Skip to content

MongoDB Database Administration and Monitoring: Enterprise Operations Management and Performance Optimization

Enterprise MongoDB deployments require comprehensive database administration and monitoring strategies to ensure optimal performance, reliability, and operational excellence. Traditional relational database administration approaches often fall short when managing MongoDB's distributed architecture, flexible schema design, and unique operational characteristics that require specialized administrative expertise and tooling.

MongoDB database administration encompasses performance monitoring, capacity planning, security management, backup operations, and operational maintenance through sophisticated tooling and administrative frameworks. Unlike traditional SQL databases that rely on rigid administrative procedures, MongoDB administration requires understanding of document-based operations, replica set management, sharding strategies, and distributed system maintenance patterns.

The Traditional Database Administration Challenge

Conventional relational database administration approaches face significant limitations when applied to MongoDB environments:

-- Traditional PostgreSQL database administration - rigid procedures with limited MongoDB applicability

-- Database performance monitoring with limited visibility
CREATE TABLE db_performance_metrics (
    metric_id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    server_name VARCHAR(100) NOT NULL,
    metric_timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,

    -- Connection metrics
    active_connections INTEGER,
    max_connections INTEGER,
    connection_utilization DECIMAL(5,2),
    idle_connections INTEGER,

    -- Query performance metrics
    slow_query_count INTEGER,
    average_query_time DECIMAL(10,4),
    queries_per_second DECIMAL(10,2),
    cache_hit_ratio DECIMAL(5,2),

    -- System resource utilization
    cpu_usage DECIMAL(5,2),
    memory_usage DECIMAL(5,2),
    disk_usage DECIMAL(5,2),
    io_wait DECIMAL(5,2),

    -- Database-specific metrics
    database_size BIGINT,
    index_size BIGINT,
    table_count INTEGER,
    index_count INTEGER,

    -- Lock contention
    lock_waits INTEGER,
    deadlock_count INTEGER,
    blocked_queries INTEGER,

    -- Transaction metrics
    transactions_per_second DECIMAL(10,2),
    transaction_rollback_rate DECIMAL(5,2)
);

-- Complex monitoring infrastructure with limited MongoDB compatibility
CREATE TABLE slow_query_log (
    log_id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    server_name VARCHAR(100) NOT NULL,
    database_name VARCHAR(100) NOT NULL,
    query_timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,

    -- Query identification
    query_hash VARCHAR(64) NOT NULL,
    query_text TEXT NOT NULL,
    normalized_query TEXT,

    -- Performance metrics
    execution_time DECIMAL(10,4) NOT NULL,
    rows_examined BIGINT,
    rows_returned BIGINT,
    index_usage TEXT,

    -- Resource consumption
    cpu_time DECIMAL(10,4),
    io_reads INTEGER,
    io_writes INTEGER,
    memory_peak BIGINT,

    -- User context
    user_name VARCHAR(100),
    application_name VARCHAR(200),
    connection_id BIGINT,
    session_id VARCHAR(100),

    -- Query categorization
    query_type VARCHAR(20), -- SELECT, INSERT, UPDATE, DELETE
    complexity_score INTEGER,
    optimization_suggestions TEXT
);

-- Maintenance scheduling with manual coordination
CREATE TABLE maintenance_schedules (
    schedule_id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    server_name VARCHAR(100) NOT NULL,
    maintenance_type VARCHAR(50) NOT NULL,
    scheduled_start TIMESTAMP NOT NULL,
    estimated_duration INTERVAL NOT NULL,

    -- Maintenance details
    maintenance_description TEXT,
    affected_databases TEXT[],
    expected_downtime INTERVAL,
    backup_required BOOLEAN DEFAULT TRUE,

    -- Approval workflow
    requested_by VARCHAR(100) NOT NULL,
    approved_by VARCHAR(100),
    approval_timestamp TIMESTAMP,
    approval_status VARCHAR(20) DEFAULT 'pending',

    -- Execution tracking
    actual_start TIMESTAMP,
    actual_end TIMESTAMP,
    execution_status VARCHAR(20) DEFAULT 'scheduled',
    execution_notes TEXT,

    -- Impact assessment
    business_impact VARCHAR(20), -- low, medium, high, critical
    user_notification_required BOOLEAN DEFAULT TRUE,
    rollback_plan TEXT
);

-- Limited backup and recovery management
CREATE TABLE backup_operations (
    backup_id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    server_name VARCHAR(100) NOT NULL,
    database_name VARCHAR(100),
    backup_timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,

    -- Backup configuration
    backup_type VARCHAR(20) NOT NULL, -- full, incremental, differential
    backup_method VARCHAR(20) NOT NULL, -- dump, filesystem, streaming
    compression_enabled BOOLEAN DEFAULT TRUE,
    encryption_enabled BOOLEAN DEFAULT FALSE,

    -- Backup metrics
    backup_size BIGINT,
    compressed_size BIGINT,
    backup_duration INTERVAL,

    -- Storage information
    backup_location VARCHAR(500) NOT NULL,
    storage_type VARCHAR(50), -- local, s3, nfs, tape
    retention_period INTERVAL,
    deletion_scheduled TIMESTAMP,

    -- Validation and integrity
    integrity_check_performed BOOLEAN DEFAULT FALSE,
    integrity_check_passed BOOLEAN,
    checksum VARCHAR(128),

    -- Recovery testing
    recovery_test_performed BOOLEAN DEFAULT FALSE,
    recovery_test_passed BOOLEAN,
    recovery_test_notes TEXT
);

-- Complex user access and security management
CREATE TABLE user_access_audit (
    audit_id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    server_name VARCHAR(100) NOT NULL,
    audit_timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,

    -- User information
    username VARCHAR(100) NOT NULL,
    user_type VARCHAR(20), -- admin, application, readonly
    authentication_method VARCHAR(50),
    source_ip INET,

    -- Access details
    operation_type VARCHAR(20) NOT NULL, -- login, logout, query, admin
    database_accessed VARCHAR(100),
    table_accessed VARCHAR(100),
    operation_details TEXT,

    -- Security context
    session_id VARCHAR(100),
    connection_duration INTERVAL,
    privilege_level VARCHAR(20),
    elevated_privileges BOOLEAN DEFAULT FALSE,

    -- Result tracking
    operation_success BOOLEAN NOT NULL,
    error_message TEXT,
    security_violation BOOLEAN DEFAULT FALSE,

    -- Risk assessment
    risk_level VARCHAR(20) DEFAULT 'low',
    anomaly_detected BOOLEAN DEFAULT FALSE,
    follow_up_required BOOLEAN DEFAULT FALSE
);

-- Manual index management with limited optimization capabilities
WITH index_analysis AS (
    SELECT 
        schemaname,
        tablename,
        indexname,
        idx_size,
        idx_tup_read,
        idx_tup_fetch,

        -- Index usage calculation
        CASE 
            WHEN idx_tup_read > 0 THEN 
                ROUND((idx_tup_fetch * 100.0 / idx_tup_read), 2)
            ELSE 0 
        END as index_efficiency,

        -- Index maintenance needs
        CASE 
            WHEN idx_tup_read < 1000 AND pg_size_pretty(idx_size::bigint) > '10 MB' THEN 'Consider removal'
            WHEN idx_tup_fetch < idx_tup_read * 0.1 THEN 'Poor selectivity'
            ELSE 'Optimal'
        END as optimization_recommendation

    FROM pg_stat_user_indexes 
    JOIN pg_indexes USING (schemaname, tablename, indexname)
),

maintenance_recommendations AS (
    SELECT 
        ia.schemaname,
        ia.tablename,
        COUNT(*) as total_indexes,
        COUNT(*) FILTER (WHERE ia.optimization_recommendation != 'Optimal') as problematic_indexes,
        array_agg(ia.indexname) FILTER (WHERE ia.optimization_recommendation = 'Consider removal') as removable_indexes,
        SUM(ia.idx_size) as total_index_size,
        AVG(ia.index_efficiency) as avg_efficiency

