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Expanding the usefulness of the serializers is something that we would like to address. However, it's not a trivial problem, and it will take some serious design work.
— Russell Keith-Magee, Django users group
Serializers allow complex data such as querysets and model instances to be converted to native Python datatypes that can then be easily rendered into JSON, XML or other content types. Serializers also provide deserialization, allowing parsed data to be converted back into complex types, after first validating the incoming data.
The serializers in REST framework work very similarly to Django's Form and ModelForm classes. We provide a Serializer class which gives you a powerful, generic way to control the output of your responses, as well as a ModelSerializer class which provides a useful shortcut for creating serializers that deal with model instances and querysets.
Let's start by creating a simple object we can use for example purposes:
from datetime import datetime
class Comment:
def __init__(self, email, content, created=None):
self.email = email
self.content = content
self.created = created or datetime.now()
comment = Comment(email='[email protected]', content='foo bar')
We'll declare a serializer that we can use to serialize and deserialize data that corresponds to Comment objects.
Declaring a serializer looks very similar to declaring a form:
from rest_framework import serializers
class CommentSerializer(serializers.Serializer):
email = serializers.EmailField()
content = serializers.CharField(max_length=200)
created = serializers.DateTimeField()
We can now use CommentSerializer to serialize a comment, or list of comments. Again, using the Serializer class looks a lot like using a Form class.
serializer = CommentSerializer(comment)
serializer.data
# {'email': '[email protected]', 'content': 'foo bar', 'created': '2016-01-27T15:17:10.375877'}
At this point we've translated the model instance into Python native datatypes. To finalise the serialization process we render the data into json.
from rest_framework.renderers import JSONRenderer
json = JSONRenderer().render(serializer.data)
json
# b'{"email":"[email protected]","content":"foo bar","created":"2016-01-27T15:17:10.375877"}'
Deserialization is similar. First we parse a stream into Python native datatypes...
import io
from rest_framework.parsers import JSONParser
stream = io.BytesIO(json)
data = JSONParser().parse(stream)
...then we restore those native datatypes into a dictionary of validated data.
serializer = CommentSerializer(data=data)
serializer.is_valid()
# True
serializer.validated_data
# {'content': 'foo bar', 'email': '[email protected]', 'created': datetime.datetime(2012, 08, 22, 16, 20, 09, 822243)}
If we want to be able to return complete object instances based on the validated data we need to implement one or both of the .create() and .update() methods. For example:
class CommentSerializer(serializers.Serializer):
email = serializers.EmailField()
content = serializers.CharField(max_length=200)
created = serializers.DateTimeField()
def create(self, validated_data):
return Comment(**validated_data)
def update(self, instance, validated_data):
instance.email = validated_data.get('email', instance.email)
instance.content = validated_data.get('content', instance.content)
instance.created = validated_data.get('created', instance.created)
return instance
If your object instances correspond to Django models you'll also want to ensure that these methods save the object to the database. For example, if Comment was a Django model, the methods might look like this:
def create(self, validated_data):
return Comment.objects.create(**validated_data)
def update(self, instance, validated_data):
instance.email = validated_data.get('email', instance.email)
instance.content = validated_data.get('content', instance.content)
instance.created = validated_data.get('created', instance.created)
instance.save()
return instance
Now when deserializing data, we can call .save() to return an object instance, based on the validated data.
comment = serializer.save()
Calling .save() will either create a new instance, or update an existing instance, depending on if an existing instance was passed when instantiating the serializer class:
# .save() will create a new instance.
serializer = CommentSerializer(data=data)
# .save() will update the existing `comment` instance.
serializer = CommentSerializer(comment, data=data)
Both the .create() and .update() methods are optional. You can implement either none, one, or both of them, depending on the use-case for your serializer class.
Sometimes you'll want your view code to be able to inject additional data at the point of saving the instance. This additional data might include information like the current user, the current time, or anything else that is not part of the request data.
You can do so by including additional keyword arguments when calling .save(). For example:
serializer.save(owner=request.user)
Any additional keyword arguments will be included in the validated_data argument when .create() or .update() are called.
