MongoDB Capped Collections and Circular Buffers: High-Performance Logging, Event Streaming, and Fixed-Size Data Management for High-Throughput Applications

-- Traditional PostgreSQL high-volume logging - storage growth and performance challenges

-- Application log table with typical structure
CREATE TABLE application_logs (
  log_id BIGSERIAL PRIMARY KEY,
  timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
  application_name VARCHAR(100) NOT NULL,
  environment VARCHAR(20) DEFAULT 'production',
  log_level VARCHAR(10) NOT NULL, -- DEBUG, INFO, WARN, ERROR, FATAL
  message TEXT NOT NULL,
  user_id UUID,
  session_id VARCHAR(100),
  request_id VARCHAR(100),

  -- Contextual information
  source_ip INET,
  user_agent TEXT,
  request_method VARCHAR(10),
  request_url TEXT,
  response_status INTEGER,
  response_time_ms INTEGER,

  -- Structured data fields
  metadata JSONB,
  tags TEXT[],

  -- Performance tracking
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,

  -- Indexing for queries
  CONSTRAINT valid_log_level CHECK (log_level IN ('DEBUG', 'INFO', 'WARN', 'ERROR', 'FATAL'))
);

-- Indexes for log querying (expensive to maintain with high write volume)
CREATE INDEX idx_application_logs_timestamp ON application_logs USING BTREE (timestamp DESC);
CREATE INDEX idx_application_logs_application ON application_logs (application_name, timestamp DESC);
CREATE INDEX idx_application_logs_level ON application_logs (log_level, timestamp DESC) WHERE log_level IN ('ERROR', 'FATAL');
CREATE INDEX idx_application_logs_user ON application_logs (user_id, timestamp DESC) WHERE user_id IS NOT NULL;
CREATE INDEX idx_application_logs_session ON application_logs (session_id, timestamp DESC) WHERE session_id IS NOT NULL;
CREATE INDEX idx_application_logs_request ON application_logs (request_id) WHERE request_id IS NOT NULL;

-- Partitioning strategy for managing large datasets (complex setup)
CREATE TABLE application_logs_y2024m01 PARTITION OF application_logs
FOR VALUES FROM ('2024-01-01') TO ('2024-02-01');

CREATE TABLE application_logs_y2024m02 PARTITION OF application_logs  
FOR VALUES FROM ('2024-02-01') TO ('2024-03-01');

CREATE TABLE application_logs_y2024m03 PARTITION OF application_logs
FOR VALUES FROM ('2024-03-01') TO ('2024-04-01');

-- Complex log rotation and cleanup procedures (operational overhead)
-- Daily log cleanup procedure
CREATE OR REPLACE FUNCTION cleanup_old_logs()
RETURNS void AS $$
DECLARE
  cutoff_date TIMESTAMP;
  affected_rows BIGINT;
  partition_name TEXT;
  partition_start DATE;
  partition_end DATE;
BEGIN
  -- Keep logs for 30 days
  cutoff_date := CURRENT_TIMESTAMP - INTERVAL '30 days';

  -- Delete old logs in batches to avoid lock contention
  LOOP
    DELETE FROM application_logs 
    WHERE timestamp < cutoff_date 
    AND log_id IN (
      SELECT log_id FROM application_logs 
      WHERE timestamp < cutoff_date 
      LIMIT 10000
    );

    GET DIAGNOSTICS affected_rows = ROW_COUNT;
    EXIT WHEN affected_rows = 0;

    -- Commit batch and pause to reduce system impact
    COMMIT;
    PERFORM pg_sleep(0.1);
  END LOOP;

  -- Drop old partitions if using partitioning
  FOR partition_name, partition_start, partition_end IN
    SELECT schemaname||'.'||tablename, 
           split_part(split_part(pg_get_expr(c.relpartbound, c.oid), '''', 2), '''', 1)::date,
           split_part(split_part(pg_get_expr(c.relpartbound, c.oid), '''', 4), '''', 1)::date
    FROM pg_tables pt
    JOIN pg_class c ON c.relname = pt.tablename
    WHERE pt.tablename LIKE 'application_logs_y%'
      AND split_part(split_part(pg_get_expr(c.relpartbound, c.oid), '''', 4), '''', 1)::date < CURRENT_DATE - INTERVAL '30 days'
  LOOP
    EXECUTE format('DROP TABLE IF EXISTS %s', partition_name);
  END LOOP;

END;
$$ LANGUAGE plpgsql;

-- Schedule daily cleanup (requires external scheduler)
-- 0 2 * * * /usr/bin/psql -d myapp -c "SELECT cleanup_old_logs();"

