Hourly Email Traffic Trends

Returns hourly email volume (received and sent) between the given start and end dates. Each day is broken down into 24 hourly buckets, showing how many emails were received and sent during each hour.

Request

Headers

Name	Type	Required	Description
Authorization	string	Yes	Bearer token

Path Parameters

Parameter	Type	Required	Description
startDate	string	Yes	Start date for the range (YYYY-MM-DD)
endDate	string	Yes	End date for the range (YYYY-MM-DD)

Parameter Format

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

Response

200 OK - Hourly email trends between given dates

{
  "message": "",
  "data": {
    "labels": [
      "00:00:00",
      "01:00:00",
      "02:00:00",
      "03:00:00",
      "04:00:00",
      "05:00:00",
      "06:00:00",
      "07:00:00",
      "08:00:00",
      "09:00:00",
      "10:00:00",
      "11:00:00",
      "12:00:00",
      "13:00:00",
      "14:00:00",
      "15:00:00",
      "16:00:00",
      "17:00:00",
      "18:00:00",
      "19:00:00",
      "20:00:00",
      "21:00:00",
      "22:00:00",
      "23:00:00"
    ],
    "series": {
      "received": [
        {
          "name": "2025-10-01",
          "data": [0, 1, 3, 2, 5, 10, 6, 8, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
        },
        {
          "name": "2025-10-02",
          "data": [0, 2, 5, 3, 8, 15, 12, 18, 25, 22, 19, 15, 12, 18, 20, 16, 14, 8, 5, 2, 1, 0, 0, 0]
        }
      ],
      "sent": [
        {
          "name": "2025-10-01",
          "data": [0, 0, 2, 1, 3, 8, 5, 8, 12, 15, 18, 14, 10, 8, 6, 4, 2, 1, 0, 0, 0, 0, 0, 0]
        },
        {
          "name": "2025-10-02",
          "data": [0, 1, 3, 2, 6, 8, 8, 10, 15, 20, 25, 22, 18, 15, 12, 10, 8, 5, 2, 1, 0, 0, 0, 0]
        }
      ]
    }
  }
}

401 Unauthorized

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

500 Internal Server Error

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

Example

curl -X GET 'http://localhost:2000/email-meter/stats/hourly-trends/2025-10-01/2025-10-20' \
  -H 'Authorization: oBearer your-jwt-token'

Data Structure

Labels Array

Field	Type	Description
labels	array	Hour labels from 00:00:00 to 23:00:00

Series Object

Field	Type	Description
received	array	Series showing received emails per hour per day
sent	array	Series showing sent emails per hour per day

Data Series Structure

Each object in received/sent arrays contains:

Field	Type	Description
name	string	Date of the data point (YYYY-MM-DD)
data	array	24-hour email counts

Data Alignment

series.received[i].data[j] corresponds to labels[j] for received emails on date series.received[i].name
series.sent[i].data[j] corresponds to labels[j] for sent emails on date series.sent[i].name
All data arrays have exactly 24 elements (one for each hour)

Use Cases

Peak Hours Analysis: Identify busiest email hours
Staff Scheduling: Optimize agent scheduling based on volume
Resource Planning: Allocate resources during peak times
Performance Monitoring: Track hourly performance patterns
Heat Map Visualization: Create hourly heat maps

Analysis Examples

Peak Hour Identification

const findPeakHours = (data) => {
  const hourlyTotals = data.labels.map((_, hourIndex) => {
    return data.series.received.reduce((sum, day) => sum + day.data[hourIndex], 0);
  });
  
  const maxVolume = Math.max(...hourlyTotals);
  const peakHour = hourlyTotals.indexOf(maxVolume);
  
  return {
    peakHour: data.labels[peakHour],
    peakVolume: maxVolume,
    peakHourIndex: peakHour
  };
};

Daily Pattern Analysis

const analyzeDailyPatterns = (data) => {
  return data.series.received.map(day => ({
    date: day.name,
    totalEmails: day.data.reduce((a, b) => a + b, 0),
    peakHour: data.labels[day.data.indexOf(Math.max(...day.data))],
    peakVolume: Math.max(...day.data)
  }));
};

