voluptuous 0.10.2

Contact

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You can also contact me directly via email or Twitter.

To file a bug, create a new issue on GitHub with a short example of how to replicate the issue.

Documentation

The documentation is provided [here] (http://alecthomas.github.io/voluptuous/).

Changelog

See CHANGELOG.md.

Show me an example

Twitter’s user search API accepts query URLs like:

$ curl 'http://api.twitter.com/1.1/users/search.json?q=python&per_page=20&page=1'

To validate this we might use a schema like:

>>> from voluptuous import Schema
>>> schema = Schema({
...   'q': str,
...   'per_page': int,
...   'page': int,
... })

This schema very succinctly and roughly describes the data required by the API, and will work fine. But it has a few problems. Firstly, it doesn’t fully express the constraints of the API. According to the API, per_page should be restricted to at most 20, defaulting to 5, for example. To describe the semantics of the API more accurately, our schema will need to be more thoroughly defined:

>>> from voluptuous import Required, All, Length, Range
>>> schema = Schema({
...   Required('q'): All(str, Length(min=1)),
...   Required('per_page', default=5): All(int, Range(min=1, max=20)),
...   'page': All(int, Range(min=0)),
... })

This schema fully enforces the interface defined in Twitter’s documentation, and goes a little further for completeness.

“q” is required:

>>> from voluptuous import MultipleInvalid, Invalid
>>> try:
...   schema({})
...   raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
...   exc = e
>>> str(exc) == "required key not provided @ data['q']"
True

…must be a string:

>>> try:
...   schema({'q': 123})
...   raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
...   exc = e
>>> str(exc) == "expected str for dictionary value @ data['q']"
True

…and must be at least one character in length:

>>> try:
...   schema({'q': ''})
...   raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
...   exc = e
>>> str(exc) == "length of value must be at least 1 for dictionary value @ data['q']"
True
>>> schema({'q': '#topic'}) == {'q': '#topic', 'per_page': 5}
True

“per_page” is a positive integer no greater than 20:

>>> try:
...   schema({'q': '#topic', 'per_page': 900})
...   raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
...   exc = e
>>> str(exc) == "value must be at most 20 for dictionary value @ data['per_page']"
True
>>> try:
...   schema({'q': '#topic', 'per_page': -10})
...   raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
...   exc = e
>>> str(exc) == "value must be at least 1 for dictionary value @ data['per_page']"
True

“page” is an integer >= 0:

>>> try:
...   schema({'q': '#topic', 'per_page': 'one'})
...   raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
...   exc = e
>>> str(exc)
"expected int for dictionary value @ data['per_page']"
>>> schema({'q': '#topic', 'page': 1}) == {'q': '#topic', 'page': 1, 'per_page': 5}
True

Defining schemas

Schemas are nested data structures consisting of dictionaries, lists, scalars and validators. Each node in the input schema is pattern matched against corresponding nodes in the input data.

>>> schema = Schema(1)
>>> schema(1)
1
>>> schema = Schema('a string')
>>> schema('a string')
'a string'

>>> schema = Schema(int)
>>> schema(1)
1
>>> try:
...   schema('one')
...   raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
...   exc = e
>>> str(exc) == "expected int"
True

>>> from voluptuous import Url
>>> schema = Schema(Url())
>>> schema('http://w3.org')
'http://w3.org'
>>> try:
...   schema('one')
...   raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
...   exc = e
>>> str(exc) == "expected a URL"
True

>>> schema = Schema([1, 'a', 'string'])
>>> schema([1])
[1]
>>> schema([1, 1, 1])
[1, 1, 1]
>>> schema(['a', 1, 'string', 1, 'string'])
['a', 1, 'string', 1, 'string']

>>> schema = Schema([])
>>> try:
...   schema([1])
...   raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
...   exc = e
>>> str(exc) == "not a valid value"
True
>>> schema([])
[]
>>> schema = Schema(list)
>>> schema([])
[]
>>> schema([1, 2])
[1, 2]

>>> from datetime import datetime
>>> def Date(fmt='%Y-%m-%d'):
...   return lambda v: datetime.strptime(v, fmt)

>>> schema = Schema(Date())
>>> schema('2013-03-03')
datetime.datetime(2013, 3, 3, 0, 0)
>>> try:
...   schema('2013-03')
...   raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
...   exc = e
>>> str(exc) == "not a valid value"
True

def Coerce(type, msg=None):
    """Coerce a value to a type.

    If the type constructor throws a ValueError, the value will be marked as
    Invalid.
    """
    def f(v):
        try:
            return type(v)
        except ValueError:
            raise Invalid(msg or ('expected %s' % type.__name__))
    return f

>>> schema = Schema({1: 'one', 2: 'two'})
>>> schema({1: 'one'})
{1: 'one'}

>>> schema = Schema({2: 3})
>>> try:
...   schema({1: 2, 2: 3})
...   raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
...   exc = e
>>> str(exc) == "extra keys not allowed @ data[1]"
True

>>> from voluptuous import ALLOW_EXTRA
>>> schema = Schema({2: 3}, extra=ALLOW_EXTRA)
>>> schema({1: 2, 2: 3})
{1: 2, 2: 3}

>>> from voluptuous import REMOVE_EXTRA
>>> schema = Schema({2: 3}, extra=REMOVE_EXTRA)
>>> schema({1: 2, 2: 3})
{2: 3}

