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










A human readable message explaining the error.


The name of the failed validator.


The value for the failed validator in the 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.


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


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).


Same as relative_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.


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.


Same as relative_path.


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.


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.


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().


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:


{} 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:

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.

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]:


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=())

ErrorTrees make it easier to check which validations failed.


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


Check whether instance[index] has any errors.


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.


Iterate (non-recursively) over the indices in the instance with errors.


Same as 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):
'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

>>> 1 in tree

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

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

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

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 at 0x7f8118aaa500>)[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.

  • 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.

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


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.


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.

  • 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”