Python - Infer Spark Cluster ConfigurationsΒΆ

Below is an example Python program for inferring the Apache Spark cluster configurations using the REST API.

It would infer the cluster configurations with latest changes and save the new results.


import requests

import json

token_url = "http://localhost:8080/oauth/token"

infer_configuration_api_url = "http://localhost:8080/api/v1/configurations/infer"

save_configuration_api_url = "http://localhost:8080/api/v1/configurations"

#Step A - resource owner supplies credential #Resource owner (enduser) credentials

#input your own username

RO_user = 'admin'

#input your own password

RO_password = 'admin'

#client (application) credentials

client_id = 'sparkflows'
client_secret = 'secret'

#step B, C - single call with resource owner credentials in the body and client credentials as the basic auth header will return#access_token

data = {'grant_type': 'password','username': RO_user, 'password': RO_password}

access_token_response =, data=data, verify=False, allow_redirects=False, auth=(client_id, client_secret))


tokens = json.loads(access_token_response.text)
print( "access token: " + tokens['access_token'])

# Step- now use the access_token to call infer configuration api and its save api.

api_call_headers = {'Authorization': 'Bearer ' + tokens['access_token']}

print( api_call_headers)

#infer the hadoop configuration

infer_configuration_api_response = requests.get(infer_configuration_api_url, headers=api_call_headers, verify=False)
print(" infer configuration response : "+ infer_configuration_api_response.text)

#save the hadoop configuration

save_configuration_api_response =,infer_configuration_api_response, headers=api_call_headers,   verify=False)

print(" configuration after save : "+save_configuration_api_response.text)