Chapter 7: Query Optimization and Performance

Overview

Query optimization and performance tuning are critical for building scalable applications with Vektagraf. This chapter covers comprehensive strategies for optimizing database operations, from automatic query optimization and intelligent indexing to advanced caching strategies and performance monitoring. You'll learn how to identify bottlenecks, implement optimization techniques, and build high-performance applications that scale efficiently.

Learning Objectives

By the end of this chapter, you will be able to:

• Implement automatic query optimization using Vektagraf's built-in optimizer
• Design and manage efficient indexing strategies for different query patterns
• Optimize vector search operations for maximum performance
• Implement advanced caching strategies to reduce query latency
• Monitor and analyze performance metrics to identify bottlenecks
• Tune memory usage and resource allocation for optimal performance
• Build scalable applications with predictable performance characteristics

Prerequisites

• Completed Chapter 4: Database Operations and Transactions
• Completed Chapter 5: Vector Search and Similarity Operations
• Completed Chapter 6: Graph Operations and Relationship Modeling
• Understanding of database indexing concepts and performance analysis

Core Concepts

Query Optimization Pipeline

Vektagraf's query optimizer follows a multi-stage optimization pipeline:

1. Query Analysis: Parse and analyze query structure
2. Index Selection: Choose optimal indexes for query execution
3. Operation Reordering: Reorder operations for maximum efficiency
4. Caching Strategy: Determine caching opportunities
5. Execution Planning: Generate optimized execution plan
6. Performance Monitoring: Track execution metrics for future optimization

Indexing Strategy

Vektagraf provides multiple indexing mechanisms:

• Primary Indexes: Automatic indexes on object IDs and types
• Property Indexes: Hash-based indexes for exact value lookups
• Sorted Indexes: Tree-based indexes for range queries and sorting
• Vector Indexes: Specialized indexes for similarity search (HNSW, IVFFlat)
• Graph Indexes: Relationship-based indexes for traversal operations

Performance Metrics

Key performance indicators to monitor:

• Query Latency: Time to execute individual queries
• Throughput: Queries processed per second
• Memory Usage: RAM consumption by indexes and caches
• Cache Hit Rate: Percentage of queries served from cache
• Index Efficiency: Ratio of indexed vs. full-scan operations## Pra ctical Examples

Automatic Query Optimization

Setting Up Query Optimization

import 'package:vektagraf/vektagraf.dart';

Future<void> setupQueryOptimization() async {
  final database = VektagrafDatabaseImpl();
  
  // Configure database with optimization enabled
  final config = VektagrafConfig(
    enableQueryOptimization: true,
    enableQueryCache: true,
    maxCacheSize: 1000,
    defaultCacheTtl: Duration(minutes: 5),
    enableAutoIndexing: true,
    indexThreshold: 10, // Create index after 10 queries
    maxIndexes: 100,
  );
  
  await database.open('optimized_database.db', config: config);
  
  try {
    // Get query optimizer instance
    final optimizer = database.queryOptimizer;
    if (optimizer != null) {
      print('Query optimization enabled');
      print('Cache enabled: ${config.enableQueryCache}');
      print('Auto-indexing enabled: ${config.enableAutoIndexing}');
    }
    
    // Example: Create sample data for optimization testing
    await _createSampleData(database);
    
    // Demonstrate different query patterns
    await _demonstrateQueryOptimization(database);
    
  } finally {
    await database.close();
  }
}

Future<void> _createSampleData(VektagrafDatabase database) async {
  final users = await database.objects<User>();
  final posts = await database.objects<Post>();
  
  // Create users with various properties for indexing
  final sampleUsers = List.generate(1000, (index) => User(
    id: VektagrafId.generate(),
    username: 'user_$index',
    email: 'user$index@example.com',
    age: 20 + (index % 50),
    city: ['New York', 'London', 'Tokyo', 'Berlin', 'Sydney'][index % 5],
    joinDate: DateTime.now().subtract(Duration(days: index % 365)),
    isActive: index % 3 == 0,
    followerCount: index * 10,
  ));
  
  // Batch insert users
  await users.saveAllInTransaction(sampleUsers);
  
  // Create posts with relationships
  final samplePosts = <Post>[];
  for (int i = 0; i < 2000; i++) {
    final author = sampleUsers[i % sampleUsers.length];
    samplePosts.add(Post(
      id: VektagrafId.generate(),
      title: 'Post $i',
      content: 'This is the content of post $i',
      authorId: author.id,
      category: ['tech', 'lifestyle', 'news', 'sports'][i % 4],
      tags: ['tag${i % 10}', 'tag${(i + 1) % 10}'],
      likeCount: i % 100,
      createdAt: DateTime.now().subtract(Duration(hours: i % 24)),
    ));
  }
  
  await posts.saveAllInTransaction(samplePosts);
  
  print('Created ${sampleUsers.length} users and ${samplePosts.length} posts');
}

Future<void> _demonstrateQueryOptimization(VektagrafDatabase database) async {
  final users = await database.objects<User>();
  final posts = await database.objects<Post>();
  
  print('\n=== Query Optimization Demonstration ===');
  
  // Query 1: Property-based filtering (will create index automatically)
  print('\n1. Property-based filtering:');
  final stopwatch = Stopwatch()..start();
  
  final activeUsers = await users
      .whereProperty('isActive', true)
      .toList();
  
  stopwatch.stop();
  print('   Found ${activeUsers.length} active users in ${stopwatch.elapsedMilliseconds}ms');
  
  // Query 2: Range query (will create sorted index)
  print('\n2. Range query optimization:');
  stopwatch.reset()..start();
  
  final youngUsers = await users
      .wherePropertyRange<int>('age', 20, 30)
      .toList();
  
  stopwatch.stop();
  print('   Found ${youngUsers.length} young users in ${stopwatch.elapsedMilliseconds}ms');
  
  // Query 3: Sorted query (uses sorted index)
  print('\n3. Sorted query optimization:');
  stopwatch.reset()..start();
  
  final topUsers = await users
      .orderByProperty<int>('followerCount', descending: true)
      .take(10)
      .toList();
  
  stopwatch.stop();
  print('   Found top 10 users by followers in ${stopwatch.elapsedMilliseconds}ms');
  
  // Query 4: Complex chained query (optimizer reorders operations)
  print('\n4. Complex chained query optimization:');
  stopwatch.reset()..start();
  
  final complexQuery = await users
      .whereProperty('city', 'New York')
      .wherePropertyRange<int>('age', 25, 35)
      .whereProperty('isActive', true)
      .orderByProperty<DateTime>('joinDate', descending: true)
      .take(5)
      .toList();
  
  stopwatch.stop();
  print('   Complex query result: ${complexQuery.length} users in ${stopwatch.elapsedMilliseconds}ms');
  
  // Show optimizer statistics
  final optimizer = database.queryOptimizer;
  if (optimizer != null) {
    print('\n=== Query Optimizer Statistics ===');
    final stats = optimizer.queryStats;
    for (final entry in stats.entries) {
      final stat = entry.value;
      print('${entry.key}:');
      print('  Executions: ${stat.totalExecutions}');
      print('  Avg time: ${stat.averageExecutionTime.inMilliseconds}ms');
      print('  Avg results: ${stat.averageResultCount.toInt()}');
    }
  }
}

// Example model classes
class User {
  final VektagrafId id;
  final String username;
  final String email;
  final int age;
  final String city;
  final DateTime joinDate;
  final bool isActive;
  final int followerCount;
  
  User({
    required this.id,
    required this.username,
    required this.email,
    required this.age,
    required this.city,
    required this.joinDate,
    required this.isActive,
    required this.followerCount,
  });
}

class Post {
  final VektagrafId id;
  final String title;
  final String content;
  final VektagrafId authorId;
  final String category;
  final List<String> tags;
  final int likeCount;
  final DateTime createdAt;
  
  Post({
    required this.id,
    required this.title,
    required this.content,
    required this.authorId,
    required this.category,
    required this.tags,
    required this.likeCount,
    required this.createdAt,
  });
}

Advanced Index Management

class IndexManager {
  final VektagrafDatabase database;
  
  IndexManager(this.database);
  
  /// Analyzes query patterns and creates optimal indexes
  Future<void> analyzeAndOptimizeIndexes() async {
    print('=== Index Analysis and Optimization ===');
    
    // Get index manager from database
    final indexManager = database.indexManager;
    if (indexManager == null) {
      print('Index manager not available');
      return;
    }
    
    // Analyze current index usage
    final indexStats = indexManager.indexStats;
    print('\nCurrent Index Statistics:');
    
    for (final entry in indexStats.entries) {
      final propertyName = entry.key;
      final stats = entry.value;
      
      print('$propertyName:');
      print('  Usage count: ${stats.usageCount}');
      print('  Last used: ${stats.lastUsed}');
      print('  Created: ${stats.created}');
      print('  Time since creation: ${stats.timeSinceCreation.inMinutes} minutes');
    }
    
    // Get memory usage statistics
    final memoryStats = indexManager.memoryStats;
    print('\nIndex Memory Usage:');
    print('  Total indexes: ${memoryStats.totalIndexes}');
    print('  Total entries: ${memoryStats.entryCount}');
    print('  Memory usage: ${(memoryStats.memoryUsage / 1024 / 1024).toStringAsFixed(2)} MB');
    
    // Identify optimization opportunities
    await _identifyOptimizationOpportunities(indexManager);
    
    // Create recommended indexes
    await _createRecommendedIndexes(indexManager);
    
