i++) {
        const memoryResult = this.memoryCache.get(keys[i]);
        if (memoryResult !== undefined) {
          results[i] = memoryResult as T;
        } else {
          redisKeys.push(keys[i]);
          redisIndexes.push(i);
        }
      }

      // Fetch remaining from Redis
      if (redisKeys.length > 0) {
        const redisResults = await this.redis.mget(...redisKeys);
        
        for (let i = 0; i < redisResults.length; i++) {
          const redisResult = redisResults[i];
          const originalIndex = redisIndexes[i];
          
          if (redisResult) {
            const data = await this.decompress(redisResult);
            const parsed = JSON.parse(data);
            results[originalIndex] = parsed as T;
            
            // Store in memory cache
            this.memoryCache.set(keys[originalIndex], parsed);
          } else {
            results[originalIndex] = null;
          }
        }
      }

      return results;
    } catch (error) {
      console.error('Cache mget error:', error);
      return new Array(keys.length).fill(null);
    }
  }

  async mset<T>(keyValuePairs: Array<{key: string, value: T}>, ttl?: number): Promise<void> {
    try {
      const pipeline = this.redis.pipeline();
      
      for (const { key, value } of keyValuePairs) {
        const serialized = JSON.stringify(value);
        const compressed = await this.compress(serialized);
        
        // Store in memory cache
        this.memoryCache.set(key, value);
        
        if (ttl) {
          pipeline.setex(key, ttl, compressed);
        } else {
          pipeline.set(key, compressed);
        }
      }

      await pipeline.exec();
    } catch (error) {
      console.error('Cache mset error:', error);
    }
  }

  // Cache warming for frequently accessed data
  async warmCache(warmingStrategy: 'tiles' | 'regions' | 'popular', config: any): Promise<void> {
    try {
      switch (warmingStrategy) {
        case 'tiles':
          await this.warmTileCache(config);
          break;
        case 'regions':
          await this.warmRegionCache(config);
          break;
        case 'popular':
          await this.warmPopularDataCache(config);
          break;
      }
    } catch (error) {
      console.error('Cache warming error:', error);
    }
  }

  private async warmTileCache(config: {
    bounds: [number, number, number, number];
    minZoom: number;
    maxZoom: number;
  }): Promise<void> {
    const { bounds, minZoom, maxZoom } = config;
    
    for (let z = minZoom; z <= maxZoom; z++) {
      const tiles = this.calculateTilesForBounds(bounds, z);
      
      // Process tiles in batches to avoid overwhelming the system
      const batchSize = 10;
      for (let i = 0; i < tiles.length; i += batchSize) {
        const batch = tiles.slice(i, i + batchSize);
        
        await Promise.allSettled(
          batch.map(async (tile) => {
            const cached = await this.getTile(tile.x, tile.y, tile.z);
            if (!cached) {
              // Generate and cache tile data
              const tileData = await this.generateTileData(tile.x, tile.y, tile.z);
              if (tileData) {
                await this.setTile(tile.x, tile.y, tile.z, tileData);
              }
            }
          })
        );

        // Small delay to prevent overwhelming the system
        await new Promise(resolve => setTimeout(resolve, 100));
      }
    }
  }

  private async warmRegionCache(config: { regions: string[] }): Promise<void> {
    // Implementation for warming region-specific cache
    console.log('Warming region cache for:', config.regions);
  }

  private async warmPopularDataCache(config: { limit: number }): Promise<void> {
    // Implementation for warming cache with popular data
    console.log('Warming popular data cache, limit:', config.limit);
  }

  // Utility methods
  private async compress(data: string): Promise<string> {
    if (data.length < this.compressionThreshold) {
      return data;
    }

    // Use gzip compression for larger data
    const zlib = require('zlib');
    const compressed = zlib.gzipSync(Buffer.from(data));
    return compressed.toString('base64');
  }

  private async decompress(data: string): Promise<string> {
    try {
      // Try to decompress first
      const zlib = require('zlib');
      const buffer = Buffer.from(data, 'base64');
      const decompressed = zlib.gunzipSync(buffer);
      return decompressed.toString();
    } catch {
      // If decompression fails, assume it's uncompressed
      return data;
    }
  }

  private hashQuery(query: string, params: any[]): string {
    const crypto = require('crypto');
    const combined = query + JSON.stringify(params);
    return crypto.createHash('md5').update(combined).digest('hex');
  }

  private extractBoundsFromKey(key: string): [number, number, number, number] | null {
    const match = key.match(/bbox:([-\d.]+),([-\d.]+),([-\d.]+),([-\d.]+)/);
    if (match) {
      return [
        parseFloat(match[1]),
        parseFloat(match[2]),
        parseFloat(match[3]),
        parseFloat(match[4])
      ];
    }
    return null;
  }

  private boundsIntersect(
    bounds1: [number, number, number, number],
    bounds2: [number, number, number, number]
  ): boolean {
    const [west1, south1, east1, north1] = bounds1;
    const [west2, south2, east2, north2] = bounds2;

    return !(
      west1 > east2 ||
      east1 < west2 ||
      south1 > north2 ||
      north1 < south2
    );
  }

  private calculateTilesForBounds(
    bounds: [number, number, number, number],
    zoom: number
  ): Array<{x: number, y: number, z: number}> {
    const [west, south, east, north] = bounds;
    const tiles: Array<{x: number, y: number, z: number}> = [];