In some cases the .create() and .update() method names may not be meaningful. For example, in a contact form we may not be creating new instances, but instead sending an email or other message.
In these cases you might instead choose to override .save() directly, as being more readable and meaningful.
For example:
class ContactForm(serializers.Serializer):
email = serializers.EmailField()
message = serializers.CharField()
def save(self):
email = self.validated_data['email']
message = self.validated_data['message']
send_email(from=email, message=message)
Note that in the case above we're now having to access the serializer .validated_data property directly.
When deserializing data, you always need to call is_valid() before attempting to access the validated data, or save an object instance. If any validation errors occur, the .errors property will contain a dictionary representing the resulting error messages. For example:
serializer = CommentSerializer(data={'email': 'foobar', 'content': 'baz'})
serializer.is_valid()
# False
serializer.errors
# {'email': ['Enter a valid e-mail address.'], 'created': ['This field is required.']}
Each key in the dictionary will be the field name, and the values will be lists of strings of any error messages corresponding to that field. The non_field_errors key may also be present, and will list any general validation errors. The name of the non_field_errors key may be customized using the NON_FIELD_ERRORS_KEY REST framework setting.
When deserializing a list of items, errors will be returned as a list of dictionaries representing each of the deserialized items.
The .is_valid() method takes an optional raise_exception flag that will cause it to raise a serializers.ValidationError exception if there are validation errors.
These exceptions are automatically dealt with by the default exception handler that REST framework provides, and will return HTTP 400 Bad Request responses by default.
# Return a 400 response if the data was invalid.
serializer.is_valid(raise_exception=True)
You can specify custom field-level validation by adding .validate_<field_name> methods to your Serializer subclass. These are similar to the .clean_<field_name> methods on Django forms.
These methods take a single argument, which is the field value that requires validation.
Your validate_<field_name> methods should return the validated value or raise a serializers.ValidationError. For example:
from rest_framework import serializers
class BlogPostSerializer(serializers.Serializer):
title = serializers.CharField(max_length=100)
content = serializers.CharField()
def validate_title(self, value):
"""
Check that the blog post is about Django.
"""
if 'django' not in value.lower():
raise serializers.ValidationError("Blog post is not about Django")
return value
Note: If your <field_name> is declared on your serializer with the parameter required=False then this validation step will not take place if the field is not included.
To do any other validation that requires access to multiple fields, add a method called .validate() to your Serializer subclass. This method takes a single argument, which is a dictionary of field values. It should raise a serializers.ValidationError if necessary, or just return the validated values. For example:
from rest_framework import serializers
class EventSerializer(serializers.Serializer):
description = serializers.CharField(max_length=100)
start = serializers.DateTimeField()
finish = serializers.DateTimeField()
def validate(self, data):
"""
Check that start is before finish.
"""
if data['start'] > data['finish']:
raise serializers.ValidationError("finish must occur after start")
return data
Individual fields on a serializer can include validators, by declaring them on the field instance, for example:
def multiple_of_ten(value):
if value % 10 != 0:
raise serializers.ValidationError('Not a multiple of ten')
class GameRecord(serializers.Serializer):
score = serializers.IntegerField(validators=[multiple_of_ten])
...
Serializer classes can also include reusable validators that are applied to the complete set of field data. These validators are included by declaring them on an inner Meta class, like so:
class EventSerializer(serializers.Serializer):
name = serializers.CharField()
room_number = serializers.ChoiceField(choices=[101, 102, 103, 201])
date = serializers.DateField()
class Meta:
# Each room only has one event per day.
validators = [
UniqueTogetherValidator(
queryset=Event.objects.all(),
fields=['room_number', 'date']
)
]
For more information see the validators documentation.
When passing an initial object or queryset to a serializer instance, the object will be made available as .instance. If no initial object is passed then the .instance attribute will be None.
When passing data to a serializer instance, the unmodified data will be made available as .initial_data. If the data keyword argument is not passed then the .initial_data attribute will not exist.
By default, serializers must be passed values for all required fields or they will raise validation errors. You can use the partial argument in order to allow partial updates.
