jsonschema¶

jsonschema is an implementation of JSON Schema for Python (supporting 2.7+ including Python 3).

>>> from jsonschema import validate

>>> # A sample schema, like what we'd get from json.load()
>>> schema = {
...     "type" : "object",
...     "properties" : {
...         "price" : {"type" : "number"},
...         "name" : {"type" : "string"},
...     },
... }

>>> # If no exception is raised by validate(), the instance is valid.
>>> validate({"name" : "Eggs", "price" : 34.99}, schema)

>>> validate(
...     {"name" : "Eggs", "price" : "Invalid"}, schema
... )                                   
Traceback (most recent call last):
    ...
ValidationError: 'Invalid' is not of type 'number'

You can find further information (installation instructions, mailing list) as well as the source code and issue tracker on our GitHub page.

Contents:

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.

Handling Validation Errors¶

When an invalid instance is encountered, a ValidationError will be raised or returned, depending on which method or function is used.

exception jsonschema.exceptions.ValidationError(message, validator=<unset>, path=(), cause=None, context=(), validator_value=<unset>, instance=<unset>, schema=<unset>, schema_path=(), parent=None)[source]¶

The instance didn’t properly validate under the provided schema.

The information carried by an error roughly breaks down into:

What Happened Why Did It Happen What Was Being Validated

message

message¶: A human readable message explaining the error.

validator¶: The name of the failed validator.

validator_value¶: The value for the failed validator in the schema.

schema¶: The full schema that this error came from. This is potentially a subschema from within the schema that was passed in originally, or even an entirely different schema if a $ref was followed.

relative_schema_path¶: A collections.deque containing the path to the failed validator within the schema.

absolute_schema_path¶: A collections.deque containing the path to the failed validator within the schema, but always relative to the original schema as opposed to any subschema (i.e. the one originally passed into a validator class, not schema).

schema_path¶: Same as relative_schema_path.

relative_path¶: A collections.deque containing the path to the offending element within the instance. The deque can be empty if the error happened at the root of the instance.

absolute_path¶: A collections.deque containing the path to the offending element within the instance. The absolute path is always relative to the original instance that was validated (i.e. the one passed into a validation method, not instance). The deque can be empty if the error happened at the root of the instance.

path¶: Same as relative_path.

instance¶: The instance that was being validated. This will differ from the instance originally passed into validate() if the validator object was in the process of validating a (possibly nested) element within the top-level instance. The path within the top-level instance (i.e. ValidationError.path) could be used to find this object, but it is provided for convenience.

context¶: If the error was caused by errors in subschemas, the list of errors from the subschemas will be available on this property. The schema_path and path of these errors will be relative to the parent error.

cause¶: If the error was caused by a non-validation error, the exception object will be here. Currently this is only used for the exception raised by a failed format checker in FormatChecker.check().

parent¶: A validation error which this error is the context of. None if there wasn’t one.

In case an invalid schema itself is encountered, a SchemaError is raised.

exception jsonschema.exceptions.SchemaError(message, validator=<unset>, path=(), cause=None, context=(), validator_value=<unset>, instance=<unset>, schema=<unset>, schema_path=(), parent=None)[source]¶

The provided schema is malformed.

The same attributes are present as for ValidationErrors.

These attributes can be clarified with a short example:

schema = {
    "items": {
        "anyOf": [
            {"type": "string", "maxLength": 2},
            {"type": "integer", "minimum": 5}
        ]
    }
}
instance = [{}, 3, "foo"]
v = Draft4Validator(schema)
errors = sorted(v.iter_errors(instance), key=lambda e: e.path)

The error messages in this situation are not very helpful on their own.

for error in errors:
    print(error.message)

outputs:

{} is not valid under any of the given schemas
3 is not valid under any of the given schemas
'foo' is not valid under any of the given schemas

If we look at path on each of the errors, we can find out which elements in the instance correspond to each of the errors. In this example, path will have only one element, which will be the index in our list.

for error in errors:
    print(list(error.path))

[0]
[1]
[2]

Since our schema contained nested subschemas, it can be helpful to look at the specific part of the instance and subschema that caused each of the errors. This can be seen with the instance and schema attributes.

