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MongoDB Distributed Caching and Session Management: High-Performance Web Application State Management and Cache Optimization

Modern web applications require sophisticated caching and session management capabilities that can handle millions of concurrent users while maintaining consistent performance across distributed infrastructure. Traditional caching approaches rely on dedicated cache servers like Redis or Memcached, creating additional infrastructure complexity and potential single points of failure, while session management often involves complex synchronization between application servers and separate session stores.

MongoDB TTL Collections and advanced document modeling provide comprehensive caching and session management capabilities that integrate seamlessly with existing application data, offering automatic expiration, flexible data structures, and distributed consistency without requiring additional infrastructure components. Unlike traditional cache-aside patterns that require complex cache invalidation logic, MongoDB's integrated approach enables intelligent caching strategies with built-in consistency guarantees and sophisticated query capabilities.

The Traditional Caching and Session Challenge

Conventional approaches to distributed caching and session management introduce significant complexity and operational overhead:

-- Traditional PostgreSQL session storage - limited scalability and complex cleanup
CREATE TABLE user_sessions (
    session_id VARCHAR(128) PRIMARY KEY,
    user_id BIGINT NOT NULL,
    session_data JSONB NOT NULL,

    -- Session lifecycle management
    created_at TIMESTAMPTZ NOT NULL DEFAULT CURRENT_TIMESTAMP,
    last_accessed TIMESTAMPTZ NOT NULL DEFAULT CURRENT_TIMESTAMP,
    expires_at TIMESTAMPTZ NOT NULL,

    -- Session metadata
    ip_address INET,
    user_agent TEXT,
    device_fingerprint VARCHAR(256),

    -- Security and fraud detection
    login_method VARCHAR(50),
    mfa_verified BOOLEAN DEFAULT FALSE,
    risk_score DECIMAL(3,2) DEFAULT 0.0,

    -- Application state
    active BOOLEAN DEFAULT TRUE,
    invalidated_at TIMESTAMPTZ,
    invalidation_reason VARCHAR(100)
);

-- Manual session cleanup (requires scheduled maintenance)
CREATE OR REPLACE FUNCTION cleanup_expired_sessions()
RETURNS INTEGER AS $$
DECLARE
    deleted_count INTEGER;
BEGIN
    -- Delete expired sessions
    DELETE FROM user_sessions 
    WHERE expires_at < CURRENT_TIMESTAMP
       OR (active = FALSE AND invalidated_at < CURRENT_TIMESTAMP - INTERVAL '1 day');

    GET DIAGNOSTICS deleted_count = ROW_COUNT;

    -- Log cleanup activity
    INSERT INTO session_cleanup_log (cleanup_date, sessions_deleted)
    VALUES (CURRENT_TIMESTAMP, deleted_count);

    RETURN deleted_count;
END;
$$ LANGUAGE plpgsql;

-- Complex cache table design with manual expiration
CREATE TABLE application_cache (
    cache_key VARCHAR(512) PRIMARY KEY,
    cache_namespace VARCHAR(100) NOT NULL,
    cache_data JSONB NOT NULL,

    -- Expiration management
    created_at TIMESTAMPTZ NOT NULL DEFAULT CURRENT_TIMESTAMP,
    expires_at TIMESTAMPTZ NOT NULL,
    last_accessed TIMESTAMPTZ DEFAULT CURRENT_TIMESTAMP,

    -- Cache metadata
    data_size INTEGER,
    cache_tags TEXT[],
    cache_version VARCHAR(50),

    -- Usage statistics
    hit_count BIGINT DEFAULT 0,
    miss_count BIGINT DEFAULT 0,
    invalidation_count INTEGER DEFAULT 0
);

-- Indexing for cache lookups (expensive maintenance overhead)
CREATE INDEX idx_cache_namespace_key ON application_cache (cache_namespace, cache_key);
CREATE INDEX idx_cache_expires_at ON application_cache (expires_at);
CREATE INDEX idx_cache_tags ON application_cache USING GIN (cache_tags);
CREATE INDEX idx_cache_last_accessed ON application_cache (last_accessed);

-- Manual cache invalidation logic (complex and error-prone)
CREATE OR REPLACE FUNCTION invalidate_cache_by_tags(tag_names TEXT[])
RETURNS INTEGER AS $$
DECLARE
    invalidated_count INTEGER;
BEGIN
    -- Invalidate cache entries with matching tags
    DELETE FROM application_cache 
    WHERE cache_tags && tag_names;

    GET DIAGNOSTICS invalidated_count = ROW_COUNT;

    -- Update invalidation statistics
    UPDATE cache_statistics 
    SET tag_invalidations = tag_invalidations + invalidated_count,
        last_invalidation = CURRENT_TIMESTAMP
    WHERE stat_date = CURRENT_DATE;

    RETURN invalidated_count;
END;
$$ LANGUAGE plpgsql;

-- Session data queries with complex join logic
WITH active_sessions AS (
    SELECT 
        us.session_id,
        us.user_id,
        us.session_data,
        us.last_accessed,
        us.expires_at,
        us.ip_address,
        us.device_fingerprint,

        -- Calculate session duration
        EXTRACT(EPOCH FROM us.last_accessed - us.created_at) / 3600 as session_hours,

        -- Determine session freshness
        CASE 
            WHEN us.last_accessed > CURRENT_TIMESTAMP - INTERVAL '15 minutes' THEN 'active'
            WHEN us.last_accessed > CURRENT_TIMESTAMP - INTERVAL '1 hour' THEN 'recent'
            WHEN us.last_accessed > CURRENT_TIMESTAMP - INTERVAL '6 hours' THEN 'idle'
            ELSE 'stale'
        END as session_status,

        -- Extract user preferences from session data
        us.session_data->>'preferences' as user_preferences,
        us.session_data->>'shopping_cart' as shopping_cart,
        us.session_data->>'last_page' as last_page

    FROM user_sessions us
    WHERE us.active = TRUE 
    AND us.expires_at > CURRENT_TIMESTAMP
),

session_analytics AS (
    SELECT 
        COUNT(*) as total_active_sessions,
        COUNT(DISTINCT user_id) as unique_users,
        AVG(session_hours) as avg_session_duration,

        COUNT(*) FILTER (WHERE session_status = 'active') as active_sessions,
        COUNT(*) FILTER (WHERE session_status = 'recent') as recent_sessions,
        COUNT(*) FILTER (WHERE session_status = 'idle') as idle_sessions,
        COUNT(*) FILTER (WHERE session_status = 'stale') as stale_sessions,

        -- Risk analysis
        COUNT(*) FILTER (WHERE risk_score > 0.7) as high_risk_sessions,
        COUNT(DISTINCT ip_address) as unique_ip_addresses,

        -- Application state analysis
        COUNT(*) FILTER (WHERE shopping_cart IS NOT NULL) as sessions_with_cart,
        AVG(CAST(shopping_cart->>'item_count' AS INTEGER)) as avg_cart_items

    FROM active_sessions
)

SELECT 
    'Session Management Report' as report_type,
    CURRENT_TIMESTAMP as generated_at,

    -- Session statistics
    sa.total_active_sessions,
    sa.unique_users,
    ROUND(sa.avg_session_duration::NUMERIC, 2) as avg_session_hours,

    -- Session distribution
    JSON_BUILD_OBJECT(
        'active', sa.active_sessions,
        'recent', sa.recent_sessions, 
        'idle', sa.idle_sessions,
        'stale', sa.stale_sessions
    ) as session_distribution,

    -- Security metrics
    sa.high_risk_sessions,
    sa.unique_ip_addresses,
    ROUND((sa.high_risk_sessions::FLOAT / sa.total_active_sessions * 100)::NUMERIC, 2) as risk_percentage,