-- Complex high-volume log insertion with batching
WITH log_batch AS (
  INSERT INTO application_logs (
    application_name, environment, log_level, message, user_id, 
    session_id, request_id, source_ip, user_agent, request_method, 
    request_url, response_status, response_time_ms, metadata, tags
  ) VALUES 
  ('web-api', 'production', 'INFO', 'User login successful', 
   '550e8400-e29b-41d4-a716-446655440000', 'sess_abc123', 'req_xyz789',
   '192.168.1.100', 'Mozilla/5.0...', 'POST', '/api/auth/login', 200, 150,
   '{"login_method": "email", "ip_geolocation": "US-CA"}', ARRAY['auth', 'login']
  ),
  ('web-api', 'production', 'WARN', 'Rate limit threshold reached', 
   '550e8400-e29b-41d4-a716-446655440001', 'sess_def456', 'req_abc123',
   '192.168.1.101', 'PostmanRuntime/7.29.0', 'POST', '/api/data/upload', 429, 50,
   '{"rate_limit": "100_per_minute", "current_count": 101}', ARRAY['rate_limiting', 'api']
  ),
  ('background-worker', 'production', 'ERROR', 'Database connection timeout', 
   NULL, NULL, 'job_456789',
   NULL, NULL, NULL, NULL, NULL, 5000,
   '{"error_code": "DB_TIMEOUT", "retry_attempt": 3, "queue_size": 1500}', ARRAY['database', 'error', 'timeout']
  ),
  ('web-api', 'production', 'DEBUG', 'Cache miss for user preferences', 
   '550e8400-e29b-41d4-a716-446655440002', 'sess_ghi789', 'req_def456',
   '192.168.1.102', 'React Native App', 'GET', '/api/user/preferences', 200, 85,
   '{"cache_key": "user_prefs_12345", "cache_ttl": 300}', ARRAY['cache', 'performance']
  )
  RETURNING log_id, timestamp, application_name, log_level
)
SELECT 
  COUNT(*) as logs_inserted,
  MIN(timestamp) as first_log_time,
  MAX(timestamp) as last_log_time,
  string_agg(DISTINCT application_name, ', ') as applications,
  string_agg(DISTINCT log_level, ', ') as log_levels
FROM log_batch;

-- Complex log analysis queries (expensive on large datasets)
WITH hourly_log_stats AS (
  SELECT 
    date_trunc('hour', timestamp) as hour_bucket,
    application_name,
    log_level,
    COUNT(*) as log_count,

    -- Error rate calculation
    COUNT(*) FILTER (WHERE log_level IN ('ERROR', 'FATAL')) as error_count,
    COUNT(*) FILTER (WHERE log_level IN ('ERROR', 'FATAL'))::float / COUNT(*) * 100 as error_rate_percent,

    -- Response time statistics
    AVG(response_time_ms) FILTER (WHERE response_time_ms IS NOT NULL) as avg_response_time,
    PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY response_time_ms) FILTER (WHERE response_time_ms IS NOT NULL) as p95_response_time,

    -- Request statistics
    COUNT(DISTINCT user_id) FILTER (WHERE user_id IS NOT NULL) as unique_users,
    COUNT(DISTINCT session_id) FILTER (WHERE session_id IS NOT NULL) as unique_sessions,

    -- Top error messages
    mode() WITHIN GROUP (ORDER BY message) FILTER (WHERE log_level IN ('ERROR', 'FATAL')) as most_common_error,

    -- Resource utilization indicators
    COUNT(*) FILTER (WHERE response_time_ms > 1000) as slow_requests,
    COUNT(*) FILTER (WHERE response_status >= 400) as client_errors,
    COUNT(*) FILTER (WHERE response_status >= 500) as server_errors

  FROM application_logs
  WHERE timestamp >= CURRENT_TIMESTAMP - INTERVAL '24 hours'
    AND timestamp < CURRENT_TIMESTAMP
  GROUP BY date_trunc('hour', timestamp), application_name, log_level
),

trend_analysis AS (
  SELECT 
    hour_bucket,
    application_name,
    log_level,
    log_count,
    error_rate_percent,
    avg_response_time,

    -- Hour-over-hour trend analysis
    LAG(log_count) OVER (
      PARTITION BY application_name, log_level 
      ORDER BY hour_bucket
    ) as prev_hour_count,

    LAG(error_rate_percent) OVER (
      PARTITION BY application_name, log_level 
      ORDER BY hour_bucket
    ) as prev_hour_error_rate,

    LAG(avg_response_time) OVER (
      PARTITION BY application_name, log_level 
      ORDER BY hour_bucket
    ) as prev_hour_response_time,

    -- Calculate trends
    CASE 
      WHEN LAG(log_count) OVER (PARTITION BY application_name, log_level ORDER BY hour_bucket) IS NOT NULL THEN
        ((log_count - LAG(log_count) OVER (PARTITION BY application_name, log_level ORDER BY hour_bucket))::float / 
         LAG(log_count) OVER (PARTITION BY application_name, log_level ORDER BY hour_bucket) * 100)
      ELSE NULL
    END as log_count_change_percent,

    -- Moving averages
    AVG(log_count) OVER (
      PARTITION BY application_name, log_level 
      ORDER BY hour_bucket 
      ROWS BETWEEN 3 PRECEDING AND CURRENT ROW
    ) as rolling_4h_avg_count,

