Validating a yaml document in python

Question

One of the benefits of XML is being able to validate a document against an XSD. YAML doesn't have this feature, so how can I validate that the YAML document I open is in the format expected by my application?

Answer 1

Given that JSON and YAML are pretty similar beasts, you could make use of JSON-Schema to validate a sizable subset of YAML. Here's a code snippet (you'll need PyYAML and jsonschema installed):

from jsonschema import validate import yaml  schema = """ type: object properties:   testing:     type: array     items:       enum:         - this         - is         - a         - test """  good_instance = """ testing: ['this', 'is', 'a', 'test'] """  validate(yaml.load(good_instance), yaml.load(schema)) # passes  # Now let's try a bad instance...  bad_instance = """ testing: ['this', 'is', 'a', 'bad', 'test'] """  validate(yaml.load(bad_instance), yaml.load(schema))  # Fails with: # ValidationError: 'bad' is not one of ['this', 'is', 'a', 'test'] # # Failed validating 'enum' in schema['properties']['testing']['items']: #     {'enum': ['this', 'is', 'a', 'test']} # # On instance['testing'][3]: #     'bad' 

One problem with this is that if your schema spans multiple files and you use "$ref" to reference the other files then those other files will need to be JSON, I think. But there are probably ways around that. In my own project, I'm playing with specifying the schema using JSON files whilst the instances are YAML.

Answer 2

I find Cerberus to be very reliable with great documentation and straightforward to use.

Here is a basic implementation example:

my_yaml.yaml:

name: 'my_name' date: 2017-10-01 metrics:     percentage:     value: 87     trend: stable 

Defining the validation schema in schema.py:

{     'name': {         'required': True,         'type': 'string'     },     'date': {         'required': True,         'type': 'date'     },     'metrics': {         'required': True,         'type': 'dict',         'schema': {             'percentage': {                 'required': True,                 'type': 'dict',                 'schema': {                     'value': {                         'required': True,                         'type': 'number',                         'min': 0,                         'max': 100                     },                     'trend': {                         'type': 'string',                         'nullable': True,                         'regex': '^(?i)(down|equal|up)$'                     }                 }             }         }     } } 

Using the PyYaml to load a yaml document:

import yaml def load_doc():     with open('./my_yaml.yaml', 'r') as stream:         try:             return yaml.load(stream)         except yaml.YAMLError as exception:             raise exception  ## Now, validating the yaml file is straightforward: from cerberus import Validator schema = eval(open('./schema.py', 'r').read())     v = Validator(schema)     doc = load_doc()     print(v.validate(doc, schema))     print(v.errors) 

Keep in mind that Cerberus is an agnostic data validation tool, which means that it can support formats other than YAML, such as JSON, XML and so on.