    const minTileX = Math.floor((west + 180) / 360 * Math.pow(2, zoom));
    const maxTileX = Math.floor((east + 180) / 360 * Math.pow(2, zoom));
    const minTileY = Math.floor((1 - Math.log(Math.tan(north * Math.PI / 180) + 1 / Math.cos(north * Math.PI / 180)) / Math.PI) / 2 * Math.pow(2, zoom));
    const maxTileY = Math.floor((1 - Math.log(Math.tan(south * Math.PI / 180) + 1 / Math.cos(south * Math.PI / 180)) / Math.PI) / 2 * Math.pow(2, zoom));

    for (let x = minTileX; x <= maxTileX; x++) {
      for (let y = minTileY; y <= maxTileY; y++) {
        tiles.push({ x, y, z: zoom });
      }
    }

    return tiles;
  }

  private async generateTileData(x: number, y: number, z: number): Promise<Buffer | null> {
    // This would integrate with your tile generation service
    // For now, return null to indicate no tile data available
    return null;
  }

  // Performance monitoring
  getCacheStats(): any {
    return {
      memoryCache: {
        size: this.memoryCache.size,
        maxSize: this.memoryCache.max,
        hitRate: this.memoryCache.calculatedSize / this.memoryCache.max
      },
      redis: {
        connected: this.redis.status === 'ready'
      }
    };
  }

  // Cleanup and shutdown
  async shutdown(): Promise<void> {
    this.memoryCache.clear();
    await this.redis.quit();
  }
}

14.5. CDN and Edge Optimization

14.5.1. CDN Configuration

// server/src/services/cdnService.ts
interface CDNConfig {
  provider: 'cloudflare' | 'aws' | 'azure';
  domains: string[];
  tileEndpoints: string[];
  cacheRules: CacheRule[];
  compressionSettings: CompressionConfig;
  geoReplication: GeoReplicationConfig;
}

interface CacheRule {
  path: string;
  ttl: number;
  vary: string[];
  browserTtl: number;
}

interface CompressionConfig {
  enabled: boolean;
  algorithms: ('gzip' | 'brotli' | 'deflate')[];
  minSize: number;
  mimeTypes: string[];
}

interface GeoReplicationConfig {
  enabled: boolean;
  regions: string[];
  syncStrategy: 'immediate' | 'lazy' | 'scheduled';
}

export class CDNOptimizationService {
  private config: CDNConfig;

  constructor(config: CDNConfig) {
    this.config = config;
  }

  // Generate optimized cache headers for different content types
  generateCacheHeaders(contentType: string, path: string): Record<string, string> {
    const rule = this.findMatchingCacheRule(path);
    const headers: Record<string, string> = {};

    // Set cache control based on content type
    if (contentType.includes('tile') || path.includes('/tiles/')) {
      // Map tiles - long cache with stale-while-revalidate
      headers['Cache-Control'] = `public, max-age=${rule.ttl}, stale-while-revalidate=86400`;
      headers['Vary'] = 'Accept-Encoding';
    } else if (contentType === 'application/json') {
      // API responses - shorter cache with revalidation
      headers['Cache-Control'] = `public, max-age=${Math.min(rule.ttl, 300)}, must-revalidate`;
      headers['Vary'] = 'Accept-Encoding, Authorization';
    } else if (contentType.startsWith('image/')) {
      // Static images - long cache
      headers['Cache-Control'] = `public, max-age=${rule.ttl}, immutable`;
    } else {
      // Default caching
      headers['Cache-Control'] = `public, max-age=${rule.ttl}`;
    }

    // Add ETags for efficient caching
    headers['ETag'] = this.generateETag(contentType, path);

    // Add compression hints
    if (this.config.compressionSettings.enabled) {
      headers['Accept-Encoding'] = this.config.compressionSettings.algorithms.join(', ');
    }

    return headers;
  }

  // Configure CDN for optimal geospatial content delivery
  async configureCDN(): Promise<void> {
    switch (this.config.provider) {
      case 'cloudflare':
        await this.configureCloudflare();
        break;
      case 'aws':
        await this.configureAWS();
        break;
      case 'azure':
        await this.configureAzure();
        break;
    }
  }

  private async configureCloudflare(): Promise<void> {
    // Cloudflare-specific configuration
    const rules = [
      {
        pattern: '*/tiles/*',
        cache_level: 'cache_everything',
        edge_cache_ttl: 86400,
        browser_cache_ttl: 86400,
        compression: 'gzip',
        mirage: true, // Optimize images
        polish: 'lossy' // Image optimization
      },
      {
        pattern: '*/api/data/*',
        cache_level: 'cache_everything',
        edge_cache_ttl: 300,
        browser_cache_ttl: 60,
        bypass_cache_on_cookie: false
      }
    ];

    // Apply page rules via Cloudflare API
    for (const rule of rules) {
      await this.applyCloudflarePageRule(rule);
    }

    // Configure Argo Smart Routing for improved performance
    await this.enableArgoSmartRouting();

