Daily Email Activity Trend

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Request

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Headers

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Path Parameters

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Parameter Format

• Format: YYYY-MM-DD (ISO 8601 date)
• Timezone: UTC
• Default: Last 7 days if not provided

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Response

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200 OK - Successfully retrieved email activity trend

{
  "message": "",
  "data": {
    "series": [
      {
        "name": "Incoming",
        "data": [12, 18, 9, 25, 14, 22, 19]
      },
      {
        "name": "Sent",
        "data": [8, 15, 7, 20, 12, 18, 16]
      }
    ],
    "labels": [
      "2025-10-01",
      "2025-10-02",
      "2025-10-03",
      "2025-10-04",
      "2025-10-05",
      "2025-10-06",
      "2025-10-07"
    ]
  }
}

​

401 Unauthorized

{
  "error": {
    "code": "UNAUTHORIZED",
    "message": "Missing or invalid token"
  }
}

​

500 Internal Server Error

{
  "error": {
    "code": "SERVER_ERROR",
    "message": "Internal server error"
  }
}

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Example

curl -X GET 'http://localhost:2000/email-meter/stats/emails-trend/2025-10-01/2025-10-07' \
  -H 'Authorization: Bearer your-jwt-token'

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Data Structure

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Series Array

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Labels Array

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Data Alignment

• series[0].data[i] corresponds to labels[i] for Incoming emails
• series[1].data[i] corresponds to labels[i] for Sent emails
• All arrays have the same length

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Use Cases

• Trend Analysis: Visualize email volume over time
• Capacity Planning: Identify peak email periods
• Performance Monitoring: Track daily email patterns
• Dashboard Visualization: Create line charts and graphs
• Seasonal Patterns: Identify weekly/monthly trends

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Visualization Examples

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Line Chart

// Example using Chart.js
const ctx = document.getElementById('emailTrend').getContext('2d');
new Chart(ctx, {
  type: 'line',
  data: {
    labels: response.data.labels,
    datasets: response.data.series
  },
  options: {
    responsive: true,
    scales: {
      y: {
        beginAtZero: true,
        title: {
          display: true,
          text: 'Email Count'
        }
      }
    }
  }
});

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Bar Chart

// Example for daily comparison
new Chart(ctx, {
  type: 'bar',
  data: {
    labels: response.data.labels,
    datasets: response.data.series
  }
});

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Analysis Metrics

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Daily Totals

const dailyTotals = response.data.labels.map((label, index) => ({
  date: label,
  total: response.data.series[0].data[index] + response.data.series[1].data[index],
  incoming: response.data.series[0].data[index],
  sent: response.data.series[1].data[index]
}));

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Trend Calculations

// Calculate moving average
const movingAverage = (data, period) => {
  return data.map((_, index) => {
    const start = Math.max(0, index - period + 1);
    const subset = data.slice(start, index + 1);
    return subset.reduce((a, b) => a + b, 0) / subset.length;
  });
};

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Best Practices

1. Date Range: Use reasonable ranges (recommended: max 90 days)
2. Caching: Cache trend data for dashboard performance
3. Real-time Updates: Update trends periodically for live dashboards
4. Empty Data: Handle days with zero emails gracefully
5. Time Zones: Consider converting to local time zones for display

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Performance Considerations

• Large Ranges: Longer date ranges may take more time to process
• Data Points: Each day represents a data point
• Caching: Results are cached for 15 minutes by default
• Memory Usage: Large datasets may impact client-side rendering

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Related Endpoints

• Use /email-meter/stats/hourly-trends for hourly breakdown
• Use /email-meter/stats/email-counts for aggregate statistics
• Use /email-meter/stats/clients/incoming-trends for client-specific trends

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Notes

• Data includes all email types (incoming, outgoing, internal)
• Weekends and holidays are included in the trend
• Zero-value days are included for complete timeline
• Data is calculated based on email timestamps in UTC
• Automated emails are included in counts unless filtered