Using validictory¶
As of 2018 this library is deprecated, please consider using jsonschema instead.
Normal use of validictory is as simple as calling validictory.validate()
,
the only thing to learn is how to craft a schema.
Sample Usage¶
JSON documents and schema must first be loaded into a Python dictionary type before it can be validated.
Parsing a simple JSON document:
>>> import validictory
>>> validictory.validate("roast beef", {"type":"string"})
Parsing a more complex JSON document:
>>> import json
>>> import validictory
>>> data = json.loads('["foo", {"bar":["baz", null, 1.0, 2]}]')
>>> schema = {
... "type":"array",
... "items":[
... {"type":"string"},
... {"type":"object",
... "properties":{
... "bar":{
... "items":[
... {"type":"string"},
... {"type":"any"},
... {"type":"number"},
... {"type":"integer"}
... ]
... }
... }
... }
... ]
... }
>>> validictory.validate(data,schema)
Catch ValueErrors to handle validation issues:
>>> import validictory
>>> try:
... validictory.validate("short", {"type":"string","minLength":15})
... except ValueError, error:
... print error
...
Length of value 'short' for field '_data' must be greater than or equal to 15
For more example usage of all schema options check out the tests within
validictory/tests
.
Schema Options¶
type
Validate that an item in the data is of a particular type.
If a list of values is provided then any of the specified types will be accepted.
Provided value can be any combination of the following:
string
- str and unicode objectsinteger
- intsnumber
- ints and floatsboolean
- boolsobject
- dictsarray
- lists and tuplesnull
- Noneany
- any type is acceptable
properties
List of validators for properties of the object.
In essence each item in the provided dict for properties is a sub-schema applied against the property (if present) with the same name in the data.
# each key in the 'properties' option matches a key in the object that you are validating,
# and the value to each key in the 'properties' option is the schema to validate
# the value of the key in the JSON you are verifying.
data = json.loads(''' {"obj1": {"obj2": 12}}''' )
schema =
{
"type": "object",
"properties": {
"obj1": {
"type": "object",
"properties": {
"obj2": {
"type": "integer"
}
}
}
}
}
validictory.validate(data, schema)
patternProperties
Define a set of patterns that validate against subschemas.
Similarly to how
properties
works, any properties in the data that have a name matching a particular pattern must validate against the provided sub-schema.
data = json.loads('''
{
"one": "hello",
"two": "helloTwo",
"thirtyThree": 12
}''')
schema = {
"type": "object",
"properties": {
"one": {
"type": "string"
},
"two": {
"type": "string"
}
},
# each subkey of the 'patternProperties' option is a
# regex, and the value is the schema to validate
# all values whose keys match said regex.
"patternProperties": {
"^.+Three$": {
"type": "number"
}
}
}
additionalProperties
Schema for all additional properties not included in properties.
Can be
False
to disallow any additional properties not inproperties
, or can be a sub-schema that all properties not included inproperties
must match.
data = json.loads('''
{
"one": [12, 13],
"two": "hello",
"three": null,
"four": null
}''')
schema = {
"type": "object",
"properties": {
"one": {
"type": "array"
},
"two": {
"type": "string"
}
},
# this will match any keys that were not listed in 'properties'
"additionalProperties": {
"type": "null"
}
}
validictory.validate(data, schema)
items
Provide a schema or list of schemas to match against a list.
If the provided value is a schema object then every item in the list will be validated against the given schema.
