MongoDB Data Compression and Storage Optimization: Advanced Techniques for Efficient Data Storage and Performance Tuning

-- Traditional PostgreSQL storage without compression - inefficient and wasteful

-- Typical relational table with poor storage efficiency
CREATE TABLE customer_interactions (
    interaction_id BIGSERIAL PRIMARY KEY,
    customer_id UUID NOT NULL,
    interaction_type VARCHAR(50) NOT NULL,
    channel_type VARCHAR(30) NOT NULL,
    interaction_timestamp TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,

    -- Repetitive and verbose data storage
    customer_full_name VARCHAR(200),
    customer_email VARCHAR(255),
    customer_phone VARCHAR(20),
    customer_address_line1 VARCHAR(200),
    customer_address_line2 VARCHAR(200),
    customer_city VARCHAR(100),
    customer_state VARCHAR(50),
    customer_postal_code VARCHAR(20),
    customer_country VARCHAR(50),

    -- Redundant categorical data (poor normalization choice for performance)
    interaction_category VARCHAR(100),
    interaction_subcategory VARCHAR(100),
    interaction_status VARCHAR(50),
    resolution_status VARCHAR(50),
    priority_level VARCHAR(20),
    department VARCHAR(100),
    agent_name VARCHAR(200),
    agent_email VARCHAR(255),
    agent_department VARCHAR(100),

    -- Large text fields without compression
    interaction_summary TEXT,
    customer_feedback TEXT,
    internal_notes TEXT,
    resolution_details TEXT,
    follow_up_actions TEXT,

    -- JSON data stored as text (no compression)
    interaction_metadata TEXT, -- JSON stored as plain text
    customer_preferences TEXT, -- JSON stored as plain text
    session_data TEXT,         -- JSON stored as plain text

    -- Additional fields causing bloat
    created_by UUID,
    updated_by UUID,
    created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,

    -- Version tracking fields
    version_number INTEGER DEFAULT 1,
    last_modified_timestamp TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP
);

-- Create indexes that consume additional storage without optimization
CREATE INDEX customer_interactions_customer_id_idx ON customer_interactions (customer_id);
CREATE INDEX customer_interactions_timestamp_idx ON customer_interactions (interaction_timestamp);
CREATE INDEX customer_interactions_type_idx ON customer_interactions (interaction_type);
CREATE INDEX customer_interactions_status_idx ON customer_interactions (interaction_status);
CREATE INDEX customer_interactions_agent_idx ON customer_interactions (agent_name);
CREATE INDEX customer_interactions_department_idx ON customer_interactions (department);

-- Composite index without optimization
CREATE INDEX customer_interactions_composite_idx ON customer_interactions (
    customer_id, interaction_type, interaction_timestamp
);

-- Example of inefficient data insertion creating massive storage bloat
INSERT INTO customer_interactions (
    customer_id,
    interaction_type,
    channel_type,
    customer_full_name,
    customer_email,
    customer_phone,
    customer_address_line1,
    customer_address_line2,
    customer_city,
    customer_state,
    customer_postal_code,
    customer_country,
    interaction_category,
    interaction_subcategory,
    interaction_status,
    resolution_status,
    priority_level,
    department,
    agent_name,
    agent_email,
    agent_department,
    interaction_summary,
    customer_feedback,
    internal_notes,
    resolution_details,
    follow_up_actions,
    interaction_metadata,
    customer_preferences,
    session_data
) VALUES (
    '123e4567-e89b-12d3-a456-426614174000',
    'Customer Support Request',
    'Email',
    'John Michael Anderson Smith Jr.',
    '[email protected]',
    '+1-555-123-4567',
    '1234 Very Long Street Name That Takes Up Lots Of Space Avenue',
    'Suite 567 - Building Complex Name That Is Unnecessarily Verbose',
    'Some Very Long City Name That Could Be Abbreviated',
    'Some Very Long State Name That Could Be Abbreviated',
    '12345-6789',
    'United States of America',
    'Technical Support and Assistance Request',
    'Software Configuration and Setup Issues',
    'Open and Awaiting Assignment to Agent',
    'Unresolved and Pending Investigation',
    'High Priority Immediate Attention Required',
    'Technical Support and Customer Success Department',
    'Sarah Elizabeth Johnson-Williams III',
    'sarah.elizabeth.johnson.williams.iii@very-long-company-domain-name.com',
    'Technical Support and Customer Success Department Division',
    'Customer reports experiencing significant technical difficulties with the software configuration and setup process. Multiple attempts to resolve the issue through standard troubleshooting procedures have been unsuccessful. Customer expresses frustration with the current state of the system and requests immediate assistance from a senior technical specialist.',
    'I am extremely frustrated with this software. I have been trying to configure it for three days now and nothing is working properly. The documentation is confusing and the interface is not intuitive. I need someone to help me immediately or I will have to consider switching to a different solution.',
    'Customer called at 2:30 PM expressing significant frustration. Initial troubleshooting performed over phone for 45 minutes. Customer became increasingly agitated during the call. Escalated to senior technical specialist for immediate follow-up. Customer database shows history of similar issues.',
    'Scheduled follow-up call with senior technical specialist for tomorrow at 10:00 AM. Will prepare detailed configuration guide specific to customer environment. Engineering team notified of recurring configuration issues for product improvement consideration.',
    'Send follow-up email with temporary workaround solution. Schedule training session for customer team. Create internal documentation update request for engineering team to improve initial setup experience.',
    '{"browser": "Chrome", "version": "118.0.5993.88", "operating_system": "Windows 11 Professional", "screen_resolution": "1920x1080", "session_duration": "00:45:23", "pages_visited": ["dashboard", "configuration", "settings", "help", "contact"], "error_codes": ["CONFIG_001", "CONFIG_003", "SETUP_ERROR_15"], "user_agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36"}',
    '{"communication_preference": "email", "timezone": "America/New_York", "language": "English", "notification_settings": {"email_notifications": true, "sms_notifications": false, "push_notifications": true}, "support_level": "premium", "account_type": "enterprise", "contract_tier": "gold"}',
    '{"session_id": "sess_987654321", "referrer": "https://www.google.com/search?q=software+setup+help", "campaign_source": "organic_search", "landing_page": "/help/setup", "conversion_tracking": {"goal_completed": false, "time_to_conversion": null}, "interaction_flow": ["landing", "help_search", "contact_form", "phone_call"]}'
);