    AVG(error_rate_percent) OVER (
      PARTITION BY application_name, log_level 
      ORDER BY hour_bucket 
      ROWS BETWEEN 3 PRECEDING AND CURRENT ROW  
    ) as rolling_4h_avg_error_rate

  FROM hourly_log_stats
)

SELECT 
  hour_bucket,
  application_name,
  log_level,
  log_count,
  ROUND(error_rate_percent::numeric, 2) as error_rate_percent,
  ROUND(avg_response_time::numeric, 2) as avg_response_time_ms,

  -- Trend indicators
  ROUND(log_count_change_percent::numeric, 1) as hourly_change_percent,
  ROUND(rolling_4h_avg_count::numeric, 0) as rolling_4h_avg,
  ROUND(rolling_4h_avg_error_rate::numeric, 2) as rolling_4h_avg_error_rate,

  -- Alert conditions
  CASE 
    WHEN error_rate_percent > rolling_4h_avg_error_rate * 2 AND error_rate_percent > 1 THEN 'HIGH_ERROR_RATE'
    WHEN log_count_change_percent > 100 THEN 'TRAFFIC_SPIKE'
    WHEN log_count_change_percent < -50 AND log_count < rolling_4h_avg * 0.5 THEN 'TRAFFIC_DROP'
    WHEN avg_response_time > 1000 THEN 'HIGH_LATENCY'
    ELSE 'NORMAL'
  END as alert_condition,

  CURRENT_TIMESTAMP as analysis_time

FROM trend_analysis
ORDER BY hour_bucket DESC, application_name, log_level;

-- Problems with traditional PostgreSQL logging approaches:
-- 1. Unlimited storage growth requiring complex rotation strategies
-- 2. Index maintenance overhead degrading write performance
-- 3. Partitioning complexity for managing large datasets
-- 4. Expensive cleanup operations impacting production performance
-- 5. Limited real-time streaming capabilities for log analysis
-- 6. Complex batching logic required for high-volume insertions
-- 7. Vacuum and maintenance operations required for table health
-- 8. Query performance degradation as table size grows
-- 9. Storage space reclamation challenges after log deletion
-- 10. Manual operational overhead for log management and cleanup

-- Additional complications:
-- - WAL (Write-Ahead Log) bloat from high-volume insertions
-- - Lock contention during peak logging periods
-- - Backup complexity due to large log table sizes
-- - Replication lag caused by high write volume
-- - Statistics staleness affecting query plan optimization
-- - Complex monitoring required for log system health
-- - Difficulty implementing real-time log streaming
-- - Storage I/O bottlenecks during cleanup operations

// MongoDB Capped Collections - efficient high-volume logging with built-in circular buffer functionality
const { MongoClient } = require('mongodb');

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

// Advanced MongoDB Capped Collections manager for high-performance logging and event streaming
class AdvancedCappedCollectionManager {
  constructor(db) {
    this.db = db;
    this.collections = new Map();
    this.tailable_cursors = new Map();
    this.streaming_handlers = new Map();

    // Configuration for different log types and use cases
    this.cappedConfigurations = {
      // Application logs with high write volume
      application_logs: {
        size: 1024 * 1024 * 1024, // 1GB
        max: 10000000, // 10 million documents
        indexing: ['timestamp', 'application', 'level'],
        streaming: true,
        compression: true,
        retention_hours: 72
      },

      // Real-time event stream
      event_stream: {
        size: 512 * 1024 * 1024, // 512MB
        max: 5000000, // 5 million events
        indexing: ['timestamp', 'event_type'],
        streaming: true,
        compression: false, // For lowest latency
        retention_hours: 24
      },

      // System metrics collection
      system_metrics: {
        size: 2048 * 1024 * 1024, // 2GB
        max: 20000000, // 20 million metrics
        indexing: ['timestamp', 'metric_type', 'host'],
        streaming: true,
        compression: true,
        retention_hours: 168 // 7 days
      },

      // User activity tracking
      user_activity: {
        size: 256 * 1024 * 1024, // 256MB
        max: 2000000, // 2 million activities
        indexing: ['timestamp', 'user_id', 'activity_type'],
        streaming: false,
        compression: true,
        retention_hours: 24
      },

      // Error and exception tracking
      error_logs: {
        size: 128 * 1024 * 1024, // 128MB
        max: 500000, // 500k errors
        indexing: ['timestamp', 'application', 'error_type', 'severity'],
        streaming: true,
        compression: false, // For immediate processing
        retention_hours: 168 // 7 days
      }
    };

    // Performance monitoring
    this.stats = {
      writes_per_second: new Map(),
      collection_sizes: new Map(),
      streaming_clients: new Map()
    };
  }

  async initializeCappedCollections() {
    console.log('Initializing advanced capped collections for high-throughput logging...');

    const initializationResults = [];

    for (const [collectionName, config] of Object.entries(this.cappedConfigurations)) {
      try {
        console.log(`Setting up capped collection: ${collectionName}`);

