Validation Rules¶

allow_unknown¶

This can be used in conjunction with the schema rule when validating a mapping in order to set the allow_unknown property of the validator for the subdocument. This rule has precedence over purge_unknown (see Purging Unknown Fields). For a full elaboration refer to this paragraph.

allowed¶

This rule takes a collectionsabc.Container of allowed values. Validates the target value if the value is in the allowed values. If the target value is an iterable, all its members must be in the allowed values.

>>> v.schema = {'role': {'type': 'list', 'allowed': ['agent', 'client', 'supplier']}}
>>> v.validate({'role': ['agent', 'supplier']})
True

>>> v.validate({'role': ['intern']})
False
>>> v.errors
{'role': ["unallowed values ['intern']"]}

>>> v.schema = {'role': {'type': 'string', 'allowed': ['agent', 'client', 'supplier']}}
>>> v.validate({'role': 'supplier'})
True

>>> v.validate({'role': 'intern'})
False
>>> v.errors
{'role': ['unallowed value intern']}

>>> v.schema = {'a_restricted_integer': {'type': 'integer', 'allowed': [-1, 0, 1]}}
>>> v.validate({'a_restricted_integer': -1})
True

>>> v.validate({'a_restricted_integer': 2})
False
>>> v.errors
{'a_restricted_integer': ['unallowed value 2']}

Changed in version 0.5.1: Added support for the int type.

allof¶

Validates if all of the provided constraints validates the field. See *of-rules for details.

New in version 0.9.

anyof¶

Validates if any of the provided constraints validates the field. See *of-rules for details.

New in version 0.9.

check_with¶

Validates the value of a field by calling either a function or method.

A function must be implemented like the following prototype:

def functionnname(field, value, error):
    if value is invalid:
        error(field, 'error message')

The error argument points to the calling validator’s _error method. See Extending Cerberus on how to submit errors.

Here’s an example that tests whether an integer is odd or not:

def oddity(field, value, error):
    if not value & 1:
        error(field, "Must be an odd number")

Then, you can validate a value like this:

>>> schema = {'amount': {'check_with': oddity}}
>>> v = Validator(schema)
>>> v.validate({'amount': 10})
False
>>> v.errors
{'amount': ['Must be an odd number']}

>>> v.validate({'amount': 9})
True

If the rule’s constraint is a string, the Validator instance must have a method with that name prefixed by _check_with_. See Methods that can be referenced by the check_with rule for an equivalent to the function-based example above.

The constraint can also be a sequence of these that will be called consecutively.

schema = {'field': {'check_with': (oddity, 'prime number')}}

Changed in version 1.3: The rule was renamed from validator to check_with

contains¶

This rule validates that the a container object contains all of the defined items.

>>> document = {'states': ['peace', 'love', 'inity']}

>>> schema = {'states': {'contains': 'peace'}}
>>> v.validate(document, schema)
True

>>> schema = {'states': {'contains': 'greed'}}
>>> v.validate(document, schema)
False

>>> schema = {'states': {'contains': ['love', 'inity']}}
>>> v.validate(document, schema)
True

>>> schema = {'states': {'contains': ['love', 'respect']}}
>>> v.validate(document, schema)
False

dependencies¶

This rule allows one to define either a single field name, a sequence of field names or a mapping of field names and a sequence of allowed values as required in the document if the field defined upon is present in the document.

>>> schema = {'field1': {'required': False}, 'field2': {'required': False, 'dependencies': 'field1'}}
>>> document = {'field1': 7}
>>> v.validate(document, schema)
True

>>> document = {'field2': 7}
>>> v.validate(document, schema)
False

>>> v.errors
{'field2': ["field 'field1' is required"]}

When multiple field names are defined as dependencies, all of these must be present in order for the target field to be validated.