With validators like anyOf, the context attribute can be used to see the sub-errors which caused the failure. Since these errors actually came from two separate subschemas, it can be helpful to look at the schema_path attribute as well to see where exactly in the schema each of these errors come from. In the case of sub-errors from the context attribute, this path will be relative to the schema_path of the parent error.

for error in errors:
    for suberror in sorted(error.context, key=lambda e: e.schema_path):
        print(list(suberror.schema_path), suberror.message, sep=", ")

[0, 'type'], {} is not of type 'string'
[1, 'type'], {} is not of type 'integer'
[0, 'type'], 3 is not of type 'string'
[1, 'minimum'], 3 is less than the minimum of 5
[0, 'maxLength'], 'foo' is too long
[1, 'type'], 'foo' is not of type 'integer'

The string representation of an error combines some of these attributes for easier debugging.

print(errors[1])

3 is not valid under any of the given schemas

Failed validating 'anyOf' in schema['items']:
    {'anyOf': [{'maxLength': 2, 'type': 'string'},
               {'minimum': 5, 'type': 'integer'}]}

On instance[1]:
    3

ErrorTrees¶

If you want to programmatically be able to query which properties or validators failed when validating a given instance, you probably will want to do so using ErrorTree objects.

class jsonschema.validators.ErrorTree(errors=())[source]¶

ErrorTrees make it easier to check which validations failed.

errors¶: The mapping of validator names to the error objects (usually ValidationErrors) at this level of the tree.

__contains__(index)[source]¶: Check whether instance[index] has any errors.

__getitem__(index)[source]¶

Retrieve the child tree one level down at the given index.

If the index is not in the instance that this tree corresponds to and is not known by this tree, whatever error would be raised by instance.__getitem__ will be propagated (usually this is some subclass of LookupError.

__iter__()[source]¶: Iterate (non-recursively) over the indices in the instance with errors.

__len__()[source]¶: Same as total_errors.

total_errors¶: The total number of errors in the entire tree, including children.

Consider the following example:

schema = {
    "type" : "array",
    "items" : {"type" : "number", "enum" : [1, 2, 3]},
    "minItems" : 3,
}
instance = ["spam", 2]

For clarity’s sake, the given instance has three errors under this schema:

v = Draft3Validator(schema)
for error in sorted(v.iter_errors(["spam", 2]), key=str):
    print(error.message)

'spam' is not of type 'number'
'spam' is not one of [1, 2, 3]
['spam', 2] is too short

Let’s construct an ErrorTree so that we can query the errors a bit more easily than by just iterating over the error objects.

tree = ErrorTree(v.iter_errors(instance))

As you can see, ErrorTree takes an iterable of ValidationErrors when constructing a tree so you can directly pass it the return value of a validator object’s iter_errors method.

ErrorTrees support a number of useful operations. The first one we might want to perform is to check whether a given element in our instance failed validation. We do so using the in operator:

>>> 0 in tree
True

>>> 1 in tree
False

The interpretation here is that the 0th index into the instance ("spam") did have an error (in fact it had 2), while the 1th index (2) did not (i.e. it was valid).

If we want to see which errors a child had, we index into the tree and look at the errors attribute.

>>> sorted(tree[0].errors)
['enum', 'type']

Here we see that the enum and type validators failed for index 0. In fact errors is a dict, whose values are the ValidationErrors, so we can get at those directly if we want them.

>>> print(tree[0].errors["type"].message)
'spam' is not of type 'number'

Of course this means that if we want to know if a given named validator failed for a given index, we check for its presence in errors:

>>> "enum" in tree[0].errors
True

>>> "minimum" in tree[0].errors
False

Finally, if you were paying close enough attention, you’ll notice that we haven’t seen our minItems error appear anywhere yet. This is because minItems is an error that applies globally to the instance itself. So it appears in the root node of the tree.

