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https://github.com/alecthomas/voluptuous

CONTRIBUTIONS ONLY: Voluptuous, despite the name, is a Python data validation library.
https://github.com/alecthomas/voluptuous

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CONTRIBUTIONS ONLY: Voluptuous, despite the name, is a Python data validation library.

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# CONTRIBUTIONS ONLY



**What does this mean?** I do not have time to fix issues myself. The only way fixes or new features will be added is by people submitting PRs.



**Current status:** Voluptuous is largely feature stable. There hasn't been a need to add new features in a while, but there are some bugs that should be fixed.



**Why?** I no longer use Voluptuous personally (in fact I no longer regularly write Python code). Rather than leave the project in a limbo of people filing issues and wondering why they're not being worked on, I believe this notice will more clearly set expectations.



# Voluptuous is a Python data validation library



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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.



## Contact



Voluptuous now has a mailing list! Send a mail to

[](mailto:[email protected]) to subscribe. Instructions

will follow.



You can also contact me directly via [email](mailto:[email protected]) or

[Twitter](https://twitter.com/alecthomas).



To file a bug, create a [new issue](https://github.com/alecthomas/voluptuous/issues/new) on GitHub with a short example of how to replicate the issue.



## Documentation



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



## Contribution to Documentation



Documentation is built using `Sphinx`. You can install it by



    pip install -r requirements.txt



For building `sphinx-apidoc` from scratch you need to set PYTHONPATH to `voluptuous/voluptuous` repository.



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



## Changelog



See [CHANGELOG.md](https://github.com/alecthomas/voluptuous/blob/master/CHANGELOG.md).



## 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.



## Show me an example



Twitter's [user search API](https://dev.twitter.com/rest/reference/get/users/search) accepts

query URLs like:



```bash

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

```



To validate this we might use a schema like:



```pycon

>>> 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:



```pycon

>>> 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:



```pycon

>>> 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:



```pycon

>>> 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:



```pycon

>>> 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:



```pycon

>>> 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:



```pycon

>>> 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.



### Literals



Literals in the schema are matched using normal equality checks:



```pycon

>>> 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:



```pycon

>>> 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

```



### URLs



URLs in the schema are matched by using `urlparse` library.



```pycon

>>> 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

```



### 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:



```pycon

>>> 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']

```



However, an empty list (`[]`) is treated as is. If you want to specify a list that can

contain anything, specify it as `list`:



```pycon

>>> schema = Schema([])

>>> try:

...   schema([1])

...   raise AssertionError('MultipleInvalid not raised')

... except MultipleInvalid as e:

...   exc = e

>>> str(exc) == "not a valid value @ data[1]"

True

>>> schema([])

[]

>>> schema = Schema(list)

>>> schema([])

[]

>>> schema([1, 2])

[1, 2]

```



### Sets and frozensets



Sets and frozensets are treated as a set of valid values. Each element

in the schema set is compared to each value in the input data:



```pycon

>>> schema = Schema({42})

>>> schema({42}) == {42}

True

>>> try:

...   schema({43})

...   raise AssertionError('MultipleInvalid not raised')

... except MultipleInvalid as e:

...   exc = e

>>> str(exc) == "invalid value in set"

True

>>> schema = Schema({int})

>>> schema({1, 2, 3}) == {1, 2, 3}

True

>>> schema = Schema({int, str})

>>> schema({1, 2, 'abc'}) == {1, 2, 'abc'}

True

>>> schema = Schema(frozenset([int]))

>>> try:

...   schema({3})

...   raise AssertionError('Invalid not raised')

... except Invalid as e:

...   exc = e

>>> str(exc) == 'expected a frozenset'

True

```



However, an empty set (`set()`) is treated as is. If you want to specify a set

that can contain anything, specify it as `set`:



```pycon

>>> schema = Schema(set())

>>> try:

...   schema({1})

...   raise AssertionError('MultipleInvalid not raised')

... except MultipleInvalid as e:

...   exc = e

>>> str(exc) == "invalid value in set"

True

>>> schema(set()) == set()

True

>>> schema = Schema(set)

>>> schema({1, 2}) == {1, 2}

True

```



### 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:



```pycon

>>> from datetime import datetime

>>> def Date(fmt='%Y-%m-%d'):

...   return lambda v: datetime.strptime(v, fmt)

```



```pycon

>>> 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

```



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:



```python

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:



```pycon

>>> 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:



```pycon

>>> 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

```



This behaviour can be altered on a per-schema basis. To allow

additional keys use

`Schema(..., extra=ALLOW_EXTRA)`:



```pycon

>>> from voluptuous import ALLOW_EXTRA

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

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

{1: 2, 2: 3}

```



To remove additional keys use

`Schema(..., extra=REMOVE_EXTRA)`:



```pycon

>>> from voluptuous import REMOVE_EXTRA

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

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

{2: 3}

```



It can also be overridden per-dictionary by using the catch-all marker

token `extra` as a key:



```pycon

>>> 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:



```pycon

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

>>> schema({3: 4})

{3: 4}

```



Similarly to how extra\_ keys work, this behaviour can be overridden

per-schema:



```pycon

>>> 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

```



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



```pycon

>>> 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}

```



#### Optional dictionary keys



If a schema has `required=True`, keys may be individually marked as

optional using the marker token `Optional(key)`:



```pycon

>>> 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

```



```pycon

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

{1: 2, 3: 4}

```



### Recursive / nested schema



You can use `voluptuous.Self` to define a nested schema:



```pycon

>>> from voluptuous import Schema, Self

>>> recursive = Schema({"more": Self, "value": int})

>>> recursive({"more": {"value": 42}, "value": 41}) == {'more': {'value': 42}, 'value': 41}

True

```



### Extending an existing Schema



Often it comes handy to have a base `Schema` that is extended with more

requirements. In that case you can use `Schema.extend` to create a new

`Schema`:



```pycon

>>> from voluptuous import Schema

>>> person = Schema({'name': str})

>>> person_with_age = person.extend({'age': int})

>>> sorted(list(person_with_age.schema.keys()))

['age', 'name']

```



The original `Schema` remains unchanged.



### Objects



Each key-value pair in a schema dictionary is validated against each

attribute-value pair in the corresponding object:



```pycon

>>> from voluptuous import Object

>>> class Structure(object):

...     def __init__(self, q=None):

...         self.q = q

...     def __repr__(self):

...         return ''.format(self)

...

>>> schema = Schema(Object({'q': 'one'}, cls=Structure))

>>> schema(Structure(q='one'))



```



### Allow None values



To allow value to be None as well, use Any:



```pycon

>>> 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.



```pycon

>>> 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:



```pycon

>>> 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:



```pycon

>>> 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:



```pycon

>>> schema([6])

[6]

```



## Multi-field validation



Validation rules that involve multiple fields can be implemented as

custom validators. It's recommended to use `All()` to do a two-pass

validation - the first pass checking the basic structure of the data,

and only after that, the second pass applying your cross-field

validator:



```python

def passwords_must_match(passwords):

    if passwords['password'] != passwords['password_again']:

        raise Invalid('passwords must match')

    return passwords



schema = Schema(All(

    # First "pass" for field types

    {'password': str, 'password_again': str},

    # Follow up the first "pass" with your multi-field rules

    passwords_must_match

))



# valid

schema({'password': '123', 'password_again': '123'})



# raises MultipleInvalid: passwords must match

schema({'password': '123', 'password_again': 'and now for something completely different'})



```



With this structure, your multi-field validator will run with

pre-validated data from the first "pass" and so will not have to do

its own type checking on its inputs.



The flipside is that if the first "pass" of validation fails, your

cross-field validator will not run:



```python

# raises Invalid because password_again is not a string

# passwords_must_match() will not run because first-pass validation already failed

schema({'password': '123', 'password_again': 1337})

```



## Running tests



Voluptuous is using `pytest`:



```bash

$ pip install pytest

$ pytest

```



To also include a coverage report:



```bash

$ pip install pytest pytest-cov coverage>=3.0

$ pytest --cov=voluptuous voluptuous/tests/

```



## Other libraries and inspirations



Voluptuous is heavily inspired by

[Validino](http://code.google.com/p/validino/), and to a lesser extent,

[jsonvalidator](http://code.google.com/p/jsonvalidator/) and

[json\_schema](http://blog.sendapatch.se/category/json_schema.html).



[pytest-voluptuous](https://github.com/F-Secure/pytest-voluptuous) is a

[pytest](https://github.com/pytest-dev/pytest) plugin that helps in

using voluptuous validators in `assert`s.



I greatly prefer the light-weight style promoted by these libraries to

the complexity of libraries like FormEncode.