    // Configure Worker for dynamic content optimization
    await this.deployOptimizationWorker();
  }

  private async configureAWS(): Promise<void> {
    // CloudFront distribution configuration
    const distributionConfig = {
      CallerReference: Date.now().toString(),
      Origins: {
        Quantity: this.config.tileEndpoints.length,
        Items: this.config.tileEndpoints.map((endpoint, index) => ({
          Id: `origin-${index}`,
          DomainName: endpoint,
          CustomOriginConfig: {
            HTTPPort: 443,
            HTTPSPort: 443,
            OriginProtocolPolicy: 'https-only',
            OriginSslProtocols: {
              Quantity: 1,
              Items: ['TLSv1.2']
            }
          }
        }))
      },
      DefaultCacheBehavior: {
        TargetOriginId: 'origin-0',
        ViewerProtocolPolicy: 'redirect-to-https',
        CachePolicyId: this.getOptimizedCachePolicyId(),
        Compress: true,
        ResponseHeadersPolicyId: this.getSecurityHeadersPolicyId()
      },
      CacheBehaviors: {
        Quantity: 2,
        Items: [
          {
            PathPattern: '/tiles/*',
            TargetOriginId: 'origin-0',
            CachePolicyId: this.getLongTermCachePolicyId(),
            ViewerProtocolPolicy: 'https-only',
            Compress: true
          },
          {
            PathPattern: '/api/*',
            TargetOriginId: 'origin-0',
            CachePolicyId: this.getAPIResponseCachePolicyId(),
            ViewerProtocolPolicy: 'https-only',
            Compress: true
          }
        ]
      },
      PriceClass: 'PriceClass_All',
      Enabled: true,
      HttpVersion: 'http2'
    };

    // Create or update distribution
    await this.createOrUpdateCloudFrontDistribution(distributionConfig);
  }

  private async configureAzure(): Promise<void> {
    // Azure CDN configuration
    const cdnProfile = {
      location: 'Global',
      sku: {
        name: 'Premium_Verizon' // For advanced caching features
      },
      tags: {
        service: 'webgis',
        environment: process.env.NODE_ENV
      }
    };

    await this.createOrUpdateAzureCDNProfile(cdnProfile);

    // Configure custom rules for geospatial content
    const customRules = [
      {
        name: 'TileOptimization',
        order: 1,
        conditions: [
          {
            name: 'UrlPath',
            parameters: {
              operator: 'Contains',
              value: '/tiles/'
            }
          }
        ],
        actions: [
          {
            name: 'CacheExpiration',
            parameters: {
              cacheBehavior: 'Override',
              cacheType: 'All',
              cacheDuration: '1.00:00:00' // 1 day
            }
          },
          {
            name: 'ResponseHeader',
            parameters: {
              headerAction: 'Overwrite',
              headerName: 'Cache-Control',
              value: 'public, max-age=86400, immutable'
            }
          }
        ]
      }
    ];

    await this.applyAzureCustomRules(customRules);
  }

  // Edge computing for dynamic tile generation
  generateEdgeWorkerScript(): string {
    return `
      addEventListener('fetch', event => {
        event.respondWith(handleRequest(event.request));
      });

      async function handleRequest(request) {
        const url = new URL(request.url);
        
        // Handle tile requests with dynamic generation
        if (url.pathname.startsWith('/tiles/')) {
          return handleTileRequest(request, url);
        }
        
        // Handle API requests with caching
        if (url.pathname.startsWith('/api/')) {
          return handleAPIRequest(request, url);
        }
        
        // Pass through other requests
        return fetch(request);
      }

      async function handleTileRequest(request, url) {
        const cacheKey = new Request(url.toString(), request);
        const cache = caches.default;
        
        // Check cache first
        let response = await cache.match(cacheKey);
        
        if (!response) {
          // Generate tile dynamically if not cached
          response = await generateTile(url);
          
          // Cache the response
          const cacheResponse = response.clone();
          cacheResponse.headers.set('Cache-Control', 'public, max-age=86400');
          cacheResponse.headers.set('Expires', new Date(Date.now() + 86400000).toUTCString());
          
          event.waitUntil(cache.put(cacheKey, cacheResponse));
        }
        
        return response;
      }

      async function generateTile(url) {
        const pathParts = url.pathname.split('/');
        const z = parseInt(pathParts[2]);
        const x = parseInt(pathParts[3]);
        const y = parseInt(pathParts[4].split('.')[0]);
        
        // Simple vector tile generation
        const bounds = tileToBounds(x, y, z);
        const features = await fetchFeaturesInBounds(bounds);
        
        const vectorTile = {
          layers: {
            data: {
              version: 2,
              name: 'data',
              extent: 4096,
              features: features.map(f => simplifyFeature(f, z))
            }
          }
        };
        
        return new Response(JSON.stringify(vectorTile), {
          headers: {
            'Content-Type': 'application/json',
            'Access-Control-Allow-Origin': '*',
            'Cache-Control': 'public, max-age=86400'
          }
        });
      }

      async function handleAPIRequest(request, url) {
        // Add request optimization for API calls
        const optimizedRequest = optimizeAPIRequest(request);
        
        // Check cache based on request parameters
        const cacheKey = generateAPICacheKey(url, request);
        const cache = caches.default;
        
        let response = await cache.match(cacheKey);
        
        if (!response) {
          response = await fetch(optimizedRequest);
          
          if (response.ok && shouldCacheAPIResponse(url)) {
            const cacheResponse = response.clone();
            cacheResponse.headers.set('Cache-Control', 'public, max-age=300');
            event.waitUntil(cache.put(cacheKey, cacheResponse));
          }
        }
        
        return response;
      }

      function tileToBounds(x, y, z) {
        const n = Math.PI - 2 * Math.PI * y / Math.pow(2, z);
        return [
          x / Math.pow(2, z) * 360 - 180,
          180 / Math.PI * Math.atan(0.5 * (Math.exp(n) - Math.exp(-n))),
          (x + 1) / Math.pow(2, z) * 360 - 180,
          180 / Math.PI * Math.atan(0.5 * (Math.exp(n - 2 * Math.PI / Math.pow(2, z)) - Math.exp(-n + 2 * Math.PI / Math.pow(2, z))))
        ];
      }