    // Clean up unused indexes
    await _cleanupUnusedIndexes(indexManager);
  }
  
  Future<void> _identifyOptimizationOpportunities(dynamic indexManager) async {
    print('\n=== Optimization Opportunities ===');
    
    // Analyze query patterns to identify missing indexes
    final users = await database.objects<User>();
    
    // Simulate various query patterns to trigger index analysis
    final queryPatterns = [
      () => users.whereProperty('city', 'New York'),
      () => users.wherePropertyRange<int>('age', 25, 35),
      () => users.orderByProperty<int>('followerCount', descending: true),
      () => users.whereProperty('isActive', true),
    ];
    
    for (int i = 0; i < queryPatterns.length; i++) {
      final pattern = queryPatterns[i];
      
      // Execute query multiple times to trigger auto-indexing
      for (int j = 0; j < 15; j++) {
        await pattern();
      }
      
      print('Executed query pattern ${i + 1} multiple times');
    }
    
    // Check which indexes were created automatically
    final newStats = indexManager.indexStats;
    print('\nAuto-created indexes:');
    for (final propertyName in newStats.keys) {
      if (indexManager.indexedProperties.contains(propertyName)) {
        print('  - $propertyName (usage: ${newStats[propertyName]?.usageCount})');
      }
    }
  }
  
  Future<void> _createRecommendedIndexes(dynamic indexManager) async {
    print('\n=== Creating Recommended Indexes ===');
    
    // Create indexes for commonly queried properties
    final recommendedIndexes = {
      'email': false, // Hash index for exact lookups
      'joinDate': true, // Sorted index for range queries
      'createdAt': true, // Sorted index for temporal queries
      'category': false, // Hash index for categorical data
    };
    
    for (final entry in recommendedIndexes.entries) {
      final propertyName = entry.key;
      final sorted = entry.value;
      
      if (!indexManager.indexedProperties.contains(propertyName)) {
        indexManager.createIndex(propertyName, sorted: sorted);
        print('Created ${sorted ? 'sorted' : 'hash'} index for $propertyName');
      }
    }
  }
  
  Future<void> _cleanupUnusedIndexes(dynamic indexManager) async {
    print('\n=== Cleaning Up Unused Indexes ===');
    
    final indexStats = indexManager.indexStats;
    final cutoffTime = DateTime.now().subtract(Duration(hours: 24));
    
    for (final entry in indexStats.entries) {
      final propertyName = entry.key;
      final stats = entry.value;
      
      // Remove indexes that haven't been used recently and have low usage
      if (stats.usageCount < 5 && 
          (stats.lastUsed == null || stats.lastUsed!.isBefore(cutoffTime))) {
        
        // Don't remove essential indexes
        if (!['id', 'type', 'createdAt', 'updatedAt'].contains(propertyName)) {
          indexManager.dropIndex(propertyName);
          print('Dropped unused index for $propertyName');
        }
      }
    }
  }
}
```#
## Vector Search Optimization

#### Optimizing Vector Space Performance

```dart
class VectorSearchOptimizer {
  final VektagrafDatabase database;
  
  VectorSearchOptimizer(this.database);
  
  /// Optimizes vector search performance based on usage patterns
  Future<void> optimizeVectorSpaces() async {
    print('=== Vector Search Optimization ===');
    
    // Create sample vector data for optimization testing
    await _createVectorData();
    
    // Test different vector space configurations
    await _testVectorSpaceConfigurations();
    
    // Optimize existing vector spaces
    await _optimizeExistingVectorSpaces();
    
    // Monitor vector search performance
    await _monitorVectorPerformance();
  }
  
  Future<void> _createVectorData() async {
    print('\n1. Creating vector data for optimization testing...');
    
    // Create different vector spaces for testing
    final documentVectors = database.vectorSpace('documents', 384);
    final productVectors = database.vectorSpace('products', 512);
    final userVectors = database.vectorSpace('users', 256);
    
    // Add vectors with different characteristics
    final random = Random();
    
    // Document vectors (text embeddings)
    for (int i = 0; i < 10000; i++) {
      final vector = List.generate(384, (_) => random.nextGaussian());
      await documentVectors.addVector(vector, metadata: {
        'document_id': 'doc_$i',
        'category': ['tech', 'science', 'business', 'health'][i % 4],
        'length': 100 + random.nextInt(1000),
        'created_at': DateTime.now().subtract(Duration(days: i % 365)),
      });
    }
    
    // Product vectors (feature embeddings)
    for (int i = 0; i < 5000; i++) {
      final vector = List.generate(512, (_) => random.nextGaussian());
      await productVectors.addVector(vector, metadata: {
        'product_id': 'prod_$i',
        'category': ['electronics', 'clothing', 'books', 'home'][i % 4],
        'price': 10.0 + random.nextDouble() * 1000,
        'rating': 1.0 + random.nextDouble() * 4,
      });
    }
    
    // User vectors (preference embeddings)
    for (int i = 0; i < 2000; i++) {
      final vector = List.generate(256, (_) => random.nextGaussian());
      await userVectors.addVector(vector, metadata: {
        'user_id': 'user_$i',
        'age_group': ['18-25', '26-35', '36-45', '46+'][i % 4],
        'activity_level': ['low', 'medium', 'high'][i % 3],
      });
    }
    
    print('Created vectors in 3 different spaces');
  }
  
  Future<void> _testVectorSpaceConfigurations() async {
    print('\n2. Testing vector space configurations...');
    
    // Test different algorithms and parameters
    final configurations = [
      {
        'name': 'hnsw_default',
        'algorithm': 'hnsw',
        'parameters': {'maxConnections': 16, 'efConstruction': 200, 'efSearch': 50},
      },
      {
        'name': 'hnsw_high_recall',
        'algorithm': 'hnsw',
        'parameters': {'maxConnections': 32, 'efConstruction': 400, 'efSearch': 100},
      },
      {
        'name': 'ivfflat_default',
        'algorithm': 'ivfflat',
        'parameters': {'nLists': 100, 'nProbe': 10},
      },
      {
        'name': 'ivfflat_high_accuracy',
        'algorithm': 'ivfflat',
        'parameters': {'nLists': 200, 'nProbe': 20},
      },
    ];
    
    final queryVector = List.generate(384, (_) => Random().nextGaussian());
    
    for (final config in configurations) {
      await _benchmarkConfiguration(config, queryVector);
    }
  }
  
  Future<void> _benchmarkConfiguration(
    Map<String, dynamic> config,
    List<double> queryVector,
  ) async {
    final name = config['name'] as String;
    print('\nTesting configuration: $name');
    
    // Create vector space with specific configuration
    final vectorSpace = database.vectorSpace('test_$name', 384);
    
    // Benchmark search performance
    final stopwatch = Stopwatch();
    final latencies = <int>[];
    
    // Warm up
    for (int i = 0; i < 5; i++) {
      await vectorSpace.similaritySearch(queryVector, 10);
    }
    
    // Measure performance
    for (int i = 0; i < 50; i++) {
      stopwatch.reset()..start();
      final results = await vectorSpace.similaritySearch(queryVector, 10);
      stopwatch.stop();
      
      latencies.add(stopwatch.elapsedMicroseconds);
    }
    
    // Calculate statistics
    latencies.sort();
    final avgLatency = latencies.reduce((a, b) => a + b) / latencies.length;
    final p50 = latencies[latencies.length ~/ 2];
    final p95 = latencies[(latencies.length * 0.95).floor()];
    
    print('  Average latency: ${(avgLatency / 1000).toStringAsFixed(2)}ms');
    print('  P50 latency: ${(p50 / 1000).toStringAsFixed(2)}ms');
    print('  P95 latency: ${(p95 / 1000).toStringAsFixed(2)}ms');
    
    // Measure memory usage (simplified)
    final vectorCount = await vectorSpace.vectorCount;
    final estimatedMemory = vectorCount * 384 * 4; // 4 bytes per float
    print('  Estimated memory: ${(estimatedMemory / 1024 / 1024).toStringAsFixed(2)}MB');
  }
  
  Future<void> _optimizeExistingVectorSpaces() async {
    print('\n3. Optimizing existing vector spaces...');
    
    // Get vector space optimizer
    final optimizer = VectorSpaceOptimizer(
      database: database,
      configurationManager: database.vectorSpaceConfigurationManager,
    );
    
    await optimizer.initialize();
    
    // Collect usage metrics
    await optimizer.collectUsageMetrics('documents');
    await optimizer.collectUsageMetrics('products');
    await optimizer.collectUsageMetrics('users');
    
    // Get optimization recommendations
    final documentRecommendations = await optimizer.getOptimizationRecommendations('documents');
    
    print('Optimization recommendations for documents vector space:');
    for (final recommendation in documentRecommendations) {
      print('  - ${recommendation.description}');
      print('    Impact: ${recommendation.impact}');
      print('    Confidence: ${(recommendation.confidence * 100).toStringAsFixed(1)}%');
      print('    Risk: ${recommendation.risk}');
    }
    
    // Apply automatic optimizations
    await optimizer.applyAutomaticOptimizations();
    
    await optimizer.dispose();
  }
  
  Future<void> _monitorVectorPerformance() async {
    print('\n4. Monitoring vector search performance...');
    
    final documentVectors = database.vectorSpace('documents', 384);
    final queryVector = List.generate(384, (_) => Random().nextGaussian());
    
    // Create performance monitor
    final monitor = VectorPerformanceMonitor(documentVectors);
    
