Sureberus

This is an implementation of the Cerberus schema format. It doesn't implement all of the features of that library, and where it does implement a feature it doesn't always implement it in the exact same way.

The main reason it exists is to support some of the things that Cerberus doesn't do.

Schema selection based on dict keys

Often times when anyof or oneof are used, what we really want to do is select a schema based on dict keys.

There are two options for this, which should be used in preference to anyof or oneof, when possible, as they provide much better error messages.

when_key_is

Use this when you have dictionaries that have a fixed key, such as "type", which specifies some specific format to use. For example, if you have data that can look like this:

{"type": "elephant", "trunk_length": 60}
{"type": "eagle", "wingspan": 50}

Then you would use when_key_is, like this:

{
    "type": "dict",
    "when_key_is": {
        "key": "type",
        "choices": {
            "elephant": {
                "schema": {"trunk_length": {"type": "integer"}}
            },
            "eagle": {
                "schema": {"wingspan": {"type": "integer"}}
            },
        }
    }
}

when_key_exists

Use this when you have dictionaries where you must choose the schema based on keys that exist in the data exclusively for their type of data. For example, if you have data that can look like this:

{"image_url": "foo.jpg", "width": 30}
{"color": "red"}

Then you would use when_key_exists, like this:

{
    "type": "dict",
    "when_key_exists": {
        "image_url": {
            "schema": {"image_url": {"type": "string"}, "width": {"type": "integer"}}
        },
        "color": {
            "schema": {"color": {"type": "string"}}
        },
    }
}

normalization inside of *of-rules

The primary important difference is that you can use sureberus if you want to use default or coerce inside of a *of-rule.

In-line schema registries

Small, reusable "chunks" of schema can be defined in-line in the schema specification, instead of requiring Python code to be written which sets up registries. This allows for easy use of recursive schemas at any point in your schema, or just a way to conveniently reuse some subschema in multiple places. For example, here is a schema that validates any nested list of strings:

{
    "registry": {
        "nested_list": {
            "type": "list",
            "schema": {
                "anyof": [
                    {"type": "string"},
                    "nested_list",
                ],
            }
        }
    },
    "type": "dict",
    "schema": {"things": "nested_list"},
}

This will validate data like {"things": ["one", ["two", ["three"]]]}.

Typically any place you can specify a schema, you can instead specify a string which will be used to find a previously registered schema (references to registered schemas are resolved lexically).

When you need to "merge in" a registered schema, you can use the schema_ref directive. This can be useful if you want to register a schema and use it at exactly the same level, for example:

{
    "registry": {
        "nested_list": {
            "type": "list",
            "schema": {"anyof": [{"type": "integer"}, "nested_list"]}
        }
    },
    "schema_ref": "nested_list",
}

This will validate data like ["one", ["two", ["three"]]].

Nullable in the face of *of-rules

Sureberus allows you to use nullable even if you have *of-rules that have type constraints. A nullable schema always allows None.

A slightly nicer schema syntax

If you want to construct a schema from Python code instead of storing it as JSON, sureberus provides a more terse syntax for it:

Here's a standard dict-based schema, using an 80-character limit and strict newline/indent-based line wrapping:

myschema = {
    'type': 'dict',
    'anyof': [
        {'schema': {'gradient': {'type': 'string'}}},
        {
            'schema': {
                'image': {'type': 'string'},
                'opacity': {'type': 'integer', 'default': 100},
            }
        },
    ],
}

And here is a sureberus.schema-based schema, using the same line-wrapping rules:

from sureberus.schema import Dict, SubSchema, String, Integer
myschema = Dict(
    anyof=[
        SubSchema(gradient=String()),
        SubSchema(image=String(), opacity=Integer(default=100))
    ]
)