    -- Business metrics
    sa.sessions_with_cart,
    ROUND(sa.avg_cart_items::NUMERIC, 1) as avg_cart_items,
    ROUND((sa.sessions_with_cart::FLOAT / sa.total_active_sessions * 100)::NUMERIC, 2) as cart_conversion_rate

FROM session_analytics sa;

-- Cache performance queries (limited analytics capabilities)
WITH cache_performance AS (
    SELECT 
        cache_namespace,
        COUNT(*) as total_entries,
        SUM(data_size) as total_size_bytes,
        AVG(data_size) as avg_entry_size,

        -- Hit ratio calculation
        SUM(hit_count) as total_hits,
        SUM(miss_count) as total_misses,
        CASE 
            WHEN SUM(hit_count + miss_count) > 0 THEN
                ROUND((SUM(hit_count)::FLOAT / SUM(hit_count + miss_count) * 100)::NUMERIC, 2)
            ELSE 0
        END as hit_ratio_percent,

        -- Expiration analysis
        COUNT(*) FILTER (WHERE expires_at <= CURRENT_TIMESTAMP) as expired_entries,
        COUNT(*) FILTER (WHERE expires_at > CURRENT_TIMESTAMP + INTERVAL '1 hour') as long_lived_entries,

        -- Access patterns
        AVG(hit_count) as avg_hits_per_entry,
        MAX(last_accessed) as most_recent_access,
        MIN(last_accessed) as oldest_access

    FROM application_cache
    GROUP BY cache_namespace
)

SELECT 
    cp.cache_namespace,
    cp.total_entries,
    ROUND((cp.total_size_bytes / 1024.0 / 1024.0)::NUMERIC, 2) as size_mb,
    ROUND(cp.avg_entry_size::NUMERIC, 0) as avg_entry_size_bytes,
    cp.hit_ratio_percent,
    cp.expired_entries,
    cp.long_lived_entries,
    cp.avg_hits_per_entry,

    -- Cache efficiency assessment
    CASE 
        WHEN cp.hit_ratio_percent > 90 THEN 'excellent'
        WHEN cp.hit_ratio_percent > 70 THEN 'good'
        WHEN cp.hit_ratio_percent > 50 THEN 'acceptable'
        ELSE 'poor'
    END as cache_efficiency,

    -- Recommendations
    CASE 
        WHEN cp.expired_entries > cp.total_entries * 0.1 THEN 'Consider shorter TTL or more frequent cleanup'
        WHEN cp.hit_ratio_percent < 50 THEN 'Review caching strategy and key patterns'
        WHEN cp.avg_entry_size > 1048576 THEN 'Consider data compression or smaller cache objects'
        ELSE 'Cache performance within normal parameters'
    END as recommendation

FROM cache_performance cp
ORDER BY cp.total_entries DESC;

-- Problems with traditional cache and session management:
-- 1. Manual expiration cleanup with potential for orphaned data
-- 2. Complex indexing strategies and maintenance overhead
-- 3. Limited scalability for high-concurrency web applications
-- 4. No built-in distributed consistency or replication
-- 5. Complex cache invalidation logic prone to race conditions
-- 6. Separate infrastructure requirements for cache and session stores
-- 7. Limited analytics and monitoring capabilities
-- 8. Manual session lifecycle management and security tracking
-- 9. No automatic compression or storage optimization
-- 10. Complex failover and disaster recovery procedures

MongoDB provides sophisticated distributed caching and session management with automatic TTL handling and advanced capabilities:

// MongoDB Distributed Caching and Session Management System
const { MongoClient, ObjectId } = require('mongodb');

const client = new MongoClient('mongodb://localhost:27017/?replicaSet=rs0');
const db = client.db('distributed_cache_system');

// Comprehensive MongoDB Caching and Session Manager
class AdvancedCacheSessionManager {
  constructor(db, config = {}) {
    this.db = db;
    this.collections = {
      userSessions: db.collection('user_sessions'),
      applicationCache: db.collection('application_cache'),
      cacheStatistics: db.collection('cache_statistics'),
      sessionEvents: db.collection('session_events'),
      cacheMetrics: db.collection('cache_metrics'),
      deviceFingerprints: db.collection('device_fingerprints')
    };

    // Advanced configuration
    this.config = {
      // Session management
      defaultSessionTTL: config.defaultSessionTTL || 7200, // 2 hours
      extendedSessionTTL: config.extendedSessionTTL || 86400, // 24 hours for "remember me"
      maxSessionsPerUser: config.maxSessionsPerUser || 10,
      sessionCleanupInterval: config.sessionCleanupInterval || 300, // 5 minutes

      // Cache management
      defaultCacheTTL: config.defaultCacheTTL || 3600, // 1 hour
      maxCacheSize: config.maxCacheSize || 16 * 1024 * 1024, // 16MB per entry
      enableCompression: config.enableCompression !== false,
      enableDistribution: config.enableDistribution !== false,

      // Security settings
      enableDeviceTracking: config.enableDeviceTracking !== false,
      enableRiskScoring: config.enableRiskScoring !== false,
      maxRiskScore: config.maxRiskScore || 0.8,
      suspiciousActivityThreshold: config.suspiciousActivityThreshold || 5,

      // Performance optimization
      enableMetrics: config.enableMetrics !== false,
      metricsRetentionDays: config.metricsRetentionDays || 30,
      enableLazyLoading: config.enableLazyLoading !== false,
      cacheWarmupEnabled: config.cacheWarmupEnabled !== false
    };

    // Initialize TTL collections and indexes
    this.initializeCollections();
    this.setupMetricsCollection();
    this.startMaintenanceTasks();
  }

  async initializeCollections() {
    console.log('Initializing MongoDB TTL collections and indexes...');

    try {
      // Configure user sessions collection with TTL
      await this.collections.userSessions.createIndex(
        { "expiresAt": 1 },
        { 
          expireAfterSeconds: 0,
          name: "session_ttl_index"
        }
      );

      // Additional session indexes for performance
      await this.collections.userSessions.createIndexes([
        { key: { sessionId: 1 }, unique: true, name: "session_id_unique" },
        { key: { userId: 1, expiresAt: 1 }, name: "user_active_sessions" },
        { key: { deviceFingerprint: 1 }, name: "device_sessions" },
        { key: { ipAddress: 1, createdAt: 1 }, name: "ip_activity" },
        { key: { riskScore: 1 }, name: "risk_analysis" },
        { key: { "sessionData.shoppingCart.items": 1 }, name: "cart_sessions", sparse: true }
      ]);

      // Configure application cache collection with TTL
      await this.collections.applicationCache.createIndex(
        { "expiresAt": 1 },
        { 
          expireAfterSeconds: 0,
          name: "cache_ttl_index"
        }
      );

      // Cache performance indexes
      await this.collections.applicationCache.createIndexes([
        { key: { cacheKey: 1 }, unique: true, name: "cache_key_unique" },
        { key: { namespace: 1, cacheKey: 1 }, name: "namespace_key_lookup" },
        { key: { tags: 1 }, name: "cache_tags" },
        { key: { lastAccessed: 1 }, name: "access_patterns" },
        { key: { dataSize: 1 }, name: "size_analysis" }
      ]);

      // Cache metrics with TTL for automatic cleanup
      await this.collections.cacheMetrics.createIndex(
        { "timestamp": 1 },
        { 
          expireAfterSeconds: this.config.metricsRetentionDays * 24 * 3600,
          name: "metrics_ttl_index"
        }
      );

      console.log('TTL collections and indexes initialized successfully');

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

  async createUserSession(userId, sessionData, options = {}) {
    console.log(`Creating new session for user: ${userId}`);

    try {
      // Clean up old sessions if user has too many
      await this.enforceSessionLimits(userId);

