Skip to main content

Documentation Index

Fetch the complete documentation index at: https://mintlify.com/dishant0406/quickleap/llms.txt

Use this file to discover all available pages before exploring further.

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

​
redirectId
string
required
The unique identifier of the redirect to retrieve time-series data for

​
Query parameters

​
start
string
Start date for the analytics period in ISO 8601 formatExample: 2024-01-01T00:00:00Z
​
end
string
End date for the analytics period in ISO 8601 formatExample: 2024-01-31T23:59:59Z
​
interval
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:
​
data
object
​
meta
object

​
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`);

​
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

Build docs developers (and LLMs) love

Get started for freeTalk to us