GenSON

by wolverdude

wolverdude / GenSON

GenSON is a powerful, user-friendly JSON Schema generator built in Python.

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GenSON

GenSON is a powerful, user-friendly

JSON Schema
_ generator built in Python.

.. note:: This is not the Python equivalent of the

Java Genson library
. If you are coming from Java and need to create JSON objects in Python, you want
Python's builtin json library
.)

GenSON's core function is to take JSON objects and generate schemas that describe them, but it is unique in its ability to merge schemas. It was originally built to describe the common structure of a large number of JSON objects, and it uses its merging ability to generate a single schema from any number of JSON objects and/or schemas.

GenSON's schema builder follows these three rules:

  1. Every object it is given must validate under the generated schema.
  2. Any object that is valid under any schema it is given must also validate under the generated schema. (there is one glaring exception to this, detailed
    below
    _)
  3. The generated schema should be as strict as possible given the first 2 rules.

JSON Schema Implementation

GenSON is compatible with JSON Schema Draft 6 and above.

It is important to note that GenSON uses only a subset of JSON Schema's capabilities. This is mainly because it doesn't know the specifics of your data model, and it tries to avoid guessing them. Its purpose is to generate the basic structure so that you can skip the boilerplate and focus on the details of the schema.

Currently, GenSON only deals with these keywords:

  • "$schema"
  • "type"
  • "items"
  • "properties"
  • "patternProperties"
  • "required"
  • "anyOf"

You should be aware that this limited vocabulary could cause GenSON to violate rules 1 and 2. If you feed it schemas with advanced keywords, it will just blindly pass them on to the final schema. Note that

"$ref"
and
id
are also not supported, so GenSON will not dereference linked nodes when building a schema.

Installation

.. code-block:: bash

$ pip install genson

CLI Tool

The package includes a

genson
executable that allows you to access this functionality from the command line. For usage info, run with
--help
:

.. code-block:: bash

$ genson --help

.. code-block::

usage: genson [-h] [--version] [-d DELIM] [-e ENCODING] [-i SPACES]
              [-s SCHEMA] [-$ SCHEMA_URI]
              ...

Generate one, unified JSON Schema from one or more JSON objects and/or JSON Schemas. Compatible with JSON-Schema Draft 4 and above.

positional arguments: object Files containing JSON objects (defaults to stdin if no arguments are passed).

optional arguments: -h, --help Show this help message and exit. --version Show version number and exit. -d DELIM, --delimiter DELIM Set a delimiter. Use this option if the input files contain multiple JSON objects/schemas. You can pass any string. A few cases ('newline', 'tab', 'space') will get converted to a whitespace character. If this option is omitted, the parser will try to auto-detect boundaries. -e ENCODING, --encoding ENCODING Use ENCODING instead of the default system encoding when reading files. ENCODING must be a valid codec name or alias. -i SPACES, --indent SPACES Pretty-print the output, indenting SPACES spaces. -s SCHEMA, --schema SCHEMA File containing a JSON Schema (can be specified multiple times to merge schemas). -$ SCHEMA_URI, --schema-uri SCHEMA_URI The value of the '$schema' keyword (defaults to 'http://json-schema.org/schema#' or can be specified in a schema with the -s option). If 'NULL' is passed, the "$schema" keyword will not be included in the result.

.. note:: The

--encoding
option is only available in Python 3.

GenSON Python API

SchemaBuilder
is the basic schema generator class.
SchemaBuilder
instances can be loaded up with existing schemas and objects before being serialized.