      // Generate secure session ID
      const sessionId = await this.generateSecureSessionId();

      // Calculate expiration time
      const ttlSeconds = options.rememberMe ? 
        this.config.extendedSessionTTL : 
        this.config.defaultSessionTTL;

      const expiresAt = new Date(Date.now() + (ttlSeconds * 1000));

      // Calculate risk score
      const riskScore = await this.calculateSessionRiskScore(userId, sessionData, options);

      // Create session document
      const session = {
        _id: new ObjectId(),
        sessionId: sessionId,
        userId: userId,

        // Session lifecycle
        createdAt: new Date(),
        lastAccessed: new Date(),
        expiresAt: expiresAt,
        ttlSeconds: ttlSeconds,

        // Session data with flexible structure
        sessionData: {
          preferences: sessionData.preferences || {},
          shoppingCart: sessionData.shoppingCart || { items: [], total: 0 },
          navigation: sessionData.navigation || { lastPage: '/', referrer: null },
          applicationState: sessionData.applicationState || {},
          temporaryData: sessionData.temporaryData || {}
        },

        // Security and device tracking
        ipAddress: options.ipAddress,
        userAgent: options.userAgent,
        deviceFingerprint: await this.generateDeviceFingerprint(options),
        riskScore: riskScore,

        // Authentication metadata
        loginMethod: options.loginMethod || 'password',
        mfaVerified: options.mfaVerified || false,
        loginLocation: options.loginLocation,

        // Session flags
        active: true,
        rememberMe: options.rememberMe || false,

        // Usage statistics
        pageViews: 0,
        actionsPerformed: 0,
        dataTransferred: 0
      };

      // Insert session with automatic TTL handling
      const result = await this.collections.userSessions.insertOne(session);

      // Log session creation event
      await this.logSessionEvent(sessionId, 'session_created', {
        userId: userId,
        riskScore: riskScore,
        ttlSeconds: ttlSeconds,
        rememberMe: options.rememberMe
      });

      // Update device fingerprint tracking
      if (this.config.enableDeviceTracking) {
        await this.updateDeviceTracking(session.deviceFingerprint, userId, sessionId);
      }

      console.log(`Session created successfully: ${sessionId}`);

      return {
        sessionId: sessionId,
        expiresAt: expiresAt,
        riskScore: riskScore,
        success: true
      };

    } catch (error) {
      console.error('Error creating user session:', error);
      throw error;
    }
  }

  async getSessionData(sessionId, options = {}) {
    console.log(`Retrieving session data: ${sessionId}`);

    try {
      // Find active session
      const session = await this.collections.userSessions.findOne({
        sessionId: sessionId,
        active: true,
        expiresAt: { $gt: new Date() }
      });

      if (!session) {
        return { success: false, reason: 'session_not_found' };
      }

      // Update last accessed timestamp
      const updateData = {
        $set: { lastAccessed: new Date() },
        $inc: { 
          pageViews: options.incrementPageView ? 1 : 0,
          actionsPerformed: options.actionPerformed ? 1 : 0
        }
      };

      await this.collections.userSessions.updateOne(
        { sessionId: sessionId },
        updateData
      );

      // Check for risk score updates
      if (this.config.enableRiskScoring && options.updateRiskScore) {
        const newRiskScore = await this.calculateSessionRiskScore(
          session.userId, 
          session.sessionData, 
          options
        );

        if (newRiskScore > this.config.maxRiskScore) {
          await this.flagHighRiskSession(sessionId, newRiskScore);
        }
      }

      return {
        success: true,
        session: session,
        userId: session.userId,
        sessionData: session.sessionData,
        expiresAt: session.expiresAt,
        riskScore: session.riskScore
      };

    } catch (error) {
      console.error('Error retrieving session data:', error);
      throw error;
    }
  }

  async updateSessionData(sessionId, updateData, options = {}) {
    console.log(`Updating session data: ${sessionId}`);

    try {
      // Prepare update operations
      const update = {
        $set: {
          lastAccessed: new Date(),
          'sessionData.preferences': updateData.preferences,
          'sessionData.shoppingCart': updateData.shoppingCart,
          'sessionData.navigation': updateData.navigation,
          'sessionData.applicationState': updateData.applicationState
        }
      };

      // Optional TTL extension
      if (options.extendTTL) {
        const newExpirationTime = new Date(Date.now() + (this.config.defaultSessionTTL * 1000));
        update.$set.expiresAt = newExpirationTime;
      }

      // Merge temporary data if provided
      if (updateData.temporaryData) {
        update.$set['sessionData.temporaryData'] = {
          ...updateData.temporaryData
        };
      }

      const result = await this.collections.userSessions.updateOne(
        { 
          sessionId: sessionId, 
          active: true,
          expiresAt: { $gt: new Date() }
        },
        update
      );

      if (result.matchedCount === 0) {
        return { success: false, reason: 'session_not_found' };
      }

      // Log session update event
      await this.logSessionEvent(sessionId, 'session_updated', {
        updateFields: Object.keys(updateData),
        extendTTL: options.extendTTL
      });

      return { success: true };

    } catch (error) {
      console.error('Error updating session data:', error);
      throw error;
    }
  }

  async invalidateSession(sessionId, reason = 'user_logout') {
    console.log(`Invalidating session: ${sessionId}, reason: ${reason}`);

    try {
      const result = await this.collections.userSessions.updateOne(
        { sessionId: sessionId },
        {
          $set: {
            active: false,
            invalidatedAt: new Date(),
            invalidationReason: reason,
            // Set immediate expiration for automatic cleanup
            expiresAt: new Date()
          }
        }
      );

      if (result.matchedCount > 0) {
        // Log session invalidation
        await this.logSessionEvent(sessionId, 'session_invalidated', {
          reason: reason,
          invalidatedAt: new Date()
        });
      }

      return { success: result.matchedCount > 0 };

    } catch (error) {
      console.error('Error invalidating session:', error);
      throw error;
    }
  }

  async setCache(cacheKey, data, options = {}) {
    console.log(`Setting cache entry: ${cacheKey}`);

    try {
      // Validate data size
      const dataSize = JSON.stringify(data).length;
      if (dataSize > this.config.maxCacheSize) {
        throw new Error(`Cache data size ${dataSize} exceeds maximum ${this.config.maxCacheSize}`);
      }

      // Calculate expiration time
      const ttlSeconds = options.ttl || this.config.defaultCacheTTL;
      const expiresAt = new Date(Date.now() + (ttlSeconds * 1000));

      // Prepare cache document
      const cacheEntry = {
        cacheKey: cacheKey,
        namespace: options.namespace || 'default',
        data: this.config.enableCompression ? await this.compressData(data) : data,
        compressed: this.config.enableCompression,

        // Expiration handling
        createdAt: new Date(),
        expiresAt: expiresAt,
        ttlSeconds: ttlSeconds,
        lastAccessed: new Date(),

        // Metadata
        tags: options.tags || [],
        version: options.version || '1.0',
        dataSize: dataSize,
        contentType: options.contentType || 'application/json',

        // Usage statistics
        hitCount: 0,
        accessHistory: [],

        // Cache strategy metadata
        cacheStrategy: options.strategy || 'default',
        invalidationRules: options.invalidationRules || []
      };

      // Upsert cache entry with automatic TTL
      await this.collections.applicationCache.replaceOne(
        { cacheKey: cacheKey },
        cacheEntry,
        { upsert: true }
      );

      // Update cache metrics
      await this.updateCacheMetrics('set', cacheKey, {
        namespace: cacheEntry.namespace,
        dataSize: dataSize,
        ttlSeconds: ttlSeconds
      });

      console.log(`Cache entry set successfully: ${cacheKey}`);
      return { success: true, expiresAt: expiresAt };