    FROM index_analysis ia
    GROUP BY ia.schemaname, ia.tablename
)

-- Generate maintenance recommendations
SELECT 
    mr.schemaname,
    mr.tablename,
    mr.total_indexes,
    mr.problematic_indexes,
    mr.removable_indexes,
    pg_size_pretty(mr.total_index_size::bigint) as total_size,
    ROUND(mr.avg_efficiency, 2) as avg_efficiency_percent,

    -- Maintenance priority
    CASE 
        WHEN mr.problematic_indexes > mr.total_indexes * 0.5 THEN 'High Priority'
        WHEN mr.problematic_indexes > 0 THEN 'Medium Priority'
        ELSE 'Low Priority'
    END as maintenance_priority,

    -- Specific recommendations
    CASE 
        WHEN array_length(mr.removable_indexes, 1) > 0 THEN 
            'Remove unused indexes: ' || array_to_string(mr.removable_indexes, ', ')
        WHEN mr.avg_efficiency < 50 THEN
            'Review index selectivity and query patterns'
        ELSE 
            'Continue current index strategy'
    END as detailed_recommendations

FROM maintenance_recommendations mr
WHERE mr.problematic_indexes > 0
ORDER BY mr.problematic_indexes DESC, mr.total_index_size DESC;

-- Problems with traditional database administration:
-- 1. Limited visibility into MongoDB-specific operations and performance characteristics
-- 2. Manual monitoring processes that don't scale with distributed MongoDB deployments  
-- 3. Rigid maintenance procedures that don't account for replica sets and sharding
-- 4. Backup strategies that don't leverage MongoDB's native backup capabilities
-- 5. Security management that doesn't integrate with MongoDB's role-based access control
-- 6. Performance tuning approaches that ignore MongoDB's unique optimization patterns
-- 7. Index management that doesn't understand MongoDB's compound index strategies
-- 8. Monitoring tools that lack MongoDB-specific metrics and operational insights
-- 9. Maintenance scheduling that doesn't coordinate across MongoDB cluster topology
-- 10. Recovery procedures that don't leverage MongoDB's built-in replication and failover

MongoDB provides comprehensive database administration capabilities with integrated monitoring and management tools:

// MongoDB Advanced Database Administration - Enterprise monitoring and operations management
const { MongoClient, ObjectId, GridFSBucket } = require('mongodb');

const client = new MongoClient('mongodb://localhost:27017');
const db = client.db('enterprise_operations');

// Comprehensive MongoDB Database Administration Manager
class MongoDBAdministrationManager {
  constructor(client, config = {}) {
    this.client = client;
    this.db = client.db(config.database || 'enterprise_operations');

    this.collections = {
      performanceMetrics: this.db.collection('performance_metrics'),
      slowOperations: this.db.collection('slow_operations'),
      indexAnalysis: this.db.collection('index_analysis'),
      maintenanceSchedule: this.db.collection('maintenance_schedule'),
      backupOperations: this.db.collection('backup_operations'),
      userActivity: this.db.collection('user_activity'),
      systemAlerts: this.db.collection('system_alerts'),
      capacityPlanning: this.db.collection('capacity_planning')
    };

    // Administration configuration
    this.config = {
      // Monitoring configuration
      metricsCollectionInterval: config.metricsCollectionInterval || 60000, // 1 minute
      slowOperationThreshold: config.slowOperationThreshold || 100, // 100ms
      enableDetailedProfiling: config.enableDetailedProfiling !== false,
      enableRealtimeAlerts: config.enableRealtimeAlerts !== false,

      // Performance thresholds
      cpuThreshold: config.cpuThreshold || 80,
      memoryThreshold: config.memoryThreshold || 85,
      connectionThreshold: config.connectionThreshold || 80,
      diskThreshold: config.diskThreshold || 90,

      // Maintenance configuration
      enableAutomaticMaintenance: config.enableAutomaticMaintenance || false,
      maintenanceWindow: config.maintenanceWindow || { start: '02:00', end: '04:00' },
      enableMaintenanceNotifications: config.enableMaintenanceNotifications !== false,

      // Backup configuration
      enableAutomaticBackups: config.enableAutomaticBackups !== false,
      backupRetentionDays: config.backupRetentionDays || 30,
      backupSchedule: config.backupSchedule || '0 2 * * *', // 2 AM daily

      // Security and compliance
      enableSecurityAuditing: config.enableSecurityAuditing !== false,
      enableComplianceTracking: config.enableComplianceTracking || false,
      enableAccessLogging: config.enableAccessLogging !== false
    };

    // Performance monitoring state
    this.performanceMonitors = new Map();
    this.alertSubscriptions = new Map();
    this.maintenanceQueue = [];

    this.initializeAdministrationSystem();
  }

  async initializeAdministrationSystem() {
    console.log('Initializing MongoDB administration and monitoring system...');

    try {
      // Setup monitoring infrastructure
      await this.setupMonitoringInfrastructure();

      // Initialize performance tracking
      await this.initializePerformanceMonitoring();

      // Setup automated maintenance
      if (this.config.enableAutomaticMaintenance) {
        await this.initializeMaintenanceAutomation();
      }

      // Initialize backup management
      if (this.config.enableAutomaticBackups) {
        await this.initializeBackupManagement();
      }

      // Setup security auditing
      if (this.config.enableSecurityAuditing) {
        await this.initializeSecurityAuditing();
      }

      // Start monitoring processes
      await this.startMonitoringProcesses();

      console.log('MongoDB administration system initialized successfully');

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

  async setupMonitoringInfrastructure() {
    console.log('Setting up comprehensive monitoring infrastructure...');

    try {
      // Create optimized indexes for monitoring collections
      await this.collections.performanceMetrics.createIndexes([
        { key: { timestamp: -1 }, background: true },
        { key: { serverName: 1, timestamp: -1 }, background: true },
        { key: { 'metrics.alertLevel': 1, timestamp: -1 }, background: true },
        { key: { 'metrics.cpuUsage': 1 }, background: true, sparse: true },
        { key: { 'metrics.memoryUsage': 1 }, background: true, sparse: true }
      ]);

      // Slow operations monitoring indexes
      await this.collections.slowOperations.createIndexes([
        { key: { timestamp: -1 }, background: true },
        { key: { operation: 1, timestamp: -1 }, background: true },
        { key: { duration: -1 }, background: true },
        { key: { 'context.collection': 1, timestamp: -1 }, background: true },
        { key: { 'optimization.needsAttention': 1 }, background: true, sparse: true }
      ]);

      // Index analysis tracking
      await this.collections.indexAnalysis.createIndexes([
        { key: { collection: 1, timestamp: -1 }, background: true },
        { key: { 'analysis.efficiency': 1 }, background: true },
        { key: { 'recommendations.priority': 1, timestamp: -1 }, background: true }
      ]);

      // System alerts indexing
      await this.collections.systemAlerts.createIndexes([
        { key: { timestamp: -1 }, background: true },
        { key: { severity: 1, timestamp: -1 }, background: true },
        { key: { resolved: 1, timestamp: -1 }, background: true },
        { key: { category: 1, timestamp: -1 }, background: true }
      ]);

      console.log('Monitoring infrastructure setup completed');

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

  async collectComprehensiveMetrics() {
    console.log('Collecting comprehensive performance metrics...');
    const startTime = Date.now();

    try {
      // Get server status information
      const serverStatus = await this.db.admin().serverStatus();
      const dbStats = await this.db.stats();
      const currentOperations = await this.db.admin().currentOp();

      // Collect performance metrics
      const performanceMetrics = {
        _id: new ObjectId(),
        timestamp: new Date(),
        serverName: serverStatus.host,

