Voluptuous is a Python data validation library

Voluptuous, despite the name, is a Python data validation library. It is primarily intended for validating data coming into Python as JSON, YAML, etc.

It has three goals:

1. Simplicity.
2. Support for complex data structures.
3. Provide useful error messages.

Table of Contents

• Show me an example
• Defining schemas
  • Literals
  • Types
  • Lists
  • Validation functions
  • Dictionaries
    • Extra dictionary keys
    • Required dictionary keys
    • Optional dictionary keys
  • Objects
• Error reporting
• Why use Voluptuous over another validation library?
• Other libraries and inspirations

Show me an example

Twitter's user search API accepts query URLs like:

$ curl 'http://api.twitter.com/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, 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)),
...   'per_page': 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:

>>> schema({})
Traceback (most recent call last):
...
MultipleInvalid: required key not provided @ data['q']

...must be a string:

>>> schema({'q': 123})
Traceback (most recent call last):
...
MultipleInvalid: expected str for dictionary value @ data['q']

...and must be at least one character in length:

>>> schema({'q': ''})
Traceback (most recent call last):
...
MultipleInvalid: length of value must be at least 1 for dictionary value @ data['q']
>>> schema({'q': '#topic'})
{'q': '#topic'}

"per_page" is a positive integer no greater than 20:

>>> schema({'q': '#topic', 'per_page': 900})
Traceback (most recent call last):
...
MultipleInvalid: value must be at most 20 for dictionary value @ data['per_page']
>>> schema({'q': '#topic', 'per_page': -10})
Traceback (most recent call last):
...
MultipleInvalid: value must be at least 1 for dictionary value @ data['per_page']

"page" is an integer >= 0:

>>> schema({'q': '#topic', 'page': 'one'})
Traceback (most recent call last):
...
MultipleInvalid: expected int for dictionary value @ data['page']
>>> schema({'q': '#topic', 'page': 1})
{'q': '#topic', 'page': 1}

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.

Literals

Literals in the schema are matched using normal equality checks:

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

Types

Types in the schema are matched by checking if the corresponding value is an instance of the type:

>>> schema = Schema(int)
>>> schema(1)
1
>>> schema('one')
Traceback (most recent call last):
...
MultipleInvalid: expected int

Lists

Lists in the schema are treated as a set of valid values. Each element in the schema list is compared to each value in the input data:

>>> 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']

Validation functions

Validators are simple callables that raise an Invalid exception when they encounter invalid data. The criteria for determining validity is entirely up to the implementation; it may check that a value is a valid username with pwd.getpwnam(), it may check that a value is of a specific type, and so on.

The simplest kind of validator is a Python function that raises ValueError when its argument is invalid. Conveniently, many builtin Python functions have this property. Here's an example of a date validator:

>>> 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)
>>> schema('2013-03')
Traceback (most recent call last):
...
MultipleInvalid: not a valid value

In addition to simply determining if a value is valid, validators may mutate the value into a valid form. An example of this is the Coerce(type) function, which returns a function that coerces its argument to the given type:

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

This example also shows a common idiom where an optional human-readable message can be provided. This can vastly improve the usefulness of the resulting error messages.

Dictionaries

Each key-value pair in a schema dictionary is validated against each key-value pair in the corresponding data dictionary:

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

Extra dictionary keys

By default any additional keys in the data, not in the schema will trigger exceptions:

>>> schema = Schema({2: 3})
>>> schema({1: 2, 2: 3})
Traceback (most recent call last):
...
MultipleInvalid: extra keys not allowed @ data[1]

This behaviour can be altered on a per-schema basis with Schema(..., extra=True):

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

It can also be overridden per-dictionary by using the catch-all marker token extra as a key:

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

Required dictionary keys

By default, keys in the schema are not required to be in the data:

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

Similarly to how extra keys work, this behaviour can be overridden per-schema:

>>> schema = Schema({1: 2, 3: 4}, required=True)
>>> schema({3: 4})
Traceback (most recent call last):
...
MultipleInvalid: required key not provided @ data[1]

And per-key, with the marker token Required(key):

>>> schema = Schema({Required(1): 2, 3: 4})
>>> schema({3: 4})
Traceback (most recent call last):
...
MultipleInvalid: required key not provided @ data[1]
>>> schema({1: 2})
{1: 2}

Optional dictionary keys

If a schema has required=True, keys may be individually marked as optional using the marker token Optional(key):

>>> from voluptuous import Optional
>>> schema = Schema({1: 2, Optional(3): 4}, required=True)
>>> schema({})
Traceback (most recent call last):
...
MultipleInvalid: required key not provided @ data[1]
>>> schema({1: 2})
{1: 2}
>>> schema({1: 2, 4: 5})
Traceback (most recent call last):
...
MultipleInvalid: extra keys not allowed @ data[4]
>>> schema({1: 2, 3: 4})
{1: 2, 3: 4}

Objects

Each key-value pair in a schema dictionary is validated against each attribute-value pair in the corresponding object:

>>> 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')>

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. This 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:

>>> schema([[6]])
Traceback (most recent call last):
...
MultipleInvalid: invalid list value @ data[0][0]

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]

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.