MongoDB Index Optimization and Query Performance Analysis: Advanced Database Performance Tuning and Query Optimization for High-Performance Applications

-- Traditional PostgreSQL query optimization - manual index management with limited analysis capabilities

-- Basic index tracking table with minimal functionality
CREATE TABLE index_usage_stats (
    index_id SERIAL PRIMARY KEY,
    schema_name VARCHAR(100) NOT NULL,
    table_name VARCHAR(100) NOT NULL,
    index_name VARCHAR(100) NOT NULL,
    index_type VARCHAR(50),

    -- Basic usage statistics (very limited visibility)
    index_scans BIGINT DEFAULT 0,
    tuples_read BIGINT DEFAULT 0,
    tuples_fetched BIGINT DEFAULT 0,

    -- Size information (manual tracking)
    index_size_bytes BIGINT,
    table_size_bytes BIGINT,

    -- Basic metadata
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    last_analyzed TIMESTAMP,
    is_unique BOOLEAN DEFAULT false,
    is_partial BOOLEAN DEFAULT false,

    -- Simple effectiveness metrics
    scan_ratio DECIMAL(10,4),
    selectivity_estimate DECIMAL(10,4)
);

-- Query performance tracking table (basic functionality)
CREATE TABLE query_performance_log (
    query_id SERIAL PRIMARY KEY,
    query_hash VARCHAR(64),
    query_text TEXT,

    -- Basic execution metrics
    execution_time_ms INTEGER,
    rows_examined BIGINT,
    rows_returned BIGINT,
    execution_timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,

    -- Resource usage (limited tracking)
    cpu_usage_ms INTEGER,
    memory_usage_kb INTEGER,
    disk_reads INTEGER,

    -- Connection information
    database_name VARCHAR(100),
    username VARCHAR(100),
    application_name VARCHAR(100),

    -- Basic query plan information (very limited)
    query_plan_hash VARCHAR(64),
    index_usage TEXT[], -- Simple array of index names

    -- Performance classification
    performance_category VARCHAR(50) DEFAULT 'unknown'
);

-- Manual query plan analysis function (very basic capabilities)
CREATE OR REPLACE FUNCTION analyze_query_performance(
    query_text_param TEXT,
    execution_count INTEGER DEFAULT 1
) RETURNS TABLE (
    avg_execution_time_ms INTEGER,
    total_rows_examined BIGINT,
    total_rows_returned BIGINT,
    selectivity_ratio DECIMAL(10,4),
    suggested_indexes TEXT[],
    performance_rating VARCHAR(20)
) AS $$
DECLARE
    total_execution_time INTEGER := 0;
    total_examined BIGINT := 0;
    total_returned BIGINT := 0;
    execution_counter INTEGER := 0;
    current_execution_time INTEGER;
    current_examined BIGINT;
    current_returned BIGINT;
    plan_info TEXT;
BEGIN
    -- Simulate multiple query executions for analysis
    WHILE execution_counter < execution_count LOOP
        -- Execute EXPLAIN ANALYZE (simplified simulation)
        BEGIN
            -- This would be an actual EXPLAIN ANALYZE in reality
            EXECUTE 'EXPLAIN ANALYZE ' || query_text_param INTO plan_info;

            -- Extract basic metrics (very simplified parsing)
            current_execution_time := (random() * 1000 + 10)::INTEGER; -- Simulated execution time
            current_examined := (random() * 10000 + 100)::BIGINT; -- Simulated rows examined
            current_returned := (random() * 1000 + 10)::BIGINT; -- Simulated rows returned

            total_execution_time := total_execution_time + current_execution_time;
            total_examined := total_examined + current_examined;
            total_returned := total_returned + current_returned;

            -- Log query performance
            INSERT INTO query_performance_log (
                query_text,
                execution_time_ms,
                rows_examined,
                rows_returned,
                query_plan_hash
            ) VALUES (
                query_text_param,
                current_execution_time,
                current_examined,
                current_returned,
                md5(plan_info)
            );

        EXCEPTION WHEN OTHERS THEN
            -- Basic error handling
            current_execution_time := 9999; -- Error indicator
            current_examined := 0;
            current_returned := 0;
        END;

        execution_counter := execution_counter + 1;
    END LOOP;

    -- Calculate average metrics
    RETURN QUERY SELECT 
        (total_execution_time / execution_count)::INTEGER,
        total_examined,
        total_returned,
        CASE 
            WHEN total_examined > 0 THEN (total_returned::DECIMAL / total_examined)
            ELSE 0
        END,

        -- Very basic index suggestions (limited analysis)
        CASE 
            WHEN total_execution_time > 1000 THEN ARRAY['Consider adding indexes on WHERE clause columns']
            WHEN total_examined > total_returned * 10 THEN ARRAY['Add indexes to improve selectivity']
            ELSE ARRAY['Performance appears acceptable']
        END::TEXT[],

        -- Simple performance rating
        CASE 
            WHEN total_execution_time < 100 THEN 'excellent'
            WHEN total_execution_time < 500 THEN 'good'
            WHEN total_execution_time < 1000 THEN 'acceptable'
            ELSE 'poor'
        END;

END;
$$ LANGUAGE plpgsql;

-- Execute query performance analysis (basic functionality)
SELECT * FROM analyze_query_performance('SELECT * FROM users WHERE email = ''[email protected]'' AND created_at > ''2023-01-01''', 5);

-- Index effectiveness monitoring (limited capabilities)
WITH index_effectiveness AS (
    SELECT 
        ius.schema_name,
        ius.table_name,
        ius.index_name,
        ius.index_type,
        ius.index_scans,
        ius.tuples_read,
        ius.tuples_fetched,
        ius.index_size_bytes,

        -- Basic effectiveness calculations
        CASE 
            WHEN ius.index_scans > 0 AND ius.tuples_read > 0 THEN
                ius.tuples_fetched::DECIMAL / ius.tuples_read
            ELSE 0
        END as fetch_ratio,

        CASE 
            WHEN ius.table_size_bytes > 0 AND ius.index_size_bytes > 0 THEN
                (ius.index_size_bytes::DECIMAL / ius.table_size_bytes) * 100
            ELSE 0
        END as size_overhead_percent,

        -- Usage frequency analysis
        CASE 
            WHEN ius.index_scans = 0 THEN 'unused'
            WHEN ius.index_scans < 10 THEN 'rarely_used'
            WHEN ius.index_scans < 100 THEN 'moderately_used'
            ELSE 'frequently_used'
        END as usage_category

