Using validictory¶

Normal use of validictory is as simple as calling validictory.validate(), the only thing to learn is how to craft a schema.

Examples¶

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 objects
integer - ints
number - ints and floats
boolean - bools
object - dicts
array - lists and tuples
null - None
any - 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.

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.

additionalProperties

Schema for all additional properties not included in properties.

Can be False to disallow any additional properties not in properties, or can be a sub-schema that all properties not included in properties must match.

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)

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.

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 is True.

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 and maximum

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.

exclusiveMinimum and exclusiveMaximum

If these values are present and set to True, they will modify the minimum and maximum tests to be exclusive.

minItems, minLength, maxItems, and maxLength

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.

uniqueItems

Indicate that all attributes in a list must be unique.

pattern

If the value is a string, this provides a regular expression that the string must match to be valid.

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.

enum

Provides an array that the value must match if present.

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 UTC

formats can be provided as the format_validators argument to validictory.validate.

divisibleBy

Ensures that the data value can be divided (without remainder) by a given divisor (not 0).

title and description

These do no validation, but if provided must be strings or a ~validictory.SchemaError will be raised.

Common Recipes¶

A situation that often arises is the need to validate that all members of a list validate according to one type of object or another. This can be achieved by combining the “items”, “type”, and “object” schema options.

To define a property that you want to be a list of one or more types you’d need to follow the following recipe (filling in foo and bar definition with normal schema definition):

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)

Using validictory¶

Examples¶

Schema Options¶

Common Recipes¶

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