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, especially if you intend to validate multiple instances with the same schema, you likely would prefer using the
Validator.validate
method directly on a specific validator (e.g.Draft7Validator.validate
).- Parameters:
instance – The instance to validate
schema – The schema to validate with
cls (Validator) – 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 keyword 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 the latest released draft.Any other provided positional and keyword arguments will be passed on when instantiating the
cls
.- Raises:
jsonschema.exceptions.ValidationError – is invalid
jsonschema.exceptions.SchemaError – is invalid
Footnotes
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 Protocol#
jsonschema
defines a protocol that all validator classes should adhere
to.
- class jsonschema.protocols.Validator(*args, **kwargs)[source]#
The protocol to which all validator classes should adhere.
- Parameters:
schema – 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
Validator.check_schema
to validate a schema first.resolver – an instance of
jsonschema.RefResolver
that will be used to resolve $ref properties (JSON references). If unprovided, one will be created.format_checker – an instance of
jsonschema.FormatChecker
whosejsonschema.FormatChecker.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. Certain formats require additional packages to be installed (ipv5, uri, color, date-time). The required packages can be found at the bottom of this page.
- FORMAT_CHECKER: ClassVar[jsonschema.FormatChecker]#
A
jsonschema.FormatChecker
that will be used when validating format properties in JSON schemas.
- META_SCHEMA: ClassVar[dict]#
An object representing the validator’s meta schema (the schema that describes valid schemas in the given version).
- TYPE_CHECKER: ClassVar[jsonschema.TypeChecker]#
A
jsonschema.TypeChecker
that will be used when validating type keywords in JSON schemas.
- VALIDATORS: ClassVar[dict]#
A mapping of validation keywords (
str
s) to functions that validate the keyword with that name. For more information see Creating or Extending Validator Classes.
- classmethod check_schema(schema)[source]#
Validate the given schema against the validator’s
META_SCHEMA
.- Raises:
jsonschema.exceptions.SchemaError
if the schema is invalid- Return type:
- evolve(**kwargs)[source]#
Create a new validator like this one, but with given changes.
Preserves all other attributes, so can be used to e.g. create a validator with a different schema but with the same $ref resolution behavior.
>>> validator = Draft202012Validator({}) >>> validator.evolve(schema={"type": "number"}) Draft202012Validator(schema={'type': 'number'}, format_checker=None)
The returned object satisfies the validator protocol, but may not be of the same concrete class! In particular this occurs when a $ref occurs to a schema with a different $schema than this one (i.e. for a different draft).
>>> validator.evolve( ... schema={"$schema": Draft7Validator.META_SCHEMA["$id"]} ... ) Draft7Validator(schema=..., format_checker=None)
- Return type:
- is_type(instance, type)[source]#
Check if the instance is of the given (JSON Schema) type.
- Return type:
- Raises:
jsonschema.exceptions.UnknownType
iftype
is not a known type.
- is_valid(instance)[source]#
Check if the instance is valid under the current
schema
.- Return type:
>>> schema = {"maxItems" : 2} >>> Draft202012Validator(schema).is_valid([2, 3, 4]) False
- iter_errors(instance)[source]#
Lazily yield each of the validation errors in the given instance.
- Return type:
an
collections.abc.Iterable
ofjsonschema.exceptions.ValidationError
s
>>> schema = { ... "type" : "array", ... "items" : {"enum" : [1, 2, 3]}, ... "maxItems" : 2, ... } >>> v = Draft202012Validator(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)[source]#
Check if the instance is valid under the current
schema
.- Raises:
jsonschema.exceptions.ValidationError
if the instance is invalid
>>> schema = {"maxItems" : 2} >>> Draft202012Validator(schema).validate([2, 3, 4]) Traceback (most recent call last): ... ValidationError: [2, 3, 4] is too long
- Return type:
All of the versioned validators that are included with
jsonschema
adhere to the protocol, and implementers 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.
Type Checking#
To handle JSON Schema’s type keyword, a Validator
uses
an associated TypeChecker
. The type checker provides an immutable
mapping between names of types and functions that can test if an instance is
of that type. The defaults are suitable for most users - each of the
versioned validators that are included with
jsonschema
have a TypeChecker
that can correctly handle their respective
versions.
- class jsonschema.TypeChecker(type_checkers=pmap({}))[source]#
A
type
property checker.A
TypeChecker
performs type checking for aValidator
. Type checks to perform are updated usingTypeChecker.redefine
orTypeChecker.redefine_many
and removed viaTypeChecker.remove
. Each of these return a newTypeChecker
object.- Parameters:
type_checkers (dict) – The initial mapping of types to their checking functions.
- is_type(instance, type)[source]#
Check if the instance is of the appropriate type.
- Parameters:
- Returns:
Whether it conformed.
- Return type:
- Raises:
jsonschema.exceptions.UndefinedTypeCheck – if type is unknown to this object.
- redefine(type, fn)[source]#
Produce a new checker with the given type redefined.
- Parameters:
type (str) – The name of the type to check.
fn (collections.abc.Callable) – A function taking exactly two parameters - the type checker calling the function and the instance to check. The function should return true if instance is of this type and false otherwise.