>>> schema = {'field1': {'required': False}, 'field2': {'required': False},
...           'field3': {'required': False, 'dependencies': ['field1', 'field2']}}
>>> document = {'field1': 7, 'field2': 11, 'field3': 13}
>>> v.validate(document, schema)
True

>>> document = {'field2': 11, 'field3': 13}
>>> v.validate(document, schema)
False

>>> v.errors
{'field3': ["field 'field1' is required"]}

When a mapping is provided, not only all dependencies must be present, but also any of their allowed values must be matched.

>>> schema = {'field1': {'required': False},
...           'field2': {'required': True, 'dependencies': {'field1': ['one', 'two']}}}

>>> document = {'field1': 'one', 'field2': 7}
>>> v.validate(document, schema)
True

>>> document = {'field1': 'three', 'field2': 7}
>>> v.validate(document, schema)
False
>>> v.errors
{'field2': ["depends on these values: {'field1': ['one', 'two']}"]}

>>> # same as using a dependencies list
>>> document = {'field2': 7}
>>> v.validate(document, schema)
False
>>> v.errors
{'field2': ["depends on these values: {'field1': ['one', 'two']}"]}


>>> # one can also pass a single dependency value
>>> schema = {'field1': {'required': False}, 'field2': {'dependencies': {'field1': 'one'}}}
>>> document = {'field1': 'one', 'field2': 7}
>>> v.validate(document, schema)
True

>>> document = {'field1': 'two', 'field2': 7}
>>> v.validate(document, schema)
False

>>> v.errors
{'field2': ["depends on these values: {'field1': 'one'}"]}

Declaring dependencies on subdocument fields with dot-notation is also supported:

>>> schema = {
...   'test_field': {'dependencies': ['a_dict.foo', 'a_dict.bar']},
...   'a_dict': {
...     'type': 'dict',
...     'schema': {
...       'foo': {'type': 'string'},
...       'bar': {'type': 'string'}
...     }
...   }
... }

>>> document = {'test_field': 'foobar', 'a_dict': {'foo': 'foo'}}
>>> v.validate(document, schema)
False

>>> v.errors
{'test_field': ["field 'a_dict.bar' is required"]}

When a subdocument is processed the lookup for a field in question starts at the level of that document. In order to address the processed document as root level, the declaration has to start with a ^. An occurrence of two initial carets (^^) is interpreted as a literal, single ^ with no special meaning.

>>> schema = {
...   'test_field': {},
...   'a_dict': {
...     'type': 'dict',
...     'schema': {
...       'foo': {'type': 'string'},
...       'bar': {'type': 'string', 'dependencies': '^test_field'}
...     }
...   }
... }

>>> document = {'a_dict': {'bar': 'bar'}}
>>> v.validate(document, schema)
False

>>> v.errors
{'a_dict': [{'bar': ["field '^test_field' is required"]}]}

Note

If you want to extend semantics of the dot-notation, you can override the _lookup_field() method.

Note

The evaluation of this rule does not consider any constraints defined with the required rule.

Changed in version 1.0.2: Support for absolute addressing with ^.

Changed in version 0.8.1: Support for sub-document fields as dependencies.

Changed in version 0.8: Support for dependencies as a dictionary.

New in version 0.7.

empty¶

If constrained with False validation of an iterable value will fail if it is empty. Per default the emptiness of a field isn’t checked and is therefore allowed when the rule isn’t defined. But defining it with the constraint True will skip the possibly defined rules allowed, forbidden, items, minlength, maxlength, regex and validator for that field when the value is considered empty.