.. code-block:: python

>>> from genson import SchemaBuilder

>>> builder = SchemaBuilder() >>> builder.add_schema({"type": "object", "properties": {}}) >>> builder.add_object({"hi": "there"}) >>> builder.add_object({"hi": 5})

>>> builder.to_schema() {'$schema': 'http://json-schema.org/schema#', 'type': 'object', 'properties': { 'hi': {'type': ['integer', 'string']}}, 'required': ['hi']}

>>> print(builder.to_json(indent=2)) { "$schema": "http://json-schema.org/schema#", "type": "object", "properties": { "hi": { "type": [ "integer", "string" ] } }, "required": [ "hi" ] }

SchemaBuilder
API +++++++++++++++++++++

__init__(schema_uri=None)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

:param schema_uri: value of the

$schema
keyword. If not given, it will use the value of the first available
$schema
keyword on an added schema or else the default:
'http://json-schema.org/schema#'
. A value of
False
or
None
will direct GenSON to leave out the
"$schema"
keyword.

add_schema(schema)
^^^^^^^^^^^^^^^^^^^^^^

Merge in a JSON schema. This can be a

dict
or another
SchemaBuilder
object.

:param schema: a JSON Schema

.. note:: There is no schema validation. If you pass in a bad schema, you might get back a bad schema.

add_object(obj)
^^^^^^^^^^^^^^^^^^^

Modify the schema to accommodate an object.

:param obj: any object or scalar that can be serialized in JSON

to_schema()
^^^^^^^^^^^^^^^

Generate a schema based on previous inputs.

:rtype:

dict

to_json()
^^^^^^^^^^^^^

Generate a schema and convert it directly to serialized JSON.

:rtype:

str

__eq__(other)
^^^^^^^^^^^^^^^^^

Check for equality with another

SchemaBuilder
object.

:param other: another

SchemaBuilder
object. Other types are accepted, but will always return
False

SchemaBuilder object interaction ++++++++++++++++++++++++++++++++

SchemaBuilder
objects can also interact with each other:
  • You can pass one schema directly to another to merge them.
  • You can compare schema equality directly.

.. code-block:: python

>>> from genson import SchemaBuilder

>>> b1 = SchemaBuilder() >>> b1.add_schema({"type": "object", "properties": { ... "hi": {"type": "string"}}}) >>> b2 = SchemaBuilder() >>> b2.add_schema({"type": "object", "properties": { ... "hi": {"type": "integer"}}}) >>> b1 == b2 False

>>> b1.add_schema(b2) >>> b2.add_schema(b1) >>> b1 == b2 True >>> b1.to_schema() {'$schema': 'http://json-schema.org/schema#', 'type': 'object', 'properties': {'hi': {'type': ['integer', 'string']}}}

Seed Schemas

There are several cases where multiple valid schemas could be generated from the same object. GenSON makes a default choice in all these ambiguous cases, but if you want it to choose differently, you can tell it what to do using a seed schema.

Seeding Arrays ++++++++++++++

For example, suppose you have a simple array with two items:

.. code-block:: python

['one', 1]

There are always two ways for GenSON to interpret any array: List and Tuple. Lists have one schema for every item, whereas Tuples have a different schema for every array position. This is analogous to the (now deprecated)

merge_arrays
option from version 0. You can read more about JSON Schema
array validation here
_.

List Validation ^^^^^^^^^^^^^^^

.. code-block:: json

{
  "type": "array",
  "items": {"type": ["integer", "string"]}
}

Tuple Validation ^^^^^^^^^^^^^^^^

.. code-block:: json

{
  "type": "array",
  "items": [{"type": "integer"}, {"type": "string"}]
}

By default, GenSON always interprets arrays using list validation, but you can tell it to use tuple validation by seeding it with a schema.

.. code-block:: python

>>> from genson import SchemaBuilder

>>> builder = SchemaBuilder() >>> builder.add_object(['one', 1]) >>> builder.to_schema() {'$schema': 'http://json-schema.org/schema#', 'type': 'array', 'items': {'type': ['integer', 'string']}}

>>> builder = SchemaBuilder() >>> seed_schema = {'type': 'array', 'items': []} >>> builder.add_schema(seed_schema) >>> builder.add_object(['one', 1]) >>> builder.to_schema() {'$schema': 'http://json-schema.org/schema#', 'type': 'array', 'items': [{'type': 'string'}, {'type': 'integer'}]}

Note that in this case, the seed schema is actually invalid. You can't have an empty array as the value for an

items
keyword. But GenSON is a generator, not a validator, so you can fudge a little. GenSON will modify the generated schema so that it is valid, provided that there aren't invalid keywords beyond the ones it knows about.

