Object schemas¶
Declaration¶
Object schemas are defined with Object
class by passing
it a dictionary mapping field names to Type
instances.
So given an object
class Person(object):
def __init__(self, name, age):
self.name = name
self.age = age
You can define it’s type like this:
from lollipop.types import Object, String, Integer
PersonType = Object({
'name': String(),
'age': Integer(),
})
It will allow serializing Person types to Python’s basic types (that you can use to serialize to JSON) or validate that basic Python data:
PersonType.dump(Person('John', 38))
# => {"name": "John", "age": 38}
PersonType.validate({"name": "John"})
# => {"age": "Value is required"}
PersonType.load({"name": "John", "age": 38})
# => {"name": "John", "age": 38}
Yet it loads to same basic type dict instead of real object. To fix that, you need to provide a data constructor to type object:
PersonType = Object({
'name': String(),
'age': Integer(),
}, constructor=Person)
PersonType.load({"name": "John", "age": 38})
# => Person(name="John", age=38)
Constructor function should take field values as keyword arguments and return constructed object.
Value access¶
When you serialize (dump) objects, field values are expected to be object attributes.
But library actually allows controlling that. This is done with
Field
class instances. When you define your object and
pass types for it’s fields, what really happens is those types are wrapped with
a Field
subclass objects. The actual object fields are
defined like this:
PersonType = Object({
'name': AttributeField(String()),
'age': AttributeField(Integer()),
})
Passing just a Type
instances for field types is just a
shortcut to wrap them all with a default field type which is
AttributeField
. You can change default field type with
Object.default_field_type
argument:
PersonType = Object({
'name': String(),
'age': Integer(),
}, default_field_type=AttributeField)
And you can actually mix fields defined with just Type
with fields defined with Field
. The first ones will be
wrapped with default field type while the later ones will be used as is.
AttributeField
is probably the one that would be used most
of the time. It extracts value for serialization from object attribute with the same
name as the field name. You can change the name of attribute to extract value from:
Person = namedtuple('Person', ['full_name'])
PersonType = Object({'name': AttributeField(String(), attribute='full_name')})
PersonType.dump(Person('John Doe')) # => {'name': 'John Doe'}
Other useful instances are MethodField
which calls given
method on the object to get value instead of getting attribute or
FunctionField
which uses given function on a serialized
object to get value. For last one there is another shortcut: if you provide a
value for a field which is not Type
and not
Field
then it will be wrapped with a
Constant
and then into default field type.
# Following lines are equivalent
Object({'answer': AttributeField(Constant(42))}).dump(object()) # => {'answer': 42}
Object({'answer': 42}).dump(object()) # => {'answer': 42}
Updating objects in-place¶
After you have created your initial version of your objects with data obtained
from user you might want to allow user to update them. And you might want to allow
your users to specify only changed attributes without sending all of them. Or after
creation your object store additional information that you do not want to expose to
users or allow users to modify, e.g. object ID or creation date. So you make them
dump only or do not include them in schema at all. But since load()
method
return you a new copy of your object, that object does not contain those additional
data.
Luckily this library allows updating existing objects in-place:
user = User.get(user_id)
try:
UserType.load_into(user, {'name': 'John Doe'})
User.save(user)
except ValidationError as ve:
# .. handle user validation error
If you do not want to alter existing object but still want your users to specify partial data on update, you can declare your object type as “immutable”. In this case it won’t modify your objects but will create new ones with data merged from existing object and data being deserialized:
UserType = Object({
'name': String(),
'birthdate': Date(),
# ...
}, constructor=User, immutable=True)
user = User.get(user_id)
try:
user1 = UserType.load_into(user, {'name': 'John Doe'})
User.save(user1)
except ValidationError as ve:
# .. handle user validation error
You can disable in-place update on per-invocation basis with inplace
argument:
user1 = UserType.load_into(user, new_data, inplace=False)
For partial update validation there is a validate_for()
:
errors = UserType.validate_for(user, new_data)
When doing partial update all new data is validated during deserialization. Also, whole-object validations are also run.
How values are put back into object is controlled by Field
subclasses that you use in object schema declaration (e.g.
AttributeField
, MethodField
or
FunctionField
. See Value access for
details).