        // Create capped collection with optimal configuration
        const collection = await this.createOptimizedCappedCollection(collectionName, config);

        // Setup indexing strategy
        await this.setupCollectionIndexes(collection, config.indexing);

        // Initialize real-time streaming if enabled
        if (config.streaming) {
          await this.setupRealTimeStreaming(collectionName, collection);
        }

        // Setup monitoring and statistics
        await this.setupCollectionMonitoring(collectionName, collection, config);

        this.collections.set(collectionName, {
          collection: collection,
          config: config,
          created_at: new Date(),
          stats: {
            documents_written: 0,
            bytes_written: 0,
            last_write: null,
            streaming_clients: 0
          }
        });

        initializationResults.push({
          collection: collectionName,
          status: 'success',
          size_mb: Math.round(config.size / (1024 * 1024)),
          max_documents: config.max,
          streaming_enabled: config.streaming
        });

      } catch (error) {
        console.error(`Failed to initialize capped collection ${collectionName}:`, error);
        initializationResults.push({
          collection: collectionName,
          status: 'error',
          error: error.message
        });
      }
    }

    console.log(`Initialized ${initializationResults.filter(r => r.status === 'success').length} capped collections`);
    return initializationResults;
  }

  async createOptimizedCappedCollection(name, config) {
    console.log(`Creating capped collection '${name}' with ${Math.round(config.size / (1024 * 1024))}MB capacity`);

    try {
      // Check if collection already exists
      const existingCollections = await this.db.listCollections({ name: name }).toArray();

      if (existingCollections.length > 0) {
        const existing = existingCollections[0];

        if (existing.options?.capped) {
          console.log(`Capped collection '${name}' already exists`);
          return this.db.collection(name);
        } else {
          throw new Error(`Collection '${name}' exists but is not capped`);
        }
      }

      // Create new capped collection with optimized settings
      const createOptions = {
        capped: true,
        size: config.size,
        max: config.max
      };

      await this.db.createCollection(name, createOptions);
      const collection = this.db.collection(name);

      console.log(`Created capped collection '${name}' successfully`);
      return collection;

    } catch (error) {
      console.error(`Error creating capped collection '${name}':`, error);
      throw error;
    }
  }

  async setupCollectionIndexes(collection, indexFields) {
    console.log(`Setting up indexes for capped collection: ${collection.collectionName}`);

    try {
      // Create indexes for efficient querying (note: capped collections have limitations on indexing)
      const indexPromises = indexFields.map(async (field) => {
        const indexSpec = {};
        indexSpec[field] = 1;

        try {
          await collection.createIndex(indexSpec, { 
            background: true,
            name: `idx_${field}`
          });
          console.log(`Created index on field: ${field}`);
        } catch (error) {
          // Some indexes may not be supported on capped collections
          console.warn(`Could not create index on ${field}: ${error.message}`);
        }
      });

      await Promise.allSettled(indexPromises);

    } catch (error) {
      console.error(`Error setting up indexes for ${collection.collectionName}:`, error);
    }
  }

  async setupRealTimeStreaming(collectionName, collection) {
    console.log(`Setting up real-time streaming for: ${collectionName}`);

    try {
      // Create tailable cursor for real-time document streaming
      const tailableCursor = collection.find({}, {
        tailable: true,
        awaitData: true,
        noCursorTimeout: true,
        maxTimeMS: 0
      });

      this.tailable_cursors.set(collectionName, tailableCursor);

      // Setup streaming event handlers
      const streamingHandler = async (document) => {
        await this.processStreamingDocument(collectionName, document);
      };

      this.streaming_handlers.set(collectionName, streamingHandler);

      // Start streaming in background
      this.startTailableStreaming(collectionName, tailableCursor, streamingHandler);

      console.log(`Real-time streaming enabled for: ${collectionName}`);

    } catch (error) {
      console.error(`Error setting up streaming for ${collectionName}:`, error);
    }
  }

  async startTailableStreaming(collectionName, cursor, handler) {
    console.log(`Starting tailable streaming for: ${collectionName}`);

    try {
      // Process documents as they are inserted
      for await (const document of cursor) {
        try {
          await handler(document);
          this.updateStreamingStats(collectionName, 'document_streamed');
        } catch (error) {
          console.error(`Error processing streamed document in ${collectionName}:`, error);
          this.updateStreamingStats(collectionName, 'streaming_error');
        }
      }
    } catch (error) {
      console.error(`Tailable streaming error for ${collectionName}:`, error);