    } catch (error) {
      console.error('Error setting cache entry:', error);
      throw error;
    }
  }

  async getCache(cacheKey, options = {}) {
    console.log(`Getting cache entry: ${cacheKey}`);

    try {
      const cacheEntry = await this.collections.applicationCache.findOneAndUpdate(
        {
          cacheKey: cacheKey,
          expiresAt: { $gt: new Date() }
        },
        {
          $set: { lastAccessed: new Date() },
          $inc: { hitCount: 1 },
          $push: {
            accessHistory: {
              $each: [{ timestamp: new Date(), source: options.source || 'application' }],
              $slice: -10 // Keep only last 10 access records
            }
          }
        },
        { returnDocument: 'after' }
      );

      if (!cacheEntry.value) {
        // Record cache miss
        await this.updateCacheMetrics('miss', cacheKey, {
          namespace: options.namespace || 'default'
        });

        return { success: false, reason: 'cache_miss' };
      }

      // Decompress data if needed
      const data = cacheEntry.value.compressed ? 
        await this.decompressData(cacheEntry.value.data) : 
        cacheEntry.value.data;

      // Record cache hit
      await this.updateCacheMetrics('hit', cacheKey, {
        namespace: cacheEntry.value.namespace,
        dataSize: cacheEntry.value.dataSize
      });

      return {
        success: true,
        data: data,
        metadata: {
          createdAt: cacheEntry.value.createdAt,
          expiresAt: cacheEntry.value.expiresAt,
          hitCount: cacheEntry.value.hitCount,
          tags: cacheEntry.value.tags,
          version: cacheEntry.value.version
        }
      };

    } catch (error) {
      console.error('Error getting cache entry:', error);
      throw error;
    }
  }

  async invalidateCache(criteria, options = {}) {
    console.log('Invalidating cache entries with criteria:', criteria);

    try {
      let query = {};

      // Build invalidation query based on criteria
      if (criteria.key) {
        query.cacheKey = criteria.key;
      } else if (criteria.pattern) {
        query.cacheKey = { $regex: criteria.pattern };
      } else if (criteria.namespace) {
        query.namespace = criteria.namespace;
      } else if (criteria.tags) {
        query.tags = { $in: criteria.tags };
      }

      // Immediate expiration for automatic cleanup
      const result = await this.collections.applicationCache.updateMany(
        query,
        {
          $set: { 
            expiresAt: new Date(),
            invalidatedAt: new Date(),
            invalidationReason: options.reason || 'manual_invalidation'
          }
        }
      );

      // Update invalidation metrics
      await this.updateCacheMetrics('invalidation', null, {
        criteriaType: Object.keys(criteria)[0],
        entriesInvalidated: result.modifiedCount,
        reason: options.reason
      });

      console.log(`Invalidated ${result.modifiedCount} cache entries`);
      return { success: true, invalidatedCount: result.modifiedCount };

    } catch (error) {
      console.error('Error invalidating cache entries:', error);
      throw error;
    }
  }

  async getUserSessionAnalytics(userId, options = {}) {
    console.log(`Generating session analytics for user: ${userId}`);

    try {
      const timeRange = options.timeRange || 24; // hours
      const startTime = new Date(Date.now() - (timeRange * 3600 * 1000));

      const analytics = await this.collections.userSessions.aggregate([
        {
          $match: {
            userId: userId,
            createdAt: { $gte: startTime }
          }
        },
        {
          $group: {
            _id: '$userId',

            // Session counts
            totalSessions: { $sum: 1 },
            activeSessions: {
              $sum: {
                $cond: [
                  { $and: [
                    { $eq: ['$active', true] },
                    { $gt: ['$expiresAt', new Date()] }
                  ]},
                  1,
                  0
                ]
              }
            },

            // Duration analysis
            averageSessionDuration: {
              $avg: {
                $divide: [
                  { $subtract: ['$lastAccessed', '$createdAt'] },
                  1000 * 60 // Convert to minutes
                ]
              }
            },

            // Activity metrics
            totalPageViews: { $sum: '$pageViews' },
            totalActions: { $sum: '$actionsPerformed' },

            // Risk analysis
            averageRiskScore: { $avg: '$riskScore' },
            highRiskSessions: {
              $sum: {
                $cond: [{ $gt: ['$riskScore', 0.7] }, 1, 0]
              }
            },

            // Device analysis
            uniqueDevices: { $addToSet: '$deviceFingerprint' },
            uniqueIpAddresses: { $addToSet: '$ipAddress' },

            // Authentication methods
            loginMethods: { $addToSet: '$loginMethod' },
            mfaUsage: {
              $sum: {
                $cond: [{ $eq: ['$mfaVerified', true] }, 1, 0]
              }
            }
          }
        },
        {
          $project: {
            userId: '$_id',
            totalSessions: 1,
            activeSessions: 1,
            averageSessionDuration: { $round: ['$averageSessionDuration', 2] },
            totalPageViews: 1,
            totalActions: 1,
            averageRiskScore: { $round: ['$averageRiskScore', 3] },
            highRiskSessions: 1,
            deviceCount: { $size: '$uniqueDevices' },
            ipAddressCount: { $size: '$uniqueIpAddresses' },
            loginMethods: 1,
            mfaUsagePercentage: {
              $round: [
                { $multiply: [
                  { $divide: ['$mfaUsage', '$totalSessions'] },
                  100
                ]},
                2
              ]
            }
          }
        }
      ]).toArray();

      return analytics[0] || null;

    } catch (error) {
      console.error('Error generating user session analytics:', error);
      throw error;
    }
  }

  async getCachePerformanceReport(namespace = null, options = {}) {
    console.log('Generating cache performance report...');

    try {
      const timeRange = options.timeRange || 24; // hours
      const startTime = new Date(Date.now() - (timeRange * 3600 * 1000));

      // Build match criteria
      const matchCriteria = {
        timestamp: { $gte: startTime }
      };

      if (namespace) {
        matchCriteria.namespace = namespace;
      }

      const report = await this.collections.cacheMetrics.aggregate([
        { $match: matchCriteria },
        {
          $group: {
            _id: '$namespace',

            // Hit/miss statistics
            totalHits: {
              $sum: {
                $cond: [{ $eq: ['$operation', 'hit'] }, 1, 0]
              }
            },
            totalMisses: {
              $sum: {
                $cond: [{ $eq: ['$operation', 'miss'] }, 1, 0]
              }
            },
            totalSets: {
              $sum: {
                $cond: [{ $eq: ['$operation', 'set'] }, 1, 0]
              }
            },
            totalInvalidations: {
              $sum: {
                $cond: [{ $eq: ['$operation', 'invalidation'] }, '$metadata.entriesInvalidated', 0]
              }
            },

            // Data size statistics
            totalDataSize: {
              $sum: {
                $cond: [{ $eq: ['$operation', 'set'] }, '$metadata.dataSize', 0]
              }
            },
            averageDataSize: {
              $avg: {
                $cond: [{ $eq: ['$operation', 'set'] }, '$metadata.dataSize', null]
              }
            },

            // TTL analysis
            averageTTL: {
              $avg: {
                $cond: [{ $eq: ['$operation', 'set'] }, '$metadata.ttlSeconds', null]
              }
            }
          }
        },
        {
          $project: {
            namespace: '$_id',

            // Performance metrics
            totalRequests: { $add: ['$totalHits', '$totalMisses'] },
            hitRatio: {
              $round: [
                {
                  $cond: [
                    { $gt: [{ $add: ['$totalHits', '$totalMisses'] }, 0] },
                    {
                      $multiply: [
                        { $divide: ['$totalHits', { $add: ['$totalHits', '$totalMisses'] }] },
                        100
                      ]
                    },
                    0
                  ]
                },
                2
              ]
            },

            totalHits: 1,
            totalMisses: 1,
            totalSets: 1,
            totalInvalidations: 1,