-- Storage analysis revealing massive inefficiencies
SELECT 
    schemaname,
    tablename,

    -- Table size information (showing bloat)
    pg_size_pretty(pg_total_relation_size(schemaname||'.'||tablename)) as total_size,
    pg_size_pretty(pg_relation_size(schemaname||'.'||tablename)) as table_size,
    pg_size_pretty(pg_total_relation_size(schemaname||'.'||tablename) - pg_relation_size(schemaname||'.'||tablename)) as indexes_size,

    -- Row count and average row size
    n_tup_ins as total_rows,
    CASE 
        WHEN n_tup_ins > 0 THEN 
            pg_relation_size(schemaname||'.'||tablename) / n_tup_ins 
        ELSE 0 
    END as avg_row_size_bytes,

    -- Bloat estimation (simplified)
    CASE 
        WHEN n_tup_ins > 0 AND n_tup_del > 0 THEN
            ROUND((n_tup_del::float / n_tup_ins::float) * 100, 2)
        ELSE 0.0
    END as estimated_bloat_percentage

FROM pg_stat_user_tables 
WHERE tablename = 'customer_interactions'
ORDER BY pg_total_relation_size(schemaname||'.'||tablename) DESC;

-- Index usage and efficiency analysis
SELECT 
    schemaname,
    tablename,
    indexname,
    pg_size_pretty(pg_relation_size(indexname::regclass)) as index_size,
    idx_tup_read as index_reads,
    idx_tup_fetch as index_fetches,

    -- Index efficiency metrics
    CASE 
        WHEN idx_tup_read > 0 THEN 
            ROUND((idx_tup_fetch::float / idx_tup_read::float) * 100, 2)
        ELSE 0.0
    END as index_selectivity_percentage,

    -- Usage frequency
    CASE 
        WHEN idx_scan > 0 THEN 'Used'
        ELSE 'Unused - Consider Dropping'
    END as usage_status

FROM pg_stat_user_indexes 
WHERE tablename = 'customer_interactions'
ORDER BY pg_relation_size(indexname::regclass) DESC;

-- Problems with traditional storage approaches:
-- 1. Massive storage bloat due to redundant data and poor schema design
-- 2. No compression at the column or row level leading to wasted space
-- 3. Inefficient text storage for JSON and large text fields
-- 4. Index bloat consuming excessive disk space
-- 5. Poor data normalization choices leading to repetitive storage
-- 6. No intelligent data lifecycle management or archiving strategies
-- 7. Inefficient backup and restore times due to storage bloat
-- 8. High network bandwidth consumption for data replication
-- 9. Poor cache efficiency due to large row sizes
-- 10. Limited compression options and no real-time compression capabilities

-- Attempt at PostgreSQL compression (limited options)
-- PostgreSQL doesn't have built-in transparent compression for most data types

-- TOAST compression for large values (automatically applied but limited)
ALTER TABLE customer_interactions ALTER COLUMN interaction_summary SET STORAGE EXTENDED;
ALTER TABLE customer_interactions ALTER COLUMN customer_feedback SET STORAGE EXTENDED;
ALTER TABLE customer_interactions ALTER COLUMN internal_notes SET STORAGE EXTENDED;

-- Limited compression through column storage optimizations
-- PostgreSQL primarily relies on TOAST for large values but lacks comprehensive compression

-- Manual compression approach (application-level - complex and inefficient)
CREATE OR REPLACE FUNCTION compress_json_data(input_json TEXT) 
RETURNS BYTEA AS $$
BEGIN
    -- Limited compression capabilities requiring custom implementation
    RETURN pg_compress(input_json::bytea);
END;
$$ LANGUAGE plpgsql;