      function simplifyFeature(feature, zoom) {
        // Simplify geometry based on zoom level
        const tolerance = Math.max(0.0001, 0.01 / Math.pow(2, zoom));
        return {
          ...feature,
          geometry: simplifyGeometry(feature.geometry, tolerance)
        };
      }

      function optimizeAPIRequest(request) {
        // Add compression headers
        const headers = new Headers(request.headers);
        headers.set('Accept-Encoding', 'gzip, br');
        
        return new Request(request.url, {
          method: request.method,
          headers: headers,
          body: request.body
        });
      }

      function generateAPICacheKey(url, request) {
        const key = url.pathname + url.search;
        return new Request(key, {
          method: request.method,
          headers: {'Cache-Key': key}
        });
      }

      function shouldCacheAPIResponse(url) {
        // Don't cache user-specific or real-time data
        return !url.pathname.includes('/user/') && 
               !url.pathname.includes('/realtime/') &&
               !url.searchParams.has('nocache');
      }
    `;
  }

  // Performance monitoring for CDN
  async monitorCDNPerformance(): Promise<any> {
    const metrics = {
      hitRatio: await this.getCacheHitRatio(),
      averageResponseTime: await this.getAverageResponseTime(),
      bandwidthUsage: await this.getBandwidthUsage(),
      errorRate: await this.getErrorRate(),
      topCachedContent: await this.getTopCachedContent()
    };

    return metrics;
  }

  // Utility methods
  private findMatchingCacheRule(path: string): CacheRule {
    for (const rule of this.config.cacheRules) {
      if (path.match(new RegExp(rule.path))) {
        return rule;
      }
    }

    // Default rule
    return {
      path: '*',
      ttl: 3600,
      vary: ['Accept-Encoding'],
      browserTtl: 300
    };
  }

  private generateETag(contentType: string, path: string): string {
    const crypto = require('crypto');
    return crypto
      .createHash('md5')
      .update(contentType + path + Date.now())
      .digest('hex');
  }

  private async applyCloudflarePageRule(rule: any): Promise<void> {
    // Implementation for Cloudflare API calls
    console.log('Applying Cloudflare page rule:', rule);
  }

  private async enableArgoSmartRouting(): Promise<void> {
    // Enable Argo Smart Routing via API
    console.log('Enabling Argo Smart Routing');
  }

  private async deployOptimizationWorker(): Promise<void> {
    const workerScript = this.generateEdgeWorkerScript();
    // Deploy worker via Cloudflare Workers API
    console.log('Deploying optimization worker');
  }

  private getOptimizedCachePolicyId(): string {
    return 'managed-caching-optimized';
  }

  private getSecurityHeadersPolicyId(): string {
    return 'managed-security-headers';
  }

  private getLongTermCachePolicyId(): string {
    return 'managed-caching-optimized-for-uncompressed-objects';
  }

  private getAPIResponseCachePolicyId(): string {
    return 'managed-caching-disabled';
  }

  private async createOrUpdateCloudFrontDistribution(config: any): Promise<void> {
    // AWS CloudFront API implementation
    console.log('Creating CloudFront distribution:', config);
  }

  private async createOrUpdateAzureCDNProfile(profile: any): Promise<void> {
    // Azure CDN API implementation
    console.log('Creating Azure CDN profile:', profile);
  }

  private async applyAzureCustomRules(rules: any[]): Promise<void> {
    // Azure CDN custom rules implementation
    console.log('Applying Azure custom rules:', rules);
  }

  private async getCacheHitRatio(): Promise<number> {
    // Implementation depends on CDN provider
    return 0.85; // Example value
  }

  private async getAverageResponseTime(): Promise<number> {
    // Implementation depends on CDN provider
    return 150; // Example value in ms
  }

  private async getBandwidthUsage(): Promise<number> {
    // Implementation depends on CDN provider
    return 1024; // Example value in MB
  }

  private async getErrorRate(): Promise<number> {
    // Implementation depends on CDN provider
    return 0.02; // Example value (2%)
  }

  private async getTopCachedContent(): Promise<string[]> {
    // Implementation depends on CDN provider
    return ['/tiles/14/8192/8192.json', '/api/features/popular'];
  }
}

14.6. Performance Monitoring and Analytics

14.6.1. Comprehensive Performance Monitoring

// server/src/services/performanceMonitoringService.ts
interface PerformanceMetric {
  timestamp: Date;
  metricType: 'response_time' | 'query_duration' | 'cache_hit' | 'memory_usage' | 'cpu_usage';
  value: number;
  metadata?: Record<string, any>;
  tags?: string[];
}

interface PerformanceBudget {
  metric: string;
  threshold: number;
  timeWindow: number; // in seconds
  severity: 'warning' | 'critical';
}

export class PerformanceMonitoringService {
  private metrics: PerformanceMetric[] = [];
  private budgets: PerformanceBudget[] = [];
  private alerting: AlertingService;

  constructor() {
    this.alerting = new AlertingService();
    this.setupDefaultBudgets();
    this.startContinuousMonitoring();
  }

  // Record performance metrics
  recordMetric(metric: PerformanceMetric): void {
    this.metrics.push({
      ...metric,
      timestamp: new Date()
    });

    // Keep only recent metrics to prevent memory leaks
    if (this.metrics.length > 10000) {
      this.metrics = this.metrics.slice(-5000);
    }

    // Check against performance budgets
    this.checkPerformanceBudgets(metric);
  }