If the provided value is a list of schemas then each item in the list must match the schema in the same position of the list. (extra items will be validated according to
additionalItems
)
# given a schema object, every list will be validated against it.
data = json.loads(''' {"results": [1, 2, 3, 4, 5]}''')
schema = {
"properties": {
"results": {
"items": {
"type": "integer"
}
}
}
}
validictory.validate(data, schema)
# given a list, each item in the list is matched against the schema
# at the same index. (entry 0 in the json will be matched against entry 0
# in the schema, etc)
dataTwo = json.loads(''' {"results": [1, "a", false, null, 5.3]} ''')
schemaTwo = {
"properties": {
"results": {
"items": [
{"type": "integer"},
{"type": "string"},
{"type": "boolean"},
{"type": "null"},
{"type": "number"}
]
}
}
}
validictory.validate(dataTwo, schemaTwo)
additionalItems
- Used in conjunction with
items
. If False then no additional items are allowed, if a schema is provided then all additional items must match the provided schema.
data = json.loads(''' {"results": [1, "a", false, null, null, null]} ''')
schema = {
"properties": {
"results": {
"items": [
{"type": "integer"},
{"type": "string"},
{"type": "boolean"}
],
# when using 'items' and providing a list (so that values in the list get validated
# by the schema at the same index), any extra values get validated using additionalItems
"additionalItems": {
"type": "null"
}
}
}
}
validictory.validate(data, schema)
required
If True, the property must be present to validate.
The default value of this parameter is set on the call to
validate()
. By default it isTrue
.
data = json.loads(''' {"one": 1, "two": 2}''')
schema = {
"type": "object",
"properties": {
"one": {
"type": "number",
},
"two": {
"type": "number",
},
# even though "three" is missing, it will pass validation
# because required = False
"three": {
"type": "number",
"required": False
}
}
}
validictory.validate(data, schema)
Note
If you are following the JSON Schema spec, this diverges from the
official spec as of v3. If you want to validate against v3 more
correctly, be sure to set required_by_default
to False.
dependencies
- Can be a single string or list of strings representing properties that must exist if the given property exists.
For example:
schema = {"prop01": {"required":False},
"prop02": {"required":False, "dependencies":"prop01"}}
# would validate
{"prop01": 7}
# would fail (missing prop01)
{"prop02": 7}
minimum
andmaximum
If the value is a number (int or float), these methods will validate that the values are less than or greater than the given minimum/maximum.
Minimum and maximum values are inclusive by default.
data = json.loads(''' {"result": 10, "resultTwo": 12}''')
schema = {
"properties": {
"result": { # passes
"minimum": 9,
"maximum": 10
},
"resultTwo": { # fails
"minimum": 13
}
}
}
exclusiveMinimum
andexclusiveMaximum
- If these values are present and set to True, they will modify the
minimum
andmaximum
tests to be exclusive.
data = json.loads(''' {"result": 10, "resultTwo": 12, "resultThree": 15}''')
schema = {
"properties": {
"result": { # fails, has to > 10
"exclusiveMaximum": 10
},
"resultTwo": { # fails, has to be > 12
"exclusiveMinimum": 12
},
"resultThree": { # passes
"exclusiveMaximum": 20,
"exclusiveMinimum": 14
}
}
}
minItems
,minLength
,maxItems
, andmaxLength
If the value is a list or str, these will test the length of the list or string.
There is no difference in implementation between the items/length variants.
data = json.loads(''' { "one": "12345", "two": "2345", "three": [1, 2, 3, 4, 5]} ''')
schema = {
"properties": {
"one": { # passes
"minLength": 4,
"maxLength": 6
},
"two": { # fails
"minLength": 6
},
"three": { # passes
"maxItems": 5
}
}
}
uniqueItems
- Indicate that all attributes in a list must be unique.
data = json.loads(''' {"one": [1, 2, 3, 4], "two": [1, 1, 2]} ''')
schema = {
"properties": {
"one": { # passes
"uniqueItems": True
},
"two": { # fails
"uniqueItems": True
}
}
}
pattern
- If the value is a string, this provides a regular expression that the string must match to be valid.
data = json.loads(''' {"twentyOne": "21", "thirtyThree": "33"} ''')
schema = {
"properties": {
"thirtyThree": {
"pattern": "^33$"
}
}
}
blank
If False, validate that string values are not blank (the empty string).