    // Simulate various query patterns
    final queryPatterns = [
      {'limit': 5, 'description': 'Small result set'},
      {'limit': 20, 'description': 'Medium result set'},
      {'limit': 100, 'description': 'Large result set'},
    ];
    
    for (final pattern in queryPatterns) {
      final limit = pattern['limit'] as int;
      final description = pattern['description'] as String;
      
      print('\nTesting $description (limit: $limit):');
      
      final results = await monitor.monitoredSearch(queryVector, limit);
      final stats = monitor.getStatistics();
      
      print('  Results: ${results.length}');
      print('  Average latency: ${stats.averageDuration.inMilliseconds}ms');
      print('  Success rate: ${(stats.successRate * 100).toStringAsFixed(1)}%');
    }
  }
}

class VectorPerformanceMonitor {
  final VektagrafVectorSpace vectorSpace;
  final List<SearchMetric> _metrics = [];
  
  VectorPerformanceMonitor(this.vectorSpace);
  
  Future<List<VectorSearchResult>> monitoredSearch(
    List<double> queryVector,
    int limit,
  ) async {
    final stopwatch = Stopwatch()..start();
    
    try {
      final results = await vectorSpace.similaritySearch(queryVector, limit);
      stopwatch.stop();
      
      _metrics.add(SearchMetric(
        timestamp: DateTime.now(),
        duration: stopwatch.elapsed,
        resultCount: results.length,
        success: true,
      ));
      
      return results;
    } catch (e) {
      stopwatch.stop();
      
      _metrics.add(SearchMetric(
        timestamp: DateTime.now(),
        duration: stopwatch.elapsed,
        resultCount: 0,
        success: false,
        error: e.toString(),
      ));
      
      rethrow;
    }
  }
  
  SearchStatistics getStatistics() {
    if (_metrics.isEmpty) {
      return SearchStatistics.empty();
    }
    
    final successfulSearches = _metrics.where((m) => m.success);
    final durations = successfulSearches.map((m) => m.duration);
    
    return SearchStatistics(
      totalSearches: _metrics.length,
      successfulSearches: successfulSearches.length,
      averageDuration: durations.isEmpty 
          ? Duration.zero
          : Duration(microseconds: 
              durations.map((d) => d.inMicroseconds).reduce((a, b) => a + b) ~/ 
              durations.length),
      successRate: _metrics.isNotEmpty 
          ? successfulSearches.length / _metrics.length 
          : 0.0,
    );
  }
}

class SearchMetric {
  final DateTime timestamp;
  final Duration duration;
  final int resultCount;
  final bool success;
  final String? error;
  
  SearchMetric({
    required this.timestamp,
    required this.duration,
    required this.resultCount,
    required this.success,
    this.error,
  });
}

class SearchStatistics {
  final int totalSearches;
  final int successfulSearches;
  final Duration averageDuration;
  final double successRate;
  
  SearchStatistics({
    required this.totalSearches,
    required this.successfulSearches,
    required this.averageDuration,
    required this.successRate,
  });
  
  factory SearchStatistics.empty() => SearchStatistics(
    totalSearches: 0,
    successfulSearches: 0,
    averageDuration: Duration.zero,
    successRate: 0.0,
  );
}

// Extension for Gaussian random numbers
extension RandomGaussian on Random {
  double nextGaussian() {
    // Box-Muller transform
    final u1 = nextDouble();
    final u2 = nextDouble();
    return sqrt(-2 * log(u1)) * cos(2 * pi * u2);
  }
}
```### Ad
vanced Caching Strategies

#### Multi-Level Caching Implementation

```dart
class AdvancedCacheManager {
  final VektagrafDatabase database;
  
  // L1 Cache: In-memory object cache
  final Map<String, CacheEntry> _l1Cache = {};
  
  // L2 Cache: Query result cache
  final Map<String, QueryCacheEntry> _l2Cache = {};
  
  // L3 Cache: Computed aggregation cache
  final Map<String, AggregationCacheEntry> _l3Cache = {};
  
  // Cache configuration
  final int _maxL1Size;
  final int _maxL2Size;
  final int _maxL3Size;
  final Duration _defaultTtl;
  
  AdvancedCacheManager(
    this.database, {
    int maxL1Size = 10000,
    int maxL2Size = 1000,
    int maxL3Size = 100,
    Duration defaultTtl = const Duration(minutes: 15),
  }) : _maxL1Size = maxL1Size,
       _maxL2Size = maxL2Size,
       _maxL3Size = maxL3Size,
       _defaultTtl = defaultTtl;
  
  /// Gets an object from L1 cache or database
  Future<T?> getObject<T>(VektagrafId id) async {
    final cacheKey = 'object_${id.toString()}';
    
    // Check L1 cache first
    final cached = _l1Cache[cacheKey];
    if (cached != null && !cached.isExpired) {
      cached.lastAccessed = DateTime.now();
      return cached.value as T?;
    }
    
    // Load from database
    final objects = await database.objects<T>();
    final object = objects.firstWhere((obj) => 
        objects.idOf(obj) == id, orElse: () => null);
    
    if (object != null) {
      // Store in L1 cache
      _putL1Cache(cacheKey, object);
    }
    
    return object;
  }
  
  /// Executes a query with L2 caching
  Future<List<T>> cachedQuery<T>(
    String queryKey,
    Future<List<T>> Function() queryFunction, {
    Duration? ttl,
  }) async {
    // Check L2 cache
    final cached = _l2Cache[queryKey];
    if (cached != null && !cached.isExpired) {
      cached.lastAccessed = DateTime.now();
      return List<T>.from(cached.results);
    }
    
    // Execute query
    final stopwatch = Stopwatch()..start();
    final results = await queryFunction();
    stopwatch.stop();
    
    // Store in L2 cache
    _putL2Cache(queryKey, results, stopwatch.elapsed, ttl ?? _defaultTtl);
    
    return results;
  }
  
  /// Gets or computes an aggregation with L3 caching
  Future<Map<String, dynamic>> cachedAggregation(
    String aggregationKey,
    Future<Map<String, dynamic>> Function() computeFunction, {
    Duration? ttl,
  }) async {
    // Check L3 cache
    final cached = _l3Cache[aggregationKey];
    if (cached != null && !cached.isExpired) {
      cached.lastAccessed = DateTime.now();
      return Map<String, dynamic>.from(cached.result);
    }
    
    // Compute aggregation
    final stopwatch = Stopwatch()..start();
    final result = await computeFunction();
    stopwatch.stop();
    
    // Store in L3 cache
    _putL3Cache(aggregationKey, result, stopwatch.elapsed, ttl ?? _defaultTtl);
    
    return result;
  }
  
  /// Invalidates cache entries based on patterns
  void invalidateCache({
    String? pattern,
    List<String>? keys,
    bool clearAll = false,
  }) {
    if (clearAll) {
      _l1Cache.clear();
      _l2Cache.clear();
      _l3Cache.clear();
      return;
    }
    
    if (keys != null) {
      for (final key in keys) {
        _l1Cache.remove(key);
        _l2Cache.remove(key);
        _l3Cache.remove(key);
      }
    }
    
    if (pattern != null) {
      final regex = RegExp(pattern);
      _l1Cache.removeWhere((key, _) => regex.hasMatch(key));
      _l2Cache.removeWhere((key, _) => regex.hasMatch(key));
      _l3Cache.removeWhere((key, _) => regex.hasMatch(key));
    }
  }
  
  /// Gets comprehensive cache statistics
  CacheStatistics getStatistics() {
    return CacheStatistics(
      l1Stats: _getCacheStats(_l1Cache, _maxL1Size),
      l2Stats: _getCacheStats(_l2Cache, _maxL2Size),
      l3Stats: _getCacheStats(_l3Cache, _maxL3Size),
    );
  }
  
  /// Performs cache maintenance (eviction, cleanup)
  void performMaintenance() {
    _evictExpiredEntries();
    _evictLeastRecentlyUsed();
  }
  
  // Private helper methods
  
  void _putL1Cache(String key, dynamic value) {
    if (_l1Cache.length >= _maxL1Size) {
      _evictLRU(_l1Cache);
    }
    
    _l1Cache[key] = CacheEntry(
      value: value,
      createdAt: DateTime.now(),
      lastAccessed: DateTime.now(),
      ttl: _defaultTtl,
    );
  }
  
  void _putL2Cache(String key, List<dynamic> results, Duration executionTime, Duration ttl) {
    if (_l2Cache.length >= _maxL2Size) {
      _evictLRU(_l2Cache);
    }
    
    _l2Cache[key] = QueryCacheEntry(
      results: List.from(results),
      createdAt: DateTime.now(),
      lastAccessed: DateTime.now(),
      ttl: ttl,
      executionTime: executionTime,
      hitCount: 0,
    );
  }
  
  void _putL3Cache(String key, Map<String, dynamic> result, Duration computeTime, Duration ttl) {
    if (_l3Cache.length >= _maxL3Size) {
      _evictLRU(_l3Cache);
    }
    
    _l3Cache[key] = AggregationCacheEntry(
      result: Map.from(result),
      createdAt: DateTime.now(),
      lastAccessed: DateTime.now(),
      ttl: ttl,
      computeTime: computeTime,
      hitCount: 0,
    );
  }
  
  void _evictLRU<T extends CacheEntryBase>(Map<String, T> cache) {
    if (cache.isEmpty) return;
    
    String? oldestKey;
    DateTime? oldestTime;
    
    for (final entry in cache.entries) {
      if (oldestTime == null || entry.value.lastAccessed.isBefore(oldestTime)) {
        oldestKey = entry.key;
        oldestTime = entry.value.lastAccessed;
      }
    }
    
    if (oldestKey != null) {
      cache.remove(oldestKey);
    }
  }
  
  void _evictExpiredEntries() {
    final now = DateTime.now();
    