  // Express middleware for automatic request monitoring
  requestMonitoringMiddleware() {
    return (req: any, res: any, next: any) => {
      const startTime = process.hrtime.bigint();
      const startMemory = process.memoryUsage();

      // Monitor request completion
      res.on('finish', () => {
        const endTime = process.hrtime.bigint();
        const endMemory = process.memoryUsage();
        
        const responseTime = Number(endTime - startTime) / 1_000_000; // Convert to milliseconds
        const memoryDelta = endMemory.heapUsed - startMemory.heapUsed;

        this.recordMetric({
          timestamp: new Date(),
          metricType: 'response_time',
          value: responseTime,
          metadata: {
            method: req.method,
            path: req.path,
            statusCode: res.statusCode,
            contentLength: res.get('Content-Length'),
            userAgent: req.get('User-Agent'),
            memoryDelta
          },
          tags: [
            `method:${req.method}`,
            `status:${res.statusCode}`,
            `path:${req.path.split('/')[1]}`
          ]
        });

        // Record memory usage if significant change
        if (Math.abs(memoryDelta) > 1024 * 1024) { // 1MB threshold
          this.recordMetric({
            timestamp: new Date(),
            metricType: 'memory_usage',
            value: endMemory.heapUsed,
            metadata: {
              delta: memoryDelta,
              request: `${req.method} ${req.path}`
            }
          });
        }
      });

      next();
    };
  }

  // Database query monitoring
  monitorDatabaseQuery<T>(
    query: string,
    params: any[],
    executor: () => Promise<T>
  ): Promise<T> {
    return new Promise(async (resolve, reject) => {
      const startTime = process.hrtime.bigint();
      
      try {
        const result = await executor();
        const endTime = process.hrtime.bigint();
        const duration = Number(endTime - startTime) / 1_000_000;

        this.recordMetric({
          timestamp: new Date(),
          metricType: 'query_duration',
          value: duration,
          metadata: {
            query: this.sanitizeQuery(query),
            paramCount: params.length,
            resultSize: this.estimateResultSize(result)
          },
          tags: ['database:postgresql']
        });

        resolve(result);
      } catch (error) {
        const endTime = process.hrtime.bigint();
        const duration = Number(endTime - startTime) / 1_000_000;

        this.recordMetric({
          timestamp: new Date(),
          metricType: 'query_duration',
          value: duration,
          metadata: {
            query: this.sanitizeQuery(query),
            error: error instanceof Error ? error.message : 'Unknown error'
          },
          tags: ['database:postgresql', 'status:error']
        });

        reject(error);
      }
    });
  }

  // Cache performance monitoring
  monitorCacheOperation(
    operation: 'hit' | 'miss' | 'set' | 'delete',
    key: string,
    duration?: number
  ): void {
    this.recordMetric({
      timestamp: new Date(),
      metricType: 'cache_hit',
      value: operation === 'hit' ? 1 : 0,
      metadata: {
        operation,
        key: this.sanitizeCacheKey(key),
        duration
      },
      tags: [`cache:${operation}`]
    });
  }

  // Real-time performance dashboard data
  getPerformanceDashboard(timeWindow: number = 3600): any {
    const since = new Date(Date.now() - timeWindow * 1000);
    const recentMetrics = this.metrics.filter(m => m.timestamp >= since);

    return {
      summary: this.calculateSummaryMetrics(recentMetrics),
      responseTime: this.analyzeResponseTimes(recentMetrics),
      databasePerformance: this.analyzeDatabasePerformance(recentMetrics),
      cachePerformance: this.analyzeCachePerformance(recentMetrics),
      memoryUsage: this.analyzeMemoryUsage(recentMetrics),
      alerts: this.getActiveAlerts(),
      trends: this.calculateTrends(recentMetrics)
    };
  }

  // Performance budgets and alerting
  private setupDefaultBudgets(): void {
    this.budgets = [
      {
        metric: 'response_time',
        threshold: 500, // 500ms
        timeWindow: 300, // 5 minutes
        severity: 'warning'
      },
      {
        metric: 'response_time',
        threshold: 1000, // 1 second
        timeWindow: 300,
        severity: 'critical'
      },
      {
        metric: 'query_duration',
        threshold: 1000, // 1 second
        timeWindow: 300,
        severity: 'warning'
      },
      {
        metric: 'memory_usage',
        threshold: 512 * 1024 * 1024, // 512MB
        timeWindow: 600, // 10 minutes
        severity: 'warning'
      }
    ];
  }

  private checkPerformanceBudgets(metric: PerformanceMetric): void {
    const relevantBudgets = this.budgets.filter(b => 
      b.metric === metric.metricType
    );

    for (const budget of relevantBudgets) {
      if (metric.value > budget.threshold) {
        this.alerting.triggerAlert({
          type: 'performance_budget_exceeded',
          severity: budget.severity,
          metric: budget.metric,
          value: metric.value,
          threshold: budget.threshold,
          metadata: metric.metadata
        });
      }
    }
  }

  // Continuous monitoring setup
  private startContinuousMonitoring(): void {
    // Monitor system resources every 30 seconds
    setInterval(() => {
      const memUsage = process.memoryUsage();
      const cpuUsage = process.cpuUsage();

      this.recordMetric({
        timestamp: new Date(),
        metricType: 'memory_usage',
        value: memUsage.heapUsed,
        metadata: {
          heapTotal: memUsage.heapTotal,
          external: memUsage.external,
          arrayBuffers: memUsage.arrayBuffers
        }
      });

      this.recordMetric({
        timestamp: new Date(),
        metricType: 'cpu_usage',
        value: cpuUsage.user + cpuUsage.system,
        metadata: {
          user: cpuUsage.user,
          system: cpuUsage.system
        }
      });
    }, 30000);