The default value of this parameter is set when initializing SchemaValidator. By default it is
False
.
data = json.loads(''' {"hello": "", "testing": ""}''')
schema = {
"properties": {
"hello": {
"blank": True # passes
},
"testing": {
"blank": False # fails
}
}
}
enum
- Provides an array that the value must match if present.
data = json.loads(''' {"today": "monday", "tomorrow": "something"}''')
dayList = ["monday", "tuesday", "wednesday", "thursday", "friday", "saturday", "sunday"]
schema = {
"properties": {
"today": {
"enum": dayList # passes
},
"tomorrow": {
"enum": dayList # does not pass, 'something' is not in the enum.
}
}
}
format
Validate that the value matches a predefined format.
By default several formats are recognized:
date-time
: ‘yyyy-mm-ddhh:mm:ssZ’date
: ‘yyyy-mm-dd’time
: ‘hh:mm::ss’utc-millisec
: number of seconds since UTCip-address
: IPv4 address, in dotted-quad string format (for example, ‘123.45.67.89’)
formats can be provided as a dictionary (of type {“formatString”: format_func} ) to the
format_validators
argument ofvalidictory.validate
.Custom formatting functions have the function signature
format_func(validator, fieldname, value, format_option):
.validator
is a reference to the SchemaValidator (or custom validator class if you passed one in for thevalidator_cls
argument invalidictory.validate
).fieldname
is the name of the field whose value you are validating in the JSON.value
is the actual value that you are validatingformat_option
is the name of the format string that was provided in the JSON, useful if you have one format function for multiple format strings.
Here is an example of writing a custom format function to validate UUIDs:
import json
import validictory
import uuid
data = json.loads(''' { "uuidInt": 117574695023396164616661330147169357159,
"uuidHex": "fad9d8cc11d64578bff327df93276964"}''')
schema = {
"title": "My test schema",
"properties": {
"uuidHex": {
"format": "uuid_hex"
},
"uuidInt": {
"format": "uuid_int"
}
}
}
def validate_uuid(validator, fieldname, value, format_option):
print("*********************")
print("validator:",validator)
print("fieldname:", fieldname)
print("value", value)
print("format_option", format_option)
print("*********************")
if format_option == "uuid_hex":
try:
uuid.UUID(hex=value)
except Exception as e:
raise validictory.FieldValidationError("Could not parse UUID \
from hex string %(uuidstr)s, reason: %(reason)s"
% {"uuidstr": value, "reason": e}, fieldname, value)
elif format_option == "uuid_int":
try:
uuid.UUID(int=value)
except Exception as e:
raise validictory.FieldValidationError("Could not parse UUID \
from int string %(uuidstr)s, reason: %(reason)s"
% {"uuidstr": value, "reason": e}, fieldname, value)
else:
raise validictory.FieldValidationError("Invalid format option for \
'validate_uuid': %(format)s" % format_option,
fieldName, value)
try:
formatdict = {"uuid_hex": validate_uuid, "uuid_int": validate_uuid}
validictory.validate(data, schema, format_validators=formatdict)
print("Successfully validated %(data)s!" % {"data": data})
except Exception as e2:
print("couldn't validate =( reason: %(reason)s" % {"reason": e})
divisibleBy
- Ensures that the data value can be divided (without remainder) by a given divisor (not 0).
data = json.loads('''{"value": 12, "valueTwo": 13} ''')
schema = {
"properties": {
"value": {
"divisibleBy": 2 # passes
},
"valueTwo": {
"divisibleBy": 2 # fails
}
}
}
title
anddescription
- These do no validation, but if provided must be strings or a
~validictory.SchemaError
will be raised.