    FROM index_usage_stats ius
    WHERE ius.last_analyzed >= CURRENT_DATE - INTERVAL '7 days'
),

query_patterns AS (
    SELECT 
        qpl.database_name,
        qpl.query_hash,
        COUNT(*) as execution_count,
        AVG(qpl.execution_time_ms) as avg_execution_time,
        MAX(qpl.execution_time_ms) as max_execution_time,
        AVG(qpl.rows_examined) as avg_rows_examined,
        AVG(qpl.rows_returned) as avg_rows_returned,

        -- Performance trend analysis (very basic)
        CASE 
            WHEN COUNT(*) > 100 AND AVG(qpl.execution_time_ms) > 500 THEN 'high_impact_slow'
            WHEN COUNT(*) > 1000 THEN 'high_frequency'
            WHEN AVG(qpl.execution_time_ms) > 1000 THEN 'slow_query'
            ELSE 'normal'
        END as query_pattern_type,

        -- Index usage analysis from query logs
        STRING_AGG(DISTINCT unnest(qpl.index_usage), ', ') as indexes_used,

        -- Execution time trends
        PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY qpl.execution_time_ms) as p95_execution_time

    FROM query_performance_log qpl
    WHERE qpl.execution_timestamp >= CURRENT_DATE - INTERVAL '7 days'
    GROUP BY qpl.database_name, qpl.query_hash
)

SELECT 
    ie.schema_name,
    ie.table_name,
    ie.index_name,
    ie.index_type,
    ie.usage_category,

    -- Index effectiveness metrics
    ie.index_scans,
    ROUND(ie.fetch_ratio, 4) as selectivity_ratio,
    ROUND(ie.size_overhead_percent, 2) as size_overhead_percent,

    -- Size analysis
    ROUND(ie.index_size_bytes / 1024.0 / 1024.0, 2) as index_size_mb,

    -- Related query patterns
    COUNT(qp.query_hash) as related_query_patterns,
    COALESCE(AVG(qp.avg_execution_time), 0) as avg_query_time_using_index,
    COALESCE(AVG(qp.avg_rows_examined), 0) as avg_rows_examined,

    -- Index recommendations (very basic logic)
    CASE 
        WHEN ie.usage_category = 'unused' AND ie.index_size_bytes > 100*1024*1024 THEN 'consider_dropping'
        WHEN ie.fetch_ratio < 0.1 AND ie.index_scans > 0 THEN 'poor_selectivity'
        WHEN ie.usage_category = 'frequently_used' AND ie.fetch_ratio > 0.8 THEN 'high_performance'
        WHEN ie.size_overhead_percent > 50 THEN 'review_necessity'
        ELSE 'monitor'
    END as recommendation,

    -- Performance impact assessment
    CASE 
        WHEN ie.usage_category IN ('frequently_used', 'moderately_used') AND ie.fetch_ratio > 0.5 THEN 'positive_impact'
        WHEN ie.usage_category = 'unused' THEN 'no_impact'
        WHEN ie.fetch_ratio < 0.1 THEN 'negative_impact'
        ELSE 'unclear_impact'
    END as performance_impact

FROM index_effectiveness ie
LEFT JOIN query_patterns qp ON qp.indexes_used LIKE '%' || ie.index_name || '%'
GROUP BY 
    ie.schema_name, ie.table_name, ie.index_name, ie.index_type, 
    ie.usage_category, ie.index_scans, ie.fetch_ratio, 
    ie.size_overhead_percent, ie.index_size_bytes
ORDER BY 
    ie.index_scans DESC, 
    ie.fetch_ratio DESC,
    ie.index_size_bytes DESC;

-- Query optimization recommendations (very limited analysis)
WITH slow_queries AS (
    SELECT 
        query_hash,
        query_text,
        COUNT(*) as execution_count,
        AVG(execution_time_ms) as avg_time,
        MAX(execution_time_ms) as max_time,
        AVG(rows_examined) as avg_examined,
        AVG(rows_returned) as avg_returned,

        -- Basic pattern detection
        CASE 
            WHEN query_text ILIKE '%WHERE%=%' THEN 'equality_filter'
            WHEN query_text ILIKE '%WHERE%>%' OR query_text ILIKE '%WHERE%<%' THEN 'range_filter'
            WHEN query_text ILIKE '%ORDER BY%' THEN 'sorting'
            WHEN query_text ILIKE '%GROUP BY%' THEN 'aggregation'
            ELSE 'unknown_pattern'
        END as query_pattern

    FROM query_performance_log
    WHERE execution_time_ms > 500  -- Focus on slow queries
    AND execution_timestamp >= CURRENT_DATE - INTERVAL '24 hours'
    GROUP BY query_hash, query_text
    HAVING COUNT(*) >= 5  -- Frequently executed slow queries
)

SELECT 
    sq.query_hash,
    LEFT(sq.query_text, 100) || '...' as query_preview,
    sq.execution_count,
    ROUND(sq.avg_time, 0) as avg_execution_ms,
    sq.max_time as max_execution_ms,
    ROUND(sq.avg_examined, 0) as avg_rows_examined,
    ROUND(sq.avg_returned, 0) as avg_rows_returned,
    sq.query_pattern,

    -- Selectivity analysis
    CASE 
        WHEN sq.avg_examined > 0 THEN 
            ROUND((sq.avg_returned / sq.avg_examined) * 100, 2)
        ELSE 0
    END as selectivity_percent,

    -- Impact assessment
    ROUND(sq.execution_count * sq.avg_time, 0) as total_time_impact_ms,

    -- Basic optimization suggestions (very limited)
    CASE 
        WHEN sq.query_pattern = 'equality_filter' AND sq.avg_examined > sq.avg_returned * 10 THEN 
            'Add single-column index on equality filter columns'
        WHEN sq.query_pattern = 'range_filter' AND sq.avg_time > 1000 THEN 
            'Consider range-optimized index or query rewrite'
        WHEN sq.query_pattern = 'sorting' AND sq.avg_time > 800 THEN 
            'Add index supporting ORDER BY clause'
        WHEN sq.query_pattern = 'aggregation' AND sq.avg_examined > 10000 THEN 
            'Consider partial index or pre-aggregated data'
        WHEN sq.avg_examined > sq.avg_returned * 100 THEN 
            'Review query selectivity and indexing strategy'
        ELSE 'Manual analysis required'
    END as optimization_suggestion,

    -- Priority assessment
    CASE 
        WHEN sq.execution_count > 100 AND sq.avg_time > 1000 THEN 'high'
        WHEN sq.execution_count > 50 OR sq.avg_time > 2000 THEN 'medium'
        ELSE 'low'
    END as optimization_priority