-- Problems with PostgreSQL compression approaches:
-- 1. Limited built-in compression options compared to modern databases
-- 2. TOAST compression only applies to large values (typically > 2KB)
-- 3. No transparent compression for regular columns and data types
-- 4. Application-level compression adds complexity and overhead
-- 5. Poor integration with indexing and query performance
-- 6. No automatic compression algorithm selection based on data patterns
-- 7. Limited control over compression levels and trade-offs
-- 8. No transparent decompression during query execution
-- 9. Complex backup and restore processes with compressed data
-- 10. Poor monitoring and optimization tools for compression effectiveness

// MongoDB Advanced Data Compression and Storage Optimization
const { MongoClient, ObjectId } = require('mongodb');

const client = new MongoClient('mongodb://localhost:27017', {
  // Connection-level compression for network optimization
  compressors: ['snappy', 'zlib', 'zstd'],
  zlibCompressionLevel: 6
});

const db = client.db('storage_optimization_demo');

// Comprehensive MongoDB Storage Optimization Manager
class MongoStorageOptimizationManager {
  constructor(db, config = {}) {
    this.db = db;
    this.collections = {
      customerInteractions: db.collection('customer_interactions'),
      products: db.collection('products'),
      orderHistory: db.collection('order_history'),
      userSessions: db.collection('user_sessions'),
      analyticsData: db.collection('analytics_data'),
      archivedData: db.collection('archived_data'),
      storageMetrics: db.collection('storage_metrics')
    };

    // Advanced storage optimization configuration
    this.optimizationConfig = {
      // Compression settings
      compression: {
        algorithm: config.compressionAlgorithm || 'snappy', // snappy, zlib, zstd
        level: config.compressionLevel || 6,
        enableDocumentCompression: config.enableDocumentCompression !== false,
        enableIndexCompression: config.enableIndexCompression !== false,
        enableBlockCompression: config.enableBlockCompression !== false
      },

      // Storage efficiency settings
      storageEfficiency: {
        enableDocumentOptimization: config.enableDocumentOptimization !== false,
        enableSchemaOptimization: config.enableSchemaOptimization !== false,
        enableIndexOptimization: config.enableIndexOptimization !== false,
        enableDataLifecycleManagement: config.enableDataLifecycleManagement !== false
      },

      // Performance optimization settings
      performanceOptimization: {
        enableCacheOptimization: config.enableCacheOptimization !== false,
        enableQueryOptimization: config.enableQueryOptimization !== false,
        enableBackgroundOptimization: config.enableBackgroundOptimization !== false,
        optimizationSchedule: config.optimizationSchedule || 'daily'
      },

      // Monitoring and analytics
      monitoring: {
        enableStorageMetrics: config.enableStorageMetrics !== false,
        enableCompressionAnalytics: config.enableCompressionAnalytics !== false,
        enablePerformanceTracking: config.enablePerformanceTracking !== false,
        metricsRetention: config.metricsRetention || '90 days'
      }
    };

    this.initializeStorageOptimization();
  }

  async initializeStorageOptimization() {
    console.log('Initializing MongoDB storage optimization system...');

    try {
      // Configure WiredTiger storage engine compression
      await this.configureWiredTigerCompression();

      // Setup optimized collection configurations
      await this.setupOptimizedCollections();

      // Initialize compression monitoring
      await this.setupCompressionMonitoring();

      // Start background optimization processes
      await this.startBackgroundOptimization();

      console.log('Storage optimization system initialized successfully');

    } catch (error) {
      console.error('Error initializing storage optimization:', error);
      throw error;
    }
  }

  async configureWiredTigerCompression() {
    console.log('Configuring WiredTiger compression settings...');

    try {
      // Configure collection compression for maximum efficiency
      const compressionConfig = {
        // Document compression using advanced algorithms
        storageEngine: {
          wiredTiger: {
            configString: 
              `block_compressor=${this.optimizationConfig.compression.algorithm},` +
              `memory_page_max=10m,` +
              `split_pct=90,` +
              `leaf_value_max=64MB,` +
              `checksum=on,` +
              `compression=${this.optimizationConfig.compression.algorithm}`
          }
        }
      };

      // Apply compression configuration to new collections
      await this.collections.customerInteractions.createIndex(
        { customerId: 1, interactionTimestamp: -1 },
        { 
          background: true,
          storageEngine: {
            wiredTiger: {
              configString: `prefix_compression=true,block_compressor=${this.optimizationConfig.compression.algorithm}`
            }
          }
        }
      );

      console.log('WiredTiger compression configured successfully');
      return compressionConfig;

    } catch (error) {
      console.error('Error configuring WiredTiger compression:', error);
      throw error;
    }
  }

  async setupOptimizedCollections() {
    console.log('Setting up collections with optimal storage configurations...');

    try {
      // Customer interactions collection with optimized schema and compression
      await this.collections.customerInteractions.createIndex(
        { "customerId": 1, "timestamp": -1 },
        { 
          background: true,
          partialFilterExpression: { "timestamp": { $gte: new Date(Date.now() - 365 * 24 * 60 * 60 * 1000) } }
        }
      );

      // Compound index with compression optimization
      await this.collections.customerInteractions.createIndex(
        { 
          "interactionType": 1, 
          "status": 1, 
          "timestamp": -1 
        },
        { 
          background: true,
          sparse: true,
          storageEngine: {
            wiredTiger: {
              configString: "prefix_compression=true"
            }
          }
        }
      );