>>> schema = {'name': {'type': 'string', 'empty': False}}
>>> document = {'name': ''}
>>> v.validate(document, schema)
False

>>> v.errors
{'name': ['empty values not allowed']}

New in version 0.0.3.

excludes¶

You can declare fields to excludes others:

>>> v = Validator()
>>> schema = {'this_field': {'type': 'dict',
...                          'excludes': 'that_field'},
...           'that_field': {'type': 'dict',
...                          'excludes': 'this_field'}}
>>> v.validate({'this_field': {}, 'that_field': {}}, schema)
False
>>> v.validate({'this_field': {}}, schema)
True
>>> v.validate({'that_field': {}}, schema)
True
>>> v.validate({}, schema)
True

You can require both field to build an exclusive or:

>>> v = Validator()
>>> schema = {'this_field': {'type': 'dict',
...                          'excludes': 'that_field',
...                          'required': True},
...           'that_field': {'type': 'dict',
...                          'excludes': 'this_field',
...                          'required': True}}
>>> v.validate({'this_field': {}, 'that_field': {}}, schema)
False
>>> v.validate({'this_field': {}}, schema)
True
>>> v.validate({'that_field': {}}, schema)
True
>>> v.validate({}, schema)
False

You can also pass multiples fields to exclude in a list :

>>> schema = {'this_field': {'type': 'dict',
...                          'excludes': ['that_field', 'bazo_field']},
...           'that_field': {'type': 'dict',
...                          'excludes': 'this_field'},
...           'bazo_field': {'type': 'dict'}}
>>> v.validate({'this_field': {}, 'bazo_field': {}}, schema)
False

forbidden¶

Opposite to allowed this validates if a value is any but one of the defined values:

>>> schema = {'user': {'forbidden': ['root', 'admin']}}
>>> document = {'user': 'root'}
>>> v.validate(document, schema)
False

New in version 1.0.

items¶

Validates the items of any iterable against a sequence of rules that must validate each index-correspondent item. The items will only be evaluated if the given iterable’s size matches the definition’s. This also applies during normalization and items of a value are not normalized when the lengths mismatch.

>>> schema = {'list_of_values': {
...              'type': 'list',
...              'items': [{'type': 'string'}, {'type': 'integer'}]}
...           }
>>> document = {'list_of_values': ['hello', 100]}
>>> v.validate(document, schema)
True
>>> document = {'list_of_values': [100, 'hello']}
>>> v.validate(document, schema)
False

See schema (list) rule for dealing with arbitrary length list types.

keysrules¶

This rules takes a set of rules as constraint that all keys of a mapping are validated with.

>>> schema = {'a_dict': {
...               'type': 'dict',
...               'keysrules': {'type': 'string', 'regex': '[a-z]+'}}
...           }
>>> document = {'a_dict': {'key': 'value'}}
>>> v.validate(document, schema)
True

>>> document = {'a_dict': {'KEY': 'value'}}
>>> v.validate(document, schema)
False

New in version 0.9.

Changed in version 1.0: Renamed from propertyschema to keyschema

Changed in version 1.3: Renamed from keyschema to keysrules

meta¶

This is actually not a validation rule but a field in a rules set that can conventionally be used for application specific data that is descriptive for the document field:

{'id': {'type': 'string', 'regex': r'[A-M]\d{,6}',
        'meta': {'label': 'Inventory Nr.'}}}

The assigned data can be of any type.

New in version 1.3.

min, max¶

Minimum and maximum value allowed for any object whose class implements comparison operations (__gt__ & __lt__).

>>> schema = {'weight': {'min': 10.1, 'max': 10.9}}
>>> document = {'weight': 10.3}
>>> v.validate(document, schema)
True

>>> document = {'weight': 12}
>>> v.validate(document, schema)
False

>>> v.errors
{'weight': ['max value is 10.9']}

Changed in version 1.0: Allows any type to be compared.

Changed in version 0.7: Added support for float and number types.

minlength, maxlength¶

Minimum and maximum length allowed for sized types that implement __len__.

>>> schema = {'numbers': {'minlength': 1, 'maxlength': 3}}
>>> document = {'numbers': [256, 2048, 23]}
>>> v.validate(document, schema)
True

>>> document = {'numbers': [256, 2048, 23, 2]}
>>> v.validate(document, schema)
False

>>> v.errors
{'numbers': ['max length is 3']}

noneof¶

Validates if none of the provided constraints validates the field. See *of-rules for details.