FROM slow_queries sq
ORDER BY 
    CASE 
        WHEN sq.execution_count > 100 AND sq.avg_time > 1000 THEN 1
        WHEN sq.execution_count > 50 OR sq.avg_time > 2000 THEN 2
        ELSE 3
    END,
    (sq.execution_count * sq.avg_time) DESC;

-- Problems with traditional query optimization approaches:
-- 1. Manual index management with no automated recommendations
-- 2. Limited query plan analysis and optimization guidance
-- 3. Basic performance metrics with no comprehensive analysis
-- 4. No real-time query performance monitoring
-- 5. Minimal index effectiveness assessment
-- 6. Complex manual tuning procedures requiring deep database expertise
-- 7. No support for compound index optimization strategies
-- 8. Limited visibility into query execution patterns and resource usage
-- 9. Basic alerting with no proactive optimization suggestions
-- 10. No integration with application performance monitoring systems

// MongoDB Advanced Index Optimization and Query Performance Analysis
const { MongoClient } = require('mongodb');
const { EventEmitter } = require('events');

// Comprehensive MongoDB Performance Optimizer
class AdvancedPerformanceOptimizer extends EventEmitter {
  constructor(mongoUri, optimizationConfig = {}) {
    super();
    this.mongoUri = mongoUri;
    this.client = null;
    this.db = null;

    // Advanced optimization configuration
    this.config = {
      // Performance analysis configuration
      enableQueryProfiling: optimizationConfig.enableQueryProfiling !== false,
      profilingSampleRate: optimizationConfig.profilingSampleRate || 0.1,
      slowQueryThresholdMs: optimizationConfig.slowQueryThresholdMs || 100,

      // Index optimization settings
      enableAutomaticIndexRecommendations: optimizationConfig.enableAutomaticIndexRecommendations !== false,
      enableIndexUsageAnalysis: optimizationConfig.enableIndexUsageAnalysis !== false,
      enableCompoundIndexOptimization: optimizationConfig.enableCompoundIndexOptimization || false,

      // Monitoring and alerting
      enablePerformanceMonitoring: optimizationConfig.enablePerformanceMonitoring !== false,
      enableRealTimeAnalysis: optimizationConfig.enableRealTimeAnalysis || false,
      enablePerformanceAlerting: optimizationConfig.enablePerformanceAlerting || false,

      // Analysis parameters
      analysisWindowHours: optimizationConfig.analysisWindowHours || 24,
      minQueryExecutions: optimizationConfig.minQueryExecutions || 10,
      indexUsageThreshold: optimizationConfig.indexUsageThreshold || 0.1,

      // Resource optimization
      enableResourceOptimization: optimizationConfig.enableResourceOptimization || false,
      enableQueryPlanCaching: optimizationConfig.enableQueryPlanCaching !== false,
      enableConnectionPoolOptimization: optimizationConfig.enableConnectionPoolOptimization || false
    };

    // Performance tracking and analysis state
    this.queryPatterns = new Map();
    this.indexUsageStats = new Map();
    this.performanceMetrics = new Map();
    this.optimizationRecommendations = [];

    // Query execution tracking
    this.queryExecutionHistory = [];
    this.slowQueryLog = [];
    this.indexEffectivenessCache = new Map();

    this.initializePerformanceOptimizer();
  }

  async initializePerformanceOptimizer() {
    console.log('Initializing advanced MongoDB performance optimizer...');

    try {
      // Connect to MongoDB
      this.client = new MongoClient(this.mongoUri, {
        // Optimized connection settings
        maxPoolSize: 20,
        minPoolSize: 5,
        maxIdleTimeMS: 30000,
        serverSelectionTimeoutMS: 5000,
        heartbeatFrequencyMS: 10000
      });

      await this.client.connect();
      this.db = this.client.db();

      // Setup performance monitoring infrastructure
      await this.setupPerformanceInfrastructure();

      // Enable query profiling if configured
      if (this.config.enableQueryProfiling) {
        await this.enableQueryProfiling();
      }

      // Start real-time monitoring if enabled
      if (this.config.enableRealTimeAnalysis) {
        await this.startRealTimeMonitoring();
      }

      // Initialize index analysis
      if (this.config.enableIndexUsageAnalysis) {
        await this.initializeIndexAnalysis();
      }

      console.log('Advanced performance optimizer initialized successfully');

    } catch (error) {
      console.error('Error initializing performance optimizer:', error);
      throw error;
    }
  }

  async setupPerformanceInfrastructure() {
    console.log('Setting up performance monitoring infrastructure...');

    try {
      // Create collections for performance tracking
      const collections = {
        queryPerformanceLog: this.db.collection('query_performance_log'),
        indexUsageStats: this.db.collection('index_usage_stats'),
        performanceMetrics: this.db.collection('performance_metrics'),
        optimizationRecommendations: this.db.collection('optimization_recommendations'),
        queryPatterns: this.db.collection('query_patterns')
      };

      // Create indexes for performance collections
      await collections.queryPerformanceLog.createIndex(
        { timestamp: -1, executionTimeMs: -1 },
        { background: true, expireAfterSeconds: 7 * 24 * 60 * 60 } // 7 days retention
      );

      await collections.indexUsageStats.createIndex(
        { collection: 1, indexName: 1, timestamp: -1 },
        { background: true }
      );

      await collections.performanceMetrics.createIndex(
        { metricType: 1, timestamp: -1 },
        { background: true, expireAfterSeconds: 30 * 24 * 60 * 60 } // 30 days retention
      );

      this.collections = collections;

    } catch (error) {
      console.error('Error setting up performance infrastructure:', error);
      throw error;
    }
  }

  async enableQueryProfiling() {
    console.log('Enabling MongoDB query profiling...');

    try {
      // Set profiling level based on configuration
      await this.db.admin().command({
        profile: 2, // Profile all operations
        slowms: this.config.slowQueryThresholdMs,
        sampleRate: this.config.profilingSampleRate
      });

      console.log(`Query profiling enabled with ${this.config.slowQueryThresholdMs}ms threshold and ${this.config.profilingSampleRate} sample rate`);

    } catch (error) {
      console.error('Error enabling query profiling:', error);
      // Don't throw - profiling is optional
    }
  }

  async analyzeQueryPerformance(timeRangeHours = 24) {
    console.log(`Analyzing query performance for the last ${timeRangeHours} hours...`);

    try {
      const analysisStartTime = new Date(Date.now() - (timeRangeHours * 60 * 60 * 1000));

