Michael Schiemer
fc3d7e6357
- Add comprehensive health check system with multiple endpoints - Add Prometheus metrics endpoint - Add production logging configurations (5 strategies) - Add complete deployment documentation suite: * QUICKSTART.md - 30-minute deployment guide * DEPLOYMENT_CHECKLIST.md - Printable verification checklist * DEPLOYMENT_WORKFLOW.md - Complete deployment lifecycle * PRODUCTION_DEPLOYMENT.md - Comprehensive technical reference * production-logging.md - Logging configuration guide * ANSIBLE_DEPLOYMENT.md - Infrastructure as Code automation * README.md - Navigation hub * DEPLOYMENT_SUMMARY.md - Executive summary - Add deployment scripts and automation - Add DEPLOYMENT_PLAN.md - Concrete plan for immediate deployment - Update README with production-ready features All production infrastructure is now complete and ready for deployment.
270 lines
11 KiB
PHP
270 lines
11 KiB
PHP
<?php
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declare(strict_types=1);
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/**
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* N+1 Detection Model Management Integration Example
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*
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* Demonstrates how the N+1 Detection ML system integrates with
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* the ML Model Management System for versioning, monitoring, and auto-tuning.
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*/
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require_once __DIR__ . '/../vendor/autoload.php';
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use App\Framework\Database\NPlusOneDetection\MachineLearning\NPlusOneModelAdapter;
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use App\Framework\Database\NPlusOneDetection\QueryExecutionContext;
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use App\Framework\MachineLearning\ModelManagement\ModelRegistry;
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use App\Framework\MachineLearning\ModelManagement\ModelPerformanceMonitor;
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use App\Framework\MachineLearning\ModelManagement\AutoTuningEngine;
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use App\Framework\Core\ValueObjects\Version;
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use App\Framework\Core\ValueObjects\Duration;
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// ============================================================================
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// Setup: Get services from DI container
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// ============================================================================
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$container = require __DIR__ . '/../bootstrap/container.php';
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$adapter = $container->get(NPlusOneModelAdapter::class);
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$registry = $container->get(ModelRegistry::class);
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$performanceMonitor = $container->get(ModelPerformanceMonitor::class);
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$autoTuning = $container->get(AutoTuningEngine::class);
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echo "=== N+1 Detection Model Management Integration ===\n\n";
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// ============================================================================
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// Example 1: Model Registration
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// ============================================================================
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echo "1. Model Registration\n";
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echo str_repeat('-', 60) . "\n";
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// Register current N+1 detector model
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$metadata = $adapter->registerCurrentModel();
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echo "✓ Registered N+1 Detector Model\n";
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echo " Name: {$metadata->modelName}\n";
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echo " Version: {$metadata->version->toString()}\n";
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echo " Type: {$metadata->modelType->value}\n";
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echo " Configuration:\n";
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foreach ($metadata->configuration as $key => $value) {
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echo " - {$key}: " . json_encode($value) . "\n";
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}
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echo "\n";
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// ============================================================================
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// Example 2: Analysis with Automatic Tracking
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// ============================================================================
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echo "2. Analysis with Automatic Tracking\n";
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echo str_repeat('-', 60) . "\n";
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// Simulate query execution contexts
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$contexts = [
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// N+1 pattern detected
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new QueryExecutionContext(
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query: 'SELECT * FROM users WHERE id = ?',
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bindParams: [1],
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executionTime: Duration::fromMilliseconds(5),
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stackTrace: ['UserRepository::find', 'UserController::show', 'index.php'],
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contextData: [
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'parent_query' => 'SELECT * FROM posts',
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'query_count_in_request' => 15,
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'similar_query_count' => 12
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]
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),
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// Normal query (no N+1)
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new QueryExecutionContext(
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query: 'SELECT * FROM users',
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bindParams: [],
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executionTime: Duration::fromMilliseconds(25),
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stackTrace: ['UserRepository::findAll', 'UserController::index', 'index.php'],
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contextData: [
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'query_count_in_request' => 3,
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'similar_query_count' => 0
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]
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),
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];
