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Radar
 * Overview
 * Concepts
    * Overview
    * Aggregation intervals
    * Bot classes
    * Confidence levels
    * Normalization methods

 * Get started
    * Overview
    * Make your first API request
    * Make comparisons
    * Radar API Error Codes
    * Share a Radar chart
    * Configure alerts

 * Investigate
    * Overview
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    * BGP anomalies Beta
    * DNS
    * Domains ranking
    * Network layer attacks
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 * Reference
    * Overview
    * Quarterly DDoS threat reports

 * API reference ↗ API
 * Glossary
 * Changelog

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On this page
Overview
 * Overview
 * Example using cURL
 * Use Python
   * Notebooks
 * Next steps


ON THIS PAGE

 * Overview
 * Example using cURL
 * Use Python
   * Notebooks
 * Next steps

 1. Products
 2. …
 3. Radar
 4. Get started
 5. Make your first API request


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 User > User Details 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:

Terminal window
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:

Terminal window
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.

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