Schema Validation#
Tip
Most of the documentation for this package assumes you’re familiar with the fundamentals of writing JSON schemas themselves, and focuses on how this library helps you validate with them in Python.
If you aren’t already comfortable with writing schemas and need an introduction which teaches about JSON Schema the specification, you may find Understanding JSON Schema
to be a good read!
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
jsonschema.protocols.Validator.validate
method directly on a specific validator (e.g.Draft202012Validator.validate
).- Parameters:
instance – The instance to validate
schema – The schema to validate with
cls (jsonschema.protocols.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 – if the instance is invalid
jsonschema.exceptions.SchemaError – if the schema itself is invalid
Footnotes
The Validator Protocol#
jsonschema
defines a protocol
that all validator classes adhere to.
Hint
If you are unfamiliar with protocols, either as a general notion or as specifically implemented by typing.Protocol
, you can think of them as a set of attributes and methods that all objects satisfying the protocol have.
Here, in the context of jsonschema
, the Validator.iter_errors
method can be called on jsonschema.validators.Draft202012Validator
, or jsonschema.validators.Draft7Validator
, or indeed any validator class, as all of them have it, along with all of the other methods described below.
- class jsonschema.protocols.Validator(schema: Mapping | bool, registry: referencing.jsonschema.SchemaRegistry, format_checker: jsonschema.FormatChecker | None = None)[source]
The protocol to which all validator classes 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.registry – a schema registry that will be used for looking up JSON references
resolver –
a resolver that will be used to resolve $ref properties (JSON references). If unprovided, one will be created.
Deprecated since version v4.18.0:
RefResolver
has been deprecated in favor of JSON (Schema) Referencing, and with it, this argument.format_checker – if provided, a checker which will be used to assert about format properties present in the schema. If unprovided, no format validation is done, and the presence of format within schemas is strictly informational. Certain formats require additional packages to be installed in order to assert against instances. Ensure you’ve installed
jsonschema
with its extra (optional) dependencies when invokingpip
.
Deprecated since version v4.12.0: Subclassing validator classes now explicitly warns this is not part of their public API.
- FORMAT_CHECKER: ClassVar[jsonschema.FormatChecker]
A
jsonschema.FormatChecker
that will be used when validating format keywords in JSON schemas.
- ID_OF: _typing.id_of
A function which given a schema returns its ID.
- META_SCHEMA: ClassVar[Mapping]
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[Mapping]
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: Mapping | bool) None [source]
Validate the given schema against the validator’s
META_SCHEMA
.- Raises:
jsonschema.exceptions.SchemaError – if the schema is invalid
- evolve(**kwargs) Validator [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)
- is_type(instance: Any, type: str) bool [source]
Check if the instance is of the given (JSON Schema) type.
- Parameters:
instance – the value to check
type – the name of a known (JSON Schema) type
- Returns:
whether the instance is of the given type
- Raises:
jsonschema.exceptions.UnknownType – if
type
is not a known type
- is_valid(instance: Any) bool [source]
Check if the instance is valid under the current
schema
.- Returns:
whether the instance is valid or not
>>> schema = {"maxItems" : 2} >>> Draft202012Validator(schema).is_valid([2, 3, 4]) False
- iter_errors(instance: Any) Iterable[ValidationError] [source]
Lazily yield each of the validation errors in the given instance.
>>> 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
Deprecated since version v4.0.0: Calling this function with a second schema argument is deprecated. Use
Validator.evolve
instead.
- schema: Mapping | bool
The schema that will be used to validate instances
- validate(instance: Any) None [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
All of the versioned validators that are included with jsonschema
adhere to the protocol, and any extensions of these validators
will as well.
For more information on creating
or extending
validators 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: Mapping[str, Callable[[TypeChecker, Any], bool]] = HashTrieMap({}))[source]
A type property checker.
A
TypeChecker
performs type checking for aValidator
, converting between the defined JSON Schema types and some associated Python types or objects.Modifying the behavior just mentioned by redefining which Python objects are considered to be of which JSON Schema types can be done using
TypeChecker.redefine
orTypeChecker.redefine_many
, and types can be removed viaTypeChecker.remove
. Each of these return a newTypeChecker
.- Parameters:
type_checkers – The initial mapping of types to their checking functions.
- is_type(instance, type: str) bool [source]
Check if the instance is of the appropriate type.
- Parameters:
instance – The instance to check
type – The name of the type that is expected.
- Raises:
jsonschema.exceptions.UndefinedTypeCheck – if
type
is unknown to this object.
- redefine(type: str, fn) TypeChecker [source]
Produce a new checker with the given type redefined.
- Parameters:
type – The name of the type to check.
fn (collections.abc.Callable) – A callable 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.