      // Attempt to restart streaming after delay
      setTimeout(() => {
        console.log(`Restarting tailable streaming for: ${collectionName}`);
        this.startTailableStreaming(collectionName, cursor, handler);
      }, 5000);
    }
  }

  async processStreamingDocument(collectionName, document) {
    // Process real-time document based on collection type
    switch (collectionName) {
      case 'application_logs':
        await this.processLogDocument(document);
        break;
      case 'event_stream':
        await this.processEventDocument(document);
        break;
      case 'system_metrics':
        await this.processMetricDocument(document);
        break;
      case 'error_logs':
        await this.processErrorDocument(document);
        break;
      default:
        console.log(`Streamed document from ${collectionName}:`, document._id);
    }
  }

  async processLogDocument(logDocument) {
    // Real-time log processing
    console.log(`Processing log: ${logDocument.level} - ${logDocument.application}`);

    // Alert on critical errors
    if (logDocument.level === 'ERROR' || logDocument.level === 'FATAL') {
      await this.triggerErrorAlert(logDocument);
    }

    // Update real-time metrics
    await this.updateLogMetrics(logDocument);
  }

  async processEventDocument(eventDocument) {
    // Real-time event processing
    console.log(`Processing event: ${eventDocument.event_type}`);

    // Update event counters
    await this.updateEventCounters(eventDocument);

    // Trigger event-based workflows
    if (eventDocument.event_type === 'user_purchase') {
      await this.triggerPurchaseWorkflow(eventDocument);
    }
  }

  async processMetricDocument(metricDocument) {
    // Real-time metrics processing
    console.log(`Processing metric: ${metricDocument.metric_type} = ${metricDocument.value}`);

    // Check thresholds
    if (metricDocument.metric_type === 'cpu_usage' && metricDocument.value > 80) {
      await this.triggerHighCPUAlert(metricDocument);
    }
  }

  async processErrorDocument(errorDocument) {
    // Real-time error processing
    console.log(`Processing error: ${errorDocument.error_type} in ${errorDocument.application}`);

    // Immediate alerting for critical errors
    if (errorDocument.severity === 'CRITICAL') {
      await this.triggerCriticalErrorAlert(errorDocument);
    }
  }

  async logEvent(collectionName, eventData, options = {}) {
    console.log(`Logging event to ${collectionName}`);

    try {
      const collectionData = this.collections.get(collectionName);
      if (!collectionData) {
        throw new Error(`Capped collection ${collectionName} not initialized`);
      }

      const collection = collectionData.collection;

      // Enhance event data with standard fields
      const enhancedEvent = {
        ...eventData,
        timestamp: new Date(),
        _logged_at: new Date(),
        _collection_type: collectionName,

        // Add metadata if specified
        ...(options.metadata && { _metadata: options.metadata }),

        // Add correlation ID for tracking
        ...(options.correlationId && { _correlation_id: options.correlationId })
      };

      // High-performance insert (no acknowledgment waiting for maximum throughput)
      const insertOptions = {
        writeConcern: options.writeConcern || { w: 0 }, // No acknowledgment for maximum speed
        ordered: false // Allow out-of-order inserts for better performance
      };

      const result = await collection.insertOne(enhancedEvent, insertOptions);

      // Update statistics
      this.updateWriteStats(collectionName, enhancedEvent);

      return {
        success: true,
        insertedId: result.insertedId,
        collection: collectionName,
        timestamp: enhancedEvent.timestamp
      };

    } catch (error) {
      console.error(`Error logging to ${collectionName}:`, error);

      // Update error statistics
      this.updateWriteStats(collectionName, null, error);

      throw error;
    }
  }

  async logEventBatch(collectionName, events, options = {}) {
    console.log(`Logging batch of ${events.length} events to ${collectionName}`);

    try {
      const collectionData = this.collections.get(collectionName);
      if (!collectionData) {
        throw new Error(`Capped collection ${collectionName} not initialized`);
      }

      const collection = collectionData.collection;
      const timestamp = new Date();

      // Enhance all events with standard fields
      const enhancedEvents = events.map((eventData, index) => ({
        ...eventData,
        timestamp: new Date(timestamp.getTime() + index), // Ensure unique timestamps
        _logged_at: timestamp,
        _collection_type: collectionName,
        _batch_id: options.batchId || require('crypto').randomUUID(),

        // Add metadata if specified
        ...(options.metadata && { _metadata: options.metadata })
      }));

      // High-performance batch insert
      const insertOptions = {
        writeConcern: options.writeConcern || { w: 0 }, // No acknowledgment
        ordered: false // Allow out-of-order inserts
      };

      const result = await collection.insertMany(enhancedEvents, insertOptions);

      // Update statistics for all events
      enhancedEvents.forEach(event => this.updateWriteStats(collectionName, event));

      return {
        success: true,
        insertedCount: result.insertedCount,
        insertedIds: result.insertedIds,
        collection: collectionName,
        batchSize: events.length,
        timestamp: timestamp
      };

    } catch (error) {
      console.error(`Error batch logging to ${collectionName}:`, error);

      // Update error statistics
      this.updateWriteStats(collectionName, null, error);

      throw error;
    }
  }

  async queryRecentEvents(collectionName, query = {}, options = {}) {
    console.log(`Querying recent events from ${collectionName}`);

    try {
      const collectionData = this.collections.get(collectionName);
      if (!collectionData) {
        throw new Error(`Capped collection ${collectionName} not initialized`);
      }

      const collection = collectionData.collection;