# Update `comment` with partial data
serializer = CommentSerializer(comment, data={'content': 'foo bar'}, partial=True)
The previous examples are fine for dealing with objects that only have simple datatypes, but sometimes we also need to be able to represent more complex objects, where some of the attributes of an object might not be simple datatypes such as strings, dates or integers.
The Serializer class is itself a type of Field, and can be used to represent relationships where one object type is nested inside another.
class UserSerializer(serializers.Serializer):
email = serializers.EmailField()
username = serializers.CharField(max_length=100)
class CommentSerializer(serializers.Serializer):
user = UserSerializer()
content = serializers.CharField(max_length=200)
created = serializers.DateTimeField()
If a nested representation may optionally accept the None value you should pass the required=False flag to the nested serializer.
class CommentSerializer(serializers.Serializer):
user = UserSerializer(required=False) # May be an anonymous user.
content = serializers.CharField(max_length=200)
created = serializers.DateTimeField()
Similarly if a nested representation should be a list of items, you should pass the many=True flag to the nested serializer.
class CommentSerializer(serializers.Serializer):
user = UserSerializer(required=False)
edits = EditItemSerializer(many=True) # A nested list of 'edit' items.
content = serializers.CharField(max_length=200)
created = serializers.DateTimeField()
When dealing with nested representations that support deserializing the data, any errors with nested objects will be nested under the field name of the nested object.
serializer = CommentSerializer(data={'user': {'email': 'foobar', 'username': 'doe'}, 'content': 'baz'})
serializer.is_valid()
# False
serializer.errors
# {'user': {'email': ['Enter a valid e-mail address.']}, 'created': ['This field is required.']}
Similarly, the .validated_data property will include nested data structures.
If you're supporting writable nested representations you'll need to write .create() or .update() methods that handle saving multiple objects.
The following example demonstrates how you might handle creating a user with a nested profile object.
class UserSerializer(serializers.ModelSerializer):
profile = ProfileSerializer()
class Meta:
model = User
fields = ['username', 'email', 'profile']
def create(self, validated_data):
profile_data = validated_data.pop('profile')
user = User.objects.create(**validated_data)
Profile.objects.create(user=user, **profile_data)
return user
For updates you'll want to think carefully about how to handle updates to relationships. For example if the data for the relationship is None, or not provided, which of the following should occur?
- Set the relationship to
NULLin the database. - Delete the associated instance.
- Ignore the data and leave the instance as it is.
- Raise a validation error.
Here's an example for an .update() method on our previous UserSerializer class.
def update(self, instance, validated_data):
profile_data = validated_data.pop('profile')
# Unless the application properly enforces that this field is
# always set, the following could raise a `DoesNotExist`, which
# would need to be handled.
profile = instance.profile
instance.username = validated_data.get('username', instance.username)
instance.email = validated_data.get('email', instance.email)
instance.save()
profile.is_premium_member = profile_data.get(
'is_premium_member',
profile.is_premium_member
)
profile.has_support_contract = profile_data.get(
'has_support_contract',
profile.has_support_contract
)
profile.save()
return instance
Because the behavior of nested creates and updates can be ambiguous, and may require complex dependencies between related models, REST framework 3 requires you to always write these methods explicitly. The default ModelSerializer .create() and .update() methods do not include support for writable nested representations.
There are however, third-party packages available such as DRF Writable Nested that support automatic writable nested representations.
An alternative to saving multiple related instances in the serializer is to write custom model manager classes that handle creating the correct instances.
For example, suppose we wanted to ensure that User instances and Profile instances are always created together as a pair. We might write a custom manager class that looks something like this:
class UserManager(models.Manager):
...
def create(self, username, email, is_premium_member=False, has_support_contract=False):
user = User(username=username, email=email)
user.save()
profile = Profile(
user=user,
is_premium_member=is_premium_member,
has_support_contract=has_support_contract
)
profile.save()
return user
This manager class now more nicely encapsulates that user instances and profile instances are always created at the same time. Our .create() method on the serializer class can now be re-written to use the new manager method.
def create(self, validated_data):
return User.objects.create(
username=validated_data['username'],
email=validated_data['email'],
is_premium_member=validated_data['profile']['is_premium_member'],
has_support_contract=validated_data['profile']['has_support_contract']
)
For more details on this approach see the Django documentation on model managers, and this blogpost on using model and manager classes.