      // Text index with compression for search optimization
      await this.collections.customerInteractions.createIndex(
        {
          "summary": "text",
          "notes": "text",
          "feedback": "text"
        },
        {
          background: true,
          weights: {
            "summary": 10,
            "notes": 5,
            "feedback": 3
          },
          name: "interaction_text_search_compressed"
        }
      );

      // Products collection with category-specific optimization
      await this.collections.products.createIndex(
        { "category": 1, "subcategory": 1, "sku": 1 },
        { 
          background: true,
          storageEngine: {
            wiredTiger: {
              configString: "prefix_compression=true,dictionary=1000"
            }
          }
        }
      );

      console.log('Optimized collection configurations completed');

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

  async insertOptimizedCustomerInteraction(interactionData) {
    console.log('Inserting customer interaction with storage optimization...');
    const startTime = Date.now();

    try {
      // Optimize document structure for storage efficiency
      const optimizedDocument = await this.optimizeDocumentStructure(interactionData);

      // Apply compression-friendly data organization
      const compressedDocument = await this.applyCompressionOptimizations(optimizedDocument);

      // Insert with optimized settings
      const insertResult = await this.collections.customerInteractions.insertOne(compressedDocument, {
        writeConcern: { w: 'majority', j: true }
      });

      // Track storage metrics
      await this.trackStorageMetrics('insert', {
        documentId: insertResult.insertedId,
        originalSize: JSON.stringify(interactionData).length,
        optimizedSize: JSON.stringify(compressedDocument).length,
        processingTime: Date.now() - startTime
      });

      return {
        insertedId: insertResult.insertedId,
        originalSize: JSON.stringify(interactionData).length,
        optimizedSize: JSON.stringify(compressedDocument).length,
        compressionRatio: (JSON.stringify(interactionData).length / JSON.stringify(compressedDocument).length).toFixed(2),
        processingTime: Date.now() - startTime
      };

    } catch (error) {
      console.error('Error inserting optimized customer interaction:', error);
      throw error;
    }
  }

  async optimizeDocumentStructure(document) {
    console.log('Optimizing document structure for storage efficiency...');

    // Create normalized and optimized document structure
    const optimized = {
      _id: document._id || new ObjectId(),

      // Optimize customer data with references instead of repetition
      customerId: document.customerId,

      // Use efficient data types and structures
      interaction: {
        type: this.normalizeCategory(document.interactionType),
        channel: this.normalizeCategory(document.channelType),
        timestamp: document.interactionTimestamp || new Date(),
        category: this.optimizeCategoryHierarchy(document.category, document.subcategory),
        status: this.normalizeStatus(document.status),
        priority: this.normalizePriority(document.priority)
      },

      // Optimize agent information
      agent: {
        id: document.agentId,
        department: this.normalizeCategory(document.department)
      },

      // Compress and optimize text content
      content: {
        summary: this.optimizeTextContent(document.summary),
        feedback: this.optimizeTextContent(document.feedback),
        notes: this.optimizeTextContent(document.notes),
        resolution: this.optimizeTextContent(document.resolution)
      },

      // Optimize metadata with compression-friendly structure
      metadata: this.optimizeMetadata(document.metadata),

      // Add optimization tracking
      optimization: {
        version: 1,
        optimizedAt: new Date(),
        compressionAlgorithm: this.optimizationConfig.compression.algorithm
      }
    };

    return optimized;
  }

  async applyCompressionOptimizations(document) {
    console.log('Applying compression optimizations to document...');

    // Apply field-level optimizations for better compression
    const optimized = { ...document };

    // Optimize repetitive categorical data
    if (optimized.interaction) {
      optimized.interaction = this.compressCategorialData(optimized.interaction);
    }

    // Optimize text content for compression
    if (optimized.content) {
      optimized.content = await this.compressTextContent(optimized.content);
    }

    // Optimize metadata structure
    if (optimized.metadata) {
      optimized.metadata = this.compressMetadata(optimized.metadata);
    }

    return optimized;
  }

  normalizeCategory(category) {
    if (!category) return null;

    // Use lookup table for common categories to reduce storage
    const categoryMapping = {
      'Customer Support Request': 'CSR',
      'Technical Support and Assistance Request': 'TSR',
      'Billing and Account Inquiry': 'BAI',
      'Product Information Request': 'PIR',
      'Complaint and Issue Resolution': 'CIR',
      'Feature Request and Suggestion': 'FRS'
    };

    return categoryMapping[category] || category;
  }

  optimizeCategoryHierarchy(category, subcategory) {
    if (!category) return null;

    // Create efficient hierarchical structure
    const hierarchy = {
      primary: this.normalizeCategory(category)
    };

    if (subcategory) {
      hierarchy.secondary = this.normalizeCategory(subcategory);
    }

    return hierarchy;
  }

  normalizeStatus(status) {
    if (!status) return null;

    const statusMapping = {
      'Open and Awaiting Assignment to Agent': 'OPEN',
      'In Progress with Agent': 'IN_PROGRESS',
      'Waiting for Customer Response': 'WAITING',
      'Resolved and Closed': 'RESOLVED',
      'Escalated to Senior Specialist': 'ESCALATED'
    };

    return statusMapping[status] || status;
  }

  normalizePriority(priority) {
    if (!priority) return null;

    const priorityMapping = {
      'High Priority Immediate Attention Required': 'HIGH',
      'Medium Priority Standard Response': 'MEDIUM',
      'Low Priority When Available': 'LOW'
    };

    return priorityMapping[priority] || priority;
  }

  optimizeTextContent(text) {
    if (!text || typeof text !== 'string') return text;

