Cisco DNA Center - Assurance

Introduction

In this post we introduced DNAC from a theoretical point of view. We continued in this post with looking at some simple Python scripts to retrieve device related information from DNAC. For this one, we will look into Clients API’s. We will write a Python script that shows us the health statistics per device category (wired or wireless).

Get Client Health

Let’s have a look at the Client API section in the API documentation:

DNAC

As a reminder, we are looking to print an overview of the various health categories (fair, poor, …) per device category.

In below Python script, you will notice we use the client-health API. Before we dive into the Python code, let’s see how the response will look like.

wauterw@WAUTERW-M-65P7 Assurance % python3 get_client_health.py
DNAC: sandboxdnac2.cisco.com
{'response': [{'scoreDetail': [
    {'clientCount': 66,
        'clientUniqueCount': 66,
        'endtime': 1587739500000,
         'scoreCategory': {'scoreCategory': 'CLIENT_TYPE', 'value': 'ALL'},
            'scoreValue': 36,
            'starttime': 1587739200000},
             {'clientCount': 2,
              'clientUniqueCount': 2,
              'endtime': 1587739500000,
               'scoreCategory': {
                   'scoreCategory': 'CLIENT_TYPE','value': 'WIRED'},
                   'scoreList': [{'clientCount': 0,
                        'clientUniqueCount': 0,
                        'endtime': 1587739500000,
                         'scoreCategory': {
                             'scoreCategory': 'SCORE_TYPE','value': 'POOR'},

So we’ll need to do some parsing to extract the data we are interested in.

Here’s what we do:

  • We store the ScoreDetails in a list called scores
  • We define two dictionaries, one for the wired devices and one for the wireless devices. We will populate those dictionaries later on.
  • We iterate over the scores list and we look at the value of the scoreCategory key. This contains essentially whether the score is related to a wired device or a wireless device.
  • Next, per category (wired or wireless) we will store ScoreList (which is an array) values in a list. As you can see, also the ScoreList has a scoreCategory itself. Now these scoreCategories are poor, fair, …. So we can now see
  • We can then loop over the values and for each value (good, fair…) we add a key to the wired or wireless dictionary with the value (poor, fair…) and the clientCount as the corresponding value

The above might be a bit confusing I admit, but essentially all the above is done to get the following dictionary:

{
    'wired': 
    {
      'POOR': 0, 'FAIR': 0, 'GOOD': 2, 'IDLE': 0, 'NODATA': 0, 'NEW': 0
    }, 
    'wireless': 
    {
       'POOR': 0, 'FAIR': 42, 'GOOD': 22, 'IDLE': 0, 'NODATA': 0, 'NEW': 0
    }
}

Note: this is in fact a dictionary inside another dictionary (cfr nested dictionaries).

With this dictionary, we have an elegant structure to show the health (as a percentage) for category. This is taken care of in the calculatePercentageHealth() function.

Here we do the following:

  • We first calculate the total of client devices we have. We need this later on to be able to calculate the percentage.
  • As we are dealing with a nested dictionary structure, we’ll need two for loops. The first loop gets us in either the wired or the wireless dictonary. The second loop will iterate over the inner dictionary and per category (poor, fair), calculate the client health as a percentage.
import requests
from authenticate import get_token
from pprint import pprint

def main():
   dnac = "sandboxdnac2.cisco.com"
   token = get_token(dnac)
   
   url = f"https://{dnac}/dna/intent/api/v1/client-health"

   headers = {
      "Content-Type": "application/json",
      "Accept": "application/json",
      "X-auth-Token": token 
   }

   querystring = { "timestamp": ""}

   response =  requests.get(url, headers=headers, params=querystring, verify=False ).json()
   scores = response['response'][0]['scoreDetail']

   d = {
      'wired' : {},
      'wireless': {}
   }
   nested_dict_wired = {}
   nested_dict_wireless = {}

   print("Overview")
   print("--------")
   for score in scores:
      if score['scoreCategory']['value'] == 'ALL':
         #print(f"Total devices - all: {score['clientCount']}")
         print('')
      
      if score['scoreCategory']['value'] == 'WIRED':
         #print(f"  Total devices - wired: {score['clientCount']}")
         values = score['scoreList']
         for value in values:
            #print(f"      {value['scoreCategory']['value']}: {value['clientCount']}")
            nested_dict_wired[value['scoreCategory']['value']] = value['clientCount']
            d['wired'] = nested_dict_wired
            
      if score['scoreCategory']['value'] == 'WIRELESS':
         #print(f"  Total devices - wireless: {score['clientCount']}")

         values = score['scoreList']
         for value in values:
            #print(f"      {value['scoreCategory']['value']}: {value['clientCount']}")
            nested_dict_wireless[value['scoreCategory']['value']] = value['clientCount']
            d['wireless'] = nested_dict_wireless
  

   calculatePercentageHealth(d)


def calculatePercentageHealth(d):
   print("Percentage Health")
   print("-----------------")
   #Calculate Totals
   sum_wired = 0
   for key, value in d['wired'].items():
      sum_wired += value
   
   sum_wireless = 0
   for key, value in d['wireless'].items():
      sum_wireless += value
  
   #Calculate Percentages
   for key, value in d.items():
      if key == 'wired':
         print(f"Sum_wired: {sum_wired}")
         for k, v in value.items():
            percentage = round((v/sum_wired) * 100)
            print(f"    For {k} => {percentage}%" )
      
      if key == 'wireless':
         print(f"Sum_wireless: {sum_wireless}")
         for k, v in value.items():
            percentage = round((v/sum_wireless) * 100)
            print(f"    For {k} => {percentage}%" )
     

if __name__ == "__main__":
   main()

Executing this script, results in the following output:

Overview
--------

Percentage Health
-----------------
Sum_wired: 2
    For POOR => 0%
    For FAIR => 0%
    For GOOD => 100%
    For IDLE => 0%
    For NODATA => 0%
    For NEW => 0%
Sum_wireless: 64
    For POOR => 0%
    For FAIR => 66%
    For GOOD => 34%
    For IDLE => 0%
    For NODATA => 0%
    For NEW => 0%

As usual, code can be found on my Github repo.