New in version 0.9.

nullable¶

If True the field value is allowed to be None. The rule will be checked on every field, regardless it’s defined or not. The rule’s constraint defaults False.

>>> v.schema = {'a_nullable_integer': {'nullable': True, 'type': 'integer'}, 'an_integer': {'type': 'integer'}}

>>> v.validate({'a_nullable_integer': 3})
True
>>> v.validate({'a_nullable_integer': None})
True

>>> v.validate({'an_integer': 3})
True
>>> v.validate({'an_integer': None})
False
>>> v.errors
{'an_integer': ['null value not allowed']}

Changed in version 0.7: nullable is valid on fields lacking type definition.

New in version 0.3.0.

*of-rules¶

These rules allow you to define different sets of rules to validate against. The field will be considered valid if it validates against the set in the list according to the prefixes logics all, any, one or none.

`allof`	Validates if all of the provided constraints validates the field.
`anyof`	Validates if any of the provided constraints validates the field.
`noneof`	Validates if none of the provided constraints validates the field.
`oneof`	Validates if exactly one of the provided constraints applies.

Note

Normalization cannot be used in the rule sets within the constraints of these rules.

Note

Before you employ these rules, you should have investigated other possible solutions for the problem at hand with and without Cerberus. Sometimes people tend to overcomplicate schemas with these rules.

For example, to verify that a field’s value is a number between 0 and 10 or 100 and 110, you could do the following:

>>> schema = {'prop1':
...           {'type': 'number',
...            'anyof':
...            [{'min': 0, 'max': 10}, {'min': 100, 'max': 110}]}}

>>> document = {'prop1': 5}
>>> v.validate(document, schema)
True

>>> document = {'prop1': 105}
>>> v.validate(document, schema)
True

>>> document = {'prop1': 55}
>>> v.validate(document, schema)
False
>>> v.errors   
{'prop1': ['no definitions validate',
           {'anyof definition 0': ['max value is 10'],
            'anyof definition 1': ['min value is 100']}]}

The anyof rule tests each rules set in the list. Hence, the above schema is equivalent to creating two separate schemas:

>>> schema1 = {'prop1': {'type': 'number', 'min':   0, 'max':  10}}
>>> schema2 = {'prop1': {'type': 'number', 'min': 100, 'max': 110}}

>>> document = {'prop1': 5}
>>> v.validate(document, schema1) or v.validate(document, schema2)
True

>>> document = {'prop1': 105}
>>> v.validate(document, schema1) or v.validate(document, schema2)
True

>>> document = {'prop1': 55}
>>> v.validate(document, schema1) or v.validate(document, schema2)
False

New in version 0.9.

*of-rules typesaver¶

You can concatenate any of-rule with an underscore and another rule with a list of rule-values to save typing:

{'foo': {'anyof_regex': ['^ham', 'spam$']}}
# is equivalent to
{'foo': {'anyof': [{'regex': '^ham'}, {'regex': 'spam$'}]}}
# but is also equivalent to
# {'foo': {'regex': r'(^ham|spam$)'}}

Thus you can use this to validate a document against several schemas without implementing your own logic:

>>> schemas = [{'department': {'required': True, 'regex': '^IT$'}, 'phone': {'nullable': True}},
...            {'department': {'required': True}, 'phone': {'required': True}}]
>>> emloyee_vldtr = Validator({'employee': {'oneof_schema': schemas, 'type': 'dict'}}, allow_unknown=True)
>>> invalid_employees_phones = []
>>> for employee in employees:
...     if not employee_vldtr.validate(employee):
...         invalid_employees_phones.append(employee)

oneof¶

Validates if exactly one of the provided constraints applies. See *of-rules for details.

