feat(Production): Complete production deployment infrastructure

- 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.

examples/nplusone-ml-integration-example.php

@@ -0,0 +1,192 @@
+<?php
+
+declare(strict_types=1);
+
+/**
+ * N+1 Detection ML Integration Example
+ *
+ * Demonstrates complete integration of N+1 Detection ML engine with
+ * the existing NPlusOneDetectionService for enhanced query analysis.
+ */
+
+require __DIR__ . '/../vendor/autoload.php';
+
+use App\Framework\Database\QueryOptimization\NPlusOneDetectionService;
+use App\Framework\Database\QueryOptimization\NPlusOneDetectionServiceInitializer;
+use App\Framework\Database\QueryOptimization\QueryLogger;
+use App\Framework\Database\QueryOptimization\Analysis\NPlusOneDetector;
+use App\Framework\Database\QueryOptimization\Analysis\EagerLoadingAnalyzer;
+use App\Framework\Database\NPlusOneDetection\MachineLearning\NPlusOneDetectionEngine;
+use App\Framework\Database\NPlusOneDetection\MachineLearning\NPlusOneDetectionEngineInitializer;
+use App\Framework\Config\Environment;
+use App\Framework\DateTime\SystemClock;
+use App\Framework\Logging\Handlers\ConsoleHandler;
+use App\Framework\Logging\Logger;
+use App\Framework\Logging\LogLevel;
+use App\Framework\DI\DefaultContainer;
+
+echo "=== N+1 Detection ML Integration Example ===\n\n";
+
+// 1. Setup Components
+$environment = new Environment();
+$clock = new SystemClock();
+$logger = new Logger([new ConsoleHandler(LogLevel::INFO)]);
+$container = new DefaultContainer();
+
+// Register logger in container
+$container->singleton(Logger::class, $logger);
+
+// 2. Initialize ML Engine
+echo "Step 1: Initializing ML Engine...\n";
+$mlEngineInitializer = new NPlusOneDetectionEngineInitializer($environment, $clock, $logger);
+$mlEngine = $mlEngineInitializer();
+$container->singleton(NPlusOneDetectionEngine::class, $mlEngine);
+
+echo "✓ ML Engine initialized\n";
+echo "  Configuration: " . json_encode($mlEngine->getConfiguration(), JSON_PRETTY_PRINT) . "\n\n";
+
+// 3. Create QueryLogger and simulate query execution
+echo "Step 2: Simulating Query Execution...\n";
+$queryLogger = new QueryLogger();
+$queryLogger->enable();
+
+// Simulate N+1 scenario: 1 query + 10 repeated queries
+echo "Logging initial query: SELECT * FROM users\n";
+$queryLogger->logQuery(
+    'SELECT * FROM users',
+    [],
+    10.5,
+    10
+);
+
+echo "Logging N+1 pattern: 10 repeated queries in loop\n";
+for ($i = 1; $i <= 10; $i++) {
+    $queryLogger->logQuery(
+        "SELECT * FROM posts WHERE user_id = $i",
+        [$i],
+        5.0 + ($i * 0.2),
+        rand(5, 15)
+    );
+}
+
+$queryLogger->disable();
+
+echo "✓ Query logging completed\n";
+echo "  Total queries logged: " . $queryLogger->getQueryCount() . "\n";
+echo "  Total execution time: " . round($queryLogger->getTotalExecutionTime(), 2) . "ms\n\n";
+
+// 4. Create NPlusOneDetectionService with ML Integration
+echo "Step 3: Creating N+1 Detection Service with ML...\n";
+$detector = new NPlusOneDetector(
+    minExecutionCount: 5,
+    minSeverityScore: 4.0
+);
+$eagerLoadingAnalyzer = new EagerLoadingAnalyzer();
+
+$detectionService = new NPlusOneDetectionService(
+    queryLogger: $queryLogger,
+    detector: $detector,
+    eagerLoadingAnalyzer: $eagerLoadingAnalyzer,
+    logger: $logger,
+    mlEngine: $mlEngine
+);
+
+echo "✓ Detection service initialized\n";
+echo "  ML engine enabled: " . ($mlEngine->isEnabled() ? 'Yes' : 'No') . "\n\n";
+
+// 5. Perform Analysis
+echo "Step 4: Analyzing Queries...\n";
+echo "=====================================\n\n";
+
+$analysisResult = $detectionService->analyze();
+
+// 6. Display Traditional Pattern-Based Results
+echo "=== Traditional Pattern Detection ===\n";
+$stats = $analysisResult['statistics'];
+echo "Query Statistics:\n";
+echo "  Total queries: " . $stats['total_queries'] . "\n";
+echo "  Total patterns: " . $stats['total_patterns'] . "\n";
+echo "  N+1 patterns detected: " . $stats['n_plus_one_patterns'] . "\n";