    // Apply text optimization techniques
    return text
      .trim()
      .replace(/\s+/g, ' ')  // Normalize whitespace
      .replace(/\n\s*\n/g, '\n')  // Remove extra line breaks
      .substring(0, 10000);  // Limit extremely long text
  }

  async compressTextContent(content) {
    console.log('Compressing text content for storage efficiency...');

    const compressed = {};

    for (const [key, text] of Object.entries(content)) {
      if (text && typeof text === 'string') {
        // Apply compression-friendly text processing
        compressed[key] = this.prepareTextForCompression(text);
      } else {
        compressed[key] = text;
      }
    }

    return compressed;
  }

  prepareTextForCompression(text) {
    if (!text || text.length < 100) return text;

    // Prepare text for optimal compression
    return text
      .trim()
      .replace(/\s+/g, ' ')  // Normalize spaces
      .replace(/([.!?])\s+/g, '$1\n')  // Normalize sentence breaks
      .replace(/\n\s*\n+/g, '\n')  // Remove extra linebreaks
      .toLowerCase()  // Lowercase for better compression patterns
      .substring(0, 5000);  // Reasonable text limit
  }

  compressCategorialData(interaction) {
    // Use efficient categorical data storage
    return {
      t: interaction.type,      // type
      c: interaction.channel,   // channel
      ts: interaction.timestamp, // timestamp
      cat: interaction.category, // category
      s: interaction.status,    // status
      p: interaction.priority   // priority
    };
  }

  compressMetadata(metadata) {
    if (!metadata || typeof metadata !== 'object') return metadata;

    // Compress metadata keys and optimize structure
    const compressed = {};

    Object.entries(metadata).forEach(([key, value]) => {
      // Use abbreviated keys for common metadata
      const keyMap = {
        'browser': 'br',
        'operating_system': 'os',
        'screen_resolution': 'res',
        'session_duration': 'dur',
        'user_agent': 'ua',
        'referrer': 'ref',
        'campaign_source': 'src'
      };

      const compressedKey = keyMap[key] || key;
      compressed[compressedKey] = this.compressMetadataValue(value);
    });

    return compressed;
  }

  compressMetadataValue(value) {
    if (typeof value === 'string') {
      // Compress common string values
      const valueMap = {
        'Chrome': 'C',
        'Firefox': 'F',
        'Safari': 'S',
        'Edge': 'E',
        'Windows': 'W',
        'MacOS': 'M',
        'Linux': 'L'
      };

      return valueMap[value] || value;
    }

    return value;
  }

  optimizeMetadata(metadata) {
    if (!metadata) return null;

    // Create optimized metadata structure
    const optimized = {
      session: {
        id: metadata.session_id,
        duration: metadata.session_duration,
        pages: metadata.pages_visited?.length || 0
      }
    };

    // Add browser information efficiently
    if (metadata.browser || metadata.operating_system) {
      optimized.env = {
        browser: this.normalizeCategory(metadata.browser),
        os: this.normalizeCategory(metadata.operating_system),
        resolution: metadata.screen_resolution
      };
    }

    // Add tracking information efficiently
    if (metadata.campaign_source || metadata.referrer) {
      optimized.tracking = {
        source: metadata.campaign_source,
        referrer: metadata.referrer ? new URL(metadata.referrer).hostname : null
      };
    }

    return optimized;
  }

  async performStorageAnalysis(collectionName) {
    console.log(`Performing comprehensive storage analysis for ${collectionName}...`);
    const startTime = Date.now();

    try {
      const collection = this.collections[collectionName] || this.db.collection(collectionName);

      // Get collection statistics
      const collStats = await this.db.runCommand({ collStats: collectionName, verbose: true });

      // Analyze document structure and compression effectiveness
      const sampleDocuments = await collection.aggregate([
        { $sample: { size: 100 } }
      ]).toArray();

      // Calculate compression metrics
      const compressionAnalysis = this.analyzeCompressionEffectiveness(sampleDocuments);

      // Get index statistics
      const indexStats = await collection.aggregate([
        { $indexStats: {} }
      ]).toArray();

      // Analyze storage efficiency
      const storageEfficiency = this.calculateStorageEfficiency(collStats, compressionAnalysis);

      const analysis = {
        collectionName: collectionName,
        timestamp: new Date(),
        processingTime: Date.now() - startTime,

        // Collection-level statistics
        collectionStats: {
          documentCount: collStats.count,
          averageDocumentSize: collStats.avgObjSize,
          totalSize: collStats.size,
          storageSize: collStats.storageSize,
          totalIndexSize: collStats.totalIndexSize,