    _l1Cache.removeWhere((_, entry) => entry.isExpired);
    _l2Cache.removeWhere((_, entry) => entry.isExpired);
    _l3Cache.removeWhere((_, entry) => entry.isExpired);
  }
  
  void _evictLeastRecentlyUsed() {
    while (_l1Cache.length > _maxL1Size) {
      _evictLRU(_l1Cache);
    }
    
    while (_l2Cache.length > _maxL2Size) {
      _evictLRU(_l2Cache);
    }
    
    while (_l3Cache.length > _maxL3Size) {
      _evictLRU(_l3Cache);
    }
  }
  
  CacheLevelStats _getCacheStats<T extends CacheEntryBase>(
    Map<String, T> cache, 
    int maxSize,
  ) {
    final totalHits = cache.values
        .where((entry) => entry is QueryCacheEntry)
        .cast<QueryCacheEntry>()
        .fold<int>(0, (sum, entry) => sum + entry.hitCount);
    
    final totalRequests = cache.length + totalHits;
    final hitRate = totalRequests > 0 ? totalHits / totalRequests : 0.0;
    
    return CacheLevelStats(
      size: cache.length,
      maxSize: maxSize,
      hitRate: hitRate,
      memoryUsage: _estimateMemoryUsage(cache),
    );
  }
  
  int _estimateMemoryUsage<T>(Map<String, T> cache) {
    // Simplified memory estimation
    return cache.length * 1024; // 1KB per entry estimate
  }
}

// Cache entry classes

abstract class CacheEntryBase {
  final DateTime createdAt;
  DateTime lastAccessed;
  final Duration ttl;
  
  CacheEntryBase({
    required this.createdAt,
    required this.lastAccessed,
    required this.ttl,
  });
  
  bool get isExpired => DateTime.now().isAfter(createdAt.add(ttl));
}

class CacheEntry extends CacheEntryBase {
  final dynamic value;
  
  CacheEntry({
    required this.value,
    required DateTime createdAt,
    required DateTime lastAccessed,
    required Duration ttl,
  }) : super(
    createdAt: createdAt,
    lastAccessed: lastAccessed,
    ttl: ttl,
  );
}

class QueryCacheEntry extends CacheEntryBase {
  final List<dynamic> results;
  final Duration executionTime;
  int hitCount;
  
  QueryCacheEntry({
    required this.results,
    required DateTime createdAt,
    required DateTime lastAccessed,
    required Duration ttl,
    required this.executionTime,
    required this.hitCount,
  }) : super(
    createdAt: createdAt,
    lastAccessed: lastAccessed,
    ttl: ttl,
  );
}

class AggregationCacheEntry extends CacheEntryBase {
  final Map<String, dynamic> result;
  final Duration computeTime;
  int hitCount;
  
  AggregationCacheEntry({
    required this.result,
    required DateTime createdAt,
    required DateTime lastAccessed,
    required Duration ttl,
    required this.computeTime,
    required this.hitCount,
  }) : super(
    createdAt: createdAt,
    lastAccessed: lastAccessed,
    ttl: ttl,
  );
}

// Statistics classes

class CacheStatistics {
  final CacheLevelStats l1Stats;
  final CacheLevelStats l2Stats;
  final CacheLevelStats l3Stats;
  
  CacheStatistics({
    required this.l1Stats,
    required this.l2Stats,
    required this.l3Stats,
  });
  
  double get overallHitRate {
    final totalHits = l1Stats.hitRate + l2Stats.hitRate + l3Stats.hitRate;
    return totalHits / 3;
  }
  
  int get totalMemoryUsage {
    return l1Stats.memoryUsage + l2Stats.memoryUsage + l3Stats.memoryUsage;
  }
}

class CacheLevelStats {
  final int size;
  final int maxSize;
  final double hitRate;
  final int memoryUsage;
  
  CacheLevelStats({
    required this.size,
    required this.maxSize,
    required this.hitRate,
    required this.memoryUsage,
  });
  
  double get utilizationRate => maxSize > 0 ? size / maxSize : 0.0;
}

Smart Cache Warming and Preloading

class SmartCacheWarmer {
  final VektagrafDatabase database;
  final AdvancedCacheManager cacheManager;
  
  SmartCacheWarmer(this.database, this.cacheManager);
  
  /// Analyzes query patterns and preloads frequently accessed data
  Future<void> warmCache() async {
    print('=== Smart Cache Warming ===');
    
    // Analyze historical query patterns
    final queryPatterns = await _analyzeQueryPatterns();
    
    // Preload frequently accessed objects
    await _preloadFrequentObjects(queryPatterns);
    
    // Precompute common aggregations
    await _precomputeAggregations();
    
    // Warm vector search caches
    await _warmVectorSearchCache();
    
    print('Cache warming completed');
  }
  
  Future<List<QueryPattern>> _analyzeQueryPatterns() async {
    print('\n1. Analyzing query patterns...');
    
    // In a real implementation, this would analyze query logs
    // For demonstration, we'll simulate common patterns
    return [
      QueryPattern(
        type: 'property_filter',
        frequency: 150,
        avgLatency: Duration(milliseconds: 25),
        parameters: {'property': 'isActive', 'value': true},
      ),
      QueryPattern(
        type: 'range_query',
        frequency: 100,
        avgLatency: Duration(milliseconds: 45),
        parameters: {'property': 'age', 'min': 25, 'max': 35},
      ),
      QueryPattern(
        type: 'sorted_query',
        frequency: 80,
        avgLatency: Duration(milliseconds: 60),
        parameters: {'property': 'followerCount', 'descending': true, 'limit': 10},
      ),
      QueryPattern(
        type: 'vector_search',
        frequency: 200,
        avgLatency: Duration(milliseconds: 100),
        parameters: {'dimensions': 384, 'limit': 20},
      ),
    ];
  }
  
  Future<void> _preloadFrequentObjects(List<QueryPattern> patterns) async {
    print('\n2. Preloading frequently accessed objects...');
    
    final users = await database.objects<User>();
    
    // Preload active users (most frequently queried)
    final activeUsersPattern = patterns.firstWhere(
      (p) => p.type == 'property_filter' && p.parameters['property'] == 'isActive',
      orElse: () => null,
    );
    
    if (activeUsersPattern != null && activeUsersPattern.frequency > 100) {
      final activeUsers = await cacheManager.cachedQuery(
        'active_users',
        () => users.whereProperty('isActive', true).toList(),
        ttl: Duration(hours: 1),
      );
      
      print('Preloaded ${activeUsers.length} active users');
      
      // Preload individual user objects
      for (final user in activeUsers.take(100)) { // Top 100 most likely to be accessed
        final userId = users.idOf(user);
        if (userId != null) {
          await cacheManager.getObject<User>(userId);
        }
      }
    }
    
    // Preload users in popular age ranges
    final ageRangePattern = patterns.firstWhere(
      (p) => p.type == 'range_query' && p.parameters['property'] == 'age',
      orElse: () => null,
    );
    
    if (ageRangePattern != null && ageRangePattern.frequency > 50) {
      final youngUsers = await cacheManager.cachedQuery(
        'young_users_25_35',
        () => users.wherePropertyRange<int>('age', 25, 35).toList(),
        ttl: Duration(hours: 2),
      );
      
      print('Preloaded ${youngUsers.length} users in age range 25-35');
    }
  }
  
  Future<void> _precomputeAggregations() async {
    print('\n3. Precomputing common aggregations...');
    
    // User statistics by city
    await cacheManager.cachedAggregation(
      'user_stats_by_city',
      () async {
        final users = await database.objects<User>();
        final userList = await users.toList();
        
        final cityStats = <String, Map<String, dynamic>>{};
        
        for (final user in userList) {
          final city = user.city;
          if (!cityStats.containsKey(city)) {
            cityStats[city] = {
              'count': 0,
              'avgAge': 0.0,
              'avgFollowers': 0.0,
              'activeCount': 0,
            };
          }
          
          final stats = cityStats[city]!;
          stats['count'] = (stats['count'] as int) + 1;
          stats['avgAge'] = ((stats['avgAge'] as double) * (stats['count'] as int - 1) + user.age) / (stats['count'] as int);
          stats['avgFollowers'] = ((stats['avgFollowers'] as double) * (stats['count'] as int - 1) + user.followerCount) / (stats['count'] as int);
          
          if (user.isActive) {
            stats['activeCount'] = (stats['activeCount'] as int) + 1;
          }
        }
        
        return {'cityStats': cityStats};
      },
      ttl: Duration(hours: 6),
    );
    
    // Daily user registration trends
    await cacheManager.cachedAggregation(
      'daily_registration_trends',
      () async {
        final users = await database.objects<User>();
        final userList = await users.toList();
        
        final dailyStats = <String, int>{};
        
        for (final user in userList) {
          final dateKey = '${user.joinDate.year}-${user.joinDate.month.toString().padLeft(2, '0')}-${user.joinDate.day.toString().padLeft(2, '0')}';
          dailyStats[dateKey] = (dailyStats[dateKey] ?? 0) + 1;
        }
        
        return {'dailyRegistrations': dailyStats};
      },
      ttl: Duration(hours: 12),
    );
    
    print('Precomputed aggregations for user statistics and trends');
  }
  
  Future<void> _warmVectorSearchCache() async {
    print('\n4. Warming vector search cache...');
    
    final documentVectors = database.vectorSpace('documents', 384);
    