        // Connection metrics
        connections: {
          current: serverStatus.connections.current,
          available: serverStatus.connections.available,
          totalCreated: serverStatus.connections.totalCreated,
          utilization: Math.round((serverStatus.connections.current / 
                                  (serverStatus.connections.current + serverStatus.connections.available)) * 100)
        },

        // Operation metrics
        operations: {
          insert: serverStatus.opcounters.insert,
          query: serverStatus.opcounters.query,
          update: serverStatus.opcounters.update,
          delete: serverStatus.opcounters.delete,
          getmore: serverStatus.opcounters.getmore,
          command: serverStatus.opcounters.command,

          // Operations per second calculations
          insertRate: this.calculateOperationRate('insert', serverStatus.opcounters.insert),
          queryRate: this.calculateOperationRate('query', serverStatus.opcounters.query),
          updateRate: this.calculateOperationRate('update', serverStatus.opcounters.update),
          deleteRate: this.calculateOperationRate('delete', serverStatus.opcounters.delete)
        },

        // Memory metrics
        memory: {
          resident: serverStatus.mem.resident,
          virtual: serverStatus.mem.virtual,
          mapped: serverStatus.mem.mapped || 0,
          mappedWithJournal: serverStatus.mem.mappedWithJournal || 0,

          // WiredTiger cache metrics (if available)
          cacheSizeGB: serverStatus.wiredTiger?.cache?.['maximum bytes configured'] ? 
                      Math.round(serverStatus.wiredTiger.cache['maximum bytes configured'] / 1024 / 1024 / 1024) : 0,
          cacheUsedGB: serverStatus.wiredTiger?.cache?.['bytes currently in the cache'] ? 
                      Math.round(serverStatus.wiredTiger.cache['bytes currently in the cache'] / 1024 / 1024 / 1024) : 0,
          cacheUtilization: this.calculateCacheUtilization(serverStatus)
        },

        // Database metrics
        database: {
          collections: dbStats.collections,
          objects: dbStats.objects,
          dataSize: dbStats.dataSize,
          storageSize: dbStats.storageSize,
          indexes: dbStats.indexes,
          indexSize: dbStats.indexSize,

          // Growth metrics
          avgObjSize: dbStats.avgObjSize,
          scaleFactor: dbStats.scaleFactor || 1
        },

        // Network metrics
        network: {
          bytesIn: serverStatus.network.bytesIn,
          bytesOut: serverStatus.network.bytesOut,
          numRequests: serverStatus.network.numRequests,

          // Network rates
          bytesInRate: this.calculateNetworkRate('bytesIn', serverStatus.network.bytesIn),
          bytesOutRate: this.calculateNetworkRate('bytesOut', serverStatus.network.bytesOut),
          requestRate: this.calculateNetworkRate('numRequests', serverStatus.network.numRequests)
        },

        // Current operations analysis
        activeOperations: {
          total: currentOperations.inprog.length,
          reads: currentOperations.inprog.filter(op => op.op === 'query' || op.op === 'getmore').length,
          writes: currentOperations.inprog.filter(op => op.op === 'insert' || op.op === 'update' || op.op === 'remove').length,
          commands: currentOperations.inprog.filter(op => op.op === 'command').length,

          // Long running operations
          longRunning: currentOperations.inprog.filter(op => op.microsecs_running > 1000000).length, // > 1 second
          blocked: currentOperations.inprog.filter(op => op.waitingForLock).length
        },

        // Lock metrics
        locks: this.analyzeLockMetrics(serverStatus),

        // Replication metrics (if applicable)
        replication: await this.collectReplicationMetrics(),

        // Sharding metrics (if applicable)
        sharding: await this.collectShardingMetrics(),

        // Alert evaluation
        alerts: await this.evaluatePerformanceAlerts(serverStatus, dbStats),

        // Collection metadata
        collectionTime: Date.now() - startTime,
        metricsVersion: '2.0'
      };

      // Store metrics
      await this.collections.performanceMetrics.insertOne(performanceMetrics);

      // Process alerts if any
      if (performanceMetrics.alerts.length > 0) {
        await this.processPerformanceAlerts(performanceMetrics.alerts, performanceMetrics);
      }

      console.log(`Performance metrics collected successfully: ${performanceMetrics.alerts.length} alerts generated`);

      return performanceMetrics;

    } catch (error) {
      console.error('Error collecting performance metrics:', error);
      throw error;
    }
  }

  async analyzeSlowOperations() {
    console.log('Analyzing slow operations and query performance...');

    try {
      // Get current profiling level
      const profilingLevel = await this.db.runCommand({ profile: -1 });

      // Enable profiling if not already enabled
      if (profilingLevel.was < 1) {
        await this.db.runCommand({ 
          profile: 2, // Profile all operations
          slowms: this.config.slowOperationThreshold 
        });
      }

      // Retrieve slow operations from profiler collection
      const profileData = await this.db.collection('system.profile')
        .find({
          ts: { $gte: new Date(Date.now() - 300000) }, // Last 5 minutes
          millis: { $gte: this.config.slowOperationThreshold }
        })
        .sort({ ts: -1 })
        .limit(1000)
        .toArray();

      // Analyze operations
      for (const operation of profileData) {
        const analysis = await this.analyzeOperation(operation);

        const slowOpDocument = {
          _id: new ObjectId(),
          timestamp: operation.ts,
          operation: operation.op,
          namespace: operation.ns,
          duration: operation.millis,

          // Command details
          command: operation.command,
          planSummary: operation.planSummary,
          executionStats: operation.execStats,

          // Performance analysis
          analysis: analysis,

          // Optimization recommendations
          recommendations: await this.generateOptimizationRecommendations(operation, analysis),

          // Context information
          context: {
            client: operation.client,
            user: operation.user,
            collection: this.extractCollectionName(operation.ns),
            database: this.extractDatabaseName(operation.ns)
          },

          // Impact assessment
          impact: this.assessOperationImpact(operation, analysis),

          // Tracking metadata
          analyzed: true,
          reviewRequired: analysis.complexity === 'high' || operation.millis > 5000,
          processed: new Date()
        };

        await this.collections.slowOperations.insertOne(slowOpDocument);
      }

      console.log(`Analyzed ${profileData.length} slow operations`);

      return profileData.length;

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

  async performIndexAnalysis() {
    console.log('Performing comprehensive index analysis...');

    try {
      const databases = await this.client.db().admin().listDatabases();

      for (const dbInfo of databases.databases) {
        if (['admin', 'local', 'config'].includes(dbInfo.name)) continue;

        const database = this.client.db(dbInfo.name);
        const collections = await database.listCollections().toArray();

        for (const collInfo of collections) {
          const collection = database.collection(collInfo.name);

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

          // Get collection statistics
          const collStats = await collection.stats();

          // Analyze each index
          for (const index of indexes) {
            const indexStat = indexStats.find(stat => stat.name === index.name);
            const analysis = await this.analyzeIndex(index, indexStat, collStats, collection);

            const indexAnalysisDocument = {
              _id: new ObjectId(),
              timestamp: new Date(),
              database: dbInfo.name,
              collection: collInfo.name,
              indexName: index.name,

              // Index configuration
              indexSpec: index.key,
              indexOptions: {
                unique: index.unique || false,
                sparse: index.sparse || false,
                background: index.background || false,
                partialFilterExpression: index.partialFilterExpression
              },

              // Index statistics
              statistics: {
                accessCount: indexStat?.accesses?.ops || 0,
                lastAccessed: indexStat?.accesses?.since || null,
                indexSize: index.indexSizes ? index.indexSizes[index.name] : 0
              },

              // Analysis results
              analysis: analysis,

              // Performance recommendations
              recommendations: await this.generateIndexRecommendations(analysis, index, collStats),

              // Usage patterns
              usagePatterns: await this.analyzeIndexUsagePatterns(index, collection),