      // Build query with time-based filtering
      const timeFilter = {};
      if (options.since) {
        timeFilter.timestamp = { $gte: options.since };
      }
      if (options.until) {
        timeFilter.timestamp = { ...timeFilter.timestamp, $lte: options.until };
      }

      const finalQuery = {
        ...query,
        ...timeFilter
      };

      // Query options for efficient retrieval
      const queryOptions = {
        sort: options.sort || { $natural: -1 }, // Natural order for capped collections
        limit: options.limit || 1000,
        projection: options.projection || {}
      };

      const cursor = collection.find(finalQuery, queryOptions);
      const results = await cursor.toArray();

      console.log(`Retrieved ${results.length} events from ${collectionName}`);

      return {
        collection: collectionName,
        count: results.length,
        events: results,
        query: finalQuery,
        options: queryOptions
      };

    } catch (error) {
      console.error(`Error querying ${collectionName}:`, error);
      throw error;
    }
  }

  async getCollectionStats(collectionName) {
    console.log(`Getting statistics for capped collection: ${collectionName}`);

    try {
      const collectionData = this.collections.get(collectionName);
      if (!collectionData) {
        throw new Error(`Capped collection ${collectionName} not initialized`);
      }

      const collection = collectionData.collection;

      // Get collection statistics
      const stats = await this.db.command({ collStats: collectionName });
      const recentStats = collectionData.stats;

      // Calculate performance metrics
      const now = Date.now();
      const timeSinceLastWrite = recentStats.last_write ? 
        (now - recentStats.last_write.getTime()) / 1000 : null;

      const writesPerSecond = this.stats.writes_per_second.get(collectionName) || 0;

      return {
        collection_name: collectionName,

        // MongoDB collection stats
        is_capped: stats.capped,
        max_size: stats.maxSize,
        max_documents: stats.max,
        current_size: stats.size,
        storage_size: stats.storageSize,
        document_count: stats.count,
        average_document_size: stats.avgObjSize,

        // Usage statistics
        size_utilization_percent: (stats.size / stats.maxSize * 100).toFixed(2),
        document_utilization_percent: stats.max ? (stats.count / stats.max * 100).toFixed(2) : null,

        // Performance metrics
        writes_per_second: writesPerSecond,
        documents_written: recentStats.documents_written,
        bytes_written: recentStats.bytes_written,
        last_write: recentStats.last_write,
        time_since_last_write_seconds: timeSinceLastWrite,

        // Streaming statistics
        streaming_enabled: collectionData.config.streaming,
        streaming_clients: recentStats.streaming_clients,

        // Configuration
        retention_hours: collectionData.config.retention_hours,
        compression_enabled: collectionData.config.compression,

        // Timestamps
        created_at: collectionData.created_at,
        stats_generated_at: new Date()
      };

    } catch (error) {
      console.error(`Error getting stats for ${collectionName}:`, error);
      throw error;
    }
  }

  async getAllCollectionStats() {
    console.log('Getting comprehensive statistics for all capped collections');

    const allStats = {};
    const promises = Array.from(this.collections.keys()).map(async (collectionName) => {
      try {
        const stats = await this.getCollectionStats(collectionName);
        allStats[collectionName] = stats;
      } catch (error) {
        allStats[collectionName] = { error: error.message };
      }
    });

    await Promise.all(promises);

    // Calculate aggregate statistics
    const aggregateStats = {
      total_collections: Object.keys(allStats).length,
      total_documents: 0,
      total_size_bytes: 0,
      total_writes_per_second: 0,
      collections_with_streaming: 0,
      average_utilization_percent: 0
    };

    let validCollections = 0;
    for (const [name, stats] of Object.entries(allStats)) {
      if (!stats.error) {
        validCollections++;
        aggregateStats.total_documents += stats.document_count || 0;
        aggregateStats.total_size_bytes += stats.current_size || 0;
        aggregateStats.total_writes_per_second += stats.writes_per_second || 0;
        if (stats.streaming_enabled) aggregateStats.collections_with_streaming++;
        aggregateStats.average_utilization_percent += parseFloat(stats.size_utilization_percent) || 0;
      }
    }

    if (validCollections > 0) {
      aggregateStats.average_utilization_percent /= validCollections;
    }

    return {
      individual_collections: allStats,
      aggregate_statistics: aggregateStats,
      generated_at: new Date()
    };
  }

  // Real-time streaming client management
  createTailableStream(collectionName, filter = {}, options = {}) {
    console.log(`Creating tailable stream for: ${collectionName}`);

    const collectionData = this.collections.get(collectionName);
    if (!collectionData || !collectionData.config.streaming) {
      throw new Error(`Collection ${collectionName} is not configured for streaming`);
    }

    const collection = collectionData.collection;