    // Generate representative query vectors for common search patterns
    final commonQueryVectors = [
      _generateCentroidVector('tech'),
      _generateCentroidVector('science'),
      _generateCentroidVector('business'),
      _generateCentroidVector('health'),
    ];
    
    // Warm cache with common searches
    for (int i = 0; i < commonQueryVectors.length; i++) {
      final queryVector = commonQueryVectors[i];
      final category = ['tech', 'science', 'business', 'health'][i];
      
      await cacheManager.cachedQuery(
        'vector_search_$category',
        () => documentVectors.similaritySearch(queryVector, 20),
        ttl: Duration(minutes: 30),
      );
    }
    
    print('Warmed vector search cache with ${commonQueryVectors.length} common query patterns');
  }
  
  List<double> _generateCentroidVector(String category) {
    // Simulate generating a centroid vector for a category
    // In practice, this would be computed from actual data
    final random = Random(category.hashCode);
    return List.generate(384, (_) => random.nextGaussian());
  }
}

class QueryPattern {
  final String type;
  final int frequency;
  final Duration avgLatency;
  final Map<String, dynamic> parameters;
  
  QueryPattern({
    required this.type,
    required this.frequency,
    required this.avgLatency,
    required this.parameters,
  });
}
```### Perf
ormance Monitoring and Analytics

#### Comprehensive Performance Monitoring

```dart
class PerformanceMonitor {
  final VektagrafDatabase database;
  final SystemMetricsCollector metricsCollector;
  
  PerformanceMonitor(this.database) 
      : metricsCollector = SystemMetricsCollector(database);
  
  /// Starts comprehensive performance monitoring
  void startMonitoring() {
    print('=== Starting Performance Monitoring ===');
    
    // Start metrics collection
    metricsCollector.start(flushInterval: Duration(seconds: 30));
    
    // Set up periodic performance analysis
    Timer.periodic(Duration(minutes: 5), (_) => _analyzePerformance());
    
    // Set up real-time alerting
    Timer.periodic(Duration(seconds: 10), (_) => _checkPerformanceAlerts());
    
    print('Performance monitoring started');
  }
  
  /// Records and analyzes a database operation
  Future<T> monitorOperation<T>(
    String operationName,
    Future<T> Function() operation, {
    Map<String, String>? labels,
  }) async {
    final stopwatch = Stopwatch()..start();
    
    try {
      final result = await operation();
      stopwatch.stop();
      
      // Record successful operation
      await metricsCollector.recordDatabaseOperation(
        operationName,
        stopwatch.elapsed,
        success: true,
      );
      
      // Record custom metrics
      if (labels != null) {
        metricsCollector.recordHistogram(
          'custom_operation_duration',
          stopwatch.elapsed.inMicroseconds / 1000000.0,
          labels: {'operation': operationName, ...labels},
        );
      }
      
      return result;
      
    } catch (e) {
      stopwatch.stop();
      
      // Record failed operation
      await metricsCollector.recordDatabaseOperation(
        operationName,
        stopwatch.elapsed,
        success: false,
      );
      
      rethrow;
    }
  }
  
  /// Gets comprehensive performance report
  Future<PerformanceReport> getPerformanceReport({
    Duration? timeWindow,
  }) async {
    final endTime = DateTime.now();
    final startTime = timeWindow != null 
        ? endTime.subtract(timeWindow)
        : endTime.subtract(Duration(hours: 1));
    
    // Query database operation metrics
    final dbMetrics = await metricsCollector.queryMetrics(
      name: 'vektagraf_database_operations_total',
      startTime: startTime,
      endTime: endTime,
    );
    
    // Query query performance metrics
    final queryMetrics = await metricsCollector.queryMetrics(
      name: 'vektagraf_query_duration_seconds',
      startTime: startTime,
      endTime: endTime,
    );
    
    // Query vector operation metrics
    final vectorMetrics = await metricsCollector.queryMetrics(
      name: 'vektagraf_vector_operations_total',
      startTime: startTime,
      endTime: endTime,
    );
    
    // Query memory usage metrics
    final memoryMetrics = await metricsCollector.queryMetrics(
      name: 'vektagraf_memory_usage_bytes',
      startTime: startTime,
      endTime: endTime,
    );
    
    return PerformanceReport(
      timeWindow: timeWindow ?? Duration(hours: 1),
      databaseOperations: _analyzeOperationMetrics(dbMetrics),
      queryPerformance: _analyzeQueryMetrics(queryMetrics),
      vectorOperations: _analyzeVectorMetrics(vectorMetrics),
      memoryUsage: _analyzeMemoryMetrics(memoryMetrics),
      generatedAt: DateTime.now(),
    );
  }
  
  /// Analyzes current performance and identifies issues
  Future<void> _analyzePerformance() async {
    final report = await getPerformanceReport(timeWindow: Duration(minutes: 5));
    
    // Check for performance issues
    final issues = <PerformanceIssue>[];
    
    // Check query latency
    if (report.queryPerformance.averageLatency.inMilliseconds > 100) {
      issues.add(PerformanceIssue(
        type: 'high_query_latency',
        severity: 'warning',
        description: 'Average query latency is ${report.queryPerformance.averageLatency.inMilliseconds}ms',
        recommendation: 'Consider adding indexes or optimizing query patterns',
      ));
    }
    
    // Check error rate
    if (report.databaseOperations.errorRate > 0.05) {
      issues.add(PerformanceIssue(
        type: 'high_error_rate',
        severity: 'critical',
        description: 'Error rate is ${(report.databaseOperations.errorRate * 100).toStringAsFixed(1)}%',
        recommendation: 'Investigate error causes and implement proper error handling',
      ));
    }
    
    // Check memory usage
    if (report.memoryUsage.currentUsage > report.memoryUsage.maxUsage * 0.9) {
      issues.add(PerformanceIssue(
        type: 'high_memory_usage',
        severity: 'warning',
        description: 'Memory usage is at ${(report.memoryUsage.currentUsage / report.memoryUsage.maxUsage * 100).toStringAsFixed(1)}% of capacity',
        recommendation: 'Consider increasing memory allocation or optimizing data structures',
      ));
    }
    
    // Log issues if found
    if (issues.isNotEmpty) {
      print('\n⚠️  Performance Issues Detected:');
      for (final issue in issues) {
        print('  ${issue.severity.toUpperCase()}: ${issue.description}');
        print('    Recommendation: ${issue.recommendation}');
      }
    }
  }
  
  /// Checks for real-time performance alerts
  Future<void> _checkPerformanceAlerts() async {
    // Get recent metrics (last 30 seconds)
    final recentMetrics = await metricsCollector.queryMetrics(
      startTime: DateTime.now().subtract(Duration(seconds: 30)),
      endTime: DateTime.now(),
    );
    
    // Check for immediate issues
    for (final metric in recentMetrics) {
      if (metric.name == 'vektagraf_query_duration_seconds' && metric.value > 1.0) {
        print('🚨 ALERT: Slow query detected (${(metric.value * 1000).toStringAsFixed(0)}ms)');
      }
      
      if (metric.name == 'vektagraf_database_operations_total' && 
          metric.labels['status'] == 'error') {
        print('🚨 ALERT: Database operation error detected');
      }
    }
  }
  
  // Analysis helper methods
  
  DatabaseOperationMetrics _analyzeOperationMetrics(List<SystemMetric> metrics) {
    if (metrics.isEmpty) {
      return DatabaseOperationMetrics.empty();
    }
    
    final successMetrics = metrics.where((m) => m.labels['status'] == 'success');
    final errorMetrics = metrics.where((m) => m.labels['status'] == 'error');
    
    final totalOperations = metrics.length;
    final successfulOperations = successMetrics.length;
    final errorRate = totalOperations > 0 ? errorMetrics.length / totalOperations : 0.0;
    
    return DatabaseOperationMetrics(
      totalOperations: totalOperations,
      successfulOperations: successfulOperations,
      errorRate: errorRate,
      operationsPerSecond: totalOperations / 300.0, // 5 minute window
    );
  }
  
  QueryPerformanceMetrics _analyzeQueryMetrics(List<SystemMetric> metrics) {
    if (metrics.isEmpty) {
      return QueryPerformanceMetrics.empty();
    }
    
    final latencies = metrics.map((m) => m.value).toList()..sort();
    final avgLatency = latencies.reduce((a, b) => a + b) / latencies.length;
    
    return QueryPerformanceMetrics(
      totalQueries: metrics.length,
      averageLatency: Duration(microseconds: (avgLatency * 1000000).round()),
      p50Latency: Duration(microseconds: (latencies[latencies.length ~/ 2] * 1000000).round()),
      p95Latency: Duration(microseconds: (latencies[(latencies.length * 0.95).floor()] * 1000000).round()),
      p99Latency: Duration(microseconds: (latencies[(latencies.length * 0.99).floor()] * 1000000).round()),
    );
  }
  
  VectorOperationMetrics _analyzeVectorMetrics(List<SystemMetric> metrics) {
    if (metrics.isEmpty) {
      return VectorOperationMetrics.empty();
    }
    
    final operationCounts = <String, int>{};
    for (final metric in metrics) {
      final operation = metric.labels['operation'] ?? 'unknown';
      operationCounts[operation] = (operationCounts[operation] ?? 0) + metric.value.round();
    }
    
    return VectorOperationMetrics(
      totalOperations: metrics.fold<int>(0, (sum, m) => sum + m.value.round()),
      operationsByType: operationCounts,
      averageVectorCount: metrics.isNotEmpty 
          ? metrics.map((m) => m.value).reduce((a, b) => a + b) / metrics.length 
          : 0.0,
    );
  }
  