- Returns:
A new
TypeChecker
instance.
- redefine_many(definitions=())[source]#
Produce a new checker with the given types redefined.
- Parameters:
definitions (dict) – A dictionary mapping types to their checking functions.
- Returns:
A new
TypeChecker
instance.
- remove(*types)[source]#
Produce a new checker with the given types forgotten.
- Parameters:
types (Iterable) – the names of the types to remove.
- Returns:
A new
TypeChecker
instance- Raises:
jsonschema.exceptions.UndefinedTypeCheck – if any given type is unknown to this object
- exception jsonschema.exceptions.UndefinedTypeCheck(type)[source]#
A type checker was asked to check a type it did not have registered.
Raised when trying to remove a type check that is not known to this TypeChecker, or when calling
jsonschema.TypeChecker.is_type
directly.
Validating With Additional Types#
Occasionally it can be useful to provide additional or alternate types when validating JSON Schema’s type keyword.
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 int
s and float
s, 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 acceptable for a validator object, then you should update an
existing TypeChecker
or create a new one. You may then create a new
Validator
via jsonschema.validators.extend
.
from jsonschema import validators
class MyInteger(object):
pass
def is_my_int(checker, instance):
return (
Draft202012Validator.TYPE_CHECKER.is_type(instance, "number") or
isinstance(instance, MyInteger)
)
type_checker = Draft202012Validator.TYPE_CHECKER.redefine(
"number", is_my_int,
)
CustomValidator = validators.extend(
Draft202012Validator,
type_checker=type_checker,
)
validator = CustomValidator(schema={"type" : "number"})
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 Validator
protocol,
which each included validator class implements.
- class jsonschema.Draft202012Validator(schema, resolver=None, format_checker=None)#
- class jsonschema.Draft201909Validator(schema, resolver=None, format_checker=None)#
- class jsonschema.Draft7Validator(schema, resolver=None, format_checker=None)#
- class jsonschema.Draft6Validator(schema, resolver=None, format_checker=None)#
- class jsonschema.Draft4Validator(schema, resolver=None, format_checker=None)#
- class jsonschema.Draft3Validator(schema, resolver=None, format_checker=None)#
For example, if you wanted to validate a schema you created against the Draft 2020-12 meta-schema, you could use:
from jsonschema import Draft202012Validator
schema = {
"$schema": Draft202012Validator.META_SCHEMA["$id"],
"type": "object",
"properties": {
"name": {"type": "string"},
"email": {"type": "string"},
},
"required": ["email"]
}
Draft202012Validator.check_schema(schema)
Validating Formats#
JSON Schema defines the format keyword which can be used to check
if primitive types (string
s, number
s, boolean
s) 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
Validator
.
>>> validate("127.0.0.1", {"format" : "ipv4"})
>>> validate(
... instance="-12",
... schema={"format" : "ipv4"},
... format_checker=draft202012_format_checker,
... )
Traceback (most recent call last):
...
ValidationError: "-12" is not a "ipv4"
- 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 returnTrue
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 theFormatChecker.checks
orFormatChecker.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
orFormatChecker.cls_checks
decorators respectively.
- classmethod cls_checks(format, raises=())[source]#
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
jsonschema.exceptions.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 the instance does not conform to
format
- Return type:
- 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
jsonschema.exceptions.ValidationError.cause
attribute of the resulting validation error.
- Return type:
Callable
[[TypeVar
(_F
, bound=Callable
[[object
],bool
])],TypeVar
(_F
, bound=Callable
[[object
],bool
])]
There are a number of default checkers that FormatChecker
s 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 easiest way to
ensure you have what is needed is to install jsonschema
using the
format
or format_nongpl
collection of optional dependencies – e.g.
$ pip install jsonschema[format]
which will install all of the below dependencies for all formats.
Or if you want to install MIT-license compatible dependencies only:
$ pip install jsonschema[format_nongpl]
The non-GPL extra is intended to not install any direct dependencies
that are GPL (but that of course end-users should do their own verification).
At the moment, it supports all the available checkers except for iri
and
iri-reference
.
The more specific list of available checkers, along with their requirement (if any,) are listed below.
Note
If the following packages are not installed when using a checker that requires it, validation will succeed without throwing an error, as specified by the JSON Schema specification.
Checker |
Notes |
---|---|
|
requires webcolors |
|
|
|
requires rfc3339-validator |
|
requires isoduration |
|
|
|
requires fqdn |
|
requires idna |
|
|
|
OS must have |
|
requires rfc3987 |
|
requires rfc3987 |
|
requires jsonpointer |
|
|
|
requires jsonpointer |
|
requires rfc3339-validator |
|
requires rfc3987 or rfc3986-validator |
|
requires rfc3987 or rfc3986-validator |
|
requires uri-template |
Note
Since in most cases “validating” an email address is an attempt
instead to confirm that mail sent to it will deliver to a recipient,
and that that recipient is the correct one the email is intended
for, and since many valid email addresses are in many places
incorrectly rejected, and many invalid email addresses are in many
places incorrectly accepted, the email
format keyword only
provides a sanity check, not full rfc5322 validation.
The same applies to the idn-email
format.