Make your first API request

To make your first request to Cloudflare’s Radar API, you must obtain your API token first. Create a Custom Token, with Account > Radar in the Permissions group, and select Read as the access level.

Once you have the token, you are ready to make your first request to Radar’s API at https://api.cloudflare.com/client/v4/radar/.

Example using cURL

In the following example, we will access the global percentage distribution of device types (like mobile and desktop traffic) for the last seven days. For more information, refer to Get device types summary endpoint:

curl "https://api.cloudflare.com/client/v4/radar/http/summary/device_type?dateRange=7d&format=json" \
--header "Authorization: Bearer <API_TOKEN>"

A successful response will look similar to the following:

{
  "success": true,
  "errors": [],
  "result": {
    "summary_0": {
      "desktop": "58.223483",
      "mobile": "41.725833",
      "other": "0.050684"
    },
    "meta": {
      "dateRange": {
        "startTime": "2022-10-26T14:00:00Z",
        "endTime": "2022-11-02T14:00:00Z"
      },
      "normalization": "PERCENTAGE",
      ...
    }
  }
}

This response means that 41% of the requests are classified as coming from mobile devices, while 58% are desktop traffic.

Note

Cloudflare Radar attempts to provide trends and insights into general Internet usage, using the traffic that goes through Cloudflare infrastructure. As such, Cloudflare Radar only provides data on traffic coming from end-users, unless otherwise specified (for example, origin fetches are excluded).

The previous example returns all traffic from bots and humans. However, you can access just the traffic classified as coming from humans (the default in Cloudflare Radar ↗) by adding botClass=LIKELY_HUMAN. You can also access traffic coming only from bots with botClass=LIKELY_AUTOMATED (refer to bot classes for more information). For example:

curl "https://api.cloudflare.com/client/v4/radar/http/summary/device_type?dateRange=7d&botClass=LIKELY_AUTOMATED&format=json" \
--header "Authorization: Bearer <API_TOKEN>"

Running the above, can you find any differences between both in the distribution of mobile versus desktop traffic?

The result.meta property

The result.meta property in the response includes metadata about the current request. In the example above, meta.dateRange returns the date range specified in the query, while meta.normalization returns the type of normalization applied to the data (refer to Normalization methods for more information).

When querying for time series, result.meta will also include the returned aggregation interval in meta.aggInterval.

When present, meta.confidenceInfo.level will also provide an indication of how much confidence Cloudflare has in the data. Confidence levels are affected either by internal issues affecting data quality or by Cloudflare not having sufficient data for a given location or Autonomous System (AS). In these cases, confidence level will be below 5 (refer to Confidence levels for more information).

Use Python

Python ↗ has become one of the standard languages in data analysis. Here is a quick example on how to chart the same data using Requests ↗ and Pandas ↗ libraries. Here, we are using format=csv in the parameters to make it easier for Pandas to import.

import io
import requests
import pandas as pd

cf_api_url = "https://api.cloudflare.com/client/v4"
params = "dateRange=7d&format=csv"
my_token = "xxx" # TODO replace
r = requests.get(f"{cf_api_url}/radar/http/summary/device_type?{params}",
                 headers={"Authorization": f"Bearer {my_token}"})
df = pd.read_csv(io.StringIO(r.text))
df.plot(kind="bar", stacked=True)

Notebooks

A notebook ↗ is a web-based interactive computing application, where text, code, and code outputs, like charts, can be combined into a single document. Refer to Radar’s companion colaboratory notebook ↗ for more examples on how the API can be used in your own projects.

Next steps

Refer to Make comparisons to learn how to compare data.