Continuing my post series on the tools I use these days in Python, this time I would like to talk about a library I really like, named voluptuous.

It's no secret that most of the time, when a program receives data from the outside, it's a big deal to handle it. Indeed, most of the time your program has no guarantee that the stream is valid and that it contains what is expected.

The robustness principle says you should be liberal in what you accept, though that is not always a good idea neither. Whatever policy you chose, you need to process those data and implement a policy that will work – lax or not.

That means that the program need to look into the data received, check that it finds everything it needs, complete what might be missing (e.g. set some default), transform some data, and maybe reject those data in the end.

Data validation

The first step is to validate the data, which means checking all the fields are there and all the types are right or understandable (parseable). Voluptuous provides a single interface for all that called a Schema.

>>> from voluptuous import Schema
>>> s = Schema({
...   'q': str,
...   'per_page': int,
...   'page': int,
... })
>>> s({"q": "hello"})
{'q': 'hello'}
>>> s({"q": "hello", "page": "world"})
voluptuous.MultipleInvalid: expected int for dictionary value @ data['page']
>>> s({"q": "hello", "unknown": "key"})
voluptuous.MultipleInvalid: extra keys not allowed @ data['unknown']

The argument to voluptuous.Schema should be the data structure that you expect. Voluptuous accepts any kind of data structure, so it could also be a simple string or an array of dict of array of integer. You get it. Here it's a dict with a few keys that if present should be validated as certain types. By default, Voluptuous does not raise an error if some keys are missing. However, it is invalid to have extra keys in a dict by default. If you want to allow extra keys, it is possible to specify it.

>>> from voluptuous import Schema
>>> s = Schema({"foo": str}, extra=True)
>>> s({"bar": 2})
{"bar": 2}

It is also possible to make some keys mandatory.

>>> from voluptuous import Schema, Required
>>> s = Schema({Required("foo"): str})
>>> s({})
voluptuous.MultipleInvalid: required key not provided @ data['foo']

You can create custom data type very easily. Voluptuous data types are actually just functions that are called with one argument, the value, and that should either return the value or raise an Invalid or ValueError exception.

>>> from voluptuous import Schema, Invalid
>>> def StringWithLength5(value):
...     if isinstance(value, str) and len(value) == 5:
...             return value
...     raise Invalid("Not a string with 5 chars")
>>> s = Schema(StringWithLength5)
>>> s("hello")
>>> s("hello world")
voluptuous.MultipleInvalid: Not a string with 5 chars

Most of the time though, there is no need to create your own data types. Voluptuous provides logical operators that can, combined with a few others provided primitives such as voluptuous.Length or voluptuous.Range, create a large range of validation scheme.

>>> from voluptuous import Schema, Length, All
>>> s = Schema(All(str, Length(min=3, max=5)))
>>> s("hello")
>>> s("hello world")
voluptuous.MultipleInvalid: length of value must be at most 5

The voluptuous documentation has a good set of examples that you can check to have a good overview of what you can do.

Data transformation

What's important to remember, is that each data type that you use is a function that is called and returns a value, if the value is considered valid. That value returned is what is actually used and returned after the schema validation:

>>> import uuid
>>> from voluptuous import Schema
>>> def UUID(value):
...     return uuid.UUID(value)
>>> s = Schema({"foo": UUID})
>>> data_converted = s({"foo": "uuid?"})
voluptuous.MultipleInvalid: not a valid value for dictionary value @ data['foo']
>>> data_converted = s({"foo": "8B7BA51C-DFF5-45DD-B28C-6911A2317D1D"})
>>> data_converted
{'foo': UUID('8b7ba51c-dff5-45dd-b28c-6911a2317d1d')}

By defining a custom UUID function that converts a value to a UUID, the schema converts the string passed in the data to a Python UUID object – validating the format at the same time.

Note a little trick here: it's not possible to use directly uuid.UUID in the schema, otherwise Voluptuous would check that the data is actually an instance of uuid.UUID:

>>> from voluptuous import Schema
>>> s = Schema({"foo": uuid.UUID})
>>> s({"foo": "8B7BA51C-DFF5-45DD-B28C-6911A2317D1D"})
voluptuous.MultipleInvalid: expected UUID for dictionary value @ data['foo']
>>> s({"foo": uuid.uuid4()})
{'foo': UUID('60b6d6c4-e719-47a7-8e2e-b4a4a30631ed')}

And that's not what is wanted here.

That mechanism is really neat to transform, for example, strings to timestamps.

>>> import datetime
>>> from voluptuous import Schema
>>> def Timestamp(value):
...     return datetime.datetime.strptime(value, "%Y-%m-%dT%H:%M:%S")
>>> s = Schema({"foo": Timestamp})
>>> s({"foo": '2015-03-03T12:12:12'})
{'foo': datetime.datetime(2015, 3, 3, 12, 12, 12)}
>>> s({"foo": '2015-03-03T12:12'})

Recursive schemas

So far, Voluptuous has one limitation so far: the ability to have recursive schemas. The simplest way to circumvent it is by using another function as an indirection.

>>> from voluptuous import Schema, Any
>>> def _MySchema(value):
...     return MySchema(value)
>>> from voluptuous import Any
>>> MySchema = Schema({"foo": Any("bar", _MySchema)})
>>> MySchema({"foo": {"foo": "bar"}})
{'foo': {'foo': 'bar'}}
>>> MySchema({"foo": {"foo": "baz"}})
voluptuous.MultipleInvalid: not a valid value for dictionary value @ data['foo']['foo']

Usage in REST API

I started to use Voluptuous to validate data in a the REST API provided by Gnocchi. So far it has been a really good tool, and we've been able to create a complete REST API that is very easy to validate on the server side. I would definitely recommend it for that. It blends with any Web framework easily.

One of the upside compared to solution like JSON Schema, is the ability to create or re-use your own custom data types while converting values at validation time. It is also very Pythonic, and extensible – it's pretty great to use for all of that. It's also not tied to any serialization format.

On the other hand, JSON Schema is language agnostic and is serializable itself as JSON. That makes it easy to be exported and provided to a consumer so it can understand the API and validate the data potentially on its side.