deb-python-jsonschema/docs/errors.rst

Handling Validation Errors

jsonschema

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

ValidationError

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

message

A human readable message explaining the error.

validator

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 into the validator, or even an entirely different schema if a $ref was followed.

schema_path

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

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.

instance

The instance that was being validated. This will differ from the instance originally passed into validate if the validator 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.

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

SchemaError

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 ~ValidationError.path on each of the errors, we can find out which elements in the instance correspond to each of the errors. In this example, ~ValidationError.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 ~ValidationError.instance and ~ValidationError.schema attributes.

With validators like anyOf, the ~ValidationError.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 ~ValidationError.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 ~ValidationError.context attribute, this path will be relative to the ~ValidationError.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'], 3.0 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.

ErrorTree

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's ~IValidator.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 ~ErrorTree.errors attribute.

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

Here we see that the enum and type validators failed for index 0. In fact ~ErrorTree.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 validator failed for a given index, we check for its presence in ~ErrorTree.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 ~ErrorTree.errors attribute, a dict, that maps the failed validator to the corresponding validation error.