New in version 0.9.

readonly¶

If True the value is readonly. Validation will fail if this field is present in the target dictionary. This is useful, for example, when receiving a payload which is to be validated before it is sent to the datastore. The field might be provided by the datastore, but should not writable.

A validator can be configured with the initialization argument purge_readonly and the property with the same name to let it delete all fields that have this rule defined positively.

Changed in version 1.0.2: Can be used in conjunction with default and default_setter, see Default Values.

regex¶

The validation will fail if the field’s value does not match the provided regular expression. It is only tested on string values.

>>> schema = {
...     'email': {
...        'type': 'string',
...        'regex': '^[a-zA-Z0-9_.+-][email protected][a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$'
...     }
... }
>>> document = {'email': '[email protected]'}
>>> v.validate(document, schema)
True

>>> document = {'email': 'john_at_example_dot_com'}
>>> v.validate(document, schema)
False

>>> v.errors
{'email': ["value does not match regex '^[a-zA-Z0-9_.+-][email protected][a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+$'"]}

For details on regular expression syntax, see the documentation on the standard library’s re-module.

Hint

Mind that one can set behavioural flags as part of the expression which is equivalent to passing flags to the re.compile() function for example. So, the constraint '(?i)holy grail' includes the equivalent of the re.I flag and matches any string that includes ‘holy grail’ or any variant of it with upper-case glyphs. Look for (?aiLmsux) in the mentioned library documentation for a description there.

New in version 0.7.

require_all¶

This can be used in conjunction with the schema rule when validating a mapping in order to set the require_all property of the validator for the subdocument. For a full elaboration refer to this paragraph.

required¶

If True the field is mandatory. Validation will fail when it is missing, unless validate() is called with update=True:

>>> v.schema = {'name': {'required': True, 'type': 'string'}, 'age': {'type': 'integer'}}
>>> document = {'age': 10}
>>> v.validate(document)
False
>>> v.errors
{'name': ['required field']}

>>> v.validate(document, update=True)
True

Note

To define all fields of a document as required see this section about the available options.

Note

String fields with empty values will still be validated, even when required is set to True. If you don’t want to accept empty values, see the empty rule.

Note

The evaluation of this rule does not consider any constraints defined with the dependencies rule.

Changed in version 0.8: Check field dependencies.

schema (dict)¶

If a field for which a schema-rule is defined has a mapping as value, that mapping will be validated against the schema that is provided as constraint.

>>> schema = {'a_dict': {'type': 'dict', 'schema': {'address': {'type': 'string'},
...                                                 'city': {'type': 'string', 'required': True}}}}
>>> document = {'a_dict': {'address': 'my address', 'city': 'my town'}}
>>> v.validate(document, schema)
True

Note

To validate arbitrary keys of a mapping, see keysrules-rule, resp. valuesrules-rule for validating arbitrary values of a mapping.

schema (list)¶

If schema-validation encounters an arbritrary sized sequence as value, all items of the sequence will be validated against the rules provided in schema’s constraint.

>>> schema = {'a_list': {'type': 'list', 'schema': {'type': 'integer'}}}
>>> document = {'a_list': [3, 4, 5]}
>>> v.validate(document, schema)
True

The schema rule on list types is also the preferred method for defining and validating a list of dictionaries.

Note

Using this rule should be accompanied with a type-rule explicitly restricting the field to the list-type like in the example. Otherwise false results can be expected when a mapping is validated against this rule with constraints for a sequence.