>>> "minItems" in tree.errors
True

That’s all you need to know to use error trees.

To summarize, each tree contains child trees that can be accessed by indexing the tree to get the corresponding child tree for a given index into the instance. Each tree and child has a errors attribute, a dict, that maps the failed validator name to the corresponding validation error.

best_match and relevance¶

The best_match() function is a simple but useful function for attempting to guess the most relevant error in a given bunch.

>>> from jsonschema import Draft4Validator
>>> from jsonschema.exceptions import best_match

>>> schema = {
...     "type": "array",
...     "minItems": 3,
... }
>>> print(best_match(Draft4Validator(schema).iter_errors(11)).message)
11 is not of type 'array'

jsonschema.exceptions.best_match(errors, key=<function relevance>)[source]¶

Try to find an error that appears to be the best match among given errors.

In general, errors that are higher up in the instance (i.e. for which ValidationError.path is shorter) are considered better matches, since they indicate “more” is wrong with the instance.

If the resulting match is either oneOf or anyOf, the opposite assumption is made – i.e. the deepest error is picked, since these validators only need to match once, and any other errors may not be relevant.

Parameters:

errors (iterable) – the errors to select from. Do not provide a mixture of errors from different validation attempts (i.e. from different instances or schemas), since it won’t produce sensical output.
key (callable) – the key to use when sorting errors. See relevance and transitively by_relevance() for more details (the default is to sort with the defaults of that function). Changing the default is only useful if you want to change the function that rates errors but still want the error context decension done by this function.

Returns:

the best matching error, or None if the iterable was empty

Note

This function is a heuristic. Its return value may change for a given set of inputs from version to version if better heuristics are added.

jsonschema.exceptions.relevance(validation_error)¶

A key function that sorts errors based on heuristic relevance.

If you want to sort a bunch of errors entirely, you can use this function to do so. Using this function as a key to e.g. sorted() or max() will cause more relevant errors to be considered greater than less relevant ones.

Within the different validators that can fail, this function considers anyOf and oneOf to be weak validation errors, and will sort them lower than other validators at the same level in the instance.

If you want to change the set of weak [or strong] validators you can create a custom version of this function with by_relevance() and provide a different set of each.

>>> schema = {
...     "properties": {
...         "name": {"type": "string"},
...         "phones": {
...             "properties": {
...                 "home": {"type": "string"}
...             },
...         },
...     },
... }
>>> instance = {"name": 123, "phones": {"home": [123]}}
>>> errors = Draft4Validator(schema).iter_errors(instance)
>>> [
...     e.path[-1]
...     for e in sorted(errors, key=exceptions.relevance)
... ]
['home', 'name']

jsonschema.exceptions.by_relevance(weak=frozenset(['oneOf', 'anyOf']), strong=frozenset([]))[source]¶

Create a key function that can be used to sort errors by relevance.

Parameters:

weak (set) – a collection of validator names to consider to be “weak”. If there are two errors at the same level of the instance and one is in the set of weak validator names, the other error will take priority. By default, anyOf and oneOf are considered weak validators and will be superceded by other same-level validation errors.
strong (set) – a collection of validator names to consider to be “strong”

Resolving JSON References¶

class jsonschema.RefResolver(base_uri, referrer, store=(), cache_remote=True, handlers=(), urljoin_cache=None, remote_cache=None)[source]¶

Resolve JSON References.

Parameters:

base_uri (str) – The URI of the referring document
referrer – The actual referring document
store (dict) – A mapping from URIs to documents to cache
cache_remote (bool) – Whether remote refs should be cached after first resolution
handlers (dict) – A mapping from URI schemes to functions that should be used to retrieve them
urljoin_cache (functools.lru_cache) – A cache that will be used for caching the results of joining the resolution scope to subscopes.
remote_cache (functools.lru_cache) – A cache that will be used for caching the results of resolved remote URLs.

classmethod from_schema(schema, *args, **kwargs)[source]¶

Construct a resolver from a JSON schema object.