      // Analyze profiler data for slow queries and patterns
      const slowQueries = await this.analyzeSlowQueries(analysisStartTime);
      const queryPatterns = await this.analyzeQueryPatterns(analysisStartTime);
      const indexUsageAnalysis = await this.analyzeIndexUsage(analysisStartTime);

      // Generate performance insights
      const performanceInsights = {
        analysisTimestamp: new Date(),
        timeRangeHours: timeRangeHours,

        // Query performance summary
        queryPerformanceSummary: {
          totalQueries: slowQueries.totalQueries,
          slowQueries: slowQueries.slowQueryCount,
          averageExecutionTime: slowQueries.averageExecutionTime,
          p95ExecutionTime: slowQueries.p95ExecutionTime,
          p99ExecutionTime: slowQueries.p99ExecutionTime,

          // Query type distribution
          queryTypeDistribution: queryPatterns.queryTypeDistribution,

          // Resource usage patterns
          resourceUsage: {
            totalExaminedDocuments: slowQueries.totalExaminedDocuments,
            totalReturnedDocuments: slowQueries.totalReturnedDocuments,
            averageSelectivityRatio: slowQueries.averageSelectivityRatio
          }
        },

        // Index effectiveness analysis
        indexEffectiveness: {
          totalIndexes: indexUsageAnalysis.totalIndexes,
          activelyUsedIndexes: indexUsageAnalysis.activelyUsedIndexes,
          unusedIndexes: indexUsageAnalysis.unusedIndexes,
          inefficientIndexes: indexUsageAnalysis.inefficientIndexes,

          // Index usage patterns
          indexUsagePatterns: indexUsageAnalysis.usagePatterns,

          // Index performance metrics
          averageIndexSelectivity: indexUsageAnalysis.averageSelectivity,
          indexSizeOverhead: indexUsageAnalysis.totalIndexSizeBytes
        },

        // Performance bottlenecks
        performanceBottlenecks: await this.identifyPerformanceBottlenecks(slowQueries, queryPatterns, indexUsageAnalysis),

        // Optimization opportunities
        optimizationOpportunities: await this.generateOptimizationRecommendations(slowQueries, queryPatterns, indexUsageAnalysis)
      };

      // Store performance analysis results
      await this.collections.performanceMetrics.insertOne({
        metricType: 'comprehensive_analysis',
        timestamp: new Date(),
        analysisResults: performanceInsights
      });

      this.emit('performanceAnalysisCompleted', performanceInsights);

      return {
        success: true,
        analysisResults: performanceInsights
      };

    } catch (error) {
      console.error('Error analyzing query performance:', error);
      return {
        success: false,
        error: error.message
      };
    }
  }

  async analyzeSlowQueries(startTime) {
    console.log('Analyzing slow query patterns...');

    try {
      // Query the profiler collection for slow queries
      const profilerCollection = this.db.collection('system.profile');

      const slowQueryAggregation = [
        {
          $match: {
            ts: { $gte: startTime },
            op: { $in: ['query', 'getmore'] }, // Focus on read operations
            millis: { $gte: this.config.slowQueryThresholdMs }
          }
        },
        {
          $addFields: {
            // Normalize query shape for pattern analysis
            queryShape: {
              $function: {
                body: function(command) {
                  // Simplified query shape normalization
                  if (!command || !command.find) return 'unknown';

                  const filter = command.find.filter || {};
                  const sort = command.find.sort || {};
                  const projection = command.find.projection || {};

                  // Create shape by replacing values with type indicators
                  const shapeFilter = Object.keys(filter).reduce((acc, key) => {
                    acc[key] = typeof filter[key];
                    return acc;
                  }, {});

                  return JSON.stringify({
                    filter: shapeFilter,
                    sort: Object.keys(sort),
                    projection: Object.keys(projection)
                  });
                },
                args: ['$command'],
                lang: 'js'
              }
            },

            // Extract collection name
            targetCollection: {
              $ifNull: ['$command.find', '$command.collection']
            },

            // Calculate selectivity ratio
            selectivityRatio: {
              $cond: [
                { $and: [{ $gt: ['$docsExamined', 0] }, { $gt: ['$nreturned', 0] }] },
                { $divide: ['$nreturned', '$docsExamined'] },
                0
              ]
            }
          }
        },
        {
          $group: {
            _id: {
              queryShape: '$queryShape',
              collection: '$targetCollection'
            },

            // Execution statistics
            executionCount: { $sum: 1 },
            totalExecutionTime: { $sum: '$millis' },
            averageExecutionTime: { $avg: '$millis' },
            maxExecutionTime: { $max: '$millis' },
            minExecutionTime: { $min: '$millis' },

            // Document examination statistics
            totalDocsExamined: { $sum: '$docsExamined' },
            totalDocsReturned: { $sum: '$nreturned' },
            averageSelectivity: { $avg: '$selectivityRatio' },

            // Index usage tracking
            indexesUsed: { $addToSet: '$planSummary' },

            // Resource usage
            totalKeysExamined: { $sum: '$keysExamined' },

            // Sample query for reference
            sampleQuery: { $first: '$command' },
            sampleTimestamp: { $first: '$ts' }
          }
        },
        {
          $addFields: {
            // Calculate performance impact
            performanceImpact: {
              $multiply: ['$executionCount', '$averageExecutionTime']
            },

            // Assess query efficiency
            queryEfficiency: {
              $cond: [
                { $gt: ['$averageSelectivity', 0.1] },
                'efficient',
                { $cond: [{ $gt: ['$averageSelectivity', 0.01] }, 'moderate', 'inefficient'] }
              ]
            }
          }
        },
        {
          $sort: { performanceImpact: -1 }
        },
        {
          $limit: 100 // Top 100 slow query patterns
        }
      ];

      const slowQueryResults = await profilerCollection.aggregate(slowQueryAggregation).toArray();

      // Calculate summary statistics
      const totalQueries = slowQueryResults.reduce((sum, query) => sum + query.executionCount, 0);
      const totalExecutionTime = slowQueryResults.reduce((sum, query) => sum + query.totalExecutionTime, 0);
      const allExecutionTimes = slowQueryResults.flatMap(query => Array(query.executionCount).fill(query.averageExecutionTime));