            // Data insights
            totalDataSizeMB: {
              $round: [{ $divide: ['$totalDataSize', 1024 * 1024] }, 2]
            },
            averageDataSizeKB: {
              $round: [{ $divide: ['$averageDataSize', 1024] }, 2]
            },
            averageTTLHours: {
              $round: [{ $divide: ['$averageTTL', 3600] }, 2]
            }
          }
        }
      ]).toArray();

      return report;

    } catch (error) {
      console.error('Error generating cache performance report:', error);
      throw error;
    }
  }

  // Utility methods
  async generateSecureSessionId() {
    const crypto = require('crypto');
    return crypto.randomBytes(32).toString('hex');
  }

  async generateDeviceFingerprint(options) {
    const crypto = require('crypto');
    const fingerprint = `${options.userAgent || ''}-${options.ipAddress || ''}-${Date.now()}`;
    return crypto.createHash('sha256').update(fingerprint).digest('hex');
  }

  async calculateSessionRiskScore(userId, sessionData, options) {
    let riskScore = 0.0;

    // IP-based risk assessment
    if (options.ipAddress) {
      const recentSessions = await this.collections.userSessions.countDocuments({
        userId: userId,
        ipAddress: { $ne: options.ipAddress },
        createdAt: { $gte: new Date(Date.now() - 24 * 3600 * 1000) }
      });

      if (recentSessions > 0) riskScore += 0.2;
    }

    // Time-based risk assessment
    const currentHour = new Date().getHours();
    if (currentHour < 6 || currentHour > 23) {
      riskScore += 0.1;
    }

    // Device change assessment
    if (this.config.enableDeviceTracking && options.userAgent) {
      const knownDevice = await this.collections.deviceFingerprints.findOne({
        userId: userId,
        userAgent: options.userAgent
      });

      if (!knownDevice) riskScore += 0.3;
    }

    return Math.min(riskScore, 1.0);
  }

  async enforceSessionLimits(userId) {
    const sessionCount = await this.collections.userSessions.countDocuments({
      userId: userId,
      active: true,
      expiresAt: { $gt: new Date() }
    });

    if (sessionCount >= this.config.maxSessionsPerUser) {
      // Remove oldest sessions
      const sessionsToRemove = await this.collections.userSessions
        .find({
          userId: userId,
          active: true,
          expiresAt: { $gt: new Date() }
        })
        .sort({ lastAccessed: 1 })
        .limit(sessionCount - this.config.maxSessionsPerUser + 1)
        .toArray();

      for (const session of sessionsToRemove) {
        await this.invalidateSession(session.sessionId, 'session_limit_exceeded');
      }
    }
  }

  async logSessionEvent(sessionId, eventType, eventData) {
    if (!this.config.enableMetrics) return;

    await this.collections.sessionEvents.insertOne({
      sessionId: sessionId,
      eventType: eventType,
      eventData: eventData,
      timestamp: new Date()
    });
  }

  async updateCacheMetrics(operation, cacheKey, metadata) {
    if (!this.config.enableMetrics) return;

    await this.collections.cacheMetrics.insertOne({
      operation: operation,
      cacheKey: cacheKey,
      namespace: metadata.namespace,
      metadata: metadata,
      timestamp: new Date()
    });
  }

  async compressData(data) {
    const zlib = require('zlib');
    const jsonString = JSON.stringify(data);
    return zlib.deflateSync(jsonString);
  }

  async decompressData(compressedData) {
    const zlib = require('zlib');
    const decompressed = zlib.inflateSync(compressedData);
    return JSON.parse(decompressed.toString());
  }

  async startMaintenanceTasks() {
    // TTL collections handle expiration automatically
    console.log('Maintenance tasks initialized - TTL collections managing automatic cleanup');

    // Optional: Set up additional cleanup for edge cases
    if (this.config.sessionCleanupInterval > 0) {
      setInterval(async () => {
        await this.performMaintenanceCleanup();
      }, this.config.sessionCleanupInterval * 1000);
    }
  }

  async performMaintenanceCleanup() {
    try {
      // Optional cleanup for orphaned records or additional maintenance
      const orphanedSessions = await this.collections.userSessions.countDocuments({
        active: false,
        invalidatedAt: { $lt: new Date(Date.now() - 24 * 3600 * 1000) }
      });

      if (orphanedSessions > 0) {
        console.log(`Found ${orphanedSessions} orphaned sessions for cleanup`);
        // TTL will handle automatic cleanup
      }
    } catch (error) {
      console.warn('Error during maintenance cleanup:', error.message);
    }
  }
}

// Benefits of MongoDB Distributed Caching and Session Management:
// - Automatic TTL expiration with no manual cleanup required
// - Flexible document structure for complex session and cache data
// - Built-in indexing and query optimization for cache and session operations
// - Integrated compression and storage optimization capabilities
// - Sophisticated analytics and metrics collection with automatic retention
// - Advanced security features including risk scoring and device tracking
// - High-performance concurrent access with MongoDB's native concurrency controls
// - Seamless integration with existing MongoDB infrastructure
// - SQL-compatible operations through QueryLeaf for familiar management patterns
// - Distributed consistency and replication support for high availability

module.exports = {
  AdvancedCacheSessionManager
};

Understanding MongoDB TTL Collections and Cache Architecture

Advanced TTL Configuration and Performance Optimization

Implement sophisticated TTL strategies for optimal performance and resource management:

// Production-ready MongoDB TTL and cache optimization
class EnterpriseCAcheManager extends AdvancedCacheSessionManager {
  constructor(db, enterpriseConfig) {
    super(db, enterpriseConfig);

    this.enterpriseConfig = {
      ...enterpriseConfig,
      enableTieredStorage: true,
      enableCacheWarmup: true,
      enablePredictiveEviction: true,
      enableLoadBalancing: true,
      enableCacheReplication: true,
      enablePerformanceOptimization: true
    };

    this.setupEnterpriseFeatures();
    this.initializeAdvancedCaching();
    this.setupPerformanceMonitoring();
  }

  async implementTieredCaching() {
    console.log('Implementing enterprise tiered caching strategy...');

    const tieredConfig = {
      // Hot tier - frequently accessed data
      hotTier: {
        ttl: 900, // 15 minutes
        maxSize: 100 * 1024 * 1024, // 100MB
        compressionLevel: 'fast'
      },

      // Warm tier - moderately accessed data  
      warmTier: {
        ttl: 3600, // 1 hour
        maxSize: 500 * 1024 * 1024, // 500MB
        compressionLevel: 'balanced'
      },

      // Cold tier - rarely accessed data
      coldTier: {
        ttl: 86400, // 24 hours
        maxSize: 2048 * 1024 * 1024, // 2GB
        compressionLevel: 'maximum'
      }
    };

    return await this.deployTieredCaching(tieredConfig);
  }

  async setupAdvancedAnalytics() {
    console.log('Setting up advanced cache and session analytics...');

    const analyticsConfig = {
      // Real-time metrics
      realtimeMetrics: {
        hitRatioThreshold: 0.8,
        latencyThreshold: 100, // ms
        errorRateThreshold: 0.01,
        memoryUsageThreshold: 0.85
      },

      // Predictive analytics
      predictiveAnalytics: {
        accessPatternLearning: true,
        capacityForecasting: true,
        anomalyDetection: true,
        performanceOptimization: true
      },