          // Compression metrics
          compressionRatio: collStats.size / collStats.storageSize,
          compressionSavings: collStats.size - collStats.storageSize,
          compressionEfficiency: ((collStats.size - collStats.storageSize) / collStats.size * 100).toFixed(2)
        },

        // Document analysis
        documentAnalysis: compressionAnalysis,

        // Index analysis
        indexAnalysis: {
          totalIndexes: indexStats.length,
          indexEfficiency: this.analyzeIndexEfficiency(indexStats),
          indexSizeBreakdown: indexStats.map(idx => ({
            name: idx.name,
            size: idx.host ? idx.host.size : 'N/A',
            usageCount: idx.accesses?.ops || 0
          }))
        },

        // Storage efficiency metrics
        storageEfficiency: storageEfficiency,

        // Optimization recommendations
        recommendations: this.generateOptimizationRecommendations(collStats, compressionAnalysis, indexStats)
      };

      // Store analysis results for tracking
      await this.collections.storageMetrics.insertOne({
        ...analysis,
        analysisId: new ObjectId(),
        createdAt: new Date()
      });

      return analysis;

    } catch (error) {
      console.error(`Error performing storage analysis for ${collectionName}:`, error);
      throw error;
    }
  }

  analyzeCompressionEffectiveness(sampleDocuments) {
    console.log('Analyzing compression effectiveness across document samples...');

    const analysis = {
      sampleSize: sampleDocuments.length,
      averageOriginalSize: 0,
      averageCompressedSize: 0,
      compressionRatio: 0,
      fieldAnalysis: {},
      recommendedOptimizations: []
    };

    if (sampleDocuments.length === 0) return analysis;

    let totalOriginalSize = 0;
    let totalCompressedSize = 0;
    const fieldSizes = {};

    sampleDocuments.forEach(doc => {
      const docString = JSON.stringify(doc);
      const originalSize = docString.length;
      totalOriginalSize += originalSize;

      // Simulate compression effectiveness
      const compressedSize = this.estimateCompressedSize(docString);
      totalCompressedSize += compressedSize;

      // Analyze individual fields
      Object.entries(doc).forEach(([field, value]) => {
        if (!fieldSizes[field]) {
          fieldSizes[field] = { totalSize: 0, count: 0 };
        }
        fieldSizes[field].totalSize += JSON.stringify(value).length;
        fieldSizes[field].count++;
      });
    });

    analysis.averageOriginalSize = totalOriginalSize / sampleDocuments.length;
    analysis.averageCompressedSize = totalCompressedSize / sampleDocuments.length;
    analysis.compressionRatio = totalOriginalSize / totalCompressedSize;

    // Analyze field-level compression opportunities
    Object.entries(fieldSizes).forEach(([field, stats]) => {
      analysis.fieldAnalysis[field] = {
        averageSize: stats.totalSize / stats.count,
        frequency: stats.count / sampleDocuments.length,
        compressionOpportunity: this.calculateFieldCompressionOpportunity(field, stats)
      };
    });

    return analysis;
  }

  estimateCompressedSize(content) {
    // Simplified compression estimation based on repetition patterns
    const patterns = content.match(/(.{3,}?)(?=.*\1)/g) || [];
    const repetitionFactor = patterns.length / content.length;
    const estimatedCompressionRatio = Math.max(0.3, 1 - repetitionFactor * 0.7);
    return Math.round(content.length * estimatedCompressionRatio);
  }

  calculateFieldCompressionOpportunity(fieldName, stats) {
    // Determine compression opportunity for specific fields
    if (stats.averageSize > 1000) return 'high';
    if (stats.averageSize > 100) return 'medium';
    return 'low';
  }

  analyzeIndexEfficiency(indexStats) {
    console.log('Analyzing index efficiency and optimization opportunities...');

    const efficiency = {
      totalIndexes: indexStats.length,
      usedIndexes: 0,
      unusedIndexes: 0,
      highUsageIndexes: [],
      lowUsageIndexes: [],
      compressionOpportunities: []
    };

    indexStats.forEach(idx => {
      const usageCount = idx.accesses?.ops || 0;

      if (usageCount > 0) {
        efficiency.usedIndexes++;

        if (usageCount > 1000) {
          efficiency.highUsageIndexes.push({
            name: idx.name,
            usage: usageCount,
            recommendation: 'Consider prefix compression optimization'
          });
        } else if (usageCount < 10) {
          efficiency.lowUsageIndexes.push({
            name: idx.name,
            usage: usageCount,
            recommendation: 'Consider removal if consistently low usage'
          });
        }
      } else {
        efficiency.unusedIndexes++;
        efficiency.lowUsageIndexes.push({
          name: idx.name,
          usage: 0,
          recommendation: 'Consider removal - unused index'
        });
      }
    });

    return efficiency;
  }

  calculateStorageEfficiency(collStats, compressionAnalysis) {
    console.log('Calculating overall storage efficiency metrics...');

    const efficiency = {
      // Current efficiency metrics
      currentCompressionRatio: collStats.size / collStats.storageSize,
      indexToDataRatio: collStats.totalIndexSize / collStats.size,
      averageDocumentEfficiency: compressionAnalysis.compressionRatio,

      // Potential improvements
      potentialSavings: this.calculatePotentialSavings(collStats, compressionAnalysis),

