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-detection-usage.php

@@ -0,0 +1,159 @@
+<?php
+
+declare(strict_types=1);
+
+/**
+ * N+1 Detection Machine Learning - Usage Example
+ *
+ * This example demonstrates how to use the N+1 Detection ML Engine
+ * to analyze query execution patterns and detect N+1 query problems.
+ */
+
+require __DIR__ . '/../vendor/autoload.php';
+
+use App\Framework\Database\NPlusOneDetection\MachineLearning\NPlusOneDetectionEngine;
+use App\Framework\Database\NPlusOneDetection\MachineLearning\NPlusOneDetectionEngineInitializer;
+use App\Framework\Database\NPlusOneDetection\QueryExecutionContext;
+use App\Framework\Core\ValueObjects\Duration;
+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;
+
+echo "=== N+1 Detection Machine Learning - Usage Example ===\n\n";
+
+// Setup
+$environment = new Environment();
+$clock = new SystemClock();
+$logger = new Logger([new ConsoleHandler(LogLevel::INFO)]);
+
+// Initialize N+1 Detection ML Engine
+$initializer = new NPlusOneDetectionEngineInitializer($environment, $clock, $logger);
+$engine = $initializer();
+
+echo "✓ N+1 Detection ML Engine initialized\n";
+echo "Configuration: " . json_encode($engine->getConfiguration(), JSON_PRETTY_PRINT) . "\n\n";
+
+// Example 1: Clean Query Pattern (No N+1)
+echo "=== Example 1: Clean Query Pattern (No N+1) ===\n";
+$cleanContext = QueryExecutionContext::fromQueries([
+    [
+        'query' => 'SELECT * FROM users WHERE id = 1',
+        'duration' => 5.0,
+        'complexity' => 0.3,
+        'joins' => 0,
+    ],
+    [
+        'query' => 'SELECT * FROM posts WHERE user_id = 1',
+        'duration' => 8.0,
+        'complexity' => 0.4,
+        'joins' => 0,
+    ],
+], executedInLoop: false);
+
+$result1 = $engine->analyzeQueryContext($cleanContext);
+echo "Analysis Result:\n";
+echo "  - Success: " . ($result1['success'] ? 'Yes' : 'No') . "\n";
+echo "  - Features Extracted: " . count($result1['features']) . "\n";
+echo "  - Anomalies Detected: " . count($result1['anomalies']) . "\n";
+echo "  - Overall Confidence: " . round($result1['overall_confidence'], 2) . "%\n";
+echo "  - Analysis Time: " . round($result1['analysis_time_ms'], 2) . "ms\n";
+echo "  - Has N+1 Pattern: " . ($cleanContext->hasNPlusOnePattern() ? 'Yes' : 'No') . "\n\n";
+
+// Example 2: Suspicious N+1 Pattern
+echo "=== Example 2: Suspicious N+1 Pattern ===\n";
+$queries = [];
+// Simulate N+1: 1 initial query + 10 repeated queries in loop
+$queries[] = [
+    'query' => 'SELECT * FROM users',
+    'duration' => 10.0,
+    'complexity' => 0.5,
+    'joins' => 0,
+];
+for ($i = 1; $i <= 10; $i++) {
+    $queries[] = [
+        'query' => "SELECT * FROM posts WHERE user_id = {$i}",  // Will be normalized to same hash
+        'duration' => 5.0 + ($i * 0.1),  // Slightly varying timing
+        'complexity' => 0.3,
+        'joins' => 0,
+    ];
+}
+
+$suspiciousContext = QueryExecutionContext::fromQueries($queries, executedInLoop: true, loopDepth: 1);
+
+$result2 = $engine->analyzeQueryContext($suspiciousContext);
+echo "Analysis Result:\n";
+echo "  - Success: " . ($result2['success'] ? 'Yes' : 'No') . "\n";
+echo "  - Features Extracted: " . count($result2['features']) . "\n";
+echo "  - Anomalies Detected: " . count($result2['anomalies']) . "\n";
+echo "  - Overall Confidence: " . round($result2['overall_confidence'], 2) . "%\n";
+echo "  - Analysis Time: " . round($result2['analysis_time_ms'], 2) . "ms\n";
+echo "  - Has N+1 Pattern: " . ($suspiciousContext->hasNPlusOnePattern() ? '⚠️  Yes' : 'No') . "\n";
+echo "  - Repetition Rate: " . round($suspiciousContext->getRepetitionRate(), 2) . "%\n";
+echo "  - Unique Queries: " . count($suspiciousContext->uniqueQueryHashes) . " / " . $suspiciousContext->queryCount . "\n\n";
+
+// Show detected anomalies
+if (!empty($result2['anomalies'])) {
+    echo "Detected Anomalies:\n";
+    foreach ($result2['anomalies'] as $anomaly) {
+        echo "  - Type: {$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($anomaly->context) . "\n";
+        }
+        echo "\n";
+    }
+}
+
+// Show extracted features
+echo "=== Extracted Features (Sample) ===\n";
+if (!empty($result2['features'])) {
+    $sampleFeatures = array_slice($result2['features'], 0, 3);
+    foreach ($sampleFeatures as $feature) {
+        echo "  - {$feature->name}: " . round($feature->value, 4) . " {$feature->unit}\n";
+        echo "    Type: {$feature->type->value}\n";
+        if (!empty($feature->metadata)) {
+            echo "    Metadata: " . json_encode($feature->metadata) . "\n";
+        }
+        echo "\n";
+    }
+}
+
+// Example 3: Disabled Engine
+echo "=== Example 3: Disabled Engine ===\n";
+$disabledEngine = new NPlusOneDetectionEngine(
+    enabled: false,
+    extractors: [],
+    detectors: [],
+    clock: $clock,
+    analysisTimeout: Duration::fromSeconds(5),
+    confidenceThreshold: \App\Framework\Core\ValueObjects\Percentage::from(60.0)
+);
+
+$result3 = $disabledEngine->analyzeQueryContext($suspiciousContext);
+echo "Analysis Result:\n";
+echo "  - Success: " . ($result3['success'] ? 'Yes' : 'No') . "\n";
+echo "  - Error: " . ($result3['error'] ?? 'None') . "\n\n";
+
+echo "=== Integration Example ===\n";
+echo "To integrate N+1 Detection ML into your application:\n\n";
+echo "1. Inject NPlusOneDetectionEngine into your services:\n";
+echo "   public function __construct(\n";
+echo "       private readonly NPlusOneDetectionEngine \$mlEngine\n";
+echo "   ) {}\n\n";
+echo "2. Collect query execution data during request processing\n\n";
+echo "3. Create QueryExecutionContext from collected queries\n\n";
+echo "4. Analyze with ML Engine:\n";
+echo "   \$result = \$this->mlEngine->analyzeQueryContext(\$context);\n\n";
+echo "5. Take action based on anomalies detected:\n";
+echo "   if (\$result['overall_confidence'] > 80.0) {\n";
+echo "       \$this->logger->warning('Potential N+1 detected', [\n";
+echo "           'anomalies' => count(\$result['anomalies']),\n";
+echo "           'confidence' => \$result['overall_confidence'],\n";
+echo "       ]);\n";
+echo "   }\n\n";
+
+echo "✓ N+1 Detection ML Usage Example Complete\n";