Seeding patternProperties +++++++++++++++++++++++++

Support for patternProperties_ is new in version 1; however, since GenSON's default behavior is to only use

properties
, this powerful keyword can only be utilized with seed schemas. You will need to supply an
object
schema with a
patternProperties
object whose keys are RegEx strings. Again, you can fudge here and set the values to null instead of creating valid subschemas.

.. code-block:: python

>>> from genson import SchemaBuilder

>>> builder = SchemaBuilder() >>> builder.add_schema({'type': 'object', 'patternProperties': {r'^\d+$': None}}) >>> builder.add_object({'1': 1, '2': 2, '3': 3}) >>> builder.to_schema() {'$schema': 'http://json-schema.org/schema#', 'type': 'object', 'patternProperties': {'^\d+$': {'type': 'integer'}}}

There are a few gotchas you should be aware of here:

  • GenSON is written in Python, so it uses the
    Python flavor of RegEx
    _.
  • GenSON still prefers
    properties
    to
    patternProperties
    if a property already exists that matches one of your patterns, the normal property will be updated, not the pattern property.
  • If a key matches multiple patterns, there is no guarantee of which one will be updated.
  • The patternProperties_ docs themselves have some more useful pointers that can save you time.

Typeless Schemas ++++++++++++++++

In version 0, GenSON did not accept a schema without a type, but in order to be flexible in the support of seed schemas, support was added for version 1. However, GenSON violates rule #2 in its handling of typeless schemas. Any object will validate under an empty schema, but GenSON incorporates typeless schemas into the first-available typed schema, and since typed schemas are stricter than typless ones, objects that would validate under an added schema will not validate under the result.

Customizing
SchemaBuilder

You can extend the

SchemaBuilder
class to add in your own logic (e.g. recording
minimum
and
maximum
for a number). In order to do this, you need to:
  1. Create a custom
    SchemaStrategy
    class.
  2. Create a
    SchemaBuilder
    subclass that includes your custom
    SchemaStrategy
    class(es).
  3. Use your custom
    SchemaBuilder
    just like you would the stock
    SchemaBuilder
    .

SchemaStrategy
Classes ++++++++++++++++++++++++++

GenSON uses the Strategy Pattern to parse, update, and serialize different kinds of schemas that behave in different ways. There are several

SchemaStrategy
classes that roughly correspond to different schema types. GenSON maps each node in an object or schema to an instance of one of these classes. Each instance stores the current schema state and updates or returns it when required.

You can modify the specific ways these classes work by extending them. You can inherit from any existing

SchemaStrategy
class, though
SchemaStrategy
and
TypedSchemaStrategy
are the most useful base classes. You should call
super
and pass along all arguments when overriding any instance methods.

The documentation below explains the public API and what you need to extend and override at a high level. Feel free to explore

the code
_ to see more, but know that the public API is documented here, and anything else you depend on could be subject to change. All
SchemaStrategy
subclasses maintain the public API though, so you can extend any of them in this way.

SchemaStrategy
API ++++++++++++++++++++++

[class constant]

KEYWORDS
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

This should be a tuple listing all of the JSON-schema keywords that this strategy knows how to handle. Any keywords encountered in added schemas will be be naively passed on to the generated schema unless they are in this list (or you override that behavior in

to_schema
).

When adding keywords to a new

SchemaStrategy
, it's best to splat the parent class's
KEYWORDS
into the new tuple.

[class method]

match_schema(cls, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Return

true
if this strategy should be used to handle the passed-in schema.

:param schema: a JSON Schema in

dict
form :rtype:
bool

[class method]

match_object(cls, obj)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Return

true
if this strategy should be used to handle the passed-in object.

:param obj: any object or scalar that can be serialized in JSON :rtype:

bool

__init__(self, node_class)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Override this method if you need to initialize an instance variable.

:param node_class: This param is not part of the public API. Pass it along to

super
.

add_schema(self, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Override this to modify how a schema is parsed and stored.