      // Business intelligence
      businessIntelligence: {
        userBehaviorAnalysis: true,
        conversionTracking: true,
        sessionQualityScoring: true,
        cacheEfficiencyOptimization: true
      }
    };

    return await this.implementAdvancedAnalytics(analyticsConfig);
  }
}

SQL-Style Caching and Session Management with QueryLeaf

QueryLeaf provides familiar SQL syntax for MongoDB TTL collections and advanced caching operations:

-- QueryLeaf advanced caching and session management with SQL-familiar syntax

-- Configure TTL collections for automatic expiration management
CREATE COLLECTION user_sessions
WITH (
  ttl_field = 'expiresAt',
  expire_after_seconds = 0,
  storage_engine = 'wiredTiger',
  compression = 'snappy'
);

CREATE COLLECTION application_cache
WITH (
  ttl_field = 'expiresAt', 
  expire_after_seconds = 0,
  storage_engine = 'wiredTiger',
  compression = 'zlib'
);

-- Advanced session management with TTL and complex queries
WITH session_analytics AS (
  SELECT 
    user_id,
    session_id,
    created_at,
    last_accessed,
    expires_at,
    ip_address,
    device_fingerprint,
    risk_score,

    -- Session duration analysis
    EXTRACT(EPOCH FROM last_accessed - created_at) / 60 as session_duration_minutes,

    -- Session status classification
    CASE 
      WHEN last_accessed > CURRENT_TIMESTAMP - INTERVAL '5 minutes' THEN 'active'
      WHEN last_accessed > CURRENT_TIMESTAMP - INTERVAL '30 minutes' THEN 'recent' 
      WHEN last_accessed > CURRENT_TIMESTAMP - INTERVAL '2 hours' THEN 'idle'
      ELSE 'stale'
    END as session_status,

    -- Shopping cart analysis
    CAST(JSON_EXTRACT(session_data, '$.shoppingCart.items') AS JSONB) as cart_items,
    CAST(JSON_EXTRACT(session_data, '$.shoppingCart.total') AS DECIMAL(10,2)) as cart_total,

    -- User preferences analysis
    JSON_EXTRACT(session_data, '$.preferences.theme') as preferred_theme,
    JSON_EXTRACT(session_data, '$.preferences.language') as preferred_language,

    -- Navigation patterns
    JSON_EXTRACT(session_data, '$.navigation.lastPage') as last_page,
    JSON_EXTRACT(session_data, '$.navigation.referrer') as referrer,

    -- Activity metrics
    page_views,
    actions_performed,
    data_transferred,

    -- Security indicators  
    mfa_verified,
    login_method,

    -- Risk assessment
    CASE 
      WHEN risk_score > 0.8 THEN 'high'
      WHEN risk_score > 0.5 THEN 'medium'
      WHEN risk_score > 0.2 THEN 'low'
      ELSE 'minimal'
    END as risk_level

  FROM user_sessions
  WHERE active = true 
    AND expires_at > CURRENT_TIMESTAMP
),

session_aggregations AS (
  SELECT 
    -- Overall session metrics
    COUNT(*) as total_active_sessions,
    COUNT(DISTINCT user_id) as unique_active_users,
    AVG(session_duration_minutes) as avg_session_duration,

    -- Session status distribution
    COUNT(*) FILTER (WHERE session_status = 'active') as active_sessions,
    COUNT(*) FILTER (WHERE session_status = 'recent') as recent_sessions,
    COUNT(*) FILTER (WHERE session_status = 'idle') as idle_sessions,
    COUNT(*) FILTER (WHERE session_status = 'stale') as stale_sessions,

    -- Business metrics
    COUNT(*) FILTER (WHERE JSON_ARRAY_LENGTH(cart_items) > 0) as sessions_with_cart,
    AVG(cart_total) FILTER (WHERE cart_total > 0) as avg_cart_value,
    COUNT(*) FILTER (WHERE JSON_ARRAY_LENGTH(cart_items) > 5) as high_volume_carts,

    -- Security metrics
    COUNT(*) FILTER (WHERE risk_level = 'high') as high_risk_sessions,
    COUNT(*) FILTER (WHERE mfa_verified = true) as mfa_verified_sessions,
    COUNT(DISTINCT device_fingerprint) as unique_devices,
    COUNT(DISTINCT ip_address) as unique_ip_addresses,

    -- Activity analysis
    SUM(page_views) as total_page_views,
    SUM(actions_performed) as total_actions,
    AVG(page_views) as avg_page_views_per_session,
    AVG(actions_performed) as avg_actions_per_session,

    -- Performance insights
    SUM(data_transferred) / (1024 * 1024) as total_data_mb_transferred

  FROM session_analytics
),

cache_performance AS (
  SELECT 
    namespace,
    cache_key,
    created_at,
    expires_at,
    last_accessed,
    data_size,
    hit_count,
    tags,

    -- Cache efficiency metrics
    EXTRACT(EPOCH FROM expires_at - created_at) / 3600 as ttl_hours,
    EXTRACT(EPOCH FROM last_accessed - created_at) / 60 as lifetime_minutes,
    EXTRACT(EPOCH FROM CURRENT_TIMESTAMP - last_accessed) / 60 as idle_minutes,

    -- Data size analysis
    CASE 
      WHEN data_size > 1024 * 1024 THEN 'large'
      WHEN data_size > 100 * 1024 THEN 'medium'
      WHEN data_size > 10 * 1024 THEN 'small'
      ELSE 'tiny'
    END as size_category,

    -- Access pattern analysis
    CASE 
      WHEN hit_count > 100 THEN 'high_traffic'
      WHEN hit_count > 10 THEN 'medium_traffic'
      WHEN hit_count > 0 THEN 'low_traffic'
      ELSE 'unused'
    END as traffic_level,

    -- Cache effectiveness
    CASE 
      WHEN hit_count = 0 THEN 'ineffective'
      WHEN hit_count / GREATEST(lifetime_minutes / 60, 1) > 10 THEN 'highly_effective'
      WHEN hit_count / GREATEST(lifetime_minutes / 60, 1) > 1 THEN 'effective'
      ELSE 'moderately_effective'
    END as effectiveness_rating

  FROM application_cache
  WHERE expires_at > CURRENT_TIMESTAMP
),

cache_analytics AS (
  SELECT 
    namespace,

    -- Volume metrics
    COUNT(*) as total_entries,
    SUM(data_size) / (1024 * 1024) as total_size_mb,
    AVG(data_size) as avg_entry_size_bytes,

    -- Performance metrics
    SUM(hit_count) as total_hits,
    AVG(hit_count) as avg_hits_per_entry,
    COUNT(*) FILTER (WHERE hit_count = 0) as unused_entries,

    -- TTL analysis
    AVG(ttl_hours) as avg_ttl_hours,
    COUNT(*) FILTER (WHERE ttl_hours > 24) as long_lived_entries,
    COUNT(*) FILTER (WHERE ttl_hours < 1) as short_lived_entries,

    -- Size distribution
    COUNT(*) FILTER (WHERE size_category = 'large') as large_entries,
    COUNT(*) FILTER (WHERE size_category = 'medium') as medium_entries,
    COUNT(*) FILTER (WHERE size_category = 'small') as small_entries,
    COUNT(*) FILTER (WHERE size_category = 'tiny') as tiny_entries,

    -- Traffic analysis
    COUNT(*) FILTER (WHERE traffic_level = 'high_traffic') as high_traffic_entries,
    COUNT(*) FILTER (WHERE traffic_level = 'medium_traffic') as medium_traffic_entries,
    COUNT(*) FILTER (WHERE traffic_level = 'low_traffic') as low_traffic_entries,
    COUNT(*) FILTER (WHERE traffic_level = 'unused') as unused_traffic_entries,