    // Generate performance reports every 5 minutes
    setInterval(() => {
      const report = this.generatePerformanceReport();
      console.log('Performance Report:', report);
      
      // Send to external monitoring service if configured
      this.sendToExternalMonitoring(report);
    }, 300000);
  }

  // Analysis methods
  private calculateSummaryMetrics(metrics: PerformanceMetric[]): any {
    const responseTimeMetrics = metrics.filter(m => m.metricType === 'response_time');
    const queryMetrics = metrics.filter(m => m.metricType === 'query_duration');
    const cacheMetrics = metrics.filter(m => m.metricType === 'cache_hit');

    return {
      totalRequests: responseTimeMetrics.length,
      averageResponseTime: this.calculateAverage(responseTimeMetrics.map(m => m.value)),
      p95ResponseTime: this.calculatePercentile(responseTimeMetrics.map(m => m.value), 95),
      averageQueryTime: this.calculateAverage(queryMetrics.map(m => m.value)),
      cacheHitRate: cacheMetrics.length > 0 
        ? cacheMetrics.filter(m => m.value === 1).length / cacheMetrics.length 
        : 0
    };
  }

  private analyzeResponseTimes(metrics: PerformanceMetric[]): any {
    const responseTimeMetrics = metrics.filter(m => m.metricType === 'response_time');
    
    if (responseTimeMetrics.length === 0) return null;

    const byPath = this.groupBy(responseTimeMetrics, m => m.metadata?.path || 'unknown');
    const pathAnalysis = Object.entries(byPath).map(([path, pathMetrics]) => ({
      path,
      count: pathMetrics.length,
      average: this.calculateAverage(pathMetrics.map(m => m.value)),
      p95: this.calculatePercentile(pathMetrics.map(m => m.value), 95),
      max: Math.max(...pathMetrics.map(m => m.value))
    }));

    return {
      overall: {
        count: responseTimeMetrics.length,
        average: this.calculateAverage(responseTimeMetrics.map(m => m.value)),
        p50: this.calculatePercentile(responseTimeMetrics.map(m => m.value), 50),
        p95: this.calculatePercentile(responseTimeMetrics.map(m => m.value), 95),
        p99: this.calculatePercentile(responseTimeMetrics.map(m => m.value), 99)
      },
      byPath: pathAnalysis.sort((a, b) => b.average - a.average).slice(0, 10)
    };
  }

  private analyzeDatabasePerformance(metrics: PerformanceMetric[]): any {
    const queryMetrics = metrics.filter(m => m.metricType === 'query_duration');
    
    if (queryMetrics.length === 0) return null;

    const slowQueries = queryMetrics.filter(m => m.value > 1000); // Slower than 1 second
    const errorQueries = queryMetrics.filter(m => m.metadata?.error);

    return {
      totalQueries: queryMetrics.length,
      averageDuration: this.calculateAverage(queryMetrics.map(m => m.value)),
      slowQueryCount: slowQueries.length,
      errorCount: errorQueries.length,
      slowQueries: slowQueries.slice(-5).map(m => ({
        duration: m.value,
        query: m.metadata?.query,
        timestamp: m.timestamp
      }))
    };
  }

  private analyzeCachePerformance(metrics: PerformanceMetric[]): any {
    const cacheMetrics = metrics.filter(m => m.metricType === 'cache_hit');
    
    if (cacheMetrics.length === 0) return null;

    const hits = cacheMetrics.filter(m => m.value === 1);
    const misses = cacheMetrics.filter(m => m.value === 0);

    return {
      totalOperations: cacheMetrics.length,
      hitCount: hits.length,
      missCount: misses.length,
      hitRate: hits.length / cacheMetrics.length,
      operationsByType: this.groupBy(cacheMetrics, m => m.metadata?.operation || 'unknown')
    };
  }

  private analyzeMemoryUsage(metrics: PerformanceMetric[]): any {
    const memoryMetrics = metrics.filter(m => m.metricType === 'memory_usage');
    
    if (memoryMetrics.length === 0) return null;

    const values = memoryMetrics.map(m => m.value);
    const latest = memoryMetrics[memoryMetrics.length - 1];

    return {
      current: latest.value,
      average: this.calculateAverage(values),
      max: Math.max(...values),
      min: Math.min(...values),
      trend: this.calculateTrend(values.slice(-10)) // Last 10 readings
    };
  }

  private getActiveAlerts(): any[] {
    // Return active alerts from alerting service
    return this.alerting.getActiveAlerts();
  }

  private calculateTrends(metrics: PerformanceMetric[]): any {
    const responseTimeMetrics = metrics.filter(m => m.metricType === 'response_time');
    const queryMetrics = metrics.filter(m => m.metricType === 'query_duration');

    return {
      responseTime: this.calculateTrend(responseTimeMetrics.map(m => m.value)),
      queryDuration: this.calculateTrend(queryMetrics.map(m => m.value))
    };
  }