Business Hours vs Non-Business Hours

const businessHoursAnalysis = (data) => {
  const businessHourIndices = [9, 10, 11, 12, 13, 14, 15, 16]; // 9 AM - 5 PM
  const nonBusinessHourIndices = [0, 1, 2, 3, 4, 5, 6, 7, 8, 17, 18, 19, 20, 21, 22, 23];
  
  const businessVolume = data.series.received.reduce((sum, day) => {
    return sum + businessHourIndices.reduce((daySum, hour) => daySum + day.data[hour], 0);
  }, 0);
  
  const nonBusinessVolume = data.series.received.reduce((sum, day) => {
    return sum + nonBusinessHourIndices.reduce((daySum, hour) => daySum + day.data[hour], 0);
  }, 0);
  
  return {
    businessHours: businessVolume,
    nonBusinessHours: nonBusinessVolume,
    businessPercentage: (businessVolume / (businessVolume + nonBusinessVolume)) * 100
  };
};

Visualization Examples

Heat Map

// Create heat map data
const heatMapData = data.series.received.map(day => day.data);

// Example using Chart.js with heatmap plugin
new Chart(ctx, {
  type: 'matrix',
  data: {
    datasets: [{
      label: 'Email Volume',
      data: heatMapData.flat().map((value, index) => ({
        x: index % 24,
        y: Math.floor(index / 24),
        v: value
      })),
      backgroundColor(ctx) {
        const value = ctx.dataset.data[ctx.dataIndex].v;
        const alpha = value / maxVolume;
        return `rgba(54, 162, 235, ${alpha})`;
      }
    }]
  }
});

Multi-Day Line Chart

const ctx = document.getElementById('hourlyTrends').getContext('2d');
new Chart(ctx, {
  type: 'line',
  data: {
    labels: data.labels,
    datasets: data.series.received.map(day => ({
      label: day.name,
      data: day.data,
      borderColor: `hsl(${Math.random() * 360}, 70%, 50%)`,
      tension: 0.4
    }))
  }
});

Best Practices

Date Range: Use reasonable ranges (recommended: max 30 days for hourly detail)
Time Zone: Convert UTC times to local time zones for display
Aggregation: Consider aggregating multiple days for pattern analysis
Visualization: Use heat maps for better pattern visualization
Business Hours: Focus analysis on business hours for operational insights

Performance Considerations

Data Volume: Hourly data generates 24x more points than daily
Processing Time: Larger date ranges take longer to process
Memory Usage: Consider client-side memory for large datasets
Caching: Cache results for dashboard performance

Use /email-meter/stats/emails-trend for daily trends
Use /email-meter/stats/email-counts for aggregate statistics
Use /email-meter/stats/response-times for hourly response analysis

Notes

All times are in UTC - convert to local time for operational analysis
Zero-value hours are included for complete timeline
Data includes all email types (incoming, outgoing, internal)
Weekends and holidays are included in hourly breakdown
Automated emails are included unless filtered specifically

Authorizations

Authorization

string

header

required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Path Parameters

startDate

string<date>

required

Start date for the range (YYYY-MM-DD)

endDate

string<date>

required

End date for the range (YYYY-MM-DD)

Response

Hourly email trends between given dates

message

string

Example:

""

data

object

Show child attributes

Setup & Integration

Email Statistics

Agent Analytics

Performance Metrics

Client Analytics

Request

Headers

Path Parameters

Parameter Format

Response

200 OK - Hourly email trends between given dates

401 Unauthorized

500 Internal Server Error

Example

Data Structure

Labels Array

Series Object

Data Series Structure

Data Alignment

Use Cases

Analysis Examples

Peak Hour Identification

Daily Pattern Analysis

Business Hours vs Non-Business Hours

Visualization Examples

Heat Map

Multi-Day Line Chart

Best Practices

Performance Considerations

Notes

Authorizations

Path Parameters

Response

Setup & Integration

Email Statistics

Agent Analytics

Performance Metrics

Client Analytics

​Request

​Headers

​Path Parameters

​Parameter Format

​Response

​200 OK - Hourly email trends between given dates

​401 Unauthorized

​500 Internal Server Error

​Example

​Data Structure

​Labels Array

​Series Object

​Data Series Structure

​Data Alignment

​Use Cases

​Analysis Examples

​Peak Hour Identification

​Daily Pattern Analysis

​Business Hours vs Non-Business Hours

​Visualization Examples

​Heat Map

​Multi-Day Line Chart

​Best Practices

​Performance Considerations

​Related Endpoints

​Notes

Authorizations

Path Parameters

Response

Request

Headers

Path Parameters

Parameter Format

Response

200 OK - Hourly email trends between given dates

401 Unauthorized

500 Internal Server Error

Example

Data Structure

Labels Array

Series Object

Data Series Structure

Data Alignment

Use Cases

Analysis Examples

Peak Hour Identification

Daily Pattern Analysis

Business Hours vs Non-Business Hours

Visualization Examples

Heat Map

Multi-Day Line Chart

Best Practices

Performance Considerations

Related Endpoints

Notes