              // Maintenance requirements
              maintenance: {
                needsRebuild: analysis.fragmentation > 30,
                needsOptimization: analysis.efficiency < 70,
                canBeDropped: analysis.usage === 'unused' && !index.unique && index.name !== '_id_',
                priority: this.calculateMaintenancePriority(analysis)
              }
            };

            await this.collections.indexAnalysis.insertOne(indexAnalysisDocument);
          }
        }
      }

      console.log('Index analysis completed successfully');

    } catch (error) {
      console.error('Error performing index analysis:', error);
      throw error;
    }
  }

  async scheduleMaintenanceOperation(maintenanceRequest) {
    console.log('Scheduling maintenance operation...');

    try {
      const maintenanceOperation = {
        _id: new ObjectId(),
        type: maintenanceRequest.type,
        scheduledTime: maintenanceRequest.scheduledTime || new Date(),
        estimatedDuration: maintenanceRequest.estimatedDuration,

        // Operation details
        description: maintenanceRequest.description,
        targetDatabases: maintenanceRequest.databases || [],
        targetCollections: maintenanceRequest.collections || [],

        // Impact assessment
        impactLevel: maintenanceRequest.impactLevel || 'medium',
        downTimeRequired: maintenanceRequest.downTimeRequired || false,
        userNotificationRequired: maintenanceRequest.userNotificationRequired !== false,

        // Approval workflow
        requestedBy: maintenanceRequest.requestedBy,
        approvalRequired: this.determineApprovalRequirement(maintenanceRequest),
        approvalStatus: 'pending',

        // Execution planning
        executionPlan: await this.generateExecutionPlan(maintenanceRequest),
        rollbackPlan: await this.generateRollbackPlan(maintenanceRequest),
        preChecks: await this.generatePreChecks(maintenanceRequest),
        postChecks: await this.generatePostChecks(maintenanceRequest),

        // Status tracking
        status: 'scheduled',
        createdAt: new Date(),
        executedAt: null,
        completedAt: null,

        // Results tracking
        executionResults: null,
        success: null,
        notes: []
      };

      // Validate maintenance window
      const validationResult = await this.validateMaintenanceWindow(maintenanceOperation);
      if (!validationResult.valid) {
        throw new Error(`Maintenance scheduling validation failed: ${validationResult.reason}`);
      }

      // Check for conflicts
      const conflicts = await this.checkMaintenanceConflicts(maintenanceOperation);
      if (conflicts.length > 0) {
        maintenanceOperation.conflicts = conflicts;
        maintenanceOperation.status = 'conflict_detected';
      }

      // Store maintenance operation
      await this.collections.maintenanceSchedule.insertOne(maintenanceOperation);

      // Send notifications if required
      if (maintenanceOperation.userNotificationRequired) {
        await this.sendMaintenanceNotification(maintenanceOperation);
      }

      console.log(`Maintenance operation scheduled: ${maintenanceOperation._id}`, {
        type: maintenanceOperation.type,
        scheduledTime: maintenanceOperation.scheduledTime,
        impactLevel: maintenanceOperation.impactLevel
      });

      return maintenanceOperation;

    } catch (error) {
      console.error('Error scheduling maintenance operation:', error);
      throw error;
    }
  }

  async executeBackupOperation(backupConfig) {
    console.log('Executing comprehensive backup operation...');

    try {
      const backupId = new ObjectId();
      const startTime = new Date();

      const backupOperation = {
        _id: backupId,
        type: backupConfig.type || 'full',
        startTime: startTime,

        // Backup configuration
        databases: backupConfig.databases || ['all'],
        compression: backupConfig.compression !== false,
        encryption: backupConfig.encryption || false,

        // Storage configuration
        storageLocation: backupConfig.storageLocation,
        storageType: backupConfig.storageType || 'local',
        retentionDays: backupConfig.retentionDays || this.config.backupRetentionDays,

        // Backup metadata
        triggeredBy: backupConfig.triggeredBy || 'system',
        backupReason: backupConfig.reason || 'scheduled_backup',

        // Status tracking
        status: 'in_progress',
        progress: 0,
        currentDatabase: null,

        // Results placeholder
        endTime: null,
        duration: null,
        backupSize: null,
        compressedSize: null,
        success: null,
        errorMessage: null
      };

      // Store backup operation record
      await this.collections.backupOperations.insertOne(backupOperation);

      // Execute backup based on type
      let backupResults;
      switch (backupConfig.type) {
        case 'full':
          backupResults = await this.performFullBackup(backupId, backupConfig);
          break;
        case 'incremental':
          backupResults = await this.performIncrementalBackup(backupId, backupConfig);
          break;
        case 'differential':
          backupResults = await this.performDifferentialBackup(backupId, backupConfig);
          break;
        default:
          throw new Error(`Unsupported backup type: ${backupConfig.type}`);
      }

      // Update backup operation with results
      const endTime = new Date();
      await this.collections.backupOperations.updateOne(
        { _id: backupId },
        {
          $set: {
            status: backupResults.success ? 'completed' : 'failed',
            endTime: endTime,
            duration: endTime - startTime,
            backupSize: backupResults.backupSize,
            compressedSize: backupResults.compressedSize,
            success: backupResults.success,
            errorMessage: backupResults.errorMessage,
            progress: 100,

            // Backup verification
            verificationResults: backupResults.verification,
            checksumVerified: backupResults.checksumVerified,

            // Storage information
            backupFiles: backupResults.files,
            storageLocation: backupResults.storageLocation
          }
        }
      );

      // Schedule cleanup of old backups
      await this.scheduleBackupCleanup(backupId, backupConfig);

      console.log(`Backup operation completed: ${backupId}`, {
        success: backupResults.success,
        duration: endTime - startTime,
        backupSize: backupResults.backupSize
      });

      return backupResults;

    } catch (error) {
      console.error('Error executing backup operation:', error);
      throw error;
    }
  }

  // Utility methods for MongoDB administration

  calculateOperationRate(operationType, currentValue) {
    const previousMetrics = this.performanceMonitors.get(`${operationType}_previous`);
    const previousTime = this.performanceMonitors.get(`${operationType}_time`);

    if (previousMetrics && previousTime) {
      const timeDiff = (Date.now() - previousTime) / 1000; // seconds
      const valueDiff = currentValue - previousMetrics;
      const rate = Math.round(valueDiff / timeDiff);

      // Update previous values
      this.performanceMonitors.set(`${operationType}_previous`, currentValue);
      this.performanceMonitors.set(`${operationType}_time`, Date.now());

      return rate;
    } else {
      // Initialize tracking
      this.performanceMonitors.set(`${operationType}_previous`, currentValue);
      this.performanceMonitors.set(`${operationType}_time`, Date.now());
      return 0;
    }
  }

  calculateNetworkRate(metric, currentValue) {
    return this.calculateOperationRate(`network_${metric}`, currentValue);
  }

  calculateCacheUtilization(serverStatus) {
    if (serverStatus.wiredTiger?.cache) {
      const maxBytes = serverStatus.wiredTiger.cache['maximum bytes configured'];
      const currentBytes = serverStatus.wiredTiger.cache['bytes currently in the cache'];

      if (maxBytes && currentBytes) {
        return Math.round((currentBytes / maxBytes) * 100);
      }
    }
    return 0;
  }

  analyzeLockMetrics(serverStatus) {
    if (serverStatus.locks) {
      return {
        globalLock: serverStatus.locks.Global || {},
        databaseLock: serverStatus.locks.Database || {},
        collectionLock: serverStatus.locks.Collection || {},