data = json.loads(''' {"hello": "testing"}''')
schema = {
"title": "My test schema",
"properties": {
"hello": {
"type": "string",
"description": Make sure the 'hello' key is a string"
}
}
}
Examples¶
Using a Schema¶
The schema can be either a deserialized JSON document or a literal python object
data = json.loads(''' {"age": 23, "name": "Steven"} ''')
# json string
schemaOne = json.loads(''' {"type": "object", "properties":
{"age": {"type": "integer"}, "name": {"type": "string"}}} ''')
# python object literal
schemaTwo = {"type": "object", "properties":
{"age": {"type": "integer"}, "name": {"type": "string"}}}
validictory.validate(data, schemaOne)
validictory.validate(data, schemaTwo)
Validating Using Builtin Types¶
data = json.loads('''
{
"name": "bob",
"age": 23,
"siblings": null,
"registeredToVote": false,
"friends": ["Jane", "Michael"],
"heightInInches": 70.2
} ''')
schema =
{
"type": "object",
"properties": {
"name": {
"type": "string"
},
"age": {
"type": "integer"
},
"siblings": {
"type": "null"
},
"registeredToVote": {
"type": "boolean"
},
"friends": {
"type": "array"
}
}
}
validictory.validate(data, schema)
the ‘number’ type can be used when you don’t care what type the number is, or ‘integer’ if you want a non floating point number
dataTwo = json.loads('''{"valueOne": 12} ''')
schemaTwo = { "properties": { "valueOne": { "type": "integer"}} }
validictory.validate(dataTwo, schemaTwo)
the ‘any’ type can be used to validate any type.
dataThree = json.loads(''' {"valueOne": 12, "valueTwo": null, "valueThree": "hello" }''')
schemaThree = {
"properties": {
"valueOne": {"type": "any"},
"valueTwo": {"type": "any"},
"valueThree": {"type": "any"}
}
}
validictory.validate(dataThree, schemaThree)
You can list multiple types as well.
dataFour = json.loads(''' {"valueOne": 12, "valueTwo": null}''')
schemaFour = {
"properties": {
"valueOne": {
"type": ["string", "number"]
},
"valueTwo": {
"type": ["null", "string"]
}
}
}
validictory.validate(dataFour, schemaFour)
Validating Nested Containers¶
data = json.loads('''
{
"results": {
"xAxis": [
[0, 1],
[1, 3],
[2, 5],
[3, 1]
],
"yAxis": [
[0, "sunday"],
[1, "monday"],
[2, "tuesday"],
[3, "wednesday"]
]
}
} ''')
schema = {
"type": "object",
"properties": {
"results": {
"type": "object",
"properties": {
"xAxis": {
"type": "array",
"items": {
"type": "array",
# use a list of schemas, so that the the schema at index 0
# matches the item in the list at index 0, etc.
"items": [{"type": "number"}, {"type": "number"}]
}
},
"yAxis": {
"type": "array",
"items": {
"type": "array",
"items": [{"type": "number"}, {"type": "string"}]
}
}
}
}
}
}
validictory.validate(data, schema)
Specifying Custom Types¶
If a list is specified for the ‘types’ option, then you can specify a schema or multiple schemas that each element in the list will be tested against. This also allows you to split up your schema definition for ease of reading, or to share schema definitions between other schemas.
schema = {
"type": "object",
"properties": {
"foo_or_bar_list": {
"type": "array",
"items": {
"type": [
{"type": "object",
# foo definition
},
{"type": "object",
# bar definition
},
]
}
}
}
}
A common example of this is the GeoJSON spec, which allows for a geometry collection to have a list of geometries (Point, MultiPoint, LineString, MultiLineString, Polygon, MultiPolygon).
Simplified GeoJSON example:
# to simplify things we make a few subschema dicts
position = {
"type": "array",
"minItems": 2,
"maxItems": 3
}
point = {
"type": "object",
"properties": {
"type": {
"pattern": "Point"
},
"coordinates": {
"type": position
}
}
}
multipoint = {
"type": "object",
"properties": {
"type": {
"pattern": "MultiPoint"
},
"coordinates": {
"type": "array",
"minItems": 2,
"items": position
}
}
}
# the main schema
simplified_geojson_geometry = {
"type": "object",
"properties": {
"type": {
"pattern": "GeometryCollection"
},
# this defines an array ('geometries') that is a list of objects
# which conform to one of the schemas in the type list
"geometries": {
"type": "array",
"items": {"type": [point, multipoint]}
}
}
}
(thanks to Jason Sanford for bringing this need to my attention, see his blog post on validating GeoJSON)