      // Calculate percentiles
      allExecutionTimes.sort((a, b) => a - b);
      const p95Index = Math.floor(allExecutionTimes.length * 0.95);
      const p99Index = Math.floor(allExecutionTimes.length * 0.99);

      return {
        slowQueryPatterns: slowQueryResults,
        totalQueries: totalQueries,
        slowQueryCount: slowQueryResults.length,
        averageExecutionTime: totalQueries > 0 ? totalExecutionTime / totalQueries : 0,
        p95ExecutionTime: allExecutionTimes[p95Index] || 0,
        p99ExecutionTime: allExecutionTimes[p99Index] || 0,
        totalExaminedDocuments: slowQueryResults.reduce((sum, query) => sum + query.totalDocsExamined, 0),
        totalReturnedDocuments: slowQueryResults.reduce((sum, query) => sum + query.totalDocsReturned, 0),
        averageSelectivityRatio: slowQueryResults.length > 0 
          ? slowQueryResults.reduce((sum, query) => sum + (query.averageSelectivity || 0), 0) / slowQueryResults.length 
          : 0
      };

    } catch (error) {
      console.error('Error analyzing slow queries:', error);
      throw error;
    }
  }

  async analyzeQueryPatterns(startTime) {
    console.log('Analyzing query execution patterns...');

    try {
      const profilerCollection = this.db.collection('system.profile');

      // Analyze query type distribution and patterns
      const queryPatternAggregation = [
        {
          $match: {
            ts: { $gte: startTime },
            op: { $in: ['query', 'getmore', 'update', 'delete', 'insert'] }
          }
        },
        {
          $addFields: {
            // Categorize query operations
            queryCategory: {
              $switch: {
                branches: [
                  {
                    case: { $eq: ['$op', 'query'] },
                    then: {
                      $cond: [
                        { $ifNull: ['$command.find.sort', false] },
                        'sorted_query',
                        { $cond: [
                          { $gt: [{ $size: { $objectToArray: { $ifNull: ['$command.find.filter', {}] } } }, 0] },
                          'filtered_query',
                          'full_scan'
                        ]}
                      ]
                    }
                  },
                  { case: { $eq: ['$op', 'update'] }, then: 'update_operation' },
                  { case: { $eq: ['$op', 'delete'] }, then: 'delete_operation' },
                  { case: { $eq: ['$op', 'insert'] }, then: 'insert_operation' }
                ],
                default: 'other_operation'
              }
            },

            // Analyze query complexity
            queryComplexity: {
              $switch: {
                branches: [
                  {
                    case: { $and: [
                      { $eq: ['$op', 'query'] },
                      { $gt: [{ $size: { $objectToArray: { $ifNull: ['$command.find.filter', {}] } } }, 5] }
                    ]},
                    then: 'complex'
                  },
                  {
                    case: { $and: [
                      { $eq: ['$op', 'query'] },
                      { $gt: [{ $size: { $objectToArray: { $ifNull: ['$command.find.filter', {}] } } }, 2] }
                    ]},
                    then: 'moderate'
                  }
                ],
                default: 'simple'
              }
            }
          }
        },
        {
          $group: {
            _id: {
              collection: { $ifNull: ['$command.find', '$command.collection', '$ns'] },
              queryCategory: '$queryCategory',
              queryComplexity: '$queryComplexity'
            },

            // Pattern statistics
            executionCount: { $sum: 1 },
            averageExecutionTime: { $avg: '$millis' },
            totalExecutionTime: { $sum: '$millis' },

            // Resource usage patterns
            averageDocsExamined: { $avg: '$docsExamined' },
            averageDocsReturned: { $avg: '$nreturned' },

            // Index usage patterns
            commonIndexes: { $addToSet: '$planSummary' },

            // Performance characteristics
            maxExecutionTime: { $max: '$millis' },
            minExecutionTime: { $min: '$millis' }
          }
        },
        {
          $sort: { totalExecutionTime: -1 }
        }
      ];

      const queryPatternResults = await profilerCollection.aggregate(queryPatternAggregation).toArray();

      // Calculate query type distribution
      const queryTypeDistribution = queryPatternResults.reduce((distribution, pattern) => {
        const category = pattern._id.queryCategory;
        if (!distribution[category]) {
          distribution[category] = {
            count: 0,
            totalTime: 0,
            avgTime: 0
          };
        }

        distribution[category].count += pattern.executionCount;
        distribution[category].totalTime += pattern.totalExecutionTime;
        distribution[category].avgTime = distribution[category].totalTime / distribution[category].count;

        return distribution;
      }, {});

      return {
        queryPatterns: queryPatternResults,
        queryTypeDistribution: queryTypeDistribution,
        totalPatterns: queryPatternResults.length
      };

    } catch (error) {
      console.error('Error analyzing query patterns:', error);
      throw error;
    }
  }

  async analyzeIndexUsage(startTime) {
    console.log('Analyzing index usage effectiveness...');

    try {
      // Get all collections for comprehensive index analysis
      const collections = await this.db.listCollections().toArray();
      const indexAnalysisResults = [];

      for (const collectionInfo of collections) {
        if (collectionInfo.type === 'collection') {
          const collection = this.db.collection(collectionInfo.name);

          // Get index information
          const indexes = await collection.indexes();

          // Analyze each index
          for (const index of indexes) {
            try {
              // Get index usage statistics
              const indexStats = await collection.aggregate([
                { $indexStats: {} },
                { $match: { name: index.name } }
              ]).toArray();

              const indexStat = indexStats[0];

              if (indexStat) {
                // Calculate index effectiveness metrics
                const indexAnalysis = {
                  collection: collectionInfo.name,
                  indexName: index.name,
                  indexKeys: index.key,
                  indexType: this.determineIndexType(index),

                  // Usage statistics
                  usageCount: indexStat.accesses?.ops || 0,
                  lastUsed: indexStat.accesses?.since || null,

                  // Size and storage information
                  indexSize: index.size || 0,

                  // Effectiveness calculations
                  usageEffectiveness: this.calculateIndexEffectiveness(indexStat, index),

                  // Index health assessment
                  healthStatus: this.assessIndexHealth(indexStat, index),

                  // Optimization opportunities
                  optimizationOpportunities: await this.identifyIndexOptimizations(collection, index, indexStat)
                };

                indexAnalysisResults.push(indexAnalysis);
              }

            } catch (indexError) {
              console.warn(`Error analyzing index ${index.name} on ${collectionInfo.name}:`, indexError.message);
            }
          }
        }
      }

      // Calculate summary statistics
      const totalIndexes = indexAnalysisResults.length;
      const activelyUsedIndexes = indexAnalysisResults.filter(index => index.usageCount > 0).length;
      const unusedIndexes = indexAnalysisResults.filter(index => index.usageCount === 0);
      const inefficientIndexes = indexAnalysisResults.filter(index => 
        index.healthStatus === 'inefficient' || index.usageEffectiveness < 0.1
      );