      // Efficiency classification
      efficiencyGrade: this.classifyStorageEfficiency(collStats, compressionAnalysis)
    };

    return efficiency;
  }

  calculatePotentialSavings(collStats, compressionAnalysis) {
    // Calculate potential storage savings through optimization
    const currentSize = collStats.storageSize;
    const potentialCompression = compressionAnalysis.compressionRatio * 1.2; // 20% improvement estimate
    const potentialSavings = currentSize * (1 - 1/potentialCompression);

    return {
      potentialSavingsBytes: potentialSavings,
      potentialSavingsPercent: (potentialSavings / currentSize * 100).toFixed(2),
      estimatedCompression: potentialCompression.toFixed(2)
    };
  }

  classifyStorageEfficiency(collStats, compressionAnalysis) {
    const compressionRatio = collStats.size / collStats.storageSize;
    const indexRatio = collStats.totalIndexSize / collStats.size;

    if (compressionRatio > 3 && indexRatio < 0.5) return 'Excellent';
    if (compressionRatio > 2 && indexRatio < 1) return 'Good';
    if (compressionRatio > 1.5) return 'Fair';
    return 'Poor';
  }

  generateOptimizationRecommendations(collStats, compressionAnalysis, indexStats) {
    console.log('Generating storage optimization recommendations...');

    const recommendations = [];

    // Compression recommendations
    const compressionRatio = collStats.size / collStats.storageSize;
    if (compressionRatio < 2) {
      recommendations.push({
        type: 'compression',
        priority: 'high',
        recommendation: 'Enable or upgrade compression algorithm',
        expectedImprovement: '30-50% storage reduction'
      });
    }

    // Index recommendations
    const indexRatio = collStats.totalIndexSize / collStats.size;
    if (indexRatio > 1) {
      recommendations.push({
        type: 'indexing',
        priority: 'medium',
        recommendation: 'Review and optimize index usage',
        expectedImprovement: '20-30% index size reduction'
      });
    }

    // Document structure recommendations
    if (compressionAnalysis.averageOriginalSize > 10000) {
      recommendations.push({
        type: 'schema',
        priority: 'medium',
        recommendation: 'Optimize document structure and field organization',
        expectedImprovement: '15-25% size reduction'
      });
    }

    return recommendations;
  }

  async setupCompressionMonitoring() {
    console.log('Setting up compression monitoring and analytics...');

    try {
      // Create monitoring collection with optimized storage
      await this.collections.storageMetrics.createIndex(
        { timestamp: -1 },
        { 
          background: true,
          expireAfterSeconds: 90 * 24 * 60 * 60 // 90 days retention
        }
      );

      await this.collections.storageMetrics.createIndex(
        { collectionName: 1, timestamp: -1 },
        { background: true }
      );

      console.log('Compression monitoring setup completed');

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

  async trackStorageMetrics(operation, metrics) {
    try {
      const metricRecord = {
        operation: operation,
        timestamp: new Date(),
        ...metrics,

        // Add system context
        systemInfo: {
          compressionAlgorithm: this.optimizationConfig.compression.algorithm,
          compressionLevel: this.optimizationConfig.compression.level,
          nodeVersion: process.version
        }
      };

      await this.collections.storageMetrics.insertOne(metricRecord);

    } catch (error) {
      console.error('Error tracking storage metrics:', error);
      // Don't throw - metrics tracking shouldn't break operations
    }
  }

  async startBackgroundOptimization() {
    if (!this.optimizationConfig.performanceOptimization.enableBackgroundOptimization) {
      return;
    }

    console.log('Starting background storage optimization processes...');

    // Schedule regular storage analysis
    setInterval(async () => {
      try {
        await this.performAutomatedOptimization();
      } catch (error) {
        console.error('Background optimization error:', error);
      }
    }, 24 * 60 * 60 * 1000); // Daily optimization
  }

  async performAutomatedOptimization() {
    console.log('Performing automated storage optimization...');

    const collections = Object.keys(this.collections);

    for (const collectionName of collections) {
      if (collectionName === 'storageMetrics') continue; // Skip metrics collection

      try {
        // Analyze storage efficiency
        const analysis = await this.performStorageAnalysis(collectionName);

        // Apply automatic optimizations based on analysis
        if (analysis.storageEfficiency.efficiencyGrade === 'Poor') {
          await this.applyAutomaticOptimizations(collectionName, analysis);
        }

      } catch (error) {
        console.error(`Error optimizing collection ${collectionName}:`, error);
      }
    }
  }

  async applyAutomaticOptimizations(collectionName, analysis) {
    console.log(`Applying automatic optimizations for ${collectionName}...`);

    const recommendations = analysis.recommendations;

    for (const rec of recommendations) {
      if (rec.priority === 'high' && rec.type === 'indexing') {
        // Remove unused indexes automatically
        await this.optimizeUnusedIndexes(collectionName, analysis.indexAnalysis);
      }
    }
  }

  async optimizeUnusedIndexes(collectionName, indexAnalysis) {
    console.log(`Optimizing unused indexes for ${collectionName}...`);

    const collection = this.collections[collectionName] || this.db.collection(collectionName);

    for (const unusedIndex of indexAnalysis.indexEfficiency.lowUsageIndexes) {
      if (unusedIndex.usage === 0 && !unusedIndex.name.startsWith('_id')) {
        try {
          await collection.dropIndex(unusedIndex.name);
          console.log(`Dropped unused index: ${unusedIndex.name}`);
        } catch (error) {
          console.error(`Error dropping index ${unusedIndex.name}:`, error);
        }
      }
    }
  }
}

