<?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";