:param schema: a JSON Schema in

dict
form

add_object(self, obj)
^^^^^^^^^^^^^^^^^^^^^^^^^

Override this to change the way a schemas are inferred from objects.

:param obj: any object or scalar that can be serialized in JSON

to_schema(self)
^^^^^^^^^^^^^^^^^^^

Override this method to customize how a schema object is constructed from the inputs. It is suggested that you invoke

super
as the basis for the return value, but it is not required.

:rtype:

dict

.. note:: There is no schema validation. If you return a bad schema from this method,

SchemaBuilder
will output a bad schema.

__eq__(self, other)
^^^^^^^^^^^^^^^^^^^^^^^

When checking for

SchemaBuilder
equality, strategies are matched using
__eq__
. The default implementation uses a simple
__dict__
equality check.

Override this method if you need to override that behavior. This may be useful if you add instance variables that aren't relevant to whether two SchemaStrategies are considered equal.

:rtype:

bool

TypedSchemaStrategy
API +++++++++++++++++++++++++++

This is an abstract schema strategy for making simple schemas that only deal with the

type
keyword, but you can extend it to add more functionality. Subclasses must define the following two class constants, but you get the entire
SchemaStrategy
interface for free.

[class constant]

JS_TYPE
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

This will be the value of the

type
keyword in the generated schema. It is also used to match any added schemas.

[class constant]

PYTHON_TYPE
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

This is a Python type or tuple of types that will be matched against an added object using

isinstance
.

Extending

SchemaBuilder
+++++++++++++++++++++++++++

Once you have extended

SchemaStrategy
types, you'll need to create a
SchemaBuilder
class that uses them, since the default
SchemaBuilder
only incorporates the default strategies. To do this, extend the
SchemaBuilder
class and define one of these two constants inside it:

[class constant]

EXTRA_STRATEGIES
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

This is the standard (and suggested) way to add strategies. Set it to a tuple of all your new strategies, and they will be added to the existing list of strategies to check. This preserves all the existing functionality.

Note that order matters. GenSON checks the list in order, so the first strategy has priority over the second and so on. All

EXTRA_STRATEGIES
have priority over the default strategies.

[class constant]

STRATEGIES
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

This clobbers the existing list of strategies and completely replaces it. Set it to a tuple just like for

EXTRA_STRATEGIES
, but note that if any object or schema gets added that your exhaustive list of strategies doesn't know how to handle, you'll get an error. You should avoid doing this unless you're extending most or all existing strategies in some way.

Example:

MinNumber
++++++++++++++++++++++

Here's some example code creating a number strategy that tracks the

minimum number
_ seen and includes it in the output schema.

.. note:: This example is written in Python 3.3+. Custom strategies also work in Python 2.7, but you need different syntax (

super
arguments & no splatting
KEYWORDS
).

.. code-block:: python

from genson import SchemaBuilder
from genson.schema.strategies import Number

class MinNumber(Number): # add 'minimum' to list of keywords KEYWORDS = (*Number.KEYWORDS, 'minimum')

# create a new instance variable
def __init__(self, node_class):
    super().__init__(node_class)
    self.min = None

# capture 'minimum's from schemas
def add_schema(self, schema):
    super().add_schema(schema)
    if self.min is None:
        self.min = schema.get('minimum')
    elif 'minimum' in schema:
        self.min = min(self.min, schema['minimum'])

# adjust minimum based on the data
def add_object(self, obj):
    super().add_object(obj)
    self.min = obj if self.min is None else min(self.min, obj)

# include 'minimum' in the output
def to_schema(self):
    schema = super().to_schema()
    schema['minimum'] = self.min
    return schema

new SchemaBuilder class that uses the MinNumber strategy in addition

to the existing strategies. Both MinNumber and Number are active, but

MinNumber has priority, so it effectively replaces Number.

class MinNumberSchemaBuilder(SchemaBuilder): """ all number nodes include minimum """ EXTRA_STRATEGIES = (MinNumber,)

this class ONLY has the MinNumber strategy. Any object that is not

a number will cause an error.

class ExclusiveMinNumberSchemaBuilder(SchemaBuilder): """ all number nodes include minimum, and only handles number """ STRATEGIES = (MinNumber,)

Now that we have the MinNumberSchemaBuilder class, let's see how it works.