- redefine_many(definitions=()) TypeChecker [source]
Produce a new checker with the given types redefined.
- Parameters:
definitions (dict) – A dictionary mapping types to their checking functions.
- remove(*types) TypeChecker [source]
Produce a new checker with the given types forgotten.
- Parameters:
types – the names of the types to remove.
- 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 jsonschema.TypeChecker
or create a new one. You may then create a new
Validator
via jsonschema.validators.extend
.
from jsonschema import validators
class MyInteger:
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"})
- exception jsonschema.exceptions.UnknownType(type, instance, schema)[source]
A validator was asked to validate an instance against an unknown 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 Validator
protocol, which each included validator class implements.
Each of the below cover a specific release of the JSON Schema specification.
- class jsonschema.Draft202012Validator(schema: bool | ~collections.abc.Mapping[str, ~typing.Any], resolver=None, format_checker: ~jsonschema._format.FormatChecker | None = None, *, registry=<Registry (20 resources)>, _resolver=None)
- class jsonschema.Draft201909Validator(schema: bool | ~collections.abc.Mapping[str, ~typing.Any], resolver=None, format_checker: ~jsonschema._format.FormatChecker | None = None, *, registry=<Registry (20 resources)>, _resolver=None)
- class jsonschema.Draft7Validator(schema: bool | ~collections.abc.Mapping[str, ~typing.Any], resolver=None, format_checker: ~jsonschema._format.FormatChecker | None = None, *, registry=<Registry (20 resources)>, _resolver=None)
- class jsonschema.Draft6Validator(schema: bool | ~collections.abc.Mapping[str, ~typing.Any], resolver=None, format_checker: ~jsonschema._format.FormatChecker | None = None, *, registry=<Registry (20 resources)>, _resolver=None)
- class jsonschema.Draft4Validator(schema: bool | ~collections.abc.Mapping[str, ~typing.Any], resolver=None, format_checker: ~jsonschema._format.FormatChecker | None = None, *, registry=<Registry (20 resources)>, _resolver=None)
- class jsonschema.Draft3Validator(schema: bool | ~collections.abc.Mapping[str, ~typing.Any], resolver=None, format_checker: ~jsonschema._format.FormatChecker | None = None, *, registry=<Registry (20 resources)>, _resolver=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, as per the specification, no validation is enforced.
Optionally however, validation can be enabled by hooking a format-checking object
into a Validator
.
>>> validate("127.0.0.1", {"format" : "ipv4"})
>>> validate(
... instance="-12",
... schema={"format" : "ipv4"},
... format_checker=Draft202012Validator.FORMAT_CHECKER,
... )
Traceback (most recent call last):
...
ValidationError: "-12" is not a "ipv4"
Some formats require additional dependencies to be installed.
The easiest way to ensure you have what is needed is to install jsonschema
using the format
or format-nongpl
extras.
For example:
$ pip install jsonschema[format]
Or if you want to avoid GPL dependencies, a second extra is available:
$ pip install jsonschema[format-nongpl]
At the moment, it supports all the available checkers except for iri
and iri-reference
.
Warning
It is your own responsibility ultimately to ensure you are license-compliant, so you should be double checking your own dependencies if you rely on this extra.
The more specific list of formats along with any additional dependencies they have is shown below.
Warning
If a dependency is not installed when using a checker that requires it, validation will succeed without throwing an error, as also specified by the 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 |
The supported mechanism for ensuring these dependencies are present is again as shown above, not by directly installing the packages.
- class jsonschema.FormatChecker(formats: Iterable[str] | None = 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 add a check for a custom format use the
FormatChecker.checks
decorator.- Parameters:
formats – 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
decorator.
- 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.
Deprecated since version v4.14.0: Use
FormatChecker.checks
on an instance instead.
- check(instance: object, format: str) None [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 – The format that instance should conform to
- Raises:
FormatError – if the instance does not conform to
format
- checks(format: str, raises: Type[Exception] | Tuple[Type[Exception], ...] = ()) Callable[[_F], _F] [source]
Register a decorated function as validating a new format.
- Parameters:
format – The format that the decorated function will check.
raises –
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.
- exception jsonschema.FormatError(message, cause=None)[source]
Validating a format failed.
Format-Specific Notes#
regex#
The JSON Schema specification recommends (but does not require) that implementations use ECMA 262 regular expressions.
Given that there is no current library in Python capable of supporting the ECMA 262 dialect, the regex
format will instead validate Python regular expressions, which are the ones used by this implementation for other keywords like pattern or patternProperties.
email#
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 RFC 5322 validation.
The same applies to the idn-email
format.
If you indeed want a particular well-specified set of emails to be considered valid, you can use FormatChecker.checks
to provide your specific definition.