Active engagement

Overview Lillo's engagement tracking system provides foundational analytics and metrics collection for community interactions. The current implementation establishes core tracking capabilities, with p

Command Metrics

Metric Collection

interface CommandMetrics {
  command: string;
  userId: number;
  chatId: number;
  duration: number;
  success: boolean;
  error?: string;
  timestamp: number;
}

Storage Implementation

  • Real-time KV store for quick access

  • PostgreSQL for long-term analysis

  • Dual-storage strategy for performance

  • Automatic data retention

Data Collection

Command Analytics

  • Usage frequency

  • Success rates

  • Response times

  • Error patterns

  • User distribution

Storage Strategy

// Quick access in KV store
const key = `metrics:cmd:${metrics.command}:${metrics.timestamp}`;
await storeInKV(key, metrics);

// Long-term storage in PostgreSQL
await sql`
  INSERT INTO command_metrics (
    command, user_id, chat_id, duration,
    success, error, timestamp
  ) VALUES (
    ${metrics.command}, ${metrics.userId},
    ${metrics.chatId}, ${metrics.duration},
    ${metrics.success}, ${metrics.error},
    ${metrics.timestamp}
  );
`;

Performance Tracking

Monitored Metrics

  • Command execution time

  • API response latency

  • Cache hit rates

  • Error frequencies

  • User engagement levels

Implementation Details

  • Real-time monitoring

  • Performance logging

  • Error tracking

  • Usage patterns

  • Resource utilization

Future Enhancements

The engagement system is being expanded to include:

  • Advanced analytics dashboard

  • User engagement scoring

  • Community health metrics

  • Trend analysis

  • Behavioral insights

  • Customizable reports

Data Analysis

Current Capabilities

  • Basic usage statistics

  • Performance metrics

  • Error tracking

  • User activity monitoring

Planned Features

  • Advanced analytics

  • User behavior analysis

  • Engagement optimization

  • Community insights

  • Trend forecasting

Best Practices

Data Collection

  • Respect user privacy

  • Optimize storage usage

  • Regular maintenance

  • Data validation

Performance Monitoring

  • Track key metrics

  • Set up alerts

  • Monitor trends

  • Regular reviews

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