    // Create tailable cursor with real-time options
    const tailableOptions = {
      tailable: true,
      awaitData: true,
      noCursorTimeout: true,
      maxTimeMS: 0,
      ...options
    };

    const cursor = collection.find(filter, tailableOptions);

    // Update streaming client count
    this.updateStreamingStats(collectionName, 'client_connected');

    return cursor;
  }

  // Utility methods for statistics and monitoring
  updateWriteStats(collectionName, eventData, error = null) {
    const collectionData = this.collections.get(collectionName);
    if (!collectionData) return;

    if (error) {
      // Handle error statistics
      collectionData.stats.errors = (collectionData.stats.errors || 0) + 1;
    } else {
      // Update write statistics
      collectionData.stats.documents_written++;
      collectionData.stats.last_write = new Date();

      if (eventData) {
        const eventSize = JSON.stringify(eventData).length;
        collectionData.stats.bytes_written += eventSize;
      }
    }

    // Update writes per second
    this.updateWritesPerSecond(collectionName);
  }

  updateWritesPerSecond(collectionName) {
    const now = Date.now();
    const key = `${collectionName}_${Math.floor(now / 1000)}`;

    if (!this.stats.writes_per_second.has(collectionName)) {
      this.stats.writes_per_second.set(collectionName, 0);
    }

    // Simple writes per second calculation
    this.stats.writes_per_second.set(
      collectionName, 
      this.stats.writes_per_second.get(collectionName) + 1
    );

    // Reset counter every second
    setTimeout(() => {
      this.stats.writes_per_second.set(collectionName, 0);
    }, 1000);
  }

  updateStreamingStats(collectionName, action) {
    const collectionData = this.collections.get(collectionName);
    if (!collectionData) return;

    switch (action) {
      case 'client_connected':
        collectionData.stats.streaming_clients++;
        break;
      case 'client_disconnected':
        collectionData.stats.streaming_clients = Math.max(0, collectionData.stats.streaming_clients - 1);
        break;
      case 'document_streamed':
        collectionData.stats.documents_streamed = (collectionData.stats.documents_streamed || 0) + 1;
        break;
      case 'streaming_error':
        collectionData.stats.streaming_errors = (collectionData.stats.streaming_errors || 0) + 1;
        break;
    }
  }

  // Alert and notification methods
  async triggerErrorAlert(logDocument) {
    console.log(`🚨 ERROR ALERT: ${logDocument.application} - ${logDocument.message}`);
    // Implement alerting logic (email, Slack, PagerDuty, etc.)
  }

  async triggerCriticalErrorAlert(errorDocument) {
    console.log(`🔥 CRITICAL ERROR: ${errorDocument.application} - ${errorDocument.error_type}`);
    // Implement critical alerting logic
  }

  async triggerHighCPUAlert(metricDocument) {
    console.log(`⚠️ HIGH CPU: ${metricDocument.host} - ${metricDocument.value}%`);
    // Implement system monitoring alerts
  }

  // Workflow triggers
  async triggerPurchaseWorkflow(eventDocument) {
    console.log(`💰 Purchase Event: User ${eventDocument.user_id} - Amount ${eventDocument.amount}`);
    // Implement purchase-related workflows
  }

  // Metrics updating methods
  async updateLogMetrics(logDocument) {
    // Update aggregated log metrics in real-time
    const metricsUpdate = {
      $inc: {
        [`hourly_logs.${new Date().getHours()}.${logDocument.level.toLowerCase()}`]: 1,
        [`application_logs.${logDocument.application}.${logDocument.level.toLowerCase()}`]: 1
      },
      $set: {
        last_updated: new Date()
      }
    };

    await this.db.collection('log_metrics').updateOne(
      { _id: 'real_time_metrics' },
      metricsUpdate,
      { upsert: true }
    );
  }

  async updateEventCounters(eventDocument) {
    // Update real-time event counters
    const counterUpdate = {
      $inc: {
        [`event_counts.${eventDocument.event_type}`]: 1,
        'total_events': 1
      },
      $set: {
        last_event: new Date(),
        last_event_type: eventDocument.event_type
      }
    };

    await this.db.collection('event_metrics').updateOne(
      { _id: 'real_time_counters' },
      counterUpdate,
      { upsert: true }
    );
  }

  async setupCollectionMonitoring(collectionName, collection, config) {
    // Setup monitoring for collection health and performance
    setInterval(async () => {
      try {
        const stats = await this.getCollectionStats(collectionName);

        // Check for potential issues
        if (stats.size_utilization_percent > 90) {
          console.warn(`⚠️ Collection ${collectionName} is ${stats.size_utilization_percent}% full`);
        }

        if (stats.writes_per_second === 0 && config.retention_hours < 24) {
          console.warn(`⚠️ No recent writes to ${collectionName}`);
        }

      } catch (error) {
        console.error(`Error monitoring ${collectionName}:`, error);
      }
    }, 60000); // Check every minute
  }
}