+echo "  N+1 queries: " . $stats['n_plus_one_queries'] . " (" . round($stats['n_plus_one_percentage'], 1) . "% of total)\n";
+echo "  Time wasted: " . round($stats['n_plus_one_time_ms'], 2) . "ms (" . round($stats['time_wasted_percentage'], 1) . "% of total)\n\n";
+
+if (!empty($analysisResult['detections'])) {
+    echo "Detected N+1 Issues:\n";
+    foreach ($analysisResult['detections'] as $index => $detection) {
+        echo "  [" . ($index + 1) . "] {$detection->getSeverityLevel()} - {$detection->pattern->getTableName()}\n";
+        echo "      Executions: {$detection->pattern->getExecutionCount()}\n";
+        echo "      Total time: " . round($detection->pattern->getTotalExecutionTimeMs(), 2) . "ms\n";
+        echo "      Impact: {$detection->getPerformanceImpact()}\n";
+    }
+    echo "\n";
+}
+
+// 7. Display ML Analysis Results
+if (isset($analysisResult['ml_analysis'])) {
+    echo "=== Machine Learning Analysis ===\n";
+    $mlAnalysis = $analysisResult['ml_analysis'];
+
+    echo "ML Analysis Status: " . ($mlAnalysis['success'] ? '✓ Success' : '✗ Failed') . "\n";
+
+    if ($mlAnalysis['success']) {
+        echo "Anomalies Detected: " . $mlAnalysis['anomalies_count'] . "\n";
+        echo "Overall Confidence: " . round($mlAnalysis['overall_confidence'], 2) . "%\n";
+        echo "Analysis Time: " . round($mlAnalysis['analysis_time_ms'], 2) . "ms\n\n";
+
+        if (!empty($mlAnalysis['anomalies'])) {
+            echo "ML-Detected Anomalies:\n";
+            foreach ($mlAnalysis['anomalies'] as $index => $anomaly) {
+                echo "  [" . ($index + 1) . "] {$anomaly->type->value}\n";
+                echo "      Confidence: " . round($anomaly->confidence->value, 2) . "%\n";
+                echo "      Severity: {$anomaly->severity->value}\n";
+                echo "      Description: {$anomaly->description}\n";
+                if (!empty($anomaly->context)) {
+                    echo "      Context: " . json_encode($anomaly->context) . "\n";
+                }
+                echo "\n";
+            }
+        }
+
+        echo "Extracted Features (Sample):\n";
+        $sampleFeatures = array_slice($mlAnalysis['features'], 0, 5);
+        foreach ($sampleFeatures as $feature) {
+            echo "  - {$feature->name}: " . round($feature->value, 4) . " {$feature->unit}\n";
+        }
+        echo "\n";
+    } else {
+        echo "Error: " . ($mlAnalysis['error'] ?? 'Unknown error') . "\n\n";
+    }
+} else {
+    echo "=== Machine Learning Analysis ===\n";
+    echo "ML engine not enabled or not available\n\n";
+}
+
+// 8. Generate Eager Loading Strategies
+if (!empty($analysisResult['strategies'])) {
+    echo "=== Optimization Strategies ===\n";
+    foreach ($analysisResult['strategies'] as $strategy) {
+        echo "Strategy for {$strategy->entityClass}:\n";
+        echo "  Relations to eager load: " . implode(', ', $strategy->relationsToLoad) . "\n";
+        echo "  Expected improvement: " . $strategy->expectedImprovement . "%\n";
+        echo "  Implementation: {$strategy->implementationHint}\n\n";
+    }
+}
+
+// 9. Summary
+echo "=== Integration Summary ===\n";
+echo "✓ Traditional pattern detection completed\n";
+echo "✓ ML-based anomaly detection completed\n";
+echo "✓ Combined analysis provides:\n";
+echo "  - Pattern-based detection (existing framework)\n";
+echo "  - ML-based anomaly detection (new capability)\n";
+echo "  - Eager loading optimization strategies\n";
+echo "  - Comprehensive performance insights\n\n";
+
+echo "Integration Benefits:\n";
+echo "1. Enhanced detection accuracy through ML\n";
+echo "2. Reduced false positives via confidence scoring\n";
+echo "3. Automatic feature extraction from query patterns\n";
+echo "4. Real-time anomaly detection with low overhead\n";
+echo "5. Seamless integration with existing detection pipeline\n\n";
+
+echo "✓ N+1 Detection ML Integration Example Complete\n";