>>> from voluptuous import Extra
>>> schema = Schema({1: {Extra: object}})
>>> schema({1: {'foo': 'bar'}})
{1: {'foo': 'bar'}}

>>> schema = Schema({}, extra=ALLOW_EXTRA)  # don't do this
>>> try:
...   schema({'extra': 1})
...   raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
...   exc = e
>>> str(exc) == "not a valid value"
True
>>> schema({})
{}
>>> schema = Schema(dict)  # do this instead
>>> schema({})
{}
>>> schema({'extra': 1})
{'extra': 1}

>>> schema = Schema({1: 2, 3: 4})
>>> schema({3: 4})
{3: 4}

>>> schema = Schema({1: 2, 3: 4}, required=True)
>>> try:
...   schema({3: 4})
...   raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
...   exc = e
>>> str(exc) == "required key not provided @ data[1]"
True

>>> schema = Schema({Required(1): 2, 3: 4})
>>> try:
...   schema({3: 4})
...   raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
...   exc = e
>>> str(exc) == "required key not provided @ data[1]"
True
>>> schema({1: 2})
{1: 2}

>>> from voluptuous import Optional
>>> schema = Schema({1: 2, Optional(3): 4}, required=True)
>>> try:
...   schema({})
...   raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
...   exc = e
>>> str(exc) == "required key not provided @ data[1]"
True
>>> schema({1: 2})
{1: 2}
>>> try:
...   schema({1: 2, 4: 5})
...   raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
...   exc = e
>>> str(exc) == "extra keys not allowed @ data[4]"
True

>>> schema({1: 2, 3: 4})
{1: 2, 3: 4}

>>> from voluptuous import Schema, Any
>>> def s2(v):
...     return s1(v)
...
>>> s1 = Schema({"key": Any(s2, "value")})
>>> s1({"key": {"key": "value"}})
{'key': {'key': 'value'}}

>>> from voluptuous import Schema
>>> person = Schema({'name': str})
>>> person_with_age = person.extend({'age': int})
>>> sorted(list(person_with_age.schema.keys()))
['age', 'name']

>>> from voluptuous import Object
>>> class Structure(object):
...     def __init__(self, q=None):
...         self.q = q
...     def __repr__(self):
...         return '<Structure(q={0.q!r})>'.format(self)
...
>>> schema = Schema(Object({'q': 'one'}, cls=Structure))
>>> schema(Structure(q='one'))
<Structure(q='one')>

>>> from voluptuous import Any

>>> schema = Schema(Any(None, int))
>>> schema(None)
>>> schema(5)
5

Error reporting

Validators must throw an Invalid exception if invalid data is passed to them. All other exceptions are treated as errors in the validator and will not be caught.

Each Invalid exception has an associated path attribute representing the path in the data structure to our currently validating value, as well as an error_message attribute that contains the message of the original exception. This is especially useful when you want to catch Invalid exceptions and give some feedback to the user, for instance in the context of an HTTP API.

>>> def validate_email(email):
...     """Validate email."""
...     if not "@" in email:
...         raise Invalid("This email is invalid.")
...     return email
>>> schema = Schema({"email": validate_email})
>>> exc = None
>>> try:
...     schema({"email": "whatever"})
... except MultipleInvalid as e:
...     exc = e
>>> str(exc)
"This email is invalid. for dictionary value @ data['email']"
>>> exc.path
['email']
>>> exc.msg
'This email is invalid.'
>>> exc.error_message
'This email is invalid.'

The path attribute is used during error reporting, but also during matching to determine whether an error should be reported to the user or if the next match should be attempted. This is determined by comparing the depth of the path where the check is, to the depth of the path where the error occurred. If the error is more than one level deeper, it is reported.

The upshot of this is that matching is depth-first and fail-fast.

To illustrate this, here is an example schema:

>>> schema = Schema([[2, 3], 6])

Each value in the top-level list is matched depth-first in-order. Given input data of [[6]], the inner list will match the first element of the schema, but the literal 6 will not match any of the elements of that list. This error will be reported back to the user immediately. No backtracking is attempted:

>>> try:
...   schema([[6]])
...   raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
...   exc = e
>>> str(exc) == "not a valid value @ data[0][0]"
True

If we pass the data [6], the 6 is not a list type and so will not recurse into the first element of the schema. Matching will continue on to the second element in the schema, and succeed:

>>> schema([6])
[6]

Running tests.

Voluptuous is using nosetests:

$ nosetests

Why use Voluptuous over another validation library?

Validators are simple callables

No need to subclass anything, just use a function.

Errors are simple exceptions.

A validator can just raise Invalid(msg) and expect the user to get useful messages.

Schemas are basic Python data structures.

Should your data be a dictionary of integer keys to strings? {int: str} does what you expect. List of integers, floats or strings? [int, float, str].

Designed from the ground up for validating more than just forms.

Nested data structures are treated in the same way as any other type. Need a list of dictionaries? [{}]

Consistency.

Types in the schema are checked as types. Values are compared as values. Callables are called to validate. Simple.

Other libraries and inspirations

Voluptuous is heavily inspired by Validino, and to a lesser extent, jsonvalidator and json_schema.

I greatly prefer the light-weight style promoted by these libraries to the complexity of libraries like FormEncode.