Parameters:	schema – the referring schema
Returns:	`RefResolver`

resolve_fragment(document, fragment)[source]¶

Resolve a fragment within the referenced document.

Parameters:	document – The referrant document fragment (str) – a URI fragment to resolve within it

resolve_remote(uri)[source]¶

Resolve a remote uri.

If called directly, does not check the store first, but after retrieving the document at the specified URI it will be saved in the store if cache_remote is True.

Note

If the requests library is present, jsonschema will use it to request the remote uri, so that the correct encoding is detected and used.

If it isn’t, or if the scheme of the uri is not http or https, UTF-8 is assumed.

Parameters:	uri (str) – The URI to resolve
Returns:	The retrieved document

resolving(*args, **kwds)[source]¶

Context manager which resolves a JSON ref and enters the resolution scope of this ref.

Parameters:	ref (str) – The reference to resolve

exception jsonschema.RefResolutionError[source]¶: A JSON reference failed to resolve.

Creating or Extending Validator Classes¶

jsonschema.validators.create(meta_schema, validators=(), version=None, default_types=None)[source]¶

Create a new validator class.

Parameters:

meta_schema (dict) – the meta schema for the new validator class
validators (dict) –
a mapping from names to callables, where each callable will validate the schema property with the given name.

Each callable should take 4 arguments:
1. a validator instance,
2. the value of the property being validated within the instance
3. the instance
4. the schema
version (str) – an identifier for the version that this validator class will validate. If provided, the returned validator class will have its __name__ set to include the version, and also will have validates() automatically called for the given version.
default_types (dict) – a default mapping to use for instances of the validator class when mapping between JSON types to Python types. The default for this argument is probably fine. Instances can still have their types customized on a per-instance basis.

Returns:

a new jsonschema.IValidator class

jsonschema.validators.extend(validator, validators, version=None)[source]¶

Create a new validator class by extending an existing one.

Parameters:

validator (jsonschema.IValidator) – an existing validator class
validators (dict) –
a mapping of new validator callables to extend with, whose structure is as in create().

Note

Any validator callables with the same name as an existing one will (silently) replace the old validator callable entirely, effectively overriding any validation done in the “parent” validator class.

If you wish to instead extend the behavior of a parent’s validator callable, delegate and call it directly in the new validator function by retrieving it using OldValidator.VALIDATORS["validator_name"].
version (str) – a version for the new validator class

Returns:

a new jsonschema.IValidator class

Note

Meta Schemas

The new validator class will have its parent’s meta schema.

If you wish to change or extend the meta schema in the new validator class, modify META_SCHEMA directly on the returned class. Note that no implicit copying is done, so a copy should likely be made before modifying it, in order to not affect the old validator.

jsonschema.validators.validator_for(schema, default=<unset>)[source]¶

Retrieve the validator class appropriate for validating the given schema.

Uses the $schema property that should be present in the given schema to look up the appropriate validator class.

Parameters:	schema – the schema to look at default – the default to return if the appropriate validator class cannot be determined. If unprovided, the default is to return `Draft4Validator`

jsonschema.validators.validates(version)[source]¶

Register the decorated validator for a version of the specification.

Registered validators and their meta schemas will be considered when parsing $schema properties’ URIs.

Parameters:	version (str) – An identifier to use as the version’s name
Returns:	a class decorator to decorate the validator with the version
Return type:	callable

Creating Validation Errors¶

Any validating function that validates against a subschema should call ValidatorMixin.descend(), rather than ValidatorMixin.iter_errors(). If it recurses into the instance, or schema, it should pass one or both of the path or schema_path arguments to ValidatorMixin.descend() in order to properly maintain where in the instance or schema respectively the error occurred.

Frequently Asked Questions¶

Why doesn’t my schema that has a default property actually set the default on my instance?¶

The basic answer is that the specification does not require that default actually do anything.