  MemoryUsageMetrics _analyzeMemoryMetrics(List<SystemMetric> metrics) {
    if (metrics.isEmpty) {
      return MemoryUsageMetrics.empty();
    }
    
    final usageValues = metrics.map((m) => m.value).toList();
    final currentUsage = usageValues.last;
    final maxUsage = usageValues.reduce((a, b) => a > b ? a : b);
    final avgUsage = usageValues.reduce((a, b) => a + b) / usageValues.length;
    
    return MemoryUsageMetrics(
      currentUsage: currentUsage,
      maxUsage: maxUsage,
      averageUsage: avgUsage,
      peakUsage: maxUsage,
    );
  }
}

// Performance report classes

class PerformanceReport {
  final Duration timeWindow;
  final DatabaseOperationMetrics databaseOperations;
  final QueryPerformanceMetrics queryPerformance;
  final VectorOperationMetrics vectorOperations;
  final MemoryUsageMetrics memoryUsage;
  final DateTime generatedAt;
  
  PerformanceReport({
    required this.timeWindow,
    required this.databaseOperations,
    required this.queryPerformance,
    required this.vectorOperations,
    required this.memoryUsage,
    required this.generatedAt,
  });
  
  void printSummary() {
    print('\n=== Performance Report (${timeWindow.inMinutes} minutes) ===');
    print('Generated at: $generatedAt');
    
    print('\nDatabase Operations:');
    print('  Total: ${databaseOperations.totalOperations}');
    print('  Success rate: ${((1 - databaseOperations.errorRate) * 100).toStringAsFixed(1)}%');
    print('  Ops/sec: ${databaseOperations.operationsPerSecond.toStringAsFixed(1)}');
    
    print('\nQuery Performance:');
    print('  Total queries: ${queryPerformance.totalQueries}');
    print('  Average latency: ${queryPerformance.averageLatency.inMilliseconds}ms');
    print('  P95 latency: ${queryPerformance.p95Latency.inMilliseconds}ms');
    print('  P99 latency: ${queryPerformance.p99Latency.inMilliseconds}ms');
    
    print('\nVector Operations:');
    print('  Total operations: ${vectorOperations.totalOperations}');
    print('  Average vector count: ${vectorOperations.averageVectorCount.toStringAsFixed(0)}');
    
    print('\nMemory Usage:');
    print('  Current: ${(memoryUsage.currentUsage / 1024 / 1024).toStringAsFixed(1)} MB');
    print('  Peak: ${(memoryUsage.peakUsage / 1024 / 1024).toStringAsFixed(1)} MB');
    print('  Average: ${(memoryUsage.averageUsage / 1024 / 1024).toStringAsFixed(1)} MB');
  }
}

class DatabaseOperationMetrics {
  final int totalOperations;
  final int successfulOperations;
  final double errorRate;
  final double operationsPerSecond;
  
  DatabaseOperationMetrics({
    required this.totalOperations,
    required this.successfulOperations,
    required this.errorRate,
    required this.operationsPerSecond,
  });
  
  factory DatabaseOperationMetrics.empty() => DatabaseOperationMetrics(
    totalOperations: 0,
    successfulOperations: 0,
    errorRate: 0.0,
    operationsPerSecond: 0.0,
  );
}

class QueryPerformanceMetrics {
  final int totalQueries;
  final Duration averageLatency;
  final Duration p50Latency;
  final Duration p95Latency;
  final Duration p99Latency;
  
  QueryPerformanceMetrics({
    required this.totalQueries,
    required this.averageLatency,
    required this.p50Latency,
    required this.p95Latency,
    required this.p99Latency,
  });
  
  factory QueryPerformanceMetrics.empty() => QueryPerformanceMetrics(
    totalQueries: 0,
    averageLatency: Duration.zero,
    p50Latency: Duration.zero,
    p95Latency: Duration.zero,
    p99Latency: Duration.zero,
  );
}

class VectorOperationMetrics {
  final int totalOperations;
  final Map<String, int> operationsByType;
  final double averageVectorCount;
  
  VectorOperationMetrics({
    required this.totalOperations,
    required this.operationsByType,
    required this.averageVectorCount,
  });
  
  factory VectorOperationMetrics.empty() => VectorOperationMetrics(
    totalOperations: 0,
    operationsByType: {},
    averageVectorCount: 0.0,
  );
}

class MemoryUsageMetrics {
  final double currentUsage;
  final double maxUsage;
  final double averageUsage;
  final double peakUsage;
  
  MemoryUsageMetrics({
    required this.currentUsage,
    required this.maxUsage,
    required this.averageUsage,
    required this.peakUsage,
  });
  
  factory MemoryUsageMetrics.empty() => MemoryUsageMetrics(
    currentUsage: 0.0,
    maxUsage: 0.0,
    averageUsage: 0.0,
    peakUsage: 0.0,
  );
}

class PerformanceIssue {
  final String type;
  final String severity;
  final String description;
  final String recommendation;
  
  PerformanceIssue({
    required this.type,
    required this.severity,
    required this.description,
    required this.recommendation,
  });
}

Automated Performance Tuning

class AutoPerformanceTuner {
  final VektagrafDatabase database;
  final PerformanceMonitor performanceMonitor;
  
  AutoPerformanceTuner(this.database, this.performanceMonitor);
  
  /// Runs automated performance tuning based on current metrics
  Future<void> runAutoTuning() async {
    print('=== Automated Performance Tuning ===');
    
    // Get current performance baseline
    final baseline = await performanceMonitor.getPerformanceReport(
      timeWindow: Duration(minutes: 15),
    );
    
    print('Current performance baseline:');
    baseline.printSummary();
    
    // Identify tuning opportunities
    final opportunities = await _identifyTuningOpportunities(baseline);
    
    // Apply tuning strategies
    for (final opportunity in opportunities) {
      await _applyTuningStrategy(opportunity);
    }
    
    // Measure improvement
    await _measureImprovement(baseline);
  }
  
  Future<List<TuningOpportunity>> _identifyTuningOpportunities(
    PerformanceReport baseline,
  ) async {
    final opportunities = <TuningOpportunity>[];
    
    // Check query performance
    if (baseline.queryPerformance.averageLatency.inMilliseconds > 50) {
      opportunities.add(TuningOpportunity(
        type: 'query_optimization',
        priority: 'high',
        description: 'High query latency detected',
        strategy: 'optimize_indexes',
        expectedImprovement: 0.3,
      ));
    }
    
    // Check memory usage
    if (baseline.memoryUsage.currentUsage > baseline.memoryUsage.maxUsage * 0.8) {
      opportunities.add(TuningOpportunity(
        type: 'memory_optimization',
        priority: 'medium',
        description: 'High memory usage detected',
        strategy: 'optimize_caching',
        expectedImprovement: 0.2,
      ));
    }
    
    // Check vector operations
    if (baseline.vectorOperations.totalOperations > 1000) {
      opportunities.add(TuningOpportunity(
        type: 'vector_optimization',
        priority: 'medium',
        description: 'High vector operation volume',
        strategy: 'optimize_vector_parameters',
        expectedImprovement: 0.25,
      ));
    }
    
    return opportunities;
  }
  
  Future<void> _applyTuningStrategy(TuningOpportunity opportunity) async {
    print('\nApplying tuning strategy: ${opportunity.strategy}');
    
    switch (opportunity.strategy) {
      case 'optimize_indexes':
        await _optimizeIndexes();
        break;
      case 'optimize_caching':
        await _optimizeCaching();
        break;
      case 'optimize_vector_parameters':
        await _optimizeVectorParameters();
        break;
    }
  }
  
  Future<void> _optimizeIndexes() async {
    print('  - Analyzing and optimizing indexes...');
    
    final indexManager = IndexManager(database);
    await indexManager.analyzeAndOptimizeIndexes();
    
    print('  - Index optimization completed');
  }
  
  Future<void> _optimizeCaching() async {
    print('  - Optimizing caching strategies...');
    
    // Adjust cache sizes based on memory usage
    final config = database.config;
    if (config != null) {
      // In a real implementation, this would update cache configuration
      print('  - Adjusted cache sizes for optimal memory usage');
    }
    
    // Warm frequently accessed data
    final cacheManager = AdvancedCacheManager(database);
    final cacheWarmer = SmartCacheWarmer(database, cacheManager);
    await cacheWarmer.warmCache();
    
    print('  - Cache optimization completed');
  }
  
  Future<void> _optimizeVectorParameters() async {
    print('  - Optimizing vector search parameters...');
    
    final vectorOptimizer = VectorSearchOptimizer(database);
    await vectorOptimizer.optimizeVectorSpaces();
    
    print('  - Vector parameter optimization completed');
  }
  
  Future<void> _measureImprovement(PerformanceReport baseline) async {
    print('\n=== Measuring Performance Improvement ===');
    
    // Wait for changes to take effect
    await Future.delayed(Duration(seconds: 30));
    
    // Get new performance metrics
    final improved = await performanceMonitor.getPerformanceReport(
      timeWindow: Duration(minutes: 5),
    );
    
    // Calculate improvements
    final queryLatencyImprovement = _calculateImprovement(
      baseline.queryPerformance.averageLatency.inMilliseconds.toDouble(),
      improved.queryPerformance.averageLatency.inMilliseconds.toDouble(),
    );
    
    final memoryUsageImprovement = _calculateImprovement(
      baseline.memoryUsage.currentUsage,
      improved.memoryUsage.currentUsage,
    );
    
    final throughputImprovement = _calculateImprovement(
      baseline.databaseOperations.operationsPerSecond,
      improved.databaseOperations.operationsPerSecond,
      higherIsBetter: true,
    );
    
    print('Performance improvements:');
    print('  Query latency: ${queryLatencyImprovement.toStringAsFixed(1)}%');
    print('  Memory usage: ${memoryUsageImprovement.toStringAsFixed(1)}%');
    print('  Throughput: ${throughputImprovement.toStringAsFixed(1)}%');
    
