Schema Validation

The Basics

The simplest way to validate an instance under a given schema is to use the validate() function.

jsonschema.validate(instance, schema, cls=None, *args, **kwargs)[source]

Validate an instance under the given schema.

>>> validate([2, 3, 4], {"maxItems": 2})
Traceback (most recent call last):
    ...
ValidationError: [2, 3, 4] is too long

validate() will first verify that the provided schema is itself valid, since not doing so can lead to less obvious error messages and fail in less obvious or consistent ways. If you know you have a valid schema already or don’t care, you might prefer using the validate() method directly on a specific validator (e.g. Draft4Validator.validate()).

Parameters:
  • instance – The instance to validate
  • schema – The schema to validate with
  • cls (IValidator) – The class that will be used to validate the instance.

If the cls argument is not provided, two things will happen in accordance with the specification. First, if the schema has a $schema property containing a known meta-schema [1] then the proper validator will be used. The specification recommends that all schemas contain $schema properties for this reason. If no $schema property is found, the default validator class is Draft4Validator.

Any other provided positional and keyword arguments will be passed on when instantiating the cls.

Raises:
  • ValidationError if the instance is invalid
  • SchemaError if the schema itself is invalid

Footnotes

[1]known by a validator registered with validates()
[2]For information on creating JSON schemas to validate your data, there is a good introduction to JSON Schema fundamentals underway at Understanding JSON Schema

The Validator Interface

jsonschema defines an (informal) interface that all validator classes should adhere to.

class jsonschema.IValidator(schema, types=(), resolver=None, format_checker=None)
Parameters:
  • schema (dict) – the schema that the validator object will validate with. It is assumed to be valid, and providing an invalid schema can lead to undefined behavior. See IValidator.check_schema() to validate a schema first.
  • types (dict or iterable of 2-tuples) – Override or extend the list of known types when validating the type property. Should map strings (type names) to class objects that will be checked via isinstance(). See Validating With Additional Types for details.
  • resolver – an instance of RefResolver that will be used to resolve $ref properties (JSON references). If unprovided, one will be created.
  • format_checker – an instance of FormatChecker whose conforms() method will be called to check and see if instances conform to each format property present in the schema. If unprovided, no validation will be done for format.
DEFAULT_TYPES

The default mapping of JSON types to Python types used when validating type properties in JSON schemas.

META_SCHEMA

An object representing the validator’s meta schema (the schema that describes valid schemas in the given version).

VALIDATORS

A mapping of validator names (strs) to functions that validate the validator property with that name. For more information see Creating or Extending Validator Classes.

schema

The schema that was passed in when initializing the object.

classmethod check_schema(schema)

Validate the given schema against the validator’s META_SCHEMA.

Raises:SchemaError if the schema is invalid
is_type(instance, type)

Check if the instance is of the given (JSON Schema) type.

Return type:bool
Raises:UnknownType if type is not a known type.
is_valid(instance)

Check if the instance is valid under the current schema.

Return type:bool
>>> schema = {"maxItems" : 2}
>>> Draft3Validator(schema).is_valid([2, 3, 4])
False
iter_errors(instance)

Lazily yield each of the validation errors in the given instance.

Return type:an iterable of ValidationErrors
>>> schema = {
...     "type" : "array",
...     "items" : {"enum" : [1, 2, 3]},
...     "maxItems" : 2,
... }
>>> v = Draft3Validator(schema)
>>> for error in sorted(v.iter_errors([2, 3, 4]), key=str):
...     print(error.message)
4 is not one of [1, 2, 3]
[2, 3, 4] is too long
validate(instance)

Check if the instance is valid under the current schema.

Raises:ValidationError if the instance is invalid
>>> schema = {"maxItems" : 2}
>>> Draft3Validator(schema).validate([2, 3, 4])
Traceback (most recent call last):
    ...
ValidationError: [2, 3, 4] is too long

All of the versioned validators that are included with jsonschema adhere to the interface, and implementors of validator classes that extend or complement the ones included should adhere to it as well. For more information see Creating or Extending Validator Classes.

Validating With Additional Types

Occasionally it can be useful to provide additional or alternate types when validating the JSON Schema’s type property. Validators allow this by taking a types argument on construction that specifies additional types, or which can be used to specify a different set of Python types to map to a given JSON type.

jsonschema tries to strike a balance between performance in the common case and generality. For instance, JSON Schema defines a number type, which can be validated with a schema such as {"type" : "number"}. By default, this will accept instances of Python numbers.Number. This includes in particular ints and floats, along with decimal.Decimal objects, complex numbers etc. For integer and object, however, rather than checking for numbers.Integral and collections.abc.Mapping, jsonschema simply checks for int and dict, since the more general instance checks can introduce significant slowdown, especially given how common validating these types are.

