Get hits over time

The time series endpoint provides granular analytics data aggregated over customizable time intervals, enabling you to visualize traffic trends and identify patterns.

Endpoint

GET /analytics/redirect/:redirectId/stats/time

Function signature

getHitsOverTime(
  redirectId: string,
  params?: { start?: string; end?: string; interval?: string }
): Promise<AxiosResponse>

Path parameters

string

required

The unique identifier of the redirect to retrieve time-series data for

Query parameters

string

Start date for the analytics period in ISO 8601 formatExample: 2024-01-01T00:00:00Z

string

End date for the analytics period in ISO 8601 formatExample: 2024-01-31T23:59:59Z

string

Time interval for aggregating hits. Determines the granularity of the returned data.Available values:

hour - Hourly aggregation
day - Daily aggregation (default)
week - Weekly aggregation
month - Monthly aggregation

Choose an interval appropriate for your date range. For example, use hour for same-day analysis, day for weekly/monthly views, and month for yearly overviews.

Response

The endpoint returns a time-series array with hit counts for each interval:

object

Show properties

array

Array of data points, each representing one time interval

Show item properties

string

ISO 8601 timestamp representing the start of this interval

number

Total number of hits during this interval

number

Number of unique visitors during this interval

number

Number of bot hits during this interval

number

Number of human hits during this interval

object

Show properties

number

Sum of all hits across all intervals

number

Average hits per interval

number

Maximum hits in a single interval

object

Show properties

string

The redirect ID for which data was retrieved

string

The time interval used for aggregation

object

Show properties

string

Start of the analyzed period

string

End of the analyzed period

Example request

import { getHitsOverTime } from '@quickleap/api';

const response = await getHitsOverTime('redirect_abc123', {
  start: '2024-01-01T00:00:00Z',
  end: '2024-01-31T23:59:59Z',
  interval: 'day'
});

const { timeSeries, summary } = response.data.data;

// Plot on a chart
timeSeries.forEach(point => {
  console.log(`${point.timestamp}: ${point.hits} hits`);
});

console.log(`Peak: ${summary.peakHits} hits`);

const { getHitsOverTime } = require('@quickleap/api');

getHitsOverTime('redirect_abc123', {
  start: '2024-01-01T00:00:00Z',
  end: '2024-01-31T23:59:59Z',
  interval: 'day'
}).then(response => {
  const data = response.data.data;
  console.log('Average hits per day:', data.summary.averageHits);
});

curl -X GET \
  'https://api.quickleap.com/analytics/redirect/redirect_abc123/stats/time?start=2024-01-01T00:00:00Z&end=2024-01-31T23:59:59Z&interval=day' \
  -H 'Authorization: Bearer YOUR_API_KEY' \
  -H 'Content-Type: application/json'

import requests
import matplotlib.pyplot as plt
from datetime import datetime

response = requests.get(
  'https://api.quickleap.com/analytics/redirect/redirect_abc123/stats/time',
  params={
    'start': '2024-01-01T00:00:00Z',
    'end': '2024-01-31T23:59:59Z',
    'interval': 'day'
  },
  headers={'Authorization': 'Bearer YOUR_API_KEY'}
)

data = response.json()['data']
timestamps = [point['timestamp'] for point in data['timeSeries']]
hits = [point['hits'] for point in data['timeSeries']]

# Create a time series plot
plt.plot(timestamps, hits)
plt.xlabel('Date')
plt.ylabel('Hits')
plt.title('Redirect Traffic Over Time')
plt.show()

Example response

{
  "data": {
    "timeSeries": [
      {
        "timestamp": "2024-01-01T00:00:00Z",
        "hits": 1234,
        "uniqueVisitors": 456,
        "botHits": 89,
        "humanHits": 1145
      },
      {
        "timestamp": "2024-01-02T00:00:00Z",
        "hits": 1456,
        "uniqueVisitors": 523,
        "botHits": 102,
        "humanHits": 1354
      },
      {
        "timestamp": "2024-01-03T00:00:00Z",
        "hits": 1678,
        "uniqueVisitors": 601,
        "botHits": 115,
        "humanHits": 1563
      }
    ],
    "summary": {
      "totalHits": 45678,
      "averageHits": 1473.5,
      "peakHits": 3892
    }
  },
  "meta": {
    "redirectId": "redirect_abc123",
    "interval": "day",
    "period": {
      "start": "2024-01-01T00:00:00Z",
      "end": "2024-01-31T23:59:59Z"
    }
  }
}

Use cases

Traffic visualization: Create line charts and graphs showing traffic trends
Pattern detection: Identify daily, weekly, or seasonal traffic patterns
Anomaly detection: Spot unusual spikes or drops in traffic
Capacity planning: Predict future traffic based on historical trends
Reporting: Generate periodic traffic reports for stakeholders
A/B testing: Compare traffic before and after redirect changes

Best practices

Match interval to range: Use hourly intervals for short periods (1-7 days), daily for medium periods (1-3 months), and weekly/monthly for longer periods
Caching: Time-series data is computationally intensive. Cache results when possible
Pagination: For very long date ranges, consider breaking requests into smaller chunks
Timezone awareness: All timestamps are in UTC. Convert to local time in your application if needed

Get basic statistics - Summary metrics without time breakdown
Get hourly traffic - Specialized endpoint for hourly patterns
Get peak traffic - Identify peak traffic periods

Getting Started

Redirects

Analytics

Rules

Payment & Billing

Get hits over time

Endpoint

Function signature

Path parameters

Query parameters

Response

Example request

Example response

Use cases

Best practices

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