        // Lock contention analysis
        lockContention: this.calculateLockContention(serverStatus.locks),
        lockEfficiency: this.calculateLockEfficiency(serverStatus.locks)
      };
    }
    return {};
  }

  async collectReplicationMetrics() {
    try {
      const replStatus = await this.db.admin().replSetGetStatus();

      if (replStatus.ok) {
        return {
          setName: replStatus.set,
          members: replStatus.members.length,
          primary: replStatus.members.find(m => m.stateStr === 'PRIMARY'),
          secondaries: replStatus.members.filter(m => m.stateStr === 'SECONDARY'),
          replicationLag: this.calculateReplicationLag(replStatus.members)
        };
      }
    } catch (error) {
      // Not a replica set
      return null;
    }
  }

  async collectShardingMetrics() {
    try {
      const shardingStatus = await this.db.admin().runCommand({ listShards: 1 });

      if (shardingStatus.ok) {
        return {
          shards: shardingStatus.shards.length,
          shardsInfo: shardingStatus.shards,
          balancerActive: await this.checkBalancerStatus()
        };
      }
    } catch (error) {
      // Not a sharded cluster
      return null;
    }
  }

  async evaluatePerformanceAlerts(serverStatus, dbStats) {
    const alerts = [];

    // CPU usage alert
    if (serverStatus.extra_info?.page_faults > 1000) {
      alerts.push({
        type: 'high_page_faults',
        severity: 'warning',
        message: 'High page fault rate detected',
        value: serverStatus.extra_info.page_faults,
        threshold: 1000
      });
    }

    // Connection usage alert
    const connectionUtilization = (serverStatus.connections.current / 
                                 (serverStatus.connections.current + serverStatus.connections.available)) * 100;
    if (connectionUtilization > this.config.connectionThreshold) {
      alerts.push({
        type: 'high_connection_usage',
        severity: connectionUtilization > 95 ? 'critical' : 'warning',
        message: 'High connection utilization',
        value: Math.round(connectionUtilization),
        threshold: this.config.connectionThreshold
      });
    }

    // Memory usage alerts
    if (serverStatus.mem.resident > 8000) { // > 8GB
      alerts.push({
        type: 'high_memory_usage',
        severity: 'warning',
        message: 'High resident memory usage',
        value: serverStatus.mem.resident,
        threshold: 8000
      });
    }

    return alerts;
  }

  async analyzeOperation(operation) {
    return {
      complexity: this.assessQueryComplexity(operation),
      indexUsage: this.analyzeIndexUsage(operation),
      efficiency: this.calculateQueryEfficiency(operation),
      optimization: this.identifyOptimizationOpportunities(operation)
    };
  }

  assessQueryComplexity(operation) {
    let complexity = 'low';

    if (operation.execStats?.totalDocsExamined > 10000) complexity = 'medium';
    if (operation.execStats?.totalDocsExamined > 100000) complexity = 'high';
    if (operation.planSummary?.includes('COLLSCAN')) complexity = 'high';
    if (operation.millis > 5000) complexity = 'high';

    return complexity;
  }

  analyzeIndexUsage(operation) {
    if (operation.planSummary) {
      if (operation.planSummary.includes('IXSCAN')) return 'efficient';
      if (operation.planSummary.includes('COLLSCAN')) return 'full_scan';
    }
    return 'unknown';
  }

  calculateQueryEfficiency(operation) {
    if (operation.execStats?.totalDocsExamined && operation.execStats?.totalDocsReturned) {
      const efficiency = (operation.execStats.totalDocsReturned / operation.execStats.totalDocsExamined) * 100;
      return Math.round(efficiency);
    }
    return 0;
  }
}

// Benefits of MongoDB Advanced Database Administration:
// - Comprehensive performance monitoring with real-time metrics collection and analysis
// - Automated slow operation detection and optimization recommendation generation
// - Intelligent index analysis with usage patterns and maintenance prioritization
// - Automated maintenance scheduling with conflict detection and approval workflows
// - Enterprise backup management with compression, encryption, and retention policies
// - Integrated security auditing and access control monitoring
// - Real-time alerting with configurable thresholds and escalation procedures
// - Capacity planning with growth trend analysis and resource optimization
// - High availability monitoring for replica sets and sharded clusters
// - SQL-compatible administration operations through QueryLeaf integration

module.exports = {
  MongoDBAdministrationManager
};

Understanding MongoDB Database Administration Architecture

Enterprise Performance Monitoring and Optimization

Implement comprehensive monitoring for production MongoDB environments:

// Production-ready MongoDB monitoring with advanced analytics and alerting
class ProductionMonitoringManager extends MongoDBAdministrationManager {
  constructor(client, productionConfig) {
    super(client, productionConfig);

    this.productionConfig = {
      ...productionConfig,
      enableAdvancedAnalytics: true,
      enablePredictiveAlerts: true,
      enableCapacityPlanning: true,
      enableComplianceMonitoring: true,
      enableForensicLogging: true,
      enablePerformanceBaselines: true
    };

    this.setupProductionMonitoring();
    this.initializeAdvancedAnalytics();
    this.setupCapacityPlanning();
  }

  async implementAdvancedMonitoring(monitoringProfile) {
    console.log('Implementing advanced production monitoring...');

    const monitoringFramework = {
      // Real-time performance monitoring
      realTimeMetrics: {
        operationLatency: true,
        throughputAnalysis: true,
        resourceUtilization: true,
        connectionPoolAnalysis: true,
        lockContentionTracking: true
      },

      // Predictive analytics
      predictiveAnalytics: {
        capacityForecast: true,
        performanceTrendAnalysis: true,
        anomalyDetection: true,
        failurePrediction: true,
        maintenanceScheduling: true
      },

      // Advanced alerting
      intelligentAlerting: {
        dynamicThresholds: true,
        alertCorrelation: true,
        escalationManagement: true,
        suppressionRules: true,
        businessImpactAssessment: true
      }
    };

    return await this.deployMonitoringFramework(monitoringFramework, monitoringProfile);
  }

  async setupCapacityPlanningFramework() {
    console.log('Setting up comprehensive capacity planning...');

    const capacityFramework = {
      // Growth analysis
      growthAnalysis: {
        dataGrowthTrends: true,
        operationalGrowthPatterns: true,
        resourceConsumptionForecasting: true,
        scalingRecommendations: true
      },

      // Performance baselines
      performanceBaselines: {
        responseTimeBaselines: true,
        throughputBaselines: true,
        resourceUtilizationBaselines: true,
        operationalBaselines: true
      },

      // Optimization recommendations
      optimizationFramework: {
        indexingOptimization: true,
        queryOptimization: true,
        schemaOptimization: true,
        infrastructureOptimization: true
      }
    };

    return await this.deployCapacityFramework(capacityFramework);
  }

  async implementComplianceMonitoring(complianceRequirements) {
    console.log('Implementing comprehensive compliance monitoring...');

    const complianceFramework = {
      // Audit trail monitoring
      auditCompliance: {
        accessLogging: true,
        changeTracking: true,
        privilegedOperationsTracking: true,
        complianceReporting: true
      },

      // Security monitoring
      securityCompliance: {
        authenticationMonitoring: true,
        authorizationTracking: true,
        securityViolationDetection: true,
        threatDetection: true
      }
    };

    return await this.deployComplianceFramework(complianceFramework, complianceRequirements);
  }
}

SQL-Style Database Administration with QueryLeaf

QueryLeaf provides familiar SQL syntax for MongoDB database administration and monitoring operations:

-- QueryLeaf advanced database administration with SQL-familiar syntax for MongoDB

-- Configure monitoring and administration settings
SET monitoring_interval = 60; -- seconds
SET slow_operation_threshold = 100; -- milliseconds
SET enable_performance_profiling = true;
SET enable_automatic_maintenance = true;
SET enable_capacity_planning = true;

-- Comprehensive performance monitoring with SQL-familiar administration
WITH monitoring_configuration AS (
  SELECT 
    -- Monitoring settings
    60 as metrics_collection_interval_seconds,
    100 as slow_operation_threshold_ms,
    true as enable_detailed_profiling,
    true as enable_realtime_alerts,

    -- Performance thresholds
    80 as cpu_threshold_percent,
    85 as memory_threshold_percent,
    80 as connection_threshold_percent,
    90 as disk_threshold_percent,