// Benefits of MongoDB Advanced Data Compression and Storage Optimization:
// - Comprehensive WiredTiger compression with multiple algorithm options (snappy, zlib, zstd)
// - Document-level optimization reducing storage requirements by 50-80%
// - Advanced index compression and optimization for improved query performance
// - Intelligent document structure optimization for better compression ratios
// - Real-time compression effectiveness monitoring and analytics
// - Automatic background optimization processes for continuous improvement
// - Field-level compression strategies based on data patterns and usage
// - Integration with MongoDB's native compression capabilities
// - Comprehensive storage analysis and optimization recommendations
// - SQL-compatible compression operations through QueryLeaf integration

module.exports = {
  MongoStorageOptimizationManager
};

// Production-ready MongoDB compression with enterprise-level optimization and monitoring
class EnterpriseCompressionManager extends MongoStorageOptimizationManager {
  constructor(db, enterpriseConfig) {
    super(db, enterpriseConfig);

    this.enterpriseConfig = {
      ...enterpriseConfig,

      // Enterprise compression features
      enableTieredStorage: true,
      enableAutomaticArchiving: true,
      enableCompressionTuning: true,
      enableCapacityPlanning: true,
      enableComplianceTracking: true,

      // Advanced optimization strategies
      compressionStrategies: {
        algorithm: 'zstd',  // Best compression ratio for enterprise
        level: 9,           // Maximum compression
        enableDictionaryCompression: true,
        enableStreamCompression: true,
        enableDifferentialCompression: true
      }
    };

    this.setupEnterpriseOptimizations();
  }

  async implementTieredStorageStrategy(collections, tieringConfig) {
    console.log('Implementing tiered storage strategy for optimal cost and performance...');

    const tieredStrategy = {
      // Hot tier - frequently accessed data
      hotTier: {
        compressionAlgorithm: 'snappy',  // Fast compression/decompression
        compressionLevel: 1,
        storageType: 'high-performance-ssd',
        retentionPeriod: '30 days',
        accessPattern: 'high-frequency'
      },

      // Warm tier - occasionally accessed data
      warmTier: {
        compressionAlgorithm: 'zlib',    // Balanced compression
        compressionLevel: 6,
        storageType: 'standard-ssd',
        retentionPeriod: '180 days',
        accessPattern: 'medium-frequency'
      },

      // Cold tier - rarely accessed data
      coldTier: {
        compressionAlgorithm: 'zstd',    // Maximum compression
        compressionLevel: 19,
        storageType: 'high-capacity-hdd',
        retentionPeriod: '2 years',
        accessPattern: 'low-frequency'
      },

      // Archive tier - long-term retention
      archiveTier: {
        compressionAlgorithm: 'zstd',
        compressionLevel: 22,            // Ultra compression
        storageType: 'archive-storage',
        retentionPeriod: '7 years',
        accessPattern: 'archive-only'
      }
    };

    return await this.deployTieredStorageStrategy(collections, tieredStrategy);
  }

  async setupAdvancedCompressionMonitoring() {
    console.log('Setting up enterprise-grade compression monitoring...');

    const monitoringConfig = {
      // Real-time compression metrics
      realTimeMetrics: {
        compressionRatio: true,
        compressionSpeed: true,
        decompressionSpeed: true,
        storageEfficiency: true,
        costOptimization: true
      },

      // Performance impact analysis
      performanceAnalysis: {
        queryLatencyImpact: true,
        throughputAnalysis: true,
        resourceUtilization: true,
        compressionOverhead: true
      },

      // Capacity planning and forecasting
      capacityPlanning: {
        growthProjections: true,
        compressionTrends: true,
        costForecasting: true,
        optimizationOpportunities: true
      },

      // Alerting and automation
      alerting: {
        compressionEfficiencyThresholds: true,
        storageCapacityWarnings: true,
        performanceDegradationAlerts: true,
        automaticOptimizationTriggers: true
      }
    };

    return await this.initializeEnterpriseMonitoring(monitoringConfig);
  }

  async performCompressionBenchmarking(testDatasets) {
    console.log('Performing comprehensive compression algorithm benchmarking...');

    const algorithms = ['snappy', 'zlib', 'zstd'];
    const levels = [1, 6, 9, 19, 22];
    const benchmarkResults = [];

    for (const algorithm of algorithms) {
      for (const level of levels) {
        if (algorithm === 'snappy' && level > 1) continue; // Snappy only has one level

        const result = await this.benchmarkCompressionAlgorithm(
          algorithm, 
          level, 
          testDatasets
        );

        benchmarkResults.push({
          algorithm,
          level,
          ...result
        });
      }
    }

    return this.analyzeCompressionBenchmarks(benchmarkResults);
  }
}