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Pytest plugin for asserting data against voluptuous schema.

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A pytest plugin for asserting data against voluptuous schema.

Common use case is to validate HTTP API responses (in your functional tests):

import requests
from pytest_voluptuous import S, Partial, Exact
from voluptuous.validators import All, Length

def test_pypi():
   resp = requests.get('')
   assert S({
      'info': Partial({
          'package_url': '',
          'platform': 'INVALID VALUE',
          'description': Length(max=10),
          'downloads': list,
          'classifiers': dict,
      'releases': {
         any: dict
      'urls': int
   }) == resp.json()

If validation fails, comparison returns False and assert fails, printing error details:

E       AssertionError: assert failed to validation error(s):
E         - info.platform: not a valid value for dictionary value @ data[u'info'][u'platform']
E         - info.description: length of value must be at most 10 for dictionary value @ data[u'info'][u'description']
E         - info.downloads: expected list for dictionary value @ data[u'info'][u'downloads']
E         - info.classifiers: expected dict for dictionary value @ data[u'info'][u'classifiers']
E         - urls: expected int for dictionary value @ data[u'urls']
E         - releases.3.1.3: expected dict for dictionary value @ data[u'releases'][u'3.1.3']


Works on python 2.7 and 3.4+:

pip install pytest-voluptuous




  • Provides utility schemas (S, Exact and Partial) to cut down boilerplate.

  • Implement a pytest hook to provide error details on assert failure.

  • Print descriptive validation failure messages.

  • Equal and Unordered validators (contributed to voluptuous project, available in 0.10+).


Because writing:

>>> r = {'info': {'package_url': ''}}
>>> assert 'info' in r
>>> assert 'package_url' in r['info']
>>> assert r['info']['package_url'] == ''

…is just way too annoying.

Why not JSON schema? It’s too verbose, too inconvenient. JSON schema will never match the convenience of a validation library that can utilize the goodies of the platform.

Why voluptuous and not some other library? No special reason - but it’s pretty easy to use and understand. Also, the syntax is quite compact.


In pytest:

>>> import requests
>>> from pytest_voluptuous import S, Partial, Exact
>>> from voluptuous.validators import All, Length
>>> resp = requests.get('')
>>> assert S({
...     'info': Partial({
...         'package_url': '',
...         'platform': 'unix',
...         'description': Length(min=100),
...         'downloads': dict,
...         'classifiers': list,
...     }),
...     'releases': {
...         any: list
...     },
...     'urls': list
... }) == resp.json()

Note: if you run this in shell, there’s no pytest magic in play and in case of failure, you’ll just get AssertionError as in:

>>> assert S({'does_not_exist': 1}) == resp.json()
Traceback (most recent call last):

Don’t worry - the promised magic comes into play when you run the validation in a pytest test.

Use == operator to do exact validation:

>>> data = {'foo': 1, 'bar': True}
>>> S({'foo': 1, 'bar': True}) == data

We omit assert in these examples (for easier doctesting).

Use <= to do partial validation (to allow extra keys, that is):

>>> S({'foo': 1}) == data  # not valid
>>> S({'foo': 1}) <= data  # valid

The operator you choose gets inherited, so with test data of:

>>> data = {
...     'outer1': {
...         'inner1': 1,
...         'inner2': True
...     },
...     'outer2': 'foo'
... }

With == you must provide exact value also in nested context:

>>> S({
...     'outer1': {
...         'inner1': 1,  # this would be valid but...
...         # missing 'inner2'
...     },
...     'outer2': 'foo'
... }) == data
>>> S({
...     'outer1': {
...         'inner1': int,  # exact/partial matching
...         'inner2': bool  # is for keys only
...     },
...     'outer2': 'foo'
... }) == data

<= implies partial matching:

>>> S({
...     'outer1': {
...         'inner1': int,
...         # 'inner2' missing but that's ok
...     },
...     # 'outer2' is missing too
... }) <= data

When you need to mix and match operators, you can loosen matching with Partial:

>>> S({
...     'outer1': Partial({
...         'inner1': int
...         # 'inner2' ok to omit as scope is partial
...     }),
...     'outer2': 'foo'  # can't be missing as outer scope is exact
... }) == data

And stricten with Exact:

>>> S({
...     'outer1': Exact({
...         'inner1': int,
...         'inner2': bool
...     }),
...     # 'outer2' can be missing as outer scope is partial
... }) <= data

Remember, matching mode is inherited, so you may end up doing stuff like this:

>>> data['outer1']['inner1'] = {'prop': 1}
>>> S({
...     'outer1': Partial({
...         'inner1': Exact({
...             'prop': 1
...         })
...     }),
...     'outer2': 'foo'
... }) == data

There is no >=. If you want to declare schema keys that may be missing, use Optional:

>>> from voluptuous.schema_builder import Optional
>>> S({Optional('foo'): str}) == {'extra': 1}
>>> S({'foo': str}) == {}
>>> S({'foo': str}) <= {}
>>> S({Optional('foo'): str}) == {}
>>> S({Optional('foo'): str}) <= {'extra': 1}

Or, if you want to make all keys optional, override required:

>>> from voluptuous.schema_builder import Required
>>> S({'foo': str}, required=False) == {}

In these cases, if you want to require a key:

>>> S({'foo': str, Required('bar'): 1}, required=False) == {}
>>> S({'foo': str, Required('bar'): 1}, required=False) == {'bar': 1}

That’s it. For available validators, look into voluptuous docs.


Voluptuous 0.9.3 and earlier:

In voluptuous pre-0.10.2 [] matches any list, not an empty list. To declare an empty list, use Equal([]).

Similarly, in voluptuous pre-0.10.2, {} doesn’t always match an empty dict. If you’re inside a Schema({...}, extra=PREVENT_EXTRA) (or Exact), {} does indeed match exactly {}. However, inside Schema({...}, extra=ALLOW_EXTRA) (or ``Partial), it matches any dict (because any extra keys are allowed). To declare an empty dict, use Equal({}).

Voluptuous 0.10.0+:

In voluptuous 0.10.0+ {} and [] evaluate as empty dict and empty list, so you don’t need above workarounds.

Always use dict and list to validate dict or list of any size. It works despite voluptuous version.

Any version:

[str, int] matches any list that contains both strings and ints (in any order and 1-n times). To validate a list of fixed length with those types in it, use ExactSequence([str, int]) and Unordered([str, int]) when the order has no meaning. You can also use values inside these as in ExactSequence([2, 3]).


Apache 2.0 licensed. See LICENSE for more details.


1.0.1 (2017-01-10)

First public version.

1.0.0 (2016-12-07)

First version.

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