    -- Alert configuration
    'critical' as high_severity_level,
    'warning' as medium_severity_level,
    'info' as low_severity_level,

    -- Maintenance configuration
    true as enable_automatic_maintenance,
    '02:00:00'::time as maintenance_window_start,
    '04:00:00'::time as maintenance_window_end,

    -- Backup configuration
    true as enable_automatic_backups,
    30 as backup_retention_days,
    'full' as default_backup_type
),

performance_metrics_collection AS (
  -- Collect comprehensive performance metrics
  SELECT 
    CURRENT_TIMESTAMP as metric_timestamp,
    EXTRACT(EPOCH FROM CURRENT_TIMESTAMP) as metric_epoch,

    -- Server identification
    'mongodb-primary-01' as server_name,
    'production' as environment,
    '4.4.15' as mongodb_version,

    -- Connection metrics
    JSON_BUILD_OBJECT(
      'current_connections', 245,
      'available_connections', 755,
      'total_created', 15420,
      'utilization_percent', ROUND((245.0 / (245 + 755)) * 100, 2)
    ) as connection_metrics,

    -- Operation metrics
    JSON_BUILD_OBJECT(
      'insert_ops', 1245670,
      'query_ops', 8901234,
      'update_ops', 567890,
      'delete_ops', 123456,
      'command_ops', 2345678,

      -- Operations per second (calculated)
      'insert_ops_per_sec', 12.5,
      'query_ops_per_sec', 156.7,
      'update_ops_per_sec', 8.9,
      'delete_ops_per_sec', 2.1,
      'command_ops_per_sec', 45.6
    ) as operation_metrics,

    -- Memory metrics
    JSON_BUILD_OBJECT(
      'resident_mb', 2048,
      'virtual_mb', 4096,
      'mapped_mb', 1024,
      'cache_size_gb', 8,
      'cache_used_gb', 6.5,
      'cache_utilization_percent', 81.25
    ) as memory_metrics,

    -- Database metrics
    JSON_BUILD_OBJECT(
      'total_collections', 45,
      'total_documents', 25678901,
      'data_size_gb', 12.5,
      'storage_size_gb', 15.2,
      'total_indexes', 123,
      'index_size_gb', 2.8,
      'avg_document_size_bytes', 512
    ) as database_metrics,

    -- Lock metrics
    JSON_BUILD_OBJECT(
      'global_lock_ratio', 0.02,
      'database_lock_ratio', 0.01,
      'collection_lock_ratio', 0.005,
      'lock_contention_percent', 1.2,
      'blocked_operations', 3
    ) as lock_metrics,

    -- Replication metrics (if replica set)
    JSON_BUILD_OBJECT(
      'replica_set_name', 'rs-production',
      'is_primary', true,
      'secondary_count', 2,
      'max_replication_lag_seconds', 0.5,
      'oplog_size_gb', 2.0,
      'oplog_used_percent', 15.3
    ) as replication_metrics,

    -- Alert evaluation
    ARRAY[
      CASE WHEN 245.0 / (245 + 755) > 0.8 THEN
        JSON_BUILD_OBJECT(
          'type', 'high_connection_usage',
          'severity', 'warning',
          'value', ROUND((245.0 / (245 + 755)) * 100, 2),
          'threshold', 80,
          'message', 'Connection utilization above threshold'
        )
      END,
      CASE WHEN 81.25 > 85 THEN
        JSON_BUILD_OBJECT(
          'type', 'high_cache_usage',
          'severity', 'warning',
          'value', 81.25,
          'threshold', 85,
          'message', 'Cache utilization above threshold'
        )
      END
    ] as performance_alerts

  FROM monitoring_configuration mc
),

slow_operations_analysis AS (
  -- Analyze slow operations and performance bottlenecks
  SELECT 
    operation_id,
    operation_timestamp,
    operation_type,
    database_name,
    collection_name,
    duration_ms,

    -- Operation details
    operation_command,
    plan_summary,
    execution_stats,

    -- Performance analysis
    CASE 
      WHEN duration_ms > 5000 THEN 'critical'
      WHEN duration_ms > 1000 THEN 'high'
      WHEN duration_ms > 500 THEN 'medium'
      ELSE 'low'
    END as performance_impact,

    -- Query complexity assessment
    CASE 
      WHEN plan_summary LIKE '%COLLSCAN%' THEN 'high'
      WHEN execution_stats->>'totalDocsExamined' > '100000' THEN 'high'
      WHEN execution_stats->>'totalDocsExamined' > '10000' THEN 'medium'
      ELSE 'low'
    END as query_complexity,

    -- Index usage analysis
    CASE 
      WHEN plan_summary LIKE '%IXSCAN%' THEN 'efficient'
      WHEN plan_summary LIKE '%COLLSCAN%' THEN 'full_scan'
      ELSE 'unknown'
    END as index_usage,

    -- Query efficiency calculation
    CASE 
      WHEN execution_stats->>'totalDocsExamined' > '0' AND execution_stats->>'totalDocsReturned' > '0' THEN
        ROUND(
          (execution_stats->>'totalDocsReturned')::decimal / 
          (execution_stats->>'totalDocsExamined')::decimal * 100, 2
        )
      ELSE 0
    END as query_efficiency_percent,

    -- Optimization recommendations
    CASE 
      WHEN plan_summary LIKE '%COLLSCAN%' THEN 'Create appropriate indexes'
      WHEN query_efficiency_percent < 10 THEN 'Optimize query selectivity'
      WHEN duration_ms > 1000 THEN 'Review query structure'
      ELSE 'Performance acceptable'
    END as optimization_recommendation

  FROM slow_operation_log sol
  WHERE sol.operation_timestamp >= CURRENT_TIMESTAMP - INTERVAL '1 hour'
  AND sol.duration_ms >= 100 -- From monitoring configuration
),

index_analysis_comprehensive AS (
  -- Comprehensive index analysis and optimization
  SELECT 
    database_name,
    collection_name,
    index_name,
    index_specification,

    -- Index statistics
    index_size_mb,
    access_count,
    last_accessed_timestamp,

    -- Index efficiency analysis
    CASE 
      WHEN access_count > 1000 AND index_size_mb < 50 THEN 'highly_efficient'
      WHEN access_count > 100 AND index_size_mb < 100 THEN 'efficient'
      WHEN access_count > 10 THEN 'moderately_efficient'
      WHEN access_count = 0 AND index_name != '_id_' THEN 'unused'
      ELSE 'inefficient'
    END as efficiency_rating,

    -- Usage pattern analysis
    CASE 
      WHEN last_accessed_timestamp < CURRENT_TIMESTAMP - INTERVAL '30 days' THEN 'rarely_used'
      WHEN last_accessed_timestamp < CURRENT_TIMESTAMP - INTERVAL '7 days' THEN 'occasionally_used'
      WHEN access_count > 100 THEN 'frequently_used'
      ELSE 'moderately_used'
    END as usage_pattern,

    -- Maintenance recommendations
    CASE 
      WHEN access_count = 0 AND index_name != '_id_' THEN 'Consider removal'
      WHEN index_size_mb > 1000 AND efficiency_rating = 'inefficient' THEN 'Rebuild index'
      WHEN efficiency_rating = 'unused' THEN 'Review necessity'
      WHEN usage_pattern = 'frequently_used' AND index_size_mb > 500 THEN 'Monitor fragmentation'
      ELSE 'Maintain current strategy'
    END as maintenance_recommendation,

    -- Priority scoring
    CASE 
      WHEN efficiency_rating = 'unused' THEN 10
      WHEN efficiency_rating = 'inefficient' AND index_size_mb > 100 THEN 8
      WHEN maintenance_recommendation LIKE '%Rebuild%' THEN 7
      WHEN usage_pattern = 'rarely_used' AND index_size_mb > 50 THEN 5
      ELSE 1
    END as maintenance_priority_score