>>> schema = {'rows': {'type': 'list',
...                    'schema': {'type': 'dict', 'schema': {'sku': {'type': 'string'},
...                                                          'price': {'type': 'integer'}}}}}
>>> document = {'rows': [{'sku': 'KT123', 'price': 100}]}
>>> v.validate(document, schema)
True

Changed in version 0.0.3: Schema rule for list types of arbitrary length

type¶

Data type allowed for the key value. Can be one of the following names:

Type Name	Python 2 Type	Python 3 Type
`boolean`	`bool`	`bool`
`binary`	`bytes` [1], `bytearray`	`bytes`, `bytearray`
`date`	`datetime.date`	`datetime.date`
`datetime`	`datetime.datetime`	`datetime.datetime`
`dict`	`collections.Mapping`	`collections.abc.Mapping`
`float`	`float`	`float`
`integer`	`int`, `long`	`int`
`list`	`collections.Sequence`, excl. `string`	`collections.abc.Sequence`, excl. `string`
`number`	`float`, `int`, `long`, excl. `bool`	`float`, `int`, excl. `bool`
`set`	`set`	`set`
`string`	`basestring()`	`str`

You can extend this list and support custom types.

A list of types can be used to allow different values:

>>> v.schema = {'quotes': {'type': ['string', 'list']}}
>>> v.validate({'quotes': 'Hello world!'})
True
>>> v.validate({'quotes': ['Do not disturb my circles!', 'Heureka!']})
True

>>> v.schema = {'quotes': {'type': ['string', 'list'], 'schema': {'type': 'string'}}}
>>> v.validate({'quotes': 'Hello world!'})
True
>>> v.validate({'quotes': [1, 'Heureka!']})
False
>>> v.errors
{'quotes': [{0: ['must be of string type']}]}

Note

While the type rule is not required to be set at all, it is not encouraged to leave it unset especially when using more complex rules such as schema. If you decide you still don’t want to set an explicit type, rules such as schema are only applied to values where the rules can actually be used (such as dict and list). Also, in the case of schema, cerberus will try to decide if a list or a dict type rule is more appropriate and infer it depending on what the schema rule looks like.

Note

Please note that type validation is performed before most others which exist for the same field (only nullable and readonly are considered beforehand). In the occurrence of a type failure subsequent validation rules on the field will be skipped and validation will continue on other fields. This allows one to safely assume that field type is correct when other (standard or custom) rules are invoked.

Changed in version 1.0: Added the binary data type.

Changed in version 0.9: If a list of types is given, the key value must match any of them.

Changed in version 0.7.1: dict and list typechecking are now performed with the more generic Mapping and Sequence types from the builtin collections module. This means that instances of custom types designed to the same interface as the builtin dict and list types can be validated with Cerberus. We exclude strings when type checking for list/Sequence because it in the validation situation it is almost certain the string was not the intended data type for a sequence.

Changed in version 0.7: Added the set data type.

Changed in version 0.6: Added the number data type.

Changed in version 0.4.0: Type validation is always executed first, and blocks other field validation rules on failure.

Changed in version 0.3.0: Added the float data type.

[1]	This is actually an alias of `str` in Python 2.

valuesrules¶

This rules takes a set of rules as constraint that all values of a mapping are validated with.

>>> schema = {'numbers':
...              {'type': 'dict',
...               'valuesrules': {'type': 'integer', 'min': 10}}
... }
>>> document = {'numbers': {'an integer': 10, 'another integer': 100}}
>>> v.validate(document, schema)
True

>>> document = {'numbers': {'an integer': 9}}
>>> v.validate(document, schema)
False

>>> v.errors
{'numbers': [{'an integer': ['min value is 10']}]}

New in version 0.7.

Changed in version 0.9: renamed keyschema to valueschema

Changed in version 1.3: renamed valueschema to valuesrules

Validation Rules¶

allow_unknown¶

allowed¶

allof¶

anyof¶

check_with¶

contains¶

dependencies¶

empty¶

excludes¶

forbidden¶

items¶

keysrules¶

meta¶

min, max¶

minlength, maxlength¶

noneof¶

nullable¶

*of-rules¶

*of-rules typesaver¶

oneof¶

readonly¶

regex¶

require_all¶

required¶

schema (dict)¶

schema (list)¶

type¶

valuesrules¶

Cerberus

Stay Informed

Useful Links

Navigation

Related Topics