    // Log tuning results
    await _logTuningResults({
      'query_latency_improvement': queryLatencyImprovement,
      'memory_usage_improvement': memoryUsageImprovement,
      'throughput_improvement': throughputImprovement,
    });
  }
  
  double _calculateImprovement(
    double baseline,
    double improved, {
    bool higherIsBetter = false,
  }) {
    if (baseline == 0) return 0.0;
    
    final change = higherIsBetter 
        ? (improved - baseline) / baseline
        : (baseline - improved) / baseline;
    
    return change * 100;
  }
  
  Future<void> _logTuningResults(Map<String, double> improvements) async {
    // In a real implementation, this would log to a monitoring system
    print('\nTuning results logged for historical analysis');
  }
}

class TuningOpportunity {
  final String type;
  final String priority;
  final String description;
  final String strategy;
  final double expectedImprovement;
  
  TuningOpportunity({
    required this.type,
    required this.priority,
    required this.description,
    required this.strategy,
    required this.expectedImprovement,
  });
}
```## Be
st Practices

### Query Design Patterns

#### Efficient Query Construction

```dart
class QueryBestPractices {
  /// Demonstrates optimal query patterns for different scenarios
  static Future<void> demonstrateOptimalPatterns(VektagrafDatabase database) async {
    final users = await database.objects<User>();
    final posts = await database.objects<Post>();
    
    print('=== Query Best Practices ===');
    
    // ✅ Good: Use property queries for indexed fields
    final activeUsers = await users.whereProperty('isActive', true);
    
    // ❌ Bad: Use predicates for simple property checks
    // final activeUsers = users.where((u) => u.isActive);
    
    // ✅ Good: Chain operations efficiently (filters first, then sorts/limits)
    final topActiveUsers = await users
        .whereProperty('isActive', true)        // Filter first (uses index)
        .wherePropertyRange('age', 25, 65)      // Further filter (uses index)
        .orderByProperty('followerCount', descending: true)  // Sort reduced set
        .take(10);                              // Limit final results
    
    // ❌ Bad: Sort everything first, then filter
    // final topActiveUsers = await users
    //     .orderByProperty('followerCount', descending: true)  // Sorts all users
    //     .whereProperty('isActive', true)                     // Filters sorted results
    //     .take(10);
    
    // ✅ Good: Use batch operations for multiple saves
    await users.saveAllInTransaction(newUsers);
    
    // ❌ Bad: Individual saves in separate transactions
    // for (final user in newUsers) {
    //   await users.save(user);
    // }
    
    // ✅ Good: Use specific property queries
    final techPosts = await posts.whereProperty('category', 'tech');
    
    // ✅ Good: Use range queries for numerical/date ranges
    final recentPosts = await posts.wherePropertyRange<DateTime>(
      'createdAt',
      DateTime.now().subtract(Duration(days: 7)),
      DateTime.now(),
    );
    
    print('Demonstrated optimal query patterns');
  }
}

Memory Management Strategies

class MemoryOptimization {
  /// Implements memory-efficient data processing patterns
  static Future<void> demonstrateMemoryPatterns(VektagrafDatabase database) async {
    print('=== Memory Optimization Patterns ===');
    
    // ✅ Good: Process large datasets in chunks
    await _processLargeDatasetInChunks(database);
    
    // ✅ Good: Use streaming for large result sets
    await _useStreamingForLargeResults(database);
    
    // ✅ Good: Implement proper resource cleanup
    await _demonstrateResourceCleanup(database);
  }
  
  static Future<void> _processLargeDatasetInChunks(VektagrafDatabase database) async {
    final users = await database.objects<User>();
    
    const chunkSize = 1000;
    int offset = 0;
    
    while (true) {
      // Process data in manageable chunks
      final chunk = await users
          .skip(offset)
          .take(chunkSize)
          .toList();
      
      if (chunk.isEmpty) break;
      
      // Process chunk
      await _processUserChunk(chunk);
      
      offset += chunkSize;
      
      // Optional: Add small delay to prevent overwhelming the system
      await Future.delayed(Duration(milliseconds: 10));
    }
    
    print('Processed large dataset in chunks of $chunkSize');
  }
  
  static Future<void> _useStreamingForLargeResults(VektagrafDatabase database) async {
    final users = await database.objects<User>();
    
    // Use streaming to avoid loading all results into memory
    await for (final user in users.stream) {
      // Process each user individually
      await _processUser(user);
      
      // Memory is automatically managed as items are processed
    }
    
    print('Used streaming for memory-efficient processing');
  }
  
  static Future<void> _demonstrateResourceCleanup(VektagrafDatabase database) async {
    VektagrafVectorSpace? vectorSpace;
    
    try {
      // Create resources
      vectorSpace = database.vectorSpace('temp_space', 384);
      
      // Use resources
      await vectorSpace.addVector([1, 2, 3, /* ... */]);
      
    } finally {
      // Always clean up resources
      if (vectorSpace != null) {
        await vectorSpace.close();
      }
    }
    
    print('Demonstrated proper resource cleanup');
  }
  
  static Future<void> _processUserChunk(List<User> chunk) async {
    // Simulate processing
    await Future.delayed(Duration(milliseconds: 1));
  }
  
  static Future<void> _processUser(User user) async {
    // Simulate processing
    await Future.delayed(Duration(microseconds: 100));
  }
}

Performance Testing Framework

class PerformanceTestSuite {
  final VektagrafDatabase database;
  
  PerformanceTestSuite(this.database);
  
  /// Runs comprehensive performance tests
  Future<void> runPerformanceTests() async {
    print('=== Performance Test Suite ===');
    
    // Test query performance
    await _testQueryPerformance();
    
    // Test vector search performance
    await _testVectorSearchPerformance();
    
    // Test concurrent operations
    await _testConcurrentOperations();
    
    // Test memory usage patterns
    await _testMemoryUsage();
    
    // Test scalability
    await _testScalability();
  }
  
  Future<void> _testQueryPerformance() async {
    print('\n1. Testing query performance...');
    
    final users = await database.objects<User>();
    
    // Test different query types
    final queryTests = [
      ('Property Query', () => users.whereProperty('isActive', true)),
      ('Range Query', () => users.wherePropertyRange<int>('age', 25, 35)),
      ('Sorted Query', () => users.orderByProperty<int>('followerCount', descending: true)),
      ('Complex Chain', () => users
          .whereProperty('isActive', true)
          .wherePropertyRange<int>('age', 25, 35)
          .orderByProperty<DateTime>('joinDate')
          .take(10)),
    ];
    
    for (final test in queryTests) {
      final testName = test.$1;
      final queryFunction = test.$2;
      
      final latencies = <Duration>[];
      
      // Warm up
      for (int i = 0; i < 3; i++) {
        await queryFunction();
      }
      
      // Measure performance
      for (int i = 0; i < 10; i++) {
        final stopwatch = Stopwatch()..start();
        await queryFunction();
        stopwatch.stop();
        latencies.add(stopwatch.elapsed);
      }
      
      final avgLatency = latencies.fold<int>(0, (sum, d) => sum + d.inMicroseconds) / latencies.length;
      print('  $testName: ${(avgLatency / 1000).toStringAsFixed(2)}ms avg');
    }
  }
  
  Future<void> _testVectorSearchPerformance() async {
    print('\n2. Testing vector search performance...');
    
    final vectorSpace = database.vectorSpace('test_vectors', 384);
    final queryVector = List.generate(384, (i) => Random().nextDouble());
    
    // Test different search parameters
    final searchTests = [
      ('Small Result Set', 5),
      ('Medium Result Set', 20),
      ('Large Result Set', 100),
    ];
    
    for (final test in searchTests) {
      final testName = test.$1;
      final limit = test.$2;
      
      final latencies = <Duration>[];
      
      // Measure performance
      for (int i = 0; i < 5; i++) {
        final stopwatch = Stopwatch()..start();
        await vectorSpace.similaritySearch(queryVector, limit);
        stopwatch.stop();
        latencies.add(stopwatch.elapsed);
      }
      
      final avgLatency = latencies.fold<int>(0, (sum, d) => sum + d.inMicroseconds) / latencies.length;
      print('  $testName (limit $limit): ${(avgLatency / 1000).toStringAsFixed(2)}ms avg');
    }
  }
  
  Future<void> _testConcurrentOperations() async {
    print('\n3. Testing concurrent operations...');
    
    final users = await database.objects<User>();
    
    // Test concurrent reads
    final readStopwatch = Stopwatch()..start();
    final readFutures = List.generate(10, (_) => 
        users.whereProperty('isActive', true).toList());
    await Future.wait(readFutures);
    readStopwatch.stop();
    
    print('  Concurrent reads (10x): ${readStopwatch.elapsedMilliseconds}ms total');
    
    // Test concurrent writes
    final writeStopwatch = Stopwatch()..start();
    final writeFutures = List.generate(5, (i) => 
        users.save(User(
          id: VektagrafId.generate(),
          username: 'concurrent_user_$i',
          email: 'concurrent$i@test.com',
          age: 25,
          city: 'Test City',
          joinDate: DateTime.now(),
          isActive: true,
          followerCount: 0,
        )));
    await Future.wait(writeFutures);
    writeStopwatch.stop();
    
    print('  Concurrent writes (5x): ${writeStopwatch.elapsedMilliseconds}ms total');
  }
  
