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:
- Every object it is given must validate under the generated schema.
- 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)
- The generated schema should be as strict as possible given the first 2 rules.
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.
$ pip install genson
The package includes a genson
executable that allows you to access this functionality from the command line. For usage info, run with --help
:
$ genson --help
usage: genson.py [-h] [-d DELIM] [-i SPACES] [-s SCHEMA] [-$ URI] ... Generate one, unified JSON Schema from one or more JSON objects and/or JSON Schemas. It's compatible with Draft 6 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 -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 -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) -$ URI, --schema-uri 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.
SchemaBuilder
is the basic schema generator class. SchemaBuilder
instances can be loaded up with existing schemas and objects before being serialized.
>>> 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"
]
}
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. |
---|
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.
Modify the schema to accommodate an object.
param obj: | any object or scalar that can be serialized in JSON |
---|
Generate a schema based on previous inputs.
rtype: | dict |
---|
Generate a schema and convert it directly to serialized JSON.
rtype: | str |
---|
Check for equality with another SchemaBuilder
object.
param other: | another SchemaBuilder object. Other types are accepted, but will always return False |
---|
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.
>>> 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()
{'$schema': 'http://json-schema.org/schema#',
'type': 'object',
'properties': {'hi': {'type': ['integer', 'string']}}}
There are several cases where multiple valid schemas could be generated from the same object. GenSON makes a default choice in all these ambiguous cases, but if you want it to choose differently, you can tell it what to do using a seed schema.
For example, suppose you have a simple array with two items:
['one', 1]
There are always two ways for GenSON to interpret any array: List and Tuple. Lists have one schema for every item, whereas Tuples have a different schema for every array position. This is analogous to the (now deprecated) merge_arrays
option from version 0. You can read more about JSON Schema array validation here.
{
"type": "array",
"items": {"type": ["integer", "string"]}
}
{
"type": "array",
"items": [{"type": "integer"}, {"type": "string"}]
}
By default, GenSON always interprets arrays using list validation, but you can tell it to use tuple validation by seeding it with a schema.
>>> from genson import SchemaBuilder
>>> builder = SchemaBuilder()
>>> builder.add_object(['one', 1])
>>> builder.to_schema()
{'$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()
{'$schema': 'http://json-schema.org/schema#',
'type': 'array',
'items': [{'type': 'string'}, {'type': 'integer'}]}
Note that in this case, the seed schema is actually invalid. You can't have an empty array as the value for an items
keyword. But GenSON is a generator, not a validator, so you can fudge a little. GenSON will modify the generated schema so that it is valid, provided that there aren't invalid keywords beyond the ones it knows about.
Support for patternProperties is new in version 1; however, since GenSON's default behavior is to only use properties
, this powerful keyword can only be utilized with seed schemas. You will need to supply an object
schema with a patternProperties
object whose keys are RegEx strings. Again, you can fudge here and set the values to null instead of creating valid subschemas.
>>> from genson import SchemaBuilder
>>> builder = SchemaBuilder()
>>> builder.add_schema({'type': 'object', 'patternProperties': {r'^\d+$': 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
topatternProperties
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.
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.
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:
- Create a custom
SchemaStrategy
class. - Create a
SchemaBuilder
subclass that includes your customSchemaStrategy
class(es). - Use your custom
SchemaBuilder
just like you would the stockSchemaBuilder
.
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.
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.
Return true
if this strategy should be used to handle the passed-in schema.
param schema: | a JSON Schema in dict form |
---|---|
rtype: | bool |
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 |
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 . |
---|
Override this to modify how a schema is parsed and stored.
param schema: | a JSON Schema in dict form |
---|
Override this to change the way a schemas are inferred from objects.
param obj: | any object or scalar that can be serialized in JSON |
---|
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.
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 |
---|
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.
This will be the value of the type
keyword in the generated schema. It is also used to match any added schemas.
This is a Python type or tuple of types that will be matched against an added object using isinstance
.
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:
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.
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.
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
).
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.
>>> 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.
>>> 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
GenSON has been tested and verified using the following versions of Python:
- Python 2.7.11
- Python 3.3.5
- Python 3.4.4
- Python 3.5.1
- Python 3.6.2
When contributing, please follow these steps:
- Clone the repo and make your changes.
- Make sure your code has test cases written against it.
- Make sure all the tests pass.
- Lint your code with Flake8.
- Ensure the docs are accurate.
- Add your name to the list of contributers.
- Submit a Pull Request.
Tests are written in unittest
. You can run them all easily with the included executable bin/test.py
.
$ bin/test.py
You can also invoke individual test suites:
$ bin/test.py --test-suite test.test_gen_single
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