    -- Effectiveness distribution
    COUNT(*) FILTER (WHERE effectiveness_rating = 'highly_effective') as highly_effective_entries,
    COUNT(*) FILTER (WHERE effectiveness_rating = 'effective') as effective_entries,
    COUNT(*) FILTER (WHERE effectiveness_rating = 'moderately_effective') as moderately_effective_entries,
    COUNT(*) FILTER (WHERE effectiveness_rating = 'ineffective') as ineffective_entries

  FROM cache_performance
  GROUP BY namespace
)

-- Comprehensive session and cache monitoring dashboard
SELECT 
  'System Performance Dashboard' as dashboard_title,
  CURRENT_TIMESTAMP as report_generated_at,

  -- Session management metrics
  JSON_OBJECT(
    'total_active_sessions', sa.total_active_sessions,
    'unique_active_users', sa.unique_active_users,
    'avg_session_duration_minutes', ROUND(sa.avg_session_duration::NUMERIC, 1),
    'session_distribution', JSON_OBJECT(
      'active', sa.active_sessions,
      'recent', sa.recent_sessions,
      'idle', sa.idle_sessions,
      'stale', sa.stale_sessions
    ),
    'security_metrics', JSON_OBJECT(
      'high_risk_sessions', sa.high_risk_sessions,
      'mfa_verified_sessions', sa.mfa_verified_sessions,
      'unique_devices', sa.unique_devices,
      'unique_ip_addresses', sa.unique_ip_addresses
    ),
    'business_metrics', JSON_OBJECT(
      'sessions_with_cart', sa.sessions_with_cart,
      'avg_cart_value', ROUND(sa.avg_cart_value::NUMERIC, 2),
      'high_volume_carts', sa.high_volume_carts,
      'cart_conversion_rate', 
        ROUND((sa.sessions_with_cart::FLOAT / sa.total_active_sessions * 100)::NUMERIC, 2)
    )
  ) as session_metrics,

  -- Cache performance metrics by namespace
  JSON_OBJECT_AGG(
    ca.namespace,
    JSON_OBJECT(
      'total_entries', ca.total_entries,
      'total_size_mb', ROUND(ca.total_size_mb::NUMERIC, 2),
      'avg_entry_size_kb', ROUND((ca.avg_entry_size_bytes / 1024)::NUMERIC, 1),
      'total_hits', ca.total_hits,
      'avg_hits_per_entry', ROUND(ca.avg_hits_per_entry::NUMERIC, 1),
      'unused_entry_percentage', 
        ROUND((ca.unused_entries::FLOAT / ca.total_entries * 100)::NUMERIC, 1),
      'cache_efficiency', 
        CASE 
          WHEN ca.unused_entries::FLOAT / ca.total_entries > 0.5 THEN 'poor'
          WHEN ca.unused_entries::FLOAT / ca.total_entries > 0.2 THEN 'fair'
          WHEN ca.unused_entries::FLOAT / ca.total_entries > 0.1 THEN 'good'
          ELSE 'excellent'
        END,
      'size_distribution', JSON_OBJECT(
        'large', ca.large_entries,
        'medium', ca.medium_entries,
        'small', ca.small_entries,
        'tiny', ca.tiny_entries
      ),
      'effectiveness_distribution', JSON_OBJECT(
        'highly_effective', ca.highly_effective_entries,
        'effective', ca.effective_entries,
        'moderately_effective', ca.moderately_effective_entries,
        'ineffective', ca.ineffective_entries
      )
    )
  ) as cache_metrics_by_namespace,

  -- System health indicators
  JSON_OBJECT(
    'session_system_health', 
      CASE 
        WHEN sa.high_risk_sessions::FLOAT / sa.total_active_sessions > 0.1 THEN 'critical'
        WHEN sa.avg_session_duration < 5 THEN 'warning'
        WHEN sa.unique_active_users::FLOAT / sa.total_active_sessions < 0.5 THEN 'warning'
        ELSE 'healthy'
      END,
    'cache_system_health',
      CASE 
        WHEN AVG(ca.unused_entries::FLOAT / ca.total_entries) > 0.5 THEN 'critical'
        WHEN AVG(ca.total_size_mb) > 1024 THEN 'warning'  
        WHEN AVG(ca.avg_hits_per_entry) < 5 THEN 'warning'
        ELSE 'healthy'
      END,
    'overall_system_status',
      CASE 
        WHEN sa.high_risk_sessions > 10 OR AVG(ca.unused_entries::FLOAT / ca.total_entries) > 0.5 THEN 'needs_attention'
        WHEN sa.avg_session_duration > 30 AND AVG(ca.avg_hits_per_entry) > 10 THEN 'optimal'
        ELSE 'normal'
      END
  ) as system_health,

  -- Operational recommendations
  ARRAY[
    CASE WHEN sa.high_risk_sessions > sa.total_active_sessions * 0.05 
         THEN 'Review session security policies and risk scoring algorithms' END,
    CASE WHEN AVG(ca.unused_entries::FLOAT / ca.total_entries) > 0.3
         THEN 'Optimize cache TTL settings and review caching strategies' END,
    CASE WHEN sa.avg_session_duration < 5
         THEN 'Investigate user engagement issues and session timeout settings' END,
    CASE WHEN AVG(ca.total_size_mb) > 512
         THEN 'Consider cache data compression and size optimization' END,
    CASE WHEN sa.sessions_with_cart::FLOAT / sa.total_active_sessions < 0.1
         THEN 'Review shopping cart functionality and user experience' END
  ]::TEXT[] as recommendations

FROM session_aggregations sa
CROSS JOIN cache_analytics ca
GROUP BY sa.total_active_sessions, sa.unique_active_users, sa.avg_session_duration,
         sa.active_sessions, sa.recent_sessions, sa.idle_sessions, sa.stale_sessions,
         sa.high_risk_sessions, sa.mfa_verified_sessions, sa.unique_devices, sa.unique_ip_addresses,
         sa.sessions_with_cart, sa.avg_cart_value, sa.high_volume_carts;

-- Advanced cache invalidation with pattern matching and conditional logic
UPDATE application_cache 
SET expires_at = CURRENT_TIMESTAMP,
    invalidated_at = CURRENT_TIMESTAMP,
    invalidation_reason = 'product_update_cascade'
WHERE 
  -- Pattern-based invalidation
  (cache_key LIKE 'product:%' OR cache_key LIKE 'catalog:%')
  AND 
  -- Conditional invalidation based on tags
  ('product_catalog' = ANY(tags) OR 'inventory' = ANY(tags))
  AND
  -- Time-based invalidation criteria
  created_at < CURRENT_TIMESTAMP - INTERVAL '1 hour'
  AND
  -- Size-based invalidation for large entries
  data_size > 1024 * 1024; -- 1MB

-- Session cleanup with advanced criteria and security considerations
UPDATE user_sessions 
SET active = false,
    expires_at = CURRENT_TIMESTAMP,
    invalidated_at = CURRENT_TIMESTAMP,
    invalidation_reason = 'security_cleanup'
WHERE 
  -- Risk-based cleanup
  risk_score > 0.8
  OR
  -- Inactive session cleanup
  (last_accessed < CURRENT_TIMESTAMP - INTERVAL '2 hours' AND remember_me = false)
  OR
  -- Device anomaly cleanup
  (device_fingerprint NOT IN (
    SELECT device_fingerprint 
    FROM user_sessions 
    WHERE user_id = user_sessions.user_id 
      AND created_at >= CURRENT_TIMESTAMP - INTERVAL '30 days'
    GROUP BY device_fingerprint
    HAVING COUNT(*) > 5
  ))
  OR
  -- Geographic anomaly cleanup
  (ip_address NOT SIMILAR TO (
    SELECT STRING_AGG(DISTINCT SUBSTRING(ip_address, 1, POSITION('.' IN ip_address, POSITION('.' IN ip_address) + 1)), '|')
    FROM user_sessions recent_sessions
    WHERE recent_sessions.user_id = user_sessions.user_id
      AND recent_sessions.created_at >= CURRENT_TIMESTAMP - INTERVAL '7 days'
  ));