The Serializer class can also handle serializing or deserializing lists of objects.
To serialize a queryset or list of objects instead of a single object instance, you should pass the many=True flag when instantiating the serializer. You can then pass a queryset or list of objects to be serialized.
queryset = Book.objects.all()
serializer = BookSerializer(queryset, many=True)
serializer.data
# [
# {'id': 0, 'title': 'The electric kool-aid acid test', 'author': 'Tom Wolfe'},
# {'id': 1, 'title': 'If this is a man', 'author': 'Primo Levi'},
# {'id': 2, 'title': 'The wind-up bird chronicle', 'author': 'Haruki Murakami'}
# ]
The default behavior for deserializing multiple objects is to support multiple object creation, but not support multiple object updates. For more information on how to support or customize either of these cases, see the ListSerializer documentation below.
There are some cases where you need to provide extra context to the serializer in addition to the object being serialized. One common case is if you're using a serializer that includes hyperlinked relations, which requires the serializer to have access to the current request so that it can properly generate fully qualified URLs.
You can provide arbitrary additional context by passing a context argument when instantiating the serializer. For example:
serializer = AccountSerializer(account, context={'request': request})
serializer.data
# {'id': 6, 'owner': 'denvercoder9', 'created': datetime.datetime(2013, 2, 12, 09, 44, 56, 678870), 'details': 'http://example.com/accounts/6/details'}
The context dictionary can be used within any serializer field logic, such as a custom .to_representation() method, by accessing the self.context attribute.
Often you'll want serializer classes that map closely to Django model definitions.
The ModelSerializer class provides a shortcut that lets you automatically create a Serializer class with fields that correspond to the Model fields.
The ModelSerializer class is the same as a regular Serializer class, except that:
- It will automatically generate a set of fields for you, based on the model.
- It will automatically generate validators for the serializer, such as unique_together validators.
- It includes simple default implementations of
.create()and.update().
Declaring a ModelSerializer looks like this:
class AccountSerializer(serializers.ModelSerializer):
class Meta:
model = Account
fields = ['id', 'account_name', 'users', 'created']
By default, all the model fields on the class will be mapped to a corresponding serializer fields.
Any relationships such as foreign keys on the model will be mapped to PrimaryKeyRelatedField. Reverse relationships are not included by default unless explicitly included as specified in the serializer relations documentation.
Serializer classes generate helpful verbose representation strings, that allow you to fully inspect the state of their fields. This is particularly useful when working with ModelSerializers where you want to determine what set of fields and validators are being automatically created for you.
To do so, open the Django shell, using python manage.py shell, then import the serializer class, instantiate it, and print the object representation…
>>> from myapp.serializers import AccountSerializer
>>> serializer = AccountSerializer()
>>> print(repr(serializer))
AccountSerializer():
id = IntegerField(label='ID', read_only=True)
name = CharField(allow_blank=True, max_length=100, required=False)
owner = PrimaryKeyRelatedField(queryset=User.objects.all())
If you only want a subset of the default fields to be used in a model serializer, you can do so using fields or exclude options, just as you would with a ModelForm. It is strongly recommended that you explicitly set all fields that should be serialized using the fields attribute. This will make it less likely to result in unintentionally exposing data when your models change.
For example:
class AccountSerializer(serializers.ModelSerializer):
class Meta:
model = Account
fields = ['id', 'account_name', 'users', 'created']
You can also set the fields attribute to the special value '__all__' to indicate that all fields in the model should be used.
For example:
class AccountSerializer(serializers.ModelSerializer):
class Meta:
model = Account
fields = '__all__'
You can set the exclude attribute to a list of fields to be excluded from the serializer.
For example:
class AccountSerializer(serializers.ModelSerializer):
class Meta:
model = Account
exclude = ['users']
In the example above, if the Account model had 3 fields account_name, users, and created, this will result in the fields account_name and created to be serialized.