      // Analyze usage patterns
      const usagePatterns = this.analyzeIndexUsagePatterns(indexAnalysisResults);

      return {
        indexAnalysisResults: indexAnalysisResults,
        totalIndexes: totalIndexes,
        activelyUsedIndexes: activelyUsedIndexes,
        unusedIndexes: unusedIndexes,
        inefficientIndexes: inefficientIndexes,
        usagePatterns: usagePatterns,
        averageSelectivity: this.calculateAverageIndexSelectivity(indexAnalysisResults),
        totalIndexSizeBytes: indexAnalysisResults.reduce((total, index) => total + (index.indexSize || 0), 0)
      };

    } catch (error) {
      console.error('Error analyzing index usage:', error);
      throw error;
    }
  }

  async generateOptimizationRecommendations(slowQueries, queryPatterns, indexUsage) {
    console.log('Generating performance optimization recommendations...');

    try {
      const recommendations = [];

      // Analyze slow queries for index recommendations
      for (const slowQuery of slowQueries.slowQueryPatterns) {
        if (slowQuery.averageSelectivity < 0.1 && slowQuery.executionCount > this.config.minQueryExecutions) {
          recommendations.push({
            type: 'index_recommendation',
            priority: 'high',
            collection: slowQuery._id.collection,
            issue: 'Low selectivity query pattern with high execution frequency',
            recommendation: await this.generateIndexRecommendation(slowQuery),
            expectedImprovement: this.estimatePerformanceImprovement(slowQuery),
            implementationComplexity: 'medium',
            estimatedImpact: slowQuery.performanceImpact
          });
        }
      }

      // Analyze unused indexes
      for (const unusedIndex of indexUsage.unusedIndexes) {
        if (unusedIndex.indexName !== '_id_') { // Never recommend dropping _id index
          recommendations.push({
            type: 'index_cleanup',
            priority: 'medium',
            collection: unusedIndex.collection,
            issue: `Unused index consuming storage space: ${unusedIndex.indexName}`,
            recommendation: `Consider dropping unused index '${unusedIndex.indexName}' to save ${Math.round((unusedIndex.indexSize || 0) / 1024 / 1024)} MB storage`,
            expectedImprovement: {
              storageReduction: unusedIndex.indexSize || 0,
              maintenanceOverheadReduction: 'low'
            },
            implementationComplexity: 'low',
            estimatedImpact: unusedIndex.indexSize || 0
          });
        }
      }

      // Analyze compound index opportunities
      if (this.config.enableCompoundIndexOptimization) {
        const compoundIndexOpportunities = await this.analyzeCompoundIndexOpportunities(queryPatterns);
        recommendations.push(...compoundIndexOpportunities);
      }

      // Sort recommendations by priority and estimated impact
      recommendations.sort((a, b) => {
        const priorityOrder = { high: 3, medium: 2, low: 1 };
        const priorityDiff = priorityOrder[b.priority] - priorityOrder[a.priority];

        if (priorityDiff !== 0) return priorityDiff;
        return (b.estimatedImpact || 0) - (a.estimatedImpact || 0);
      });

      return recommendations.slice(0, 20); // Return top 20 recommendations

    } catch (error) {
      console.error('Error generating optimization recommendations:', error);
      return [];
    }
  }

  async generateIndexRecommendation(slowQuery) {
    try {
      // Analyze the query shape to determine optimal index structure
      const queryShape = JSON.parse(slowQuery._id.queryShape);
      const filterFields = Object.keys(queryShape.filter || {});
      const sortFields = queryShape.sort || [];

      let recommendedIndex = {};

      // Build compound index recommendation based on query patterns
      // Rule 1: Equality filters first
      filterFields.forEach(field => {
        if (queryShape.filter[field] === 'string' || queryShape.filter[field] === 'number') {
          recommendedIndex[field] = 1;
        }
      });

      // Rule 2: Range filters after equality filters
      filterFields.forEach(field => {
        if (queryShape.filter[field] === 'object') { // Likely range query
          if (!recommendedIndex[field]) {
            recommendedIndex[field] = 1;
          }
        }
      });

      // Rule 3: Sort fields last
      sortFields.forEach(field => {
        if (!recommendedIndex[field]) {
          recommendedIndex[field] = 1;
        }
      });

      return {
        suggestedIndex: recommendedIndex,
        indexCommand: `db.${slowQuery._id.collection}.createIndex(${JSON.stringify(recommendedIndex)})`,
        reasoning: `Compound index optimized for query pattern with ${filterFields.length} filter fields and ${sortFields.length} sort fields`,
        estimatedSize: this.estimateIndexSize(recommendedIndex, slowQuery._id.collection)
      };

    } catch (error) {
      console.error('Error generating index recommendation:', error);
      return {
        suggestedIndex: {},
        indexCommand: 'Manual analysis required',
        reasoning: 'Unable to analyze query pattern automatically',
        estimatedSize: 0
      };
    }
  }

  async explainQuery(collection, query, options = {}) {
    console.log(`Explaining query execution plan for collection: ${collection}`);

    try {
      const targetCollection = this.db.collection(collection);

      // Execute explain with detailed execution stats
      const explainResult = await targetCollection
        .find(query.filter || {}, options)
        .sort(query.sort || {})
        .limit(query.limit || 0)
        .explain('executionStats');

      // Analyze execution plan
      const executionAnalysis = this.analyzeExecutionPlan(explainResult);

      // Generate optimization insights
      const optimizationInsights = await this.generateQueryOptimizationInsights(
        collection, 
        query, 
        explainResult, 
        executionAnalysis
      );

      return {
        success: true,
        query: query,
        executionPlan: explainResult,
        executionAnalysis: executionAnalysis,
        optimizationInsights: optimizationInsights,
        explainTimestamp: new Date()
      };

    } catch (error) {
      console.error(`Error explaining query for collection ${collection}:`, error);
      return {
        success: false,
        collection: collection,
        query: query,
        error: error.message
      };
    }
  }

  analyzeExecutionPlan(explainResult) {
    try {
      const executionStats = explainResult.executionStats;
      const winningPlan = explainResult.queryPlanner?.winningPlan;

      const analysis = {
        // Basic execution metrics
        executionTime: executionStats.executionTimeMillis,
        documentsExamined: executionStats.totalDocsExamined,
        documentsReturned: executionStats.totalDocsReturned,
        keysExamined: executionStats.totalKeysExamined,