  // Utility methods
  private calculateAverage(values: number[]): number {
    return values.length > 0 ? values.reduce((sum, val) => sum + val, 0) / values.length : 0;
  }

  private calculatePercentile(values: number[], percentile: number): number {
    if (values.length === 0) return 0;
    
    const sorted = [...values].sort((a, b) => a - b);
    const index = Math.ceil((percentile / 100) * sorted.length) - 1;
    return sorted[Math.max(0, index)];
  }

  private calculateTrend(values: number[]): 'increasing' | 'decreasing' | 'stable' {
    if (values.length < 2) return 'stable';
    
    const firstHalf = values.slice(0, Math.floor(values.length / 2));
    const secondHalf = values.slice(Math.floor(values.length / 2));
    
    const firstAvg = this.calculateAverage(firstHalf);
    const secondAvg = this.calculateAverage(secondHalf);
    
    const changePercent = Math.abs((secondAvg - firstAvg) / firstAvg) * 100;
    
    if (changePercent < 5) return 'stable';
    return secondAvg > firstAvg ? 'increasing' : 'decreasing';
  }

  private groupBy<T>(array: T[], keyFn: (item: T) => string): Record<string, T[]> {
    return array.reduce((groups, item) => {
      const key = keyFn(item);
      if (!groups[key]) {
        groups[key] = [];
      }
      groups[key].push(item);
      return groups;
    }, {} as Record<string, T[]>);
  }

  private sanitizeQuery(query: string): string {
    // Remove sensitive data from query strings for logging
    return query
      .replace(/\$\d+/g, '?') // Replace parameter placeholders
      .substring(0, 100); // Limit length
  }

  private sanitizeCacheKey(key: string): string {
    // Remove sensitive data from cache keys
    return key.substring(0, 50);
  }

  private estimateResultSize(result: any): number {
    try {
      return JSON.stringify(result).length;
    } catch {
      return 0;
    }
  }

  private generatePerformanceReport(): any {
    const dashboard = this.getPerformanceDashboard();
    
    return {
      timestamp: new Date(),
      summary: dashboard.summary,
      issues: this.identifyPerformanceIssues(dashboard),
      recommendations: this.generateRecommendations(dashboard)
    };
  }

  private identifyPerformanceIssues(dashboard: any): string[] {
    const issues: string[] = [];
    
    if (dashboard.summary.averageResponseTime > 500) {
      issues.push('High average response time detected');
    }
    
    if (dashboard.summary.cacheHitRate < 0.8) {
      issues.push('Low cache hit rate detected');
    }
    
    if (dashboard.databasePerformance?.slowQueryCount > 0) {
      issues.push('Slow database queries detected');
    }
    
    return issues;
  }

  private generateRecommendations(dashboard: any): string[] {
    const recommendations: string[] = [];
    
    if (dashboard.summary.averageResponseTime > 500) {
      recommendations.push('Consider implementing response caching or optimizing slow endpoints');
    }
    
    if (dashboard.summary.cacheHitRate < 0.8) {
      recommendations.push('Review cache invalidation strategy and increase cache TTL where appropriate');
    }
    
    return recommendations;
  }

  private sendToExternalMonitoring(report: any): void {
    // Send to external monitoring services (DataDog, New Relic, etc.)
    console.log('Sending performance report to external monitoring');
  }
}

// Alerting service for performance issues
class AlertingService {
  private activeAlerts: any[] = [];

  triggerAlert(alert: any): void {
    this.activeAlerts.push({
      ...alert,
      id: this.generateAlertId(),
      timestamp: new Date(),
      status: 'active'
    });

    // Send notifications (email, Slack, etc.)
    this.sendNotification(alert);
  }

  getActiveAlerts(): any[] {
    return this.activeAlerts.filter(alert => alert.status === 'active');
  }

  private generateAlertId(): string {
    return Math.random().toString(36).substring(2, 15);
  }

  private sendNotification(alert: any): void {
    // Implementation for sending notifications
    console.log('Sending alert notification:', alert);
  }
}

14.7. Summary

Performance optimization in Web GIS applications requires a comprehensive approach addressing frontend rendering, backend processing, network delivery, and continuous monitoring. The unique challenges of geospatial data—large datasets, complex geometries, and real-time requirements—demand specialized optimization strategies beyond traditional web application techniques.

Frontend optimization focuses on efficient rendering through level-of-detail management, progressive loading, feature clustering, and memory management. Component-level optimizations using React memoization, throttled updates, and virtualized rendering ensure smooth user interactions even with large datasets.

Backend optimization emphasizes spatial indexing, query optimization, and multi-level caching strategies. Database partitioning, materialized views, and optimized spatial queries provide the foundation for high-performance data access. Caching strategies spanning memory, Redis, and CDN layers ensure optimal data delivery across different access patterns.

CDN and edge computing configurations enable global performance optimization through strategic content placement, dynamic tile generation, and intelligent caching rules. Edge workers provide opportunities for real-time data processing closer to users, reducing latency and improving responsiveness.

Comprehensive performance monitoring provides the insights needed to identify bottlenecks, track performance trends, and ensure applications meet performance budgets. Real-time metrics, automated alerting, and detailed analysis enable proactive performance management and continuous optimization.

These optimization strategies form the foundation for deploying scalable Web GIS applications that provide excellent user experiences regardless of data complexity or user location.

14.8. Exercises

14.8.1. Exercise 14.1: Frontend Performance Optimization

Objective: Implement comprehensive frontend performance optimizations for a mapping application with large datasets.

Instructions:

1. Level-of-detail rendering system:

   • Implement dynamic geometry simplification based on zoom level

   • Create feature filtering based on importance and viewport

   • Build progressive loading with user feedback

   • Add memory management and cleanup routines

2. Component optimization:

   • Implement React.memo for expensive map components

   • Add throttled viewport change handlers

   • Create virtualized layer rendering for large layer counts

   • Build performance monitoring components

3. Rendering optimization:

   • Implement efficient clustering for point data

   • Add WebGL-based rendering where appropriate

   • Create efficient update mechanisms for dynamic data

   • Optimize tile loading and caching strategies

Deliverable: A highly optimized mapping component with measurable performance improvements and built-in monitoring.

