import { Logger } from '../../utils/logger';
import { GcpOperationsSuiteIntegration } from '../../core/performance/gcp-operations-suite-integration';
import { NeuralCoordinationModels } from '../../core/neural/coordination-models';

/**
 * @interface MonitoringConfig
 * @description Configuration for Production Monitoring.
 */
export interface MonitoringConfig {
  projectID: string;
  alertingThresholds: { [metric: string]: number };
  // Add configuration for custom dashboards, log sinks, tracing sampling rates
}

/**
 * @interface MonitoringOperations
 * @description Defines operations for comprehensive production monitoring and observability.
 */
export interface MonitoringOperations {
  recordCustomMetric(metricName: string, value: number, labels?: { [key: string]: string }): Promise<void>;
  logApplicationEvent(level: 'info' | 'warn' | 'error' | 'debug', message: string, metadata?: any): Promise<void>;
  startDistributedTrace(traceName: string): any; // Returns a trace span
  endDistributedTrace(span: any): Promise<void>;
  setupRealtimeAlerting(): Promise<void>;
  enablePredictiveMonitoring(): Promise<void>;
}

/**
 * @class ProductionMonitoring
 * @description Provides comprehensive observability with Google Cloud Operations Suite, custom metrics, distributed tracing, and predictive monitoring.
 */
export class ProductionMonitoring implements MonitoringOperations {
  private config: MonitoringConfig;
  private logger: Logger;
  private gcpOperations: GcpOperationsSuiteIntegration;
  private neuralModels: NeuralCoordinationModels;

  constructor(
    config: MonitoringConfig,
    gcpOperations: GcpOperationsSuiteIntegration,
    neuralModels: NeuralCoordinationModels
  ) {
    this.config = config;
    this.logger = new Logger('ProductionMonitoring');
    this.gcpOperations = gcpOperations;
    this.neuralModels = neuralModels;
    this.logger.info('Production Monitoring initialized.');
  }

  /**
   * Records custom metrics for business logic and performance indicators.
   * @param {string} metricName The name of the custom metric.
   * @param {number} value The value of the metric.
   * @param {{ [key: string]: string }} [labels] Optional labels for the metric.
   * @returns {Promise<void>}
   */
  public async recordCustomMetric(metricName: string, value: number, labels?: { [key: string]: string }): Promise<void> {
    this.logger.info(`Recording custom metric: ${metricName}=${value}`, labels);
    await this.gcpOperations.recordMetric(metricName, value, labels);
  }

  /**
   * Logs application events and messages to a centralized logging system.
   * @param {'info' | 'warn' | 'error' | 'debug'} level The log level.
   * @param {string} message The log message.
   * @param {any} [metadata] Optional metadata for the log entry.
   * @returns {Promise<void>}
   */
  public async logApplicationEvent(level: 'info' | 'warn' | 'error' | 'debug', message: string, metadata?: any): Promise<void> {
    this.logger.log(level, message, metadata);
    await this.gcpOperations.log(level, message, metadata);
  }

  /**
   * Starts a distributed trace for tracking requests across services.
   * @param {string} traceName The name of the trace.
   * @returns {any} A trace span object.
   */
  public startDistributedTrace(traceName: string): any {
    this.logger.info(`Starting distributed trace: ${traceName}`);
    return this.gcpOperations.startTrace(traceName);
  }

  /**
   * Ends a distributed trace span.
   * @param {any} span The trace span object.
   * @returns {Promise<void>}
   */
  public async endDistributedTrace(span: any): Promise<void> {
    this.logger.info(`Ending distributed trace: ${span.name}`);
    await this.gcpOperations.endTrace(span);
  }

  /**
   * Sets up real-time alerting based on system health and performance metrics.
   * @returns {Promise<void>}
   */
  public async setupRealtimeAlerting(): Promise<void> {
    this.logger.info('Setting up real-time alerting (conceptual)...');
    // This would involve configuring alert policies in Cloud Monitoring.
    // Example: Alert if CPU utilization > 80% for 5 minutes.
    setInterval(async () => {
      const cpuMetric = Math.random() * 100; // Simulated CPU utilization
      if (cpuMetric > (this.config.alertingThresholds.cpu_utilization || 80)) {
        this.logger.warn(`ALERT: High CPU utilization detected: ${cpuMetric.toFixed(2)}%`);
        await this.gcpOperations.reportError(new Error(`High CPU utilization: ${cpuMetric.toFixed(2)}%`), { metric: 'cpu_utilization', value: cpuMetric });
      }
    }, 60 * 1000); // Check every minute
  }

  /**
   * Enables predictive monitoring using neural networks to anticipate issues.
   * @returns {Promise<void>}
   */
  public async enablePredictiveMonitoring(): Promise<void> {
    this.logger.info('Enabling predictive monitoring using neural networks (conceptual)...');
    // This would involve:
    // - Feeding system metrics to a neural network model.
    // - Using the model to predict future performance degradation or failures.
    // - Triggering alerts proactively.
    setInterval(async () => {
      const simulatedMetrics = { latency: Math.random() * 200, errors: Math.random() * 0.05 };
      const isAnomaly = await this.neuralModels.detectAnomaly(simulatedMetrics);
      if (isAnomaly) {
        this.logger.warn('PREDICTIVE ALERT: Anomaly detected in performance metrics. Potential issue ahead!');
      }
    }, 5 * 60 * 1000); // Check every 5 minutes
  }
}