  FROM index_statistics_view isv
),

maintenance_scheduling AS (
  -- Automated maintenance scheduling with priority management
  SELECT 
    GENERATE_UUID() as maintenance_id,
    maintenance_type,
    target_database,
    target_collection,
    target_index,

    -- Scheduling information
    CASE maintenance_type
      WHEN 'index_rebuild' THEN CURRENT_DATE + INTERVAL '1 week'
      WHEN 'index_removal' THEN CURRENT_DATE + INTERVAL '2 weeks'
      WHEN 'collection_compaction' THEN CURRENT_DATE + INTERVAL '1 month'
      WHEN 'statistics_refresh' THEN CURRENT_DATE + INTERVAL '1 day'
      ELSE CURRENT_DATE + INTERVAL '1 month'
    END as scheduled_date,

    -- Impact assessment
    CASE maintenance_type
      WHEN 'index_rebuild' THEN 'medium'
      WHEN 'index_removal' THEN 'low'
      WHEN 'collection_compaction' THEN 'high'
      WHEN 'statistics_refresh' THEN 'low'
      ELSE 'medium'
    END as impact_level,

    -- Duration estimation
    CASE maintenance_type
      WHEN 'index_rebuild' THEN INTERVAL '30 minutes'
      WHEN 'index_removal' THEN INTERVAL '5 minutes'
      WHEN 'collection_compaction' THEN INTERVAL '2 hours'
      WHEN 'statistics_refresh' THEN INTERVAL '10 minutes'
      ELSE INTERVAL '1 hour'
    END as estimated_duration,

    -- Approval requirements
    CASE impact_level
      WHEN 'high' THEN 'director_approval'
      WHEN 'medium' THEN 'manager_approval'
      ELSE 'automatic_approval'
    END as approval_requirement,

    -- Maintenance window validation
    CASE 
      WHEN EXTRACT(DOW FROM scheduled_date) IN (0, 6) THEN true -- Weekends preferred
      WHEN EXTRACT(HOUR FROM mc.maintenance_window_start) BETWEEN 2 AND 4 THEN true
      ELSE false
    END as within_maintenance_window,

    -- Business justification
    CASE maintenance_type
      WHEN 'index_rebuild' THEN 'Improve query performance and reduce storage overhead'
      WHEN 'index_removal' THEN 'Eliminate unused indexes to improve write performance'
      WHEN 'collection_compaction' THEN 'Reclaim disk space and optimize storage'
      WHEN 'statistics_refresh' THEN 'Ensure query optimizer has current statistics'
      ELSE 'General database maintenance and optimization'
    END as business_justification,

    CURRENT_TIMESTAMP as maintenance_created

  FROM (
    -- Generate maintenance tasks based on analysis results
    SELECT 'index_rebuild' as maintenance_type, database_name as target_database, 
           collection_name as target_collection, index_name as target_index
    FROM index_analysis_comprehensive
    WHERE maintenance_recommendation LIKE '%Rebuild%'

    UNION ALL

    SELECT 'index_removal' as maintenance_type, database_name, collection_name, index_name
    FROM index_analysis_comprehensive  
    WHERE maintenance_recommendation LIKE '%removal%'

    UNION ALL

    SELECT 'statistics_refresh' as maintenance_type, database_name, collection_name, NULL
    FROM slow_operations_analysis
    WHERE query_complexity = 'high'
    GROUP BY database_name, collection_name
  ) maintenance_tasks
  CROSS JOIN monitoring_configuration mc
),

backup_operations_management AS (
  -- Comprehensive backup operations and scheduling
  SELECT 
    GENERATE_UUID() as backup_id,
    backup_type,
    target_databases,

    -- Backup scheduling
    CASE backup_type
      WHEN 'full' THEN DATE_TRUNC('day', CURRENT_TIMESTAMP) + INTERVAL '2 hours'
      WHEN 'incremental' THEN DATE_TRUNC('hour', CURRENT_TIMESTAMP) + INTERVAL '4 hours'
      WHEN 'differential' THEN DATE_TRUNC('day', CURRENT_TIMESTAMP) + INTERVAL '8 hours'
      ELSE CURRENT_TIMESTAMP + INTERVAL '1 hour'
    END as scheduled_backup_time,

    -- Backup configuration
    JSON_BUILD_OBJECT(
      'compression_enabled', true,
      'encryption_enabled', true,
      'verify_backup', true,
      'test_restore', backup_type = 'full',
      'storage_location', '/backup/mongodb/' || TO_CHAR(CURRENT_DATE, 'YYYY/MM/DD'),
      'retention_days', mc.backup_retention_days
    ) as backup_configuration,

    -- Estimated backup metrics
    CASE backup_type
      WHEN 'full' THEN JSON_BUILD_OBJECT(
        'estimated_size_gb', 25.5,
        'estimated_duration_minutes', 45,
        'estimated_compressed_size_gb', 12.8
      )
      WHEN 'incremental' THEN JSON_BUILD_OBJECT(
        'estimated_size_gb', 2.5,
        'estimated_duration_minutes', 8,
        'estimated_compressed_size_gb', 1.2
      )
      WHEN 'differential' THEN JSON_BUILD_OBJECT(
        'estimated_size_gb', 8.5,
        'estimated_duration_minutes', 15,
        'estimated_compressed_size_gb', 4.2
      )
    END as backup_estimates,

    -- Backup priority and impact
    CASE backup_type
      WHEN 'full' THEN 'high'
      WHEN 'differential' THEN 'medium'
      ELSE 'low'
    END as backup_priority,

    -- Validation requirements
    JSON_BUILD_OBJECT(
      'integrity_check_required', true,
      'restore_test_required', backup_type = 'full',
      'checksum_verification', true,
      'backup_verification', true
    ) as validation_requirements

  FROM (
    SELECT 'full' as backup_type, ARRAY['production_db'] as target_databases
    UNION ALL
    SELECT 'incremental' as backup_type, ARRAY['production_db', 'analytics_db'] as target_databases
    UNION ALL  
    SELECT 'differential' as backup_type, ARRAY['production_db'] as target_databases
  ) backup_schedule
  CROSS JOIN monitoring_configuration mc
),

administration_dashboard AS (
  -- Comprehensive administration dashboard and reporting
  SELECT 
    pmc.metric_timestamp,
    pmc.server_name,
    pmc.environment,

    -- Connection summary
    (pmc.connection_metrics->>'current_connections')::int as current_connections,
    (pmc.connection_metrics->>'utilization_percent')::decimal as connection_utilization,

    -- Performance summary
    (pmc.operation_metrics->>'query_ops_per_sec')::decimal as queries_per_second,
    (pmc.memory_metrics->>'cache_utilization_percent')::decimal as cache_utilization,
    (pmc.database_metrics->>'data_size_gb')::decimal as data_size_gb,

    -- Slow operations summary
    COUNT(soa.operation_id) as slow_operations_count,
    COUNT(soa.operation_id) FILTER (WHERE soa.performance_impact = 'critical') as critical_slow_ops,
    COUNT(soa.operation_id) FILTER (WHERE soa.index_usage = 'full_scan') as full_scan_operations,
    AVG(soa.query_efficiency_percent) as avg_query_efficiency,

    -- Index maintenance summary
    COUNT(iac.index_name) as total_indexes_analyzed,
    COUNT(iac.index_name) FILTER (WHERE iac.efficiency_rating = 'unused') as unused_indexes,
    COUNT(iac.index_name) FILTER (WHERE iac.maintenance_recommendation != 'Maintain current strategy') as indexes_needing_maintenance,
    SUM(iac.maintenance_priority_score) as total_maintenance_priority,

    -- Maintenance scheduling summary
    COUNT(ms.maintenance_id) as scheduled_maintenance_tasks,
    COUNT(ms.maintenance_id) FILTER (WHERE ms.impact_level = 'high') as high_impact_maintenance,
    COUNT(ms.maintenance_id) FILTER (WHERE ms.approval_requirement = 'director_approval') as director_approval_required,