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foreach ($contexts as $i => $context) {
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echo "\nAnalyzing Query " . ($i + 1) . "...\n";
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// Analyze with automatic tracking (ground truth provided for training)
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$groundTruth = $i === 0; // First query is N+1, second is not
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$result = $adapter->analyzeWithTracking($context, $groundTruth);
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echo " Query: {$context->query}\n";
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echo " Execution Time: {$context->executionTime->toMilliseconds()}ms\n";
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echo " Detection Result:\n";
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echo " - Success: " . ($result['success'] ? 'Yes' : 'No') . "\n";
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echo " - Anomalies Found: " . count($result['anomalies']) . "\n";
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echo " - Confidence: " . sprintf('%.2f', $result['overall_confidence']) . "\n";
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echo " Tracking Info:\n";
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echo " - Prediction: " . ($result['tracking']['prediction'] ? 'N+1 Detected' : 'Normal') . "\n";
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echo " - Ground Truth: " . ($groundTruth ? 'N+1 Pattern' : 'Normal Query') . "\n";
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echo " - Tracked: " . ($result['tracking']['tracked'] ? 'Yes' : 'No') . "\n";
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}
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echo "\n";
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// ============================================================================
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// Example 3: Real-Time Performance Monitoring
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// ============================================================================
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echo "3. Real-Time Performance Monitoring\n";
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echo str_repeat('-', 60) . "\n";
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// Get current performance metrics
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$metrics = $adapter->getCurrentPerformanceMetrics();
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echo "Performance Metrics (last 1 hour):\n";
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echo " Total Predictions: {$metrics['total_predictions']}\n";
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echo " Correct Predictions: {$metrics['correct_predictions']}\n";
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echo " Accuracy: " . sprintf('%.4f', $metrics['accuracy']) . "\n";
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echo " Precision: " . sprintf('%.4f', $metrics['precision']) . "\n";
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echo " Recall: " . sprintf('%.4f', $metrics['recall']) . "\n";
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echo " F1-Score: " . sprintf('%.4f', $metrics['f1_score']) . "\n";
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echo " Average Confidence: " . sprintf('%.4f', $metrics['average_confidence']) . "\n";
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echo "\n";
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// Check for performance degradation
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$degradationInfo = $adapter->checkPerformanceDegradation(thresholdPercent: 0.05);
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echo "Performance Degradation Check:\n";
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echo " Has Degraded: " . ($degradationInfo['has_degraded'] ? 'Yes' : 'No') . "\n";
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echo " Baseline Accuracy: " . sprintf('%.4f', $degradationInfo['baseline_accuracy']) . "\n";
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echo " Current Accuracy: " . sprintf('%.4f', $degradationInfo['current_accuracy']) . "\n";
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echo " Degradation: " . sprintf('%.2f%%', $degradationInfo['degradation_percent']) . "\n";
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echo "\nRecommendation: {$degradationInfo['recommendation']}\n\n";
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// ============================================================================
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// Example 4: Automatic Threshold Optimization
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// ============================================================================
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echo "4. Automatic Threshold Optimization\n";
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echo str_repeat('-', 60) . "\n";
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// Optimize confidence threshold for maximum F1-score
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$optimizationResult = $autoTuning->optimizeThreshold(
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modelName: 'n1-detector',
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version: Version::fromString('1.0.0'),
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metricToOptimize: 'f1_score',
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thresholdRange: [0.5, 0.9],
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step: 0.05,
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timeWindow: Duration::fromHours(1)
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);
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echo "Threshold Optimization Results:\n";
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echo " Current Threshold: " . sprintf('%.2f', $optimizationResult['current_threshold']) . "\n";
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echo " Optimal Threshold: " . sprintf('%.2f', $optimizationResult['optimal_threshold']) . "\n";
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echo " Current F1-Score: " . sprintf('%.4f', $optimizationResult['current_metric_value']) . "\n";
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echo " Optimal F1-Score: " . sprintf('%.4f', $optimizationResult['optimal_metric_value']) . "\n";
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echo " Improvement: " . sprintf('%+.2f%%', $optimizationResult['improvement_percent']) . "\n";
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echo "\nRecommendation: {$optimizationResult['recommendation']}\n\n";
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// ============================================================================
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// Example 5: Adaptive Threshold Adjustment
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// ============================================================================
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echo "5. Adaptive Threshold Adjustment\n";
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echo str_repeat('-', 60) . "\n";
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// Automatically adjust threshold based on FP/FN rates