// Example usage: High-performance logging system setup
async function setupHighPerformanceLogging() {
  console.log('Setting up comprehensive high-performance logging system with capped collections...');

  const cappedManager = new AdvancedCappedCollectionManager(db);

  // Initialize all capped collections
  await cappedManager.initializeCappedCollections();

  // Example: High-volume application logging
  const logEntries = [
    {
      application: 'web-api',
      level: 'INFO',
      message: 'User authentication successful',
      user_id: '507f1f77bcf86cd799439011',
      session_id: 'sess_abc123',
      request_id: 'req_xyz789',
      source_ip: '192.168.1.100',
      user_agent: 'Mozilla/5.0...',
      request_method: 'POST',
      request_url: '/api/auth/login',
      response_status: 200,
      response_time_ms: 150,
      metadata: {
        login_method: 'email',
        ip_geolocation: 'US-CA',
        device_type: 'desktop'
      }
    },
    {
      application: 'background-worker',
      level: 'ERROR',
      message: 'Database connection timeout',
      error_type: 'DatabaseTimeout',
      error_code: 'DB_CONN_TIMEOUT',
      stack_trace: 'Error: Connection timeout...',
      job_id: 'job_456789',
      queue_name: 'high_priority',
      retry_attempt: 3,
      metadata: {
        connection_pool_size: 10,
        active_connections: 9,
        queue_size: 1500
      }
    },
    {
      application: 'payment-service',
      level: 'WARN',
      message: 'Payment processing took longer than expected',
      user_id: '507f1f77bcf86cd799439012',
      transaction_id: 'txn_abc456',
      payment_method: 'credit_card',
      amount: 99.99,
      currency: 'USD',
      processing_time_ms: 5500,
      metadata: {
        gateway: 'stripe',
        gateway_response_time: 4800,
        fraud_check_time: 700
      }
    }
  ];

  // Batch insert logs for maximum performance
  await cappedManager.logEventBatch('application_logs', logEntries, {
    batchId: 'batch_001',
    metadata: { source: 'demo', environment: 'production' }
  });

  // Example: Real-time event streaming
  const events = [
    {
      event_type: 'user_signup',
      user_id: '507f1f77bcf86cd799439013',
      email: '[email protected]',
      signup_method: 'google_oauth',
      referrer: 'organic_search',
      metadata: {
        utm_source: 'google',
        utm_medium: 'organic',
        landing_page: '/pricing'
      }
    },
    {
      event_type: 'user_purchase',
      user_id: '507f1f77bcf86cd799439013',
      order_id: '507f1f77bcf86cd799439014',
      amount: 299.99,
      currency: 'USD',
      product_ids: ['prod_001', 'prod_002'],
      payment_method: 'stripe',
      metadata: {
        discount_applied: 50.00,
        coupon_code: 'SAVE50',
        affiliate_id: 'aff_123'
      }
    }
  ];

  await cappedManager.logEventBatch('event_stream', events);

  // Example: System metrics collection
  const metrics = [
    {
      metric_type: 'cpu_usage',
      host: 'web-server-01',
      value: 78.5,
      unit: 'percent',
      tags: ['production', 'web-tier']
    },
    {
      metric_type: 'memory_usage',
      host: 'web-server-01', 
      value: 6.2,
      unit: 'gb',
      tags: ['production', 'web-tier']
    },
    {
      metric_type: 'disk_io',
      host: 'db-server-01',
      value: 1250,
      unit: 'ops_per_second',
      tags: ['production', 'database-tier']
    }
  ];

  await cappedManager.logEventBatch('system_metrics', metrics);

  // Query recent events
  const recentErrors = await cappedManager.queryRecentEvents('error_logs', 
    { level: 'ERROR' }, 
    { limit: 100, since: new Date(Date.now() - 60 * 60 * 1000) } // Last hour
  );

  console.log(`Found ${recentErrors.count} recent errors`);

  // Get comprehensive statistics
  const stats = await cappedManager.getAllCollectionStats();
  console.log('Capped Collections System Status:', JSON.stringify(stats.aggregate_statistics, null, 2));

  // Setup real-time streaming
  const logStream = cappedManager.createTailableStream('application_logs', 
    { level: { $in: ['ERROR', 'FATAL'] } }
  );

  console.log('Real-time error log streaming started...');

  return cappedManager;
}

// Benefits of MongoDB Capped Collections:
// - Fixed-size collections with automatic space management
// - Built-in circular buffer functionality for efficient storage utilization
// - Optimized for high-throughput write operations with minimal overhead
// - Tailable cursors for real-time streaming and event processing
// - Natural insertion order preservation without additional indexing
// - No fragmentation issues compared to traditional log rotation
// - Automatic old document removal without manual cleanup processes
// - Superior performance for append-only workloads like logging
// - Built-in MongoDB integration with replication and sharding support
// - SQL-compatible operations through QueryLeaf for familiar management

module.exports = {
  AdvancedCappedCollectionManager,
  setupHighPerformanceLogging
};