14.8.2. Exercise 14.2: Database and Backend Optimization

Objective: Optimize database queries and backend processing for spatial data operations.

Instructions:

1. Spatial indexing strategy:

   • Create appropriate spatial indexes for different query patterns

   • Implement table partitioning for large datasets

   • Build materialized views for common aggregations

   • Add query optimization monitoring

2. Query optimization:

   • Implement efficient bounding box queries

   • Create optimized nearest neighbor searches

   • Build clustering queries for map visualization

   • Add query performance monitoring and alerting

3. Backend processing optimization:

   • Implement efficient data transformation pipelines

   • Create background processing for expensive operations

   • Build request batching and optimization

   • Add comprehensive error handling and recovery

Deliverable: An optimized backend system with significant query performance improvements and monitoring capabilities.

14.8.3. Exercise 14.3: Multi-Level Caching Implementation

Objective: Design and implement a comprehensive caching strategy spanning multiple layers.

Instructions:

1. Memory caching layer:

   • Implement LRU cache for frequently accessed data

   • Add cache warming strategies

   • Create efficient cache invalidation mechanisms

   • Build cache performance monitoring

2. Redis distributed caching:

   • Implement spatial-aware cache keys

   • Create efficient batch operations

   • Add compression for large cache entries

   • Build cache clustering for high availability

3. CDN and edge caching:

   • Configure CDN for optimal geospatial content delivery

   • Implement edge workers for dynamic content generation

   • Create intelligent cache invalidation strategies

   • Add CDN performance monitoring and optimization

Deliverable: A comprehensive multi-level caching system with measurable performance improvements and monitoring.

14.8.4. Exercise 14.4: CDN and Edge Computing Setup

Objective: Configure and optimize CDN infrastructure for global Web GIS application delivery.

Instructions:

1. CDN configuration:

   • Set up CDN with optimized cache rules for different content types

   • Configure compression and optimization settings

   • Implement geographic content distribution

   • Add security and performance headers

2. Edge computing implementation:

   • Deploy edge workers for dynamic tile generation

   • Implement API optimization at the edge

   • Create intelligent request routing

   • Add edge-side caching and optimization

3. Global optimization:

   • Configure multi-region deployment

   • Implement intelligent traffic routing

   • Add performance monitoring across regions

   • Create automated failover and recovery

Deliverable: A globally optimized CDN infrastructure with edge computing capabilities and comprehensive monitoring.

14.8.5. Exercise 14.5: Performance Monitoring and Analytics

Objective: Implement comprehensive performance monitoring with automated alerting and optimization recommendations.

Instructions:

1. Metrics collection:

   • Implement automatic request and response time monitoring

   • Add database query performance tracking

   • Create cache performance analytics

   • Build system resource monitoring

2. Performance budgets and alerting:

   • Define performance budgets for different application components

   • Implement automated alerting for budget violations

   • Create escalation and notification systems

   • Add trend analysis and prediction

3. Analytics and optimization:

   • Build real-time performance dashboards

   • Implement automated performance analysis

   • Create optimization recommendations

   • Add capacity planning and scaling insights

Deliverable: A comprehensive performance monitoring system with automated alerting and optimization recommendations.

14.8.6. Exercise 14.6: Load Testing and Optimization

Objective: Conduct comprehensive load testing and implement optimizations based on results.

Instructions:

1. Load testing framework:

   • Design realistic load testing scenarios for Web GIS applications

   • Implement automated load testing with various user patterns

   • Create geographic distribution testing

   • Add performance baseline establishment

2. Bottleneck identification:

   • Analyze load testing results to identify bottlenecks

   • Create detailed performance profiling

   • Implement automated bottleneck detection

   • Add root cause analysis capabilities

3. Optimization implementation:

   • Implement targeted optimizations based on testing results

   • Create before/after performance comparisons

   • Add automated optimization testing

   • Build continuous performance improvement processes

Deliverable: A comprehensive load testing framework with automated optimization identification and implementation.

14.8.7. Exercise 14.7: Progressive Web App Optimization

Objective: Optimize a Web GIS application as a Progressive Web App with offline capabilities.

Instructions:

1. Service worker implementation:

   • Implement intelligent caching strategies for map tiles and data

   • Create offline functionality for critical features

   • Add background synchronization for data updates

   • Build efficient cache management and cleanup

2. Progressive enhancement:

   • Implement adaptive loading based on connection quality

   • Create fallback strategies for poor connectivity

   • Add progressive image and tile loading

   • Build intelligent prefetching and preloading

3. Performance optimization for mobile:

   • Optimize touch interactions and gestures

   • Implement efficient memory management for mobile devices

   • Create adaptive quality settings based on device capabilities

   • Add battery usage optimization

Deliverable: A fully optimized Progressive Web App with excellent offline capabilities and mobile performance.

Reflection Questions:

• How do the performance requirements of Web GIS applications differ from traditional web applications?

• What are the most effective caching strategies for different types of geospatial data?

• How can edge computing improve the global performance of Web GIS applications?

• What metrics are most important for monitoring Web GIS application performance?

14.9. Further Reading

• Web Performance Best Practices

• PostgreSQL Performance Tuning

• Redis Caching Strategies

• CDN Best Practices for Geographic Content

• MapLibre GL JS Performance Guide