.. code-block:: python

>>> builder = MinNumberSchemaBuilder()
>>> builder.add_object(5)
>>> builder.add_object(7)
>>> builder.to_schema()
{'$schema': 'http://json-schema.org/schema#', 'type': 'integer', 'minimum': 5}
>>> builder.add_object(-2)
>>> builder.to_schema()
{'$schema': 'http://json-schema.org/schema#', 'type': 'integer', 'minimum': -2}
>>> builder.add_schema({'$schema': 'http://json-schema.org/schema#', 'type': 'integer', 'minimum': -7})
>>> builder.to_schema()
{'$schema': 'http://json-schema.org/schema#', 'type': 'integer', 'minimum': -7}

Note that the exclusive builder is much more particular.

.. code-block:: python

>>> builder = MinNumberSchemaBuilder()
>>> picky_builder = ExclusiveMinNumberSchemaBuilder()
>>> picky_builder.add_object(5)
>>> picky_builder.to_schema()
{'$schema': 'http://json-schema.org/schema#', 'type': 'integer', 'minimum': 5}
>>> builder.add_object(None) # this is fine
>>> picky_builder.add_object(None) # this fails
genson.schema.node.SchemaGenerationError: Could not find matching schema type for object: None

Contributing

When contributing, please follow these steps:

  1. Clone the repo and make your changes.
  2. Make sure your code has test cases written against it.
  3. Lint your code with
    Flake8
    _.
  4. Run
    tox
    _ to make sure the test suite passes.
  5. Ensure the docs are accurate.
  6. Add your name to the list of contributers.
  7. Submit a Pull Request.

Tests +++++

Tests are written in

unittest
and are run using
tox
_ and
nose
_. Tox will run all tests with coverage against each supported Python version that is installed on your machine.

.. code-block:: bash

$ tox

You should always run tox before submitting a PR, but it takes some time, so when developing, it may be faster just to run

nosetests
in your own environment. e.g.

.. code-block:: bash

$ nosetests
$ nosetests --with-coverate --cover-package=genson
$ nosetests test.test_gen_single:TestBasicTypes.test_number

Integration +++++++++++

When you submit a PR,

Travis CI
_ performs the following steps:
  1. Lints the code with Flake8
  2. Runs the entire test suite against each supported Python version.
  3. Ensures that test coverage is at least 90%

If any of these steps fail, your PR cannot be merged until it is fixed.

Potential Future Features +++++++++++++++++++++++++

The following are extra features under consideration.

  • recognize every validation keyword and ignore any that don't apply
  • option to set error level
  • custom serializer plugins
  • logical support for more keywords:

    • enum
    • minimum
      /
      maximum
    • minLength
      /
      maxLength
    • minItems
      /
      maxItems
    • minProperties
      /
      maxProperties
    • additionalItems
    • additionalProperties
    • format
      &
      pattern
    • $ref
      &
      id

.. _JSON Schema: http://json-schema.org/ .. _Java Genson library: https://owlike.github.io/genson/ .. _

Python's builtin json library
: https://docs.python.org/library/json.html .. _below: #typeless-schemas .. _array validation here: https://spacetelescope.github.io/understanding-json-schema/reference/array.html#items .. _patternProperties: https://spacetelescope.github.io/understanding-json-schema/reference/object.html#pattern-properties .. _Python flavor of RegEx: https://docs.python.org/3.6/library/re.html .. _the code: https://github.com/wolverdude/GenSON/tree/master/genson/schema/strategies .. _minimum number: https://json-schema.org/understanding-json-schema/reference/numeric.html#range .. _Flake8: https://pypi.python.org/pypi/flake8 .. _tox: https://pypi.python.org/pypi/tox .. _Travis CI: https://travis-ci.com/github/wolverdude/GenSON

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