deb-python-jsonschema/docs/validate.rst

Schema Validation

jsonschema

The Basics

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

validate

The Validator Interface

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

argument dict 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 IValidator.check_schema to validate a schema first.

argument types

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-types for details.

type types

dict or iterable of 2-tuples

argument resolver

an instance of RefResolver that will be used to resolve $ref properties (JSON references). If unprovided, one will be created.

argument 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-validators.

schema

The schema that was passed in when initializing the object.

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.

type type

str

rtype

bool

raises

UnknownType if type is not a known type.

is_valid(instance)

Check if the instance is valid under the current schema.

rtype

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.

rtype

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 <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-validators.

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.

Draft3Validator

Draft4Validator

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"

FormatChecker

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.

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.

argument str format

the format that the decorated function will check

argument Exception raises

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.

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 email	OS must have socket.inet_pton function
uri	requires rfc3987
date-time date time regex	requires strict-rfc33391
color	requires webcolors

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1. For information on creating JSON schemas to validate your data, there is a good introduction to JSON Schema fundamentals underway at Understanding JSON Schema↩︎