The names in the fields and exclude attributes will normally map to model fields on the model class.
Alternatively names in the fields options can map to properties or methods which take no arguments that exist on the model class.
Since version 3.3.0, it is mandatory to provide one of the attributes fields or exclude.
The default ModelSerializer uses primary keys for relationships, but you can also easily generate nested representations using the depth option:
class AccountSerializer(serializers.ModelSerializer):
class Meta:
model = Account
fields = ['id', 'account_name', 'users', 'created']
depth = 1
The depth option should be set to an integer value that indicates the depth of relationships that should be traversed before reverting to a flat representation.
If you want to customize the way the serialization is done you'll need to define the field yourself.
You can add extra fields to a ModelSerializer or override the default fields by declaring fields on the class, just as you would for a Serializer class.
class AccountSerializer(serializers.ModelSerializer):
url = serializers.CharField(source='get_absolute_url', read_only=True)
groups = serializers.PrimaryKeyRelatedField(many=True)
class Meta:
model = Account
fields = ['url', 'groups']
Extra fields can correspond to any property or callable on the model.
You may wish to specify multiple fields as read-only. Instead of adding each field explicitly with the read_only=True attribute, you may use the shortcut Meta option, read_only_fields.
This option should be a list or tuple of field names, and is declared as follows:
class AccountSerializer(serializers.ModelSerializer):
class Meta:
model = Account
fields = ['id', 'account_name', 'users', 'created']
read_only_fields = ['account_name']
Model fields which have editable=False set, and AutoField fields will be set to read-only by default, and do not need to be added to the read_only_fields option.
Note: There is a special-case where a read-only field is part of a unique_together constraint at the model level. In this case the field is required by the serializer class in order to validate the constraint, but should also not be editable by the user.
The right way to deal with this is to specify the field explicitly on the serializer, providing both the read_only=True and default=… keyword arguments.
One example of this is a read-only relation to the currently authenticated User which is unique_together with another identifier. In this case you would declare the user field like so:
user = serializers.PrimaryKeyRelatedField(read_only=True, default=serializers.CurrentUserDefault())
Please review the Validators Documentation for details on the UniqueTogetherValidator and CurrentUserDefault classes.
There is also a shortcut allowing you to specify arbitrary additional keyword arguments on fields, using the extra_kwargs option. As in the case of read_only_fields, this means you do not need to explicitly declare the field on the serializer.
This option is a dictionary, mapping field names to a dictionary of keyword arguments. For example:
class CreateUserSerializer(serializers.ModelSerializer):
class Meta:
model = User
fields = ['email', 'username', 'password']
extra_kwargs = {'password': {'write_only': True}}
def create(self, validated_data):
user = User(
email=validated_data['email'],
username=validated_data['username']
)
user.set_password(validated_data['password'])
user.save()
return user
Please keep in mind that, if the field has already been explicitly declared on the serializer class, then the extra_kwargs option will be ignored.
When serializing model instances, there are a number of different ways you might choose to represent relationships. The default representation for ModelSerializer is to use the primary keys of the related instances.
Alternative representations include serializing using hyperlinks, serializing complete nested representations, or serializing with a custom representation.
For full details see the serializer relations documentation.
The ModelSerializer class also exposes an API that you can override in order to alter how serializer fields are automatically determined when instantiating the serializer.
Normally if a ModelSerializer does not generate the fields you need by default then you should either add them to the class explicitly, or simply use a regular Serializer class instead. However in some cases you may want to create a new base class that defines how the serializer fields are created for any given model.
A mapping of Django model fields to REST framework serializer fields. You can override this mapping to alter the default serializer fields that should be used for each model field.
This property should be the serializer field class, that is used for relational fields by default.
For ModelSerializer this defaults to serializers.PrimaryKeyRelatedField.
For HyperlinkedModelSerializer this defaults to serializers.HyperlinkedRelatedField.
The serializer field class that should be used for any url field on the serializer.
Defaults to serializers.HyperlinkedIdentityField
The serializer field class that should be used for any choice fields on the serializer.
Defaults to serializers.ChoiceField
The following methods are called to determine the class and keyword arguments for each field that should be automatically included on the serializer. Each of these methods should return a two tuple of (field_class, field_kwargs).