Object Schema Inheritance¶
To be able to allow reusing parts of schema, you can supply a base
Object
:
BaseType = Object({'base': String()})
InheritedType = Object(BaseType, {'foo': Integer()})
# is the same as
InheritedType = Object({'base': String(), 'foo': Integer()})
You can actually supply multple base types which allows using them as mixins:
TimeStamped = Object({'created_at': DateTime(), 'updated_at': DateTime()})
BaseType = Object({'base': String()})
InheritedType = Object([BaseType, TimeStamped], {'foo': Integer()})
Polymorphic types¶
Sometimes you need a way to serialize and deserialize values of different types put in the same list. Or maybe you value can be of either one of given types. E.g. you have a graphical application which operates with objects of different shapes:
class Point(object):
def __init__(self, x, y):
self.x = x
self.y = y
class Shape(object):
pass
class Circle(Shape):
def __init__(self, center, radius):
self.center = center
self.radius = radius
class Rectangle(Shape):
def __init__(self, left_top, right_bottom):
self.left_top = left_top
self.right_bottom = right_bottom
PointType = Object({'x': Integer(), 'y': Integer()}, constructor=Point)
CircleType = Object({
'center': PointType,
'radius': Integer
}, constructor=Circle)
RectangleType = Object({
'left_top': PointType,
'right_bottom': PointType,
}, constructor=Rectangle)
To support that library provides a special type - OneOf
:
def with_type_annotation(subject_type, type_name):
return Object(subject_type, {'type': type_name},
constructor=subject_type.constructor)
AnyShapeType = OneOf(
{
'circle': with_type_annotation(CircleType, 'circle'),
'rectangle': with_type_annotation(RectangleType, 'rectangle'),
},
dump_hint=lambda obj: obj.__class__.__name__.lower(),
load_hint=dict_value_hint('type'),
)
dumped = List(AnyShapeType).dump([
Circle(Point(5, 8), 4), Rectangle(Point(1, 10), Point(10, 1))
])
# => [
# {'type': 'circle',
# 'center': {'x': 5, 'y': 8},
# 'radius': 4},
# {'type': 'rectangle',
# 'left_top': {'x': 1, 'y': 10},
# 'right_bottom': {'x': 10, 'y': 1}}]
List(AnyShapeType).load(dumped)
# => [Circle(Point(5, 8), 4), Rectangle(Point(1, 10), Point(10, 1))]
OneOf
uses user supplied functions to determine which
particular type to use during serialization/deserialization. It helps returning
proper error messages. If you’re not interested in providing detailed error message,
you can just supply all types as a list. OneOf
will try
to use each of them in given order returning first successfull result. If all types
return errors it will provide generic error message.
Two-way type references¶
Nesting object types inside another objects is very easy since object types are just another types. But sometimes you might have multiple application entities that reference each other. E.g. you model a library and inside you have Person model and Book model. Person can be author of multiple books and each book has (for simplicity lets assume only one) author. You want your Person type to have a reference to Book and Book to have reference to Person types.
For that matter library provides a storage for types which can provide you with delayed type resolving:
import lollipop.types as lt
from lollupop.type_registry import TypeRegistry
TYPES = TypeRegistry()
PersonType = TYPES.add('Person', lt.Object({
'name': lt.String(),
'books': lt.List(lt.Object(TYPES['Book'], exclude='author')),
}, constructor=Person))
BookType = TYPES.add('Book', lt.Object({
'title': lt.String(),
'author': lt.Object(TYPES['Person'], exclude='books'),
}, constructor=Book))
Here you can see that we get a types from our registry to use them as a base object types and then customize them (e.g. exclude some fields to eliminate circular dependency). The Object type is designed to not access base class’ properties and methods until is needed thus allowing to postpone actual type resolution and thus allowing forward references to types.
Type type registry is not a global instance, but instance local to whatever degree you want it to be local. If your application schemas can fit into one module, you declare registry in that module. If your schemas span multiple modules, it is better to put registry in a separate module (along with any custom type declarations that you might have) and import it where needed.
You can even do self references inside Object declarations. Here is example of type declaration for lollipop errors format:
TYPES = TypeRegistry()
ErrorsType = TYPES.add('Errors', lt.OneOf([
lt.String,
lt.List(lt.String()),
lt.Dict(TYPES['Errors']),
]))