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$adaptiveResult = $autoTuning->adaptiveThresholdAdjustment(
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'n1-detector',
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Version::fromString('1.0.0')
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);
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echo "Adaptive Adjustment Results:\n";
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echo " Current Threshold: " . sprintf('%.2f', $adaptiveResult['current_threshold']) . "\n";
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echo " Recommended Threshold: " . sprintf('%.2f', $adaptiveResult['recommended_threshold']) . "\n";
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echo " Adjustment Reason: {$adaptiveResult['adjustment_reason']}\n";
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echo "\nExpected Improvement:\n";
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echo " Accuracy: " . sprintf('%+.4f', $adaptiveResult['expected_improvement']['accuracy']) . "\n";
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echo " Precision: " . sprintf('%+.4f', $adaptiveResult['expected_improvement']['precision']) . "\n";
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echo " Recall: " . sprintf('%+.4f', $adaptiveResult['expected_improvement']['recall']) . "\n";
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echo "\n";
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// ============================================================================
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// Example 6: Model Configuration Update
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// ============================================================================
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echo "6. Model Configuration Update\n";
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echo str_repeat('-', 60) . "\n";
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// Update model configuration based on optimization results
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if ($optimizationResult['improvement_percent'] > 5.0) {
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echo "Applying optimized threshold...\n";
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$newConfiguration = $metadata->configuration;
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$newConfiguration['confidence_threshold'] = $optimizationResult['optimal_threshold'];
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$adapter->updateConfiguration($newConfiguration);
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echo "✓ Configuration Updated\n";
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echo " New Threshold: " . sprintf('%.2f', $optimizationResult['optimal_threshold']) . "\n";
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echo " Expected F1-Score Improvement: " . sprintf('%+.2f%%', $optimizationResult['improvement_percent']) . "\n";
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} else {
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echo "Current configuration is optimal. No update needed.\n";
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}
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echo "\n";
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// ============================================================================
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// Example 7: Production Deployment
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// ============================================================================
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echo "7. Production Deployment\n";
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echo str_repeat('-', 60) . "\n";
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// Deploy to production when performance is validated
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if ($metrics['accuracy'] >= 0.90 && !$degradationInfo['has_degraded']) {
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echo "Deploying N+1 Detector to production...\n";
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$adapter->deployToProduction();
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// Verify deployment
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$deployedMetadata = $adapter->getModelMetadata();
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echo "✓ Successfully deployed to production\n";
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echo " Environment: {$deployedMetadata->environment}\n";
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echo " Deployed At: {$deployedMetadata->deployedAt->format('Y-m-d H:i:s')}\n";
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echo " Accuracy: " . sprintf('%.4f', $deployedMetadata->getAccuracy()) . "\n";
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} else {
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echo "⚠ Deployment criteria not met:\n";
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if ($metrics['accuracy'] < 0.90) {
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echo " - Accuracy too low: " . sprintf('%.4f', $metrics['accuracy']) . " (required: 0.90)\n";
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}
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if ($degradationInfo['has_degraded']) {
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echo " - Performance degradation detected\n";
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}
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}
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echo "\n";
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// ============================================================================
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// Example 8: Model Versioning
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// ============================================================================
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echo "8. Model Versioning\n";
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echo str_repeat('-', 60) . "\n";
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// List all versions of N+1 detector
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$allVersions = $registry->getAll('n1-detector');
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echo "N+1 Detector Versions:\n";
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foreach ($allVersions as $versionMetadata) {
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echo " - v{$versionMetadata->version->toString()}\n";
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echo " Created: {$versionMetadata->createdAt->format('Y-m-d H:i:s')}\n";
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if ($versionMetadata->deployedAt) {
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echo " Deployed: {$versionMetadata->deployedAt->format('Y-m-d H:i:s')} ({$versionMetadata->environment})\n";
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}
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if ($versionMetadata->getAccuracy()) {
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echo " Accuracy: " . sprintf('%.4f', $versionMetadata->getAccuracy()) . "\n";
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}
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}
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echo "\n=== Integration Complete ===\n";
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