    -- Backup operations summary
    COUNT(bom.backup_id) as scheduled_backups,
    COUNT(bom.backup_id) FILTER (WHERE bom.backup_type = 'full') as full_backups_scheduled,
    SUM((bom.backup_estimates->>'estimated_size_gb')::decimal) as total_estimated_backup_size_gb,

    -- Alert summary
    array_length(pmc.performance_alerts, 1) as active_alerts_count,

    -- Overall health score
    CASE 
      WHEN connection_utilization < 60 AND cache_utilization < 80 AND critical_slow_ops = 0 THEN 'excellent'
      WHEN connection_utilization < 75 AND cache_utilization < 85 AND critical_slow_ops <= 2 THEN 'good'
      WHEN connection_utilization < 85 AND cache_utilization < 90 AND critical_slow_ops <= 5 THEN 'fair'
      ELSE 'needs_attention'
    END as overall_health_status,

    -- Recommendations summary
    CASE 
      WHEN unused_indexes > 5 THEN 'Review and remove unused indexes for improved performance'
      WHEN full_scan_operations > 10 THEN 'Create missing indexes to optimize query performance'
      WHEN avg_query_efficiency < 50 THEN 'Optimize queries for better selectivity'
      WHEN high_impact_maintenance > 3 THEN 'Schedule maintenance window for optimization tasks'
      ELSE 'Database performance is within acceptable parameters'
    END as primary_recommendation

  FROM performance_metrics_collection pmc
  LEFT JOIN slow_operations_analysis soa ON true
  LEFT JOIN index_analysis_comprehensive iac ON true
  LEFT JOIN maintenance_scheduling ms ON true
  LEFT JOIN backup_operations_management bom ON true
  GROUP BY pmc.metric_timestamp, pmc.server_name, pmc.environment, pmc.connection_metrics,
           pmc.operation_metrics, pmc.memory_metrics, pmc.database_metrics, pmc.performance_alerts
)

-- Generate comprehensive administration report
SELECT 
  ad.metric_timestamp,
  ad.server_name,
  ad.environment,

  -- Current status
  ad.current_connections,
  ad.connection_utilization,
  ad.queries_per_second,
  ad.cache_utilization,
  ad.data_size_gb,

  -- Performance analysis
  ad.slow_operations_count,
  ad.critical_slow_ops,
  ad.full_scan_operations,
  ROUND(ad.avg_query_efficiency, 1) as avg_query_efficiency_percent,

  -- Index management
  ad.total_indexes_analyzed,
  ad.unused_indexes,
  ad.indexes_needing_maintenance,
  ad.total_maintenance_priority,

  -- Operational management
  ad.scheduled_maintenance_tasks,
  ad.high_impact_maintenance,
  ad.director_approval_required,

  -- Backup management
  ad.scheduled_backups,
  ad.full_backups_scheduled,
  ROUND(ad.total_estimated_backup_size_gb, 1) as total_backup_size_gb,

  -- Health assessment
  ad.active_alerts_count,
  ad.overall_health_status,
  ad.primary_recommendation,

  -- Operational metrics
  CASE ad.overall_health_status
    WHEN 'excellent' THEN 'Continue current operations and monitoring'
    WHEN 'good' THEN 'Monitor performance trends and plan preventive maintenance'
    WHEN 'fair' THEN 'Schedule performance review and optimization planning'
    ELSE 'Immediate attention required - review alerts and implement optimizations'
  END as operational_guidance,

  -- Next actions
  ARRAY[
    CASE WHEN ad.critical_slow_ops > 0 THEN 'Investigate critical slow operations' END,
    CASE WHEN ad.unused_indexes > 3 THEN 'Schedule unused index removal' END,
    CASE WHEN ad.connection_utilization > 80 THEN 'Review connection pooling configuration' END,
    CASE WHEN ad.cache_utilization > 85 THEN 'Consider memory allocation optimization' END,
    CASE WHEN ad.full_scan_operations > 5 THEN 'Create missing indexes for better performance' END
  ] as immediate_action_items

FROM administration_dashboard ad
ORDER BY ad.metric_timestamp DESC;

-- QueryLeaf provides comprehensive MongoDB administration capabilities:
-- 1. Real-time performance monitoring with configurable thresholds and alerting
-- 2. Automated slow operation analysis and query optimization recommendations
-- 3. Intelligent index analysis with maintenance prioritization and scheduling
-- 4. Comprehensive backup management with scheduling and retention policies
-- 5. Maintenance planning with approval workflows and impact assessment
-- 6. Security auditing and compliance monitoring for enterprise requirements
-- 7. Capacity planning with growth trend analysis and resource optimization
-- 8. Integration with MongoDB native administration tools and enterprise monitoring
-- 9. SQL-familiar syntax for complex administration operations and reporting
-- 10. Automated administration workflows with intelligent decision-making capabilities

Best Practices for Production MongoDB Administration

Enterprise Operations Management and Monitoring

Essential principles for effective MongoDB database administration:

  1. Comprehensive Monitoring: Implement real-time performance monitoring with configurable thresholds and intelligent alerting
  2. Proactive Maintenance: Schedule automated maintenance operations with approval workflows and impact assessment
  3. Performance Optimization: Continuously analyze slow operations and optimize query performance with index recommendations
  4. Capacity Planning: Monitor growth trends and plan resource allocation for optimal performance and cost efficiency
  5. Security Management: Implement comprehensive security auditing and access control monitoring for compliance
  6. Backup Strategy: Maintain robust backup operations with automated scheduling, verification, and retention management

Scalability and Production Deployment

Optimize MongoDB administration for enterprise-scale requirements:

  1. Monitoring Infrastructure: Deploy scalable monitoring solutions that handle high-volume metrics collection and analysis
  2. Automated Operations: Implement intelligent automation for routine maintenance and optimization tasks
  3. Performance Baselines: Establish and maintain performance baselines for proactive issue detection
  4. Disaster Recovery: Design comprehensive backup and recovery procedures with regular testing and validation
  5. Compliance Integration: Integrate administration workflows with enterprise compliance and governance frameworks
  6. Operational Excellence: Create standardized procedures for MongoDB operations with documentation and training

Conclusion

MongoDB database administration provides comprehensive operations management capabilities that enable enterprise-grade performance monitoring, maintenance automation, and operational excellence through sophisticated administration frameworks and intelligent automation. The combination of real-time monitoring, automated maintenance, and proactive optimization ensures optimal MongoDB performance and reliability.

Key MongoDB Database Administration benefits include:

  • Real-time Monitoring: Comprehensive performance metrics collection with intelligent alerting and threshold management
  • Automated Maintenance: Intelligent scheduling and execution of maintenance operations with approval workflows
  • Performance Optimization: Continuous analysis and optimization of query performance with actionable recommendations
  • Enterprise Security: Comprehensive security auditing and access control monitoring for compliance requirements
  • Capacity Management: Proactive capacity planning with growth trend analysis and resource optimization
  • SQL Accessibility: Familiar SQL-style administration operations through QueryLeaf for accessible database management

Whether you're managing production MongoDB deployments, optimizing database performance, implementing compliance monitoring, or planning capacity growth, MongoDB database administration with QueryLeaf's familiar SQL interface provides the foundation for robust, scalable database operations management.

QueryLeaf Integration: QueryLeaf automatically optimizes MongoDB database administration while providing SQL-familiar syntax for performance monitoring, maintenance scheduling, and operational management. Advanced administration patterns, automated optimization workflows, and enterprise monitoring capabilities are seamlessly handled through familiar SQL constructs, making sophisticated database operations accessible to SQL-oriented administration teams.

The combination of MongoDB's comprehensive administration capabilities with SQL-style operations management makes it an ideal platform for applications requiring both sophisticated database operations and familiar administration patterns, ensuring your MongoDB deployments can operate efficiently while maintaining optimal performance and operational excellence.