-- Real-time TTL and performance monitoring
CREATE VIEW cache_session_health_monitor AS
WITH real_time_metrics AS (
  SELECT 
    -- Current timestamp for dashboard refresh
    CURRENT_TIMESTAMP as monitor_timestamp,

    -- Active session metrics
    (SELECT COUNT(*) FROM user_sessions 
     WHERE active = true AND expires_at > CURRENT_TIMESTAMP) as current_active_sessions,

    (SELECT COUNT(DISTINCT user_id) FROM user_sessions 
     WHERE active = true AND expires_at > CURRENT_TIMESTAMP) as current_unique_users,

    -- Cache metrics
    (SELECT COUNT(*) FROM application_cache 
     WHERE expires_at > CURRENT_TIMESTAMP) as current_cache_entries,

    (SELECT SUM(data_size) / (1024 * 1024) FROM application_cache 
     WHERE expires_at > CURRENT_TIMESTAMP) as current_cache_size_mb,

    -- Recent activity (last 5 minutes)
    (SELECT COUNT(*) FROM user_sessions 
     WHERE last_accessed >= CURRENT_TIMESTAMP - INTERVAL '5 minutes') as recent_session_activity,

    (SELECT COUNT(*) FROM application_cache 
     WHERE last_accessed >= CURRENT_TIMESTAMP - INTERVAL '5 minutes') as recent_cache_activity,

    -- TTL efficiency metrics
    (SELECT COUNT(*) FROM user_sessions 
     WHERE expires_at <= CURRENT_TIMESTAMP + INTERVAL '1 minute') as sessions_expiring_soon,

    (SELECT COUNT(*) FROM application_cache 
     WHERE expires_at <= CURRENT_TIMESTAMP + INTERVAL '1 minute') as cache_expiring_soon,

    -- Risk indicators
    (SELECT COUNT(*) FROM user_sessions 
     WHERE active = true AND risk_score > 0.7) as high_risk_active_sessions
)

SELECT 
  monitor_timestamp,

  -- Session health indicators
  current_active_sessions,
  current_unique_users,
  ROUND(current_unique_users::FLOAT / NULLIF(current_active_sessions, 0), 2) as user_session_ratio,
  recent_session_activity,
  sessions_expiring_soon,

  -- Cache health indicators  
  current_cache_entries,
  ROUND(current_cache_size_mb::NUMERIC, 2) as cache_size_mb,
  recent_cache_activity,
  cache_expiring_soon,

  -- Security indicators
  high_risk_active_sessions,
  CASE 
    WHEN high_risk_active_sessions > current_active_sessions * 0.1 THEN 'critical'
    WHEN high_risk_active_sessions > current_active_sessions * 0.05 THEN 'warning'
    ELSE 'normal'
  END as security_status,

  -- Performance indicators
  CASE 
    WHEN current_active_sessions > 10000 THEN 'high_load'
    WHEN current_active_sessions > 5000 THEN 'medium_load'
    WHEN current_active_sessions > 1000 THEN 'normal_load'
    ELSE 'low_load'
  END as system_load,

  -- Cache efficiency
  CASE 
    WHEN recent_cache_activity::FLOAT / NULLIF(current_cache_entries, 0) > 0.1 THEN 'highly_active'
    WHEN recent_cache_activity::FLOAT / NULLIF(current_cache_entries, 0) > 0.05 THEN 'moderately_active'
    ELSE 'low_activity'
  END as cache_activity_level,

  -- TTL management effectiveness
  CASE 
    WHEN sessions_expiring_soon + cache_expiring_soon > 100 THEN 'high_turnover'
    WHEN sessions_expiring_soon + cache_expiring_soon > 50 THEN 'moderate_turnover'
    ELSE 'stable_turnover'
  END as ttl_turnover_rate

FROM real_time_metrics;

-- QueryLeaf provides comprehensive MongoDB TTL and caching capabilities:
-- 1. Automatic TTL expiration management with SQL-familiar syntax
-- 2. Advanced session lifecycle management with security features
-- 3. Intelligent cache invalidation patterns and strategies
-- 4. Real-time performance monitoring and health assessments
-- 5. Flexible document structure support for complex cache and session data
-- 6. Built-in compression and storage optimization capabilities
-- 7. Sophisticated analytics and business intelligence integration
-- 8. Advanced security features including risk scoring and anomaly detection
-- 9. High-performance concurrent access with MongoDB's native capabilities
-- 10. Enterprise-grade scalability and distributed consistency support

Best Practices for Production Caching and Session Management

TTL Collection Strategy Design

Essential principles for effective MongoDB TTL implementation:

  1. TTL Configuration: Design TTL strategies that balance performance, storage costs, and data availability requirements
  2. Index Optimization: Implement appropriate indexing strategies for cache and session access patterns
  3. Data Compression: Use MongoDB compression features to optimize storage for large cache and session data
  4. Security Integration: Implement comprehensive security measures including risk scoring and device tracking
  5. Performance Monitoring: Deploy real-time monitoring and alerting for cache and session system health
  6. Scalability Planning: Design caching architecture that can scale with user growth and data volume increases

Enterprise Deployment Considerations

Optimize caching and session management for production environments:

  1. High Availability: Implement distributed session and cache management across multiple nodes
  2. Data Consistency: Ensure cache and session consistency across distributed infrastructure
  3. Disaster Recovery: Design backup and recovery procedures for critical session and cache data
  4. Compliance Integration: Meet regulatory requirements for session data handling and cache security
  5. Cost Optimization: Monitor and optimize caching costs while maintaining performance requirements
  6. Operational Integration: Integrate with existing monitoring, alerting, and operational workflows

Conclusion

MongoDB TTL Collections provide comprehensive distributed caching and session management capabilities that eliminate the complexity of traditional cache servers and session stores while offering superior flexibility, performance, and integration with existing application infrastructure. Native TTL expiration, advanced document modeling, and integrated analytics enable sophisticated caching strategies without requiring additional operational overhead.

Key MongoDB caching and session management benefits include:

  • Automatic TTL Management: Built-in expiration handling with no manual cleanup or maintenance required
  • Flexible Data Models: Support for complex nested session and cache data structures with efficient querying
  • Integrated Security: Comprehensive security features including risk scoring, device tracking, and anomaly detection
  • High Performance: Native MongoDB performance optimizations for concurrent cache and session operations
  • Advanced Analytics: Sophisticated metrics and business intelligence capabilities for optimization insights
  • SQL Accessibility: Familiar SQL-style operations through QueryLeaf for accessible cache and session management

Whether you're building high-traffic web applications, e-commerce platforms, IoT systems, or enterprise applications requiring sophisticated state management, MongoDB TTL Collections with QueryLeaf's familiar SQL interface provide the foundation for reliable, scalable, and efficient distributed caching and session management.

QueryLeaf Integration: QueryLeaf automatically translates SQL-style TTL and caching operations into MongoDB's native TTL collections and indexing strategies, making advanced caching and session management accessible to SQL-oriented development teams. Complex cache invalidation patterns, session analytics, and performance optimization are seamlessly handled through familiar SQL constructs, enabling sophisticated distributed state management without requiring deep MongoDB TTL expertise.

The combination of MongoDB's robust TTL capabilities with SQL-style caching operations makes it an ideal platform for applications requiring both high-performance state management and familiar database interaction patterns, ensuring your distributed systems can maintain optimal performance and user experience as they scale and evolve.