If you do want the generality, or just want to add a few specific additional types as being acceptible for a validator object, IValidators have a types argument that can be used to provide additional or new types.

class MyInteger(object):
    ...

Draft3Validator(
    schema={"type" : "number"},
    types={"number" : (numbers.Number, MyInteger)},
)

The list of default Python types for each JSON type is available on each validator object in the IValidator.DEFAULT_TYPES attribute. Note that you need to specify all types to match if you override one of the existing JSON types, so you may want to access the set of default types when specifying your additional type.

Versioned Validators

jsonschema ships with validator classes for various versions of the JSON Schema specification. For details on the methods and attributes that each validator class provides see the IValidator interface, which each included validator class implements.

class jsonschema.Draft3Validator(schema, types=(), resolver=None, format_checker=None)
class jsonschema.Draft4Validator(schema, types=(), resolver=None, format_checker=None)

For example, if you wanted to validate a schema you created against the Draft 4 meta-schema, you could use:

from jsonschema import Draft4Validator

schema = {
    "$schema": "http://json-schema.org/schema#",

    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "email": {"type": "string"},
    },
    "required": ["email"]
}
Draft4Validator.check_schema(schema)

Validating Formats

JSON Schema defines the format property which can be used to check if primitive types (strings, numbers, booleans) conform to well-defined formats. By default, no validation is enforced, but optionally, validation can be enabled by hooking in a format-checking object into an IValidator.

>>> validate("localhost", {"format" : "hostname"})
>>> validate(
...     "-12", {"format" : "hostname"}, format_checker=FormatChecker(),
... )
Traceback (most recent call last):
    ...
ValidationError: "-12" is not a "hostname"
class jsonschema.FormatChecker(formats=None)[source]

A format property checker.

JSON Schema does not mandate that the format property actually do any validation. If validation is desired however, instances of this class can be hooked into validators to enable format validation.

FormatChecker objects always return True when asked about formats that they do not know how to validate.

To check a custom format using a function that takes an instance and returns a bool, use the FormatChecker.checks() or FormatChecker.cls_checks() decorators.

Parameters:formats (iterable) – The known formats to validate. This argument can be used to limit which formats will be used during validation.
checkers

A mapping of currently known formats to tuple of functions that validate them and errors that should be caught. New checkers can be added and removed either per-instance or globally for all checkers using the FormatChecker.checks() or FormatChecker.cls_checks() decorators respectively.

classmethod cls_checks(format, raises=())

Register a decorated function as globally validating a new format.

Any instance created after this function is called will pick up the supplied checker.

Parameters:
  • format (str) – the format that the decorated function will check
  • raises (Exception) – the exception(s) raised by the decorated function when an invalid instance is found. The exception object will be accessible as the ValidationError.cause attribute of the resulting validation error.
check(instance, format)[source]

Check whether the instance conforms to the given format.

Parameters:
  • instance (any primitive type, i.e. str, number, bool) – The instance to check
  • format (str) – The format that instance should conform to
Raises:

FormatError if instance does not conform to format

checks(format, raises=())[source]

Register a decorated function as validating a new format.

Parameters:
  • format (str) – The format that the decorated function will check.
  • raises (Exception) –

    The exception(s) raised by the decorated function when an invalid instance is found.

    The exception object will be accessible as the ValidationError.cause attribute of the resulting validation error.

conforms(instance, format)[source]

Check whether the instance conforms to the given format.

Parameters:
  • instance (any primitive type, i.e. str, number, bool) – The instance to check
  • format (str) – The format that instance should conform to
Returns:

Whether it conformed

Return type:

bool

There are a number of default checkers that FormatCheckers know how to validate. Their names can be viewed by inspecting the FormatChecker.checkers attribute. Certain checkers will only be available if an appropriate package is available for use. The available checkers, along with their requirement (if any,) are listed below.

Checker Notes
hostname  
ipv4  
ipv6 OS must have socket.inet_pton() function
email  
uri requires rfc3987
date-time requires strict-rfc3339 [2]
date  
time  
regex  
color requires webcolors
[3]For backwards compatibility, isodate is also supported, but it will allow any ISO 8601 date-time, not just RFC 3339 as mandated by the JSON Schema specification.