For an inkling as to why it doesn’t actually do anything, consider that none of the other validators modify the instance either. More importantly, having default modify the instance can produce quite peculiar things. It’s perfectly valid (and perhaps even useful) to have a default that is not valid under the schema it lives in! So an instance modified by the default would pass validation the first time, but fail the second!

Still, filling in defaults is a thing that is useful. jsonschema allows you to define your own validator classes and callables, so you can easily create a IValidator that does do default setting. Here’s some code to get you started. (In this code, we add the default properties to each object before the properties are validated, so the default values themselves will need to be valid under the schema.)

from jsonschema import Draft4Validator, validators


def extend_with_default(validator_class):
    validate_properties = validator_class.VALIDATORS["properties"]

    def set_defaults(validator, properties, instance, schema):
        for property, subschema in properties.iteritems():
            if "default" in subschema:
                instance.setdefault(property, subschema["default"])

        for error in validate_properties(
            validator, properties, instance, schema,
        ):
            yield error

    return validators.extend(
        validator_class, {"properties" : set_defaults},
    )


DefaultValidatingDraft4Validator = extend_with_default(Draft4Validator)


# Example usage:
obj = {}
schema = {'properties': {'foo': {'default': 'bar'}}}
# Note jsonschem.validate(obj, schema, cls=DefaultValidatingDraft4Validator)
# will not work because the metaschema contains `default` directives.
DefaultValidatingDraft4Validator(schema).validate(obj)
assert obj == {'foo': 'bar'}

See the above-linked document for more info on how this works, but basically, it just extends the properties validator on a Draft4Validator to then go ahead and update all the defaults.

Note

If you’re interested in a more interesting solution to a larger class of these types of transformations, keep an eye on Seep, which is an experimental data transformation and extraction library written on top of jsonschema.

Hint

The above code can provide default values for an entire object and all of its properties, but only if your schema provides a default value for the object itself, like so:

schema = {
    "type": "object",
    "properties": {
        "outer-object": {
            "type": "object",
            "properties" : {
                "inner-object": {
                    "type": "string",
                    "default": "INNER-DEFAULT"
                }
            },
            "default": {} # <-- MUST PROVIDE DEFAULT OBJECT
        }
    }
}

obj = {}
DefaultValidatingDraft4Validator(schema).validate(obj)
assert obj == {'outer-object': {'inner-object': 'INNER-DEFAULT'}}

...but if you don’t provide a default value for your object, then it won’t be instantiated at all, much less populated with default properties.

del schema["properties"]["outer-object"]["default"]
obj2 = {}
DefaultValidatingDraft4Validator(schema).validate(obj2)
assert obj2 == {} # whoops

How do jsonschema version numbers work?¶

jsonschema tries to follow the Semantic Versioning specification.

This means broadly that no backwards-incompatible changes should be made in minor releases (and certainly not in dot releases).

The full picture requires defining what constitutes a backwards-incompatible change.

The following are simple examples of things considered public API, and therefore should not be changed without bumping a major version number:

module names and contents, when not marked private by Python convention (a single leading underscore)

function and object signature (parameter order and name)

The following are not considered public API and may change without notice:

the exact wording and contents of error messages; typical reasons to do this seem to involve unit tests. API users are encouraged to use the extensive introspection provided in ValidationErrors instead to make meaningful assertions about what failed.

the order in which validation errors are returned or raised

the compat.py module, which is for internal compatibility use

anything marked private

With the exception of the last two of those, flippant changes are avoided, but changes can and will be made if there is improvement to be had. Feel free to open an issue ticket if there is a specific issue or question worth raising.

jsonschema¶

Schema Validation¶

The Basics¶

The Validator Interface¶

Validating With Additional Types¶

Versioned Validators¶

Validating Formats¶

Handling Validation Errors¶

ErrorTrees¶

best_match and relevance¶

Resolving JSON References¶

Creating or Extending Validator Classes¶

Creating Validation Errors¶

Frequently Asked Questions¶

Why doesn’t my schema that has a default property actually set the default on my instance?¶

How do jsonschema version numbers work?¶

Indices and tables¶

Table Of Contents