        // Efficiency calculations
        selectivityRatio: executionStats.totalDocsExamined > 0 
          ? executionStats.totalDocsReturned / executionStats.totalDocsExamined 
          : 0,

        indexEfficiency: executionStats.totalKeysExamined > 0 
          ? executionStats.totalDocsReturned / executionStats.totalKeysExamined 
          : 0,

        // Plan analysis
        planType: this.identifyPlanType(winningPlan),
        indexesUsed: this.extractIndexesUsed(winningPlan),
        hasSort: this.hasSortStage(winningPlan),
        hasBlockingSort: this.hasBlockingSortStage(winningPlan),

        // Performance assessment
        performanceRating: this.assessQueryPerformance(executionStats, winningPlan),

        // Resource usage
        workingSetSize: executionStats.workingSetSize || 0,

        // Optimization opportunities
        needsOptimization: this.needsOptimization(executionStats, winningPlan)
      };

      return analysis;

    } catch (error) {
      console.error('Error analyzing execution plan:', error);
      return {
        error: 'Failed to analyze execution plan',
        executionTime: 0,
        documentsExamined: 0,
        documentsReturned: 0,
        needsOptimization: true
      };
    }
  }

  async generateQueryOptimizationInsights(collection, query, explainResult, executionAnalysis) {
    try {
      const insights = [];

      // Check for full collection scans
      if (executionAnalysis.planType === 'COLLSCAN') {
        insights.push({
          type: 'full_scan_detected',
          severity: 'high',
          message: 'Query is performing a full collection scan',
          recommendation: 'Add an appropriate index to avoid collection scanning',
          suggestedIndex: await this.suggestIndexForQuery(query)
        });
      }

      // Check for low selectivity
      if (executionAnalysis.selectivityRatio < 0.1) {
        insights.push({
          type: 'low_selectivity',
          severity: 'medium',
          message: `Query selectivity is low (${(executionAnalysis.selectivityRatio * 100).toFixed(2)}%)`,
          recommendation: 'Consider more selective query conditions or compound indexes',
          currentSelectivity: executionAnalysis.selectivityRatio
        });
      }

      // Check for blocking sorts
      if (executionAnalysis.hasBlockingSort) {
        insights.push({
          type: 'blocking_sort',
          severity: 'high',
          message: 'Query requires in-memory sorting which can be expensive',
          recommendation: 'Create an index that supports the sort order',
          suggestedIndex: this.suggestSortIndex(query.sort)
        });
      }

      // Check for excessive key examination
      if (executionAnalysis.keysExamined > executionAnalysis.documentsReturned * 10) {
        insights.push({
          type: 'excessive_key_examination',
          severity: 'medium',
          message: 'Query is examining significantly more keys than documents returned',
          recommendation: 'Consider compound indexes to improve key examination efficiency',
          keysExamined: executionAnalysis.keysExamined,
          documentsReturned: executionAnalysis.documentsReturned
        });
      }

      // Check execution time
      if (executionAnalysis.executionTime > this.config.slowQueryThresholdMs) {
        insights.push({
          type: 'slow_execution',
          severity: executionAnalysis.executionTime > this.config.slowQueryThresholdMs * 5 ? 'high' : 'medium',
          message: `Query execution time (${executionAnalysis.executionTime}ms) exceeds threshold`,
          recommendation: 'Consider query optimization or index improvements',
          executionTime: executionAnalysis.executionTime,
          threshold: this.config.slowQueryThresholdMs
        });
      }

      return insights;

    } catch (error) {
      console.error('Error generating query optimization insights:', error);
      return [];
    }
  }

  async getPerformanceMetrics(timeRangeHours = 24) {
    console.log(`Retrieving performance metrics for the last ${timeRangeHours} hours...`);

    try {
      const startTime = new Date(Date.now() - (timeRangeHours * 60 * 60 * 1000));

      // Get comprehensive performance metrics
      const metrics = await this.collections.performanceMetrics
        .find({
          timestamp: { $gte: startTime }
        })
        .sort({ timestamp: -1 })
        .toArray();

      // Calculate summary statistics
      const performanceSummary = this.calculatePerformanceSummary(metrics);

      // Get current optimization recommendations
      const currentRecommendations = await this.collections.optimizationRecommendations
        .find({
          createdAt: { $gte: startTime },
          status: { $ne: 'implemented' }
        })
        .sort({ priority: -1, estimatedImpact: -1 })
        .limit(10)
        .toArray();

      return {
        success: true,
        timeRangeHours: timeRangeHours,
        metricsCollected: metrics.length,
        performanceSummary: performanceSummary,
        currentRecommendations: currentRecommendations,
        lastUpdated: new Date()
      };

    } catch (error) {
      console.error('Error retrieving performance metrics:', error);
      return {
        success: false,
        error: error.message,
        timeRangeHours: timeRangeHours
      };
    }
  }

  calculatePerformanceSummary(metrics) {
    if (metrics.length === 0) {
      return {
        totalQueries: 0,
        averageExecutionTime: 0,
        slowQueries: 0,
        indexEffectiveness: 'unknown'
      };
    }

    // Extract metrics from analysis results
    const analysisResults = metrics
      .filter(metric => metric.metricType === 'comprehensive_analysis')
      .map(metric => metric.analysisResults);

    if (analysisResults.length === 0) {
      return {
        totalQueries: 0,
        averageExecutionTime: 0,
        slowQueries: 0,
        indexEffectiveness: 'no_data'
      };
    }

    const latestAnalysis = analysisResults[0];

    return {
      totalQueries: latestAnalysis.queryPerformanceSummary?.totalQueries || 0,
      averageExecutionTime: latestAnalysis.queryPerformanceSummary?.averageExecutionTime || 0,
      p95ExecutionTime: latestAnalysis.queryPerformanceSummary?.p95ExecutionTime || 0,
      slowQueries: latestAnalysis.queryPerformanceSummary?.slowQueries || 0,

      // Index effectiveness
      indexEffectiveness: {
        totalIndexes: latestAnalysis.indexEffectiveness?.totalIndexes || 0,
        activelyUsedIndexes: latestAnalysis.indexEffectiveness?.activelyUsedIndexes || 0,
        unusedIndexes: latestAnalysis.indexEffectiveness?.unusedIndexes?.length || 0,
        averageSelectivity: latestAnalysis.indexEffectiveness?.averageIndexSelectivity || 0
      },

      // Performance trends
      performanceBottlenecks: latestAnalysis.performanceBottlenecks || [],
      optimizationOpportunities: latestAnalysis.optimizationOpportunities?.length || 0
    };
  }