  Future<void> _testMemoryUsage() async {
    print('\n4. Testing memory usage patterns...');
    
    // Simulate memory-intensive operations
    final users = await database.objects<User>();
    
    // Test large result set handling
    final largeResultSet = await users.take(10000).toList();
    print('  Large result set (10k items): ${largeResultSet.length} loaded');
    
    // Test streaming vs. loading all
    final streamStopwatch = Stopwatch()..start();
    int streamCount = 0;
    await for (final user in users.take(1000).stream) {
      streamCount++;
    }
    streamStopwatch.stop();
    
    final loadAllStopwatch = Stopwatch()..start();
    final allUsers = await users.take(1000).toList();
    loadAllStopwatch.stop();
    
    print('  Streaming 1k items: ${streamStopwatch.elapsedMilliseconds}ms');
    print('  Loading 1k items: ${loadAllStopwatch.elapsedMilliseconds}ms');
  }
  
  Future<void> _testScalability() async {
    print('\n5. Testing scalability...');
    
    final users = await database.objects<User>();
    
    // Test performance with different data sizes
    final dataSizes = [100, 1000, 10000];
    
    for (final size in dataSizes) {
      final stopwatch = Stopwatch()..start();
      final results = await users.take(size).toList();
      stopwatch.stop();
      
      final throughput = size / (stopwatch.elapsedMicroseconds / 1000000);
      print('  $size items: ${stopwatch.elapsedMilliseconds}ms (${throughput.toStringAsFixed(0)} items/sec)');
    }
  }
}

Advanced Topics

Custom Query Optimizers

class CustomQueryOptimizer {
  /// Implements domain-specific query optimization strategies
  static Future<void> implementCustomOptimizations(VektagrafDatabase database) async {
    print('=== Custom Query Optimization ===');
    
    // Implement time-based query optimization
    await _implementTimeBasedOptimization(database);
    
    // Implement user behavior-based optimization
    await _implementBehaviorBasedOptimization(database);
    
    // Implement predictive caching
    await _implementPredictiveCaching(database);
  }
  
  static Future<void> _implementTimeBasedOptimization(VektagrafDatabase database) async {
    print('\n1. Time-based query optimization...');
    
    final posts = await database.objects<Post>();
    
    // Optimize queries based on time of day
    final hour = DateTime.now().hour;
    
    if (hour >= 9 && hour <= 17) {
      // Business hours: optimize for recent content
      final recentPosts = await posts
          .wherePropertyRange<DateTime>(
            'createdAt',
            DateTime.now().subtract(Duration(hours: 24)),
            DateTime.now(),
          )
          .orderByProperty<DateTime>('createdAt', descending: true)
          .take(50)
          .toList();
      
      print('  Optimized for recent content during business hours');
    } else {
      // Off hours: optimize for popular content
      final popularPosts = await posts
          .orderByProperty<int>('likeCount', descending: true)
          .take(50)
          .toList();
      
      print('  Optimized for popular content during off hours');
    }
  }
  
  static Future<void> _implementBehaviorBasedOptimization(VektagrafDatabase database) async {
    print('\n2. User behavior-based optimization...');
    
    // Analyze user query patterns and optimize accordingly
    final queryPatterns = await _analyzeUserQueryPatterns();
    
    for (final pattern in queryPatterns) {
      if (pattern.frequency > 100) {
        // Pre-execute frequently used queries
        await _preExecuteQuery(database, pattern);
      }
    }
    
    print('  Applied behavior-based optimizations for ${queryPatterns.length} patterns');
  }
  
  static Future<void> _implementPredictiveCaching(VektagrafDatabase database) async {
    print('\n3. Predictive caching...');
    
    // Predict future queries based on current patterns
    final predictions = await _predictFutureQueries();
    
    for (final prediction in predictions) {
      if (prediction.confidence > 0.8) {
        // Pre-cache predicted queries
        await _preCacheQuery(database, prediction);
      }
    }
    
    print('  Implemented predictive caching for ${predictions.length} predicted queries');
  }
  
  static Future<List<QueryPattern>> _analyzeUserQueryPatterns() async {
    // Simulate query pattern analysis
    return [
      QueryPattern(
        type: 'recent_posts',
        frequency: 150,
        avgLatency: Duration(milliseconds: 30),
        parameters: {'timeRange': '24h', 'limit': 20},
      ),
      QueryPattern(
        type: 'user_profile',
        frequency: 200,
        avgLatency: Duration(milliseconds: 15),
        parameters: {'includeStats': true},
      ),
    ];
  }
  
  static Future<void> _preExecuteQuery(VektagrafDatabase database, QueryPattern pattern) async {
    // Pre-execute and cache frequently used queries
    print('    Pre-executing ${pattern.type} (frequency: ${pattern.frequency})');
  }
  
  static Future<List<QueryPrediction>> _predictFutureQueries() async {
    // Simulate query prediction
    return [
      QueryPrediction(
        queryType: 'trending_content',
        confidence: 0.85,
        predictedTime: DateTime.now().add(Duration(minutes: 5)),
      ),
      QueryPrediction(
        queryType: 'user_recommendations',
        confidence: 0.92,
        predictedTime: DateTime.now().add(Duration(minutes: 10)),
      ),
    ];
  }
  
  static Future<void> _preCacheQuery(VektagrafDatabase database, QueryPrediction prediction) async {
    // Pre-cache predicted queries
    print('    Pre-caching ${prediction.queryType} (confidence: ${prediction.confidence})');
  }
}

class QueryPrediction {
  final String queryType;
  final double confidence;
  final DateTime predictedTime;
  
  QueryPrediction({
    required this.queryType,
    required this.confidence,
    required this.predictedTime,
  });
}

Reference

Performance Configuration

class VektagrafPerformanceConfig {
  // Query optimization settings
  final bool enableQueryOptimization;
  final bool enableQueryCache;
  final int maxCacheSize;
  final Duration defaultCacheTtl;
  
  // Indexing settings
  final bool enableAutoIndexing;
  final int indexThreshold;
  final int maxIndexes;
  
  // Memory management
  final int maxMemoryBytes;
  final double memoryPressureThreshold;
  final bool enableMemoryCompaction;
  
  // Concurrency settings
  final int maxConcurrentQueries;
  final int maxConcurrentTransactions;
  final Duration queryTimeout;
  
  // Vector search optimization
  final Map<String, VectorSpaceConfig> vectorSpaceConfigs;
  
  // Monitoring settings
  final bool enablePerformanceMonitoring;
  final Duration metricsCollectionInterval;
  final Duration performanceReportInterval;
  
  VektagrafPerformanceConfig({
    this.enableQueryOptimization = true,
    this.enableQueryCache = true,
    this.maxCacheSize = 1000,
    this.defaultCacheTtl = const Duration(minutes: 5),
    this.enableAutoIndexing = true,
    this.indexThreshold = 10,
    this.maxIndexes = 100,
    this.maxMemoryBytes = 1024 * 1024 * 1024, // 1GB
    this.memoryPressureThreshold = 0.8,
    this.enableMemoryCompaction = true,
    this.maxConcurrentQueries = 100,
    this.maxConcurrentTransactions = 50,
    this.queryTimeout = const Duration(seconds: 30),
    this.vectorSpaceConfigs = const {},
    this.enablePerformanceMonitoring = true,
    this.metricsCollectionInterval = const Duration(seconds: 30),
    this.performanceReportInterval = const Duration(minutes: 5),
  });
}

class VectorSpaceConfig {
  final String algorithm; // 'hnsw', 'ivfflat', 'memory'
  final Map<String, dynamic> parameters;
  final int memoryBudgetBytes;
  final double cpuBudget;
  
  VectorSpaceConfig({
    required this.algorithm,
    required this.parameters,
    required this.memoryBudgetBytes,
    required this.cpuBudget,
  });
}

Performance Metrics Reference

Key Performance Indicators

Metric	Description	Target Range
Query Latency	Average time to execute queries	< 50ms
Throughput	Queries processed per second	> 1000 QPS
Cache Hit Rate	Percentage of queries served from cache	> 80%
Memory Usage	RAM consumption by database	< 80% of available
Index Efficiency	Ratio of indexed vs. full-scan operations	> 90%
Error Rate	Percentage of failed operations	< 1%

Optimization Thresholds

Component	Warning Threshold	Critical Threshold
Query Latency	100ms	500ms
Memory Usage	80%	95%
Cache Miss Rate	30%	50%
Index Fragmentation	20%	40%
Concurrent Connections	80% of max	95% of max

Summary

This chapter covered comprehensive query optimization and performance tuning strategies for Vektagraf. Key takeaways include:

• Automatic Optimization: Built-in query optimizer with intelligent index management and operation reordering
• Multi-Level Caching: L1 object cache, L2 query cache, and L3 aggregation cache for maximum performance
• Vector Search Optimization: Algorithm-specific tuning for HNSW, IVFFlat, and memory-based vector spaces
• Performance Monitoring: Real-time metrics collection with automated alerting and performance analysis
• Memory Management: Efficient memory usage patterns and resource cleanup strategies
• Automated Tuning: Self-optimizing system that adapts to usage patterns and performance requirements

Next Steps

• Chapter 11: Learn about multi-tenant architecture and resource isolation
• Chapter 14: Explore advanced performance tuning and optimization techniques
• Chapter 13: Implement comprehensive monitoring and observability

Related Resources

• Configuration Reference
• Performance Tuning Guide
• Monitoring and Observability