colander schema for mapping where keys are variable but value are arrays

Question

How do I define the schema in colander for JSON of the following form?

{
    'data' : {
        'key_1' : [123, 567],
        'key_2' : ['abc','def'],
        'frank_underwood' : [666.66, 333.333],
        ... etc ...
    }
}

The keys inside 'data' could be any string and values are arrays.

Currently, I have the following but it doesn't really put any constraints on the types of values the mapping can have.

class Query(colander.MappingSchema):
    data = colander.SchemaNode(
        colander.Mapping(unknown='preserve'),
        missing={}
    )

What's the proper way of describing this?

Answer 1

A possible solution is to use a custom validator.

Here is a full working example of a custom validator that checks if all values of an arbitrary map are singularly typed arrays.

import colander


def values_are_singularly_typed_arrays(node, mapping):
    for val in mapping.values():
        if not isinstance(val, list):
            raise colander.Invalid(node, "one or more value(s) is not a list")
        if not len(set(map(type, val))) == 1:
            raise colander.Invalid(node, "one or more value(s) is a list with mixed types")

class MySchema(colander.MappingSchema):
    data = colander.SchemaNode(
        colander.Mapping(unknown='preserve'),
        validator=values_are_singularly_typed_arrays
    )

def main():
    valid_data = {
        'data' : {
            'numbers' : [1,2,3],
            'reals' : [1.2,3.4,5.6],
        }
    }
    not_list = {
        'data' : {
            'numbers' : [1,2,3],
            'error_here' : 123
        }
    }
    mixed_type = {
        'data' : {
            'numbers' : [1,2,3],
            'error_here' : [123, 'for the watch']
        }
    }

    schema = MySchema()
    schema.deserialize(valid_data)

    try:
        schema.deserialize(not_list)
    except colander.Invalid as e:
        print(e.asdict())

    try:
        schema.deserialize(mixed_type)
    except colander.Invalid as e:
        print(e.asdict())

if __name__ == '__main__':
    main()

Answer 2

I don't know about colander but you could use Spyne.

class Data(ComplexModel):
    key_1 = Array(Integer)
    key_2 = Array(Unicode)
    frank_underwood = Array(Double)

class Wrapper(ComplexModel):
    data = Data

Full working example: https://gist.github.com/plq/3081280856ed1c0515de

Spyne's model docs: http://spyne.io/docs/2.10/manual/03_types.html

---

However, turns out that's not what you need. If you want a more loosely-specified dictionary, then you need to resort to using a custom type:

class DictOfUniformArray(AnyDict):
    @staticmethod  # yes staticmethod
    def validate_native(cls, inst):
        for k, v in inst.items():
            if not isinstance(k, six.string_types):
                raise ValidationError(type(k), "Invalid key type %r")
            if not isinstance(v, list):
                raise ValidationError(type(v), "Invalid value type %r")
            # log_repr prevents too much data going in the logs.
            if not len(set(map(type, v))) == 1:
                raise ValidationError(log_repr(v),
                                      "List %s is not uniform")
        return True

class Wrapper(ComplexModel):
    data = DictOfUniformArray

Full working exaple: https://github.com/arskom/spyne/blob/spyne-2.12.5-beta/examples/custom_type.py