  // Utility methods for performance analysis

  determineIndexType(index) {
    if (index.name === '_id_') return 'primary';
    if (index.unique) return 'unique';
    if (index.sparse) return 'sparse';
    if (index.partialFilterExpression) return 'partial';
    if (Object.values(index.key).includes('text')) return 'text';
    if (Object.values(index.key).includes('2dsphere')) return 'geospatial';
    if (Object.keys(index.key).length > 1) return 'compound';
    return 'single';
  }

  calculateIndexEffectiveness(indexStat, index) {
    const usageCount = indexStat.accesses?.ops || 0;
    const indexSize = index.size || 0;

    // Calculate effectiveness based on usage frequency and size efficiency
    if (usageCount === 0) return 0;
    if (indexSize === 0) return 1;

    // Simple effectiveness metric: usage per MB of index size
    const sizeInMB = indexSize / (1024 * 1024);
    return Math.min(usageCount / Math.max(sizeInMB, 1), 100);
  }

  assessIndexHealth(indexStat, index) {
    const usageCount = indexStat.accesses?.ops || 0;
    const effectiveness = this.calculateIndexEffectiveness(indexStat, index);

    if (usageCount === 0) return 'unused';
    if (effectiveness < 0.1) return 'inefficient';
    if (effectiveness > 10) return 'highly_effective';
    return 'moderate';
  }

  identifyPlanType(winningPlan) {
    if (!winningPlan) return 'unknown';
    if (winningPlan.stage === 'COLLSCAN') return 'COLLSCAN';
    if (winningPlan.stage === 'IXSCAN') return 'IXSCAN';
    if (winningPlan.inputStage?.stage === 'IXSCAN') return 'IXSCAN';
    return winningPlan.stage || 'unknown';
  }

  extractIndexesUsed(winningPlan) {
    const indexes = [];

    function extractFromStage(stage) {
      if (stage.indexName) {
        indexes.push(stage.indexName);
      }
      if (stage.inputStage) {
        extractFromStage(stage.inputStage);
      }
      if (stage.inputStages) {
        stage.inputStages.forEach(extractFromStage);
      }
    }

    if (winningPlan) {
      extractFromStage(winningPlan);
    }

    return [...new Set(indexes)]; // Remove duplicates
  }

  hasSortStage(winningPlan) {
    if (!winningPlan) return false;

    function checkForSort(stage) {
      if (stage.stage === 'SORT') return true;
      if (stage.inputStage) return checkForSort(stage.inputStage);
      if (stage.inputStages) return stage.inputStages.some(checkForSort);
      return false;
    }

    return checkForSort(winningPlan);
  }

  hasBlockingSortStage(winningPlan) {
    if (!winningPlan) return false;

    function checkForBlockingSort(stage) {
      // A sort is blocking if it's not supported by an index
      if (stage.stage === 'SORT' && !stage.inputStage?.stage?.includes('IXSCAN')) {
        return true;
      }
      if (stage.inputStage) return checkForBlockingSort(stage.inputStage);
      if (stage.inputStages) return stage.inputStages.some(checkForBlockingSort);
      return false;
    }

    return checkForBlockingSort(winningPlan);
  }

  assessQueryPerformance(executionStats, winningPlan) {
    const executionTime = executionStats.executionTimeMillis || 0;
    const selectivityRatio = executionStats.totalDocsExamined > 0 
      ? executionStats.totalDocsReturned / executionStats.totalDocsExamined 
      : 0;

    // Performance rating based on multiple factors
    let score = 100;

    // Penalize slow execution
    if (executionTime > 1000) score -= 40;
    else if (executionTime > 500) score -= 20;
    else if (executionTime > 100) score -= 10;

    // Penalize low selectivity
    if (selectivityRatio < 0.01) score -= 30;
    else if (selectivityRatio < 0.1) score -= 15;

    // Penalize full collection scans
    if (winningPlan?.stage === 'COLLSCAN') score -= 25;

    // Penalize blocking sorts
    if (this.hasBlockingSortStage(winningPlan)) score -= 15;

    if (score >= 80) return 'excellent';
    if (score >= 60) return 'good';
    if (score >= 40) return 'fair';
    return 'poor';
  }

  needsOptimization(executionStats, winningPlan) {
    const executionTime = executionStats.executionTimeMillis || 0;
    const selectivityRatio = executionStats.totalDocsExamined > 0 
      ? executionStats.totalDocsReturned / executionStats.totalDocsExamined 
      : 0;

    return executionTime > this.config.slowQueryThresholdMs ||
           selectivityRatio < 0.1 ||
           winningPlan?.stage === 'COLLSCAN' ||
           this.hasBlockingSortStage(winningPlan);
  }

  estimatePerformanceImprovement(slowQuery) {
    return {
      executionTimeReduction: '60-80%',
      documentExaminationReduction: '90-95%',
      resourceUsageReduction: '70-85%',
      confidenceLevel: 'high'
    };
  }

  estimateIndexSize(indexKeys, collection) {
    // Simplified index size estimation
    const keyCount = Object.keys(indexKeys).length;
    const estimatedDocumentSize = 100; // Average document size estimate
    const estimatedCollectionSize = 100000; // Estimate

    return keyCount * estimatedDocumentSize * estimatedCollectionSize * 0.1;
  }

  async shutdown() {
    console.log('Shutting down performance optimizer...');

    try {
      // Disable profiling
      if (this.config.enableQueryProfiling) {
        await this.db.admin().command({ profile: 0 });
      }

      // Close MongoDB connection
      if (this.client) {
        await this.client.close();
      }

      console.log('Performance optimizer shutdown complete');

    } catch (error) {
      console.error('Error during shutdown:', error);
    }
  }

  // Additional methods would include implementations for:
  // - startRealTimeMonitoring()
  // - initializeIndexAnalysis()
  // - identifyPerformanceBottlenecks()
  // - analyzeIndexUsagePatterns()
  // - calculateAverageIndexSelectivity()
  // - analyzeCompoundIndexOpportunities()
  // - identifyIndexOptimizations()
  // - suggestIndexForQuery()
  // - suggestSortIndex()
}

// Benefits of MongoDB Advanced Performance Optimization:
// - Comprehensive query performance analysis and monitoring
// - Intelligent index optimization recommendations
// - Real-time performance bottleneck identification
// - Advanced execution plan analysis and insights
// - Automated slow query detection and optimization
// - Index usage effectiveness assessment
// - Compound index optimization strategies
// - SQL-compatible performance operations through QueryLeaf integration
// - Production-ready monitoring and alerting capabilities
// - Enterprise-grade performance tuning automation

module.exports = {
  AdvancedPerformanceOptimizer
};