Question

Writing a simple data dictionary to csv using the Looker API and the Python requests library

  • 8 December 2016
  • 7 replies
  • 351 views

Userlevel 4

There are many reasons to come up with a document that explains what fields/explores/models are available or exposed in our Looker application. This script is used to parse through the model definition and print out a csv of fields. I use the Looker API to get the model Metadata.


I use python, and while you can use the Looker SDK, I prefer to use the python requests library.


With some other changes, you can easily figure out how fields are set up and audit the model for items such as:



  • Do we follow a consistent naming convention?

  • Do we have redundant or similar fields?

  • Did we do a good job annotating fields via descriptions?


We’ve already done this into an interactive webpage using the Ruby SDK. You can see that here:

https://discourse.looker.com/t/creating-a-data-dictionary-using-lookers-api/3589


endpoints


To get this started, I need to have endpoints for authentication, get_model and get_explore endpoints. I do that with an API class:


class LookerApi(object):

def __init__(self, token, secret, host):

self.token = token
self.secret = secret
self.host = host

self.session = requests.Session()
self.session.verify = False
self.auth()

def auth(self):
url = '{}{}'.format(self.host,'login')
params = {'client_id':self.token,
'client_secret':self.secret
}
r = self.session.post(url,params=params)
access_token = r.json().get('access_token')
# print access_token
self.session.headers.update({'Authorization': 'token {}'.format(access_token)})

# GET /lookml_models/{{NAME}}
def get_model(self,model_name=None,fields={}):
url = '{}{}/{}'.format(self.host,'lookml_models', model_name)
# print url
params = fields
r = self.session.get(url,params=params)
if r.status_code == requests.codes.ok:
return r.json()

# GET /lookml_models/{{NAME}}/explores/{{NAME}}
def get_explore(self,model_name=None,explore_name=None,fields={}):
url = '{}{}/{}/{}/{}'.format(self.host,'lookml_models', model_name, 'explores', explore_name)
print url
params = fields
r = self.session.get(url,params=params)
if r.status_code == requests.codes.ok:
return r.json()

csv writing


Once we can call those endpoints, The script should call for all models, and parse through each explore: calling for all the field information in a loop. We then will write each field and it’s metadata to a new row. For each row, I have created a function to call:


def write_fields(explore, fields):

### First, compile the fields you need for your row

explore_fields=explore['fields']
try:
connection_name = str(explore['connection_name'])
except:
connection_name = ''
for dimension in explore_fields[fields]:
# print dimension

field_type = fields
project = str(dimension['project_name'])
explore = str(explore_def['name'])
view=str(dimension['view'])
view_label=str(dimension['view_label'])
name=str(dimension['name'])
hidden=str(dimension['hidden'])
label=str(dimension['label'])
label_short=str(dimension['label_short'])
description=str(dimension['description'])
sql=str(dimension['sql'])
ftype=str(dimension['type'])
value_format=str(dimension['value_format'])
source = str(dimension['source_file'])

### compile the line - this is possible to combine above, but here to keep things simple
rowout = []
rowout.append(connection_name)
rowout.append(field_type)
rowout.append(project)
rowout.append(explore)
rowout.append(view)
rowout.append(view_label)
rowout.append(name)
rowout.append(hidden)
rowout.append(label)
rowout.append(label_short)
rowout.append(description)
rowout.append(sql)
rowout.append(ftype)
rowout.append(value_format)
rowout.append(source)

w.writerow(rowout)

csv formatting


Then all I need is to instantiate the API, open a CSV, write the header, and then iterate through my models. using the csv library we can start a csv with this code:


csvfile= open('dictionary.csv', 'wb')

w = csv.writer(csvfile, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
header = ['connection_name',
'field_type',
'project',
'explore',
'view',
'view_label',
'name',
'hidden',
'label',
'label_short',
'description',
'sql',
'ftype',
'value_format',
'source']

w.writerow(header)

Parse the model.


The rest of the script looks like this:




  • Get looker API 3.0 Credentials




  • Call for the model




  • Parse through the model and write each field as a row into our csv




  • close the file


    f = open(‘config.yml’)

    params = yaml.load(f)

    f.close()


    hostname = ‘localhost’


    my_host = params[‘hosts’][hostname][‘host’]

    my_secret = params[‘hosts’][hostname][‘secret’]

    my_token = params[‘hosts’][hostname][‘token’]


    looker = LookerApi(host=my_host,

    token=my_token,

    secret = my_secret)


    --------- API Calls -------------


    – Get all models –


    models = looker.get_model("")


    pp(models)


    for model in models:

    model_name = model[‘name’]


      ## -- Get single model --
    model_def = looker.get_model(model_name)
    # pp(model_def)

    ## -- Get single explore --
    for explore_def in model_def['explores']:
    explore=looker.get_explore(model_name, explore_def['name'])
    # pp(explore)
    ## -- parse explore --

    try:
    write_fields(explore,'measures')
    except:
    print 'Problem measure fields in ', explore_def['name']
    try:
    write_fields(explore,'dimensions')
    except:
    print 'Problem dimension fields in ', explore_def['name']



The end result of executing this file is a csv file called “dictionary.csv”



Check out the full script called get_data_dictionary.py here: https://github.com/llooker/python_api_samples



Note: the link will use a LookerAPI.py file to hold the class, and a configuration file for keys. Check the readme for setting this up.



7 replies

Userlevel 1

I’m trying to get this working, but have used Python only a couple times before. I have 2.7 installed, and am getting stuck on trying to import yaml. I’m guessing it’s a basic config/operator error, can you offer any suggestions?


C:\Python27>python get_data_dictionary.py
Traceback (most recent call last):
File "get_data_dictionary.py", line 2, in <module>
import yaml
ImportError: No module named yaml

===============================================


It never fails, after I feel stuck enough to post, I find the answer, in a comment in another sample module:


import yaml ### YOU NEED THE pyyaml PACKAGE : [sudo] pip install pyyaml


That makes sense, and it works.

Userlevel 3
Badge

@la5rocks, the module yaml is provided by PyYAML package, so pip install PyYAML should get the package installed for you.

Please let us know if that sorts out this issue for you.

Hey! Awesome post. trying to run same get_data_dictionary.py i’m getting this:

https://looker-xxx.com:19999/api/3.1/lookml_models/


None


Traceback (most recent call last):


File “get_data_dictionary.py”, line 121, in <module>


for model in models:


TypeError: ‘NoneType’ object is not iterable


that means no data is being extracted/written to the csv.

when accessing directly the link in the outpur (https://looker-xxx.com:19999/api/3.1/lookml_models/) i got a requires authentication error. anyone knows what i’m doing wrong?

thanks,

Mihai

Userlevel 7
Badge +1

The requires authentication error makes me think it’s likely related to the API keys, secret, or host URL specified on lines 11-26. Are you absolutely sure they’re correct? Or, could you try running another one of those scripts that’s more simple like get_look to see if it’s a problem with your configuration, or a problem with that script?

Thanks!

Manage to fix it ultimately. i added wrong/viceversa the id and secret key 🙂

Cheers

Userlevel 1

This is also very useful to get a list of strings for localization

Userlevel 4

Looker now offers a native Data Dictionary in beta!


As of version 7.8, you can download the Data Dictionary from your Marketplace. You can find documentation here. It only takes a few clicks to install, and comes with a host of functionality such as:



  • Dedicated UX for searching through field descriptions and metadata

  • Quick filters to quickly identify and audit fields (e.g. find all fields without a description)

  • Preview field values by showing the top 10 values for any given field

  • Simple embedding for consumption in external applications



Please feel free to post any feedback here once you’re up and running!

Reply