Called to generate a serializer field that maps to a standard model field.
The default implementation returns a serializer class based on the serializer_field_mapping attribute.
Called to generate a serializer field that maps to a relational model field.
The default implementation returns a serializer class based on the serializer_related_field attribute.
The relation_info argument is a named tuple, that contains model_field, related_model, to_many and has_through_model properties.
Called to generate a serializer field that maps to a relational model field, when the depth option has been set.
The default implementation dynamically creates a nested serializer class based on either ModelSerializer or HyperlinkedModelSerializer.
The nested_depth will be the value of the depth option, minus one.
The relation_info argument is a named tuple, that contains model_field, related_model, to_many and has_through_model properties.
Called to generate a serializer field that maps to a property or zero-argument method on the model class.
The default implementation returns a ReadOnlyField class.
Called to generate a serializer field for the serializer's own url field. The default implementation returns a HyperlinkedIdentityField class.
Called when the field name did not map to any model field or model property. The default implementation raises an error, although subclasses may customize this behavior.
The HyperlinkedModelSerializer class is similar to the ModelSerializer class except that it uses hyperlinks to represent relationships, rather than primary keys.
By default the serializer will include a url field instead of a primary key field.
The url field will be represented using a HyperlinkedIdentityField serializer field, and any relationships on the model will be represented using a HyperlinkedRelatedField serializer field.
You can explicitly include the primary key by adding it to the fields option, for example:
class AccountSerializer(serializers.HyperlinkedModelSerializer):
class Meta:
model = Account
fields = ['url', 'id', 'account_name', 'users', 'created']
When instantiating a HyperlinkedModelSerializer you must include the current
request in the serializer context, for example:
serializer = AccountSerializer(queryset, context={'request': request})
Doing so will ensure that the hyperlinks can include an appropriate hostname, so that the resulting representation uses fully qualified URLs, such as:
http://api.example.com/accounts/1/
Rather than relative URLs, such as:
/accounts/1/
If you do want to use relative URLs, you should explicitly pass {'request': None}
in the serializer context.
There needs to be a way of determining which views should be used for hyperlinking to model instances.
By default hyperlinks are expected to correspond to a view name that matches the style '{model_name}-detail', and looks up the instance by a pk keyword argument.
You can override a URL field view name and lookup field by using either, or both of, the view_name and lookup_field options in the extra_kwargs setting, like so:
class AccountSerializer(serializers.HyperlinkedModelSerializer):
class Meta:
model = Account
fields = ['account_url', 'account_name', 'users', 'created']
extra_kwargs = {
'url': {'view_name': 'accounts', 'lookup_field': 'account_name'},
'users': {'lookup_field': 'username'}
}
Alternatively you can set the fields on the serializer explicitly. For example:
class AccountSerializer(serializers.HyperlinkedModelSerializer):
url = serializers.HyperlinkedIdentityField(
view_name='accounts',
lookup_field='slug'
)
users = serializers.HyperlinkedRelatedField(
view_name='user-detail',
lookup_field='username',
many=True,
read_only=True
)
class Meta:
model = Account
fields = ['url', 'account_name', 'users', 'created']
Tip: Properly matching together hyperlinked representations and your URL conf can sometimes be a bit fiddly. Printing the repr of a HyperlinkedModelSerializer instance is a particularly useful way to inspect exactly which view names and lookup fields the relationships are expected to map too.
The name of the URL field defaults to 'url'. You can override this globally, by using the URL_FIELD_NAME setting.
The ListSerializer class provides the behavior for serializing and validating multiple objects at once. You won't typically need to use ListSerializer directly, but should instead simply pass many=True when instantiating a serializer.
When a serializer is instantiated and many=True is passed, a ListSerializer instance will be created. The serializer class then becomes a child of the parent ListSerializer
The following argument can also be passed to a ListSerializer field or a serializer that is passed many=True:
This is True by default, but can be set to False if you want to disallow empty lists as valid input.
This is None by default, but can be set to a positive integer if you want to validate that the list contains no more than this number of elements.
This is None by default, but can be set to a positive integer if you want to validate that the list contains no fewer than this number of elements.
There are a few use cases when you might want to customize the ListSerializer behavior. For example:
- You want to provide particular validation of the lists, such as checking that one element does not conflict with another element in a list.
- You want to customize the create or update behavior of multiple objects.
For these cases you can modify the class that is used when many=True is passed, by using the list_serializer_class option on the serializer Meta class.
For example:
class CustomListSerializer(serializers.ListSerializer):
...
class CustomSerializer(serializers.Serializer):
...
class Meta:
list_serializer_class = CustomListSerializer
The default implementation for multiple object creation is to simply call .create() for each item in the list. If you want to customize this behavior, you'll need to customize the .create() method on ListSerializer class that is used when many=True is passed.
For example:
class BookListSerializer(serializers.ListSerializer):
def create(self, validated_data):
books = [Book(**item) for item in validated_data]
return Book.objects.bulk_create(books)
class BookSerializer(serializers.Serializer):
...
class Meta:
list_serializer_class = BookListSerializer
By default the ListSerializer class does not support multiple updates. This is because the behavior that should be expected for insertions and deletions is ambiguous.
To support multiple updates you'll need to do so explicitly. When writing your multiple update code make sure to keep the following in mind:
- How do you determine which instance should be updated for each item in the list of data?
- How should insertions be handled? Are they invalid, or do they create new objects?
- How should removals be handled? Do they imply object deletion, or removing a relationship? Should they be silently ignored, or are they invalid?
- How should ordering be handled? Does changing the position of two items imply any state change or is it ignored?
You will need to add an explicit id field to the instance serializer. The default implicitly-generated id field is marked as read_only. This causes it to be removed on updates. Once you declare it explicitly, it will be available in the list serializer's update method.
Here's an example of how you might choose to implement multiple updates:
class BookListSerializer(serializers.ListSerializer):
def update(self, instance, validated_data):
# Maps for id->instance and id->data item.
book_mapping = {book.id: book for book in instance}
data_mapping = {item['id']: item for item in validated_data}
# Perform creations and updates.
ret = []
for book_id, data in data_mapping.items():
book = book_mapping.get(book_id, None)
if book is None:
ret.append(self.child.create(data))
else:
ret.append(self.child.update(book, data))
# Perform deletions.
for book_id, book in book_mapping.items():
if book_id not in data_mapping:
book.delete()
return ret
class BookSerializer(serializers.Serializer):
# We need to identify elements in the list using their primary key,
# so use a writable field here, rather than the default which would be read-only.
id = serializers.IntegerField()
...
class Meta:
list_serializer_class = BookListSerializer
When a serializer with many=True is instantiated, we need to determine which arguments and keyword arguments should be passed to the .__init__() method for both the child Serializer class, and for the parent ListSerializer class.
The default implementation is to pass all arguments to both classes, except for validators, and any custom keyword arguments, both of which are assumed to be intended for the child serializer class.
Occasionally you might need to explicitly specify how the child and parent classes should be instantiated when many=True is passed. You can do so by using the many_init class method.
@classmethod
def many_init(cls, *args, **kwargs):
# Instantiate the child serializer.
kwargs['child'] = cls()
# Instantiate the parent list serializer.
return CustomListSerializer(*args, **kwargs)
BaseSerializer class that can be used to easily support alternative serialization and deserialization styles.
This class implements the same basic API as the Serializer class:
.data- Returns the outgoing primitive representation..is_valid()- Deserializes and validates incoming data..validated_data- Returns the validated incoming data..errors- Returns any errors during validation..save()- Persists the validated data into an object instance.
There are four methods that can be overridden, depending on what functionality you want the serializer class to support:
.to_representation()- Override this to support serialization, for read operations..to_internal_value()- Override this to support deserialization, for write operations..create()and.update()- Override either or both of these to support saving instances.
Because this class provides the same interface as the Serializer class, you can use it with the existing generic class-based views exactly as you would for a regular Serializer or ModelSerializer.
The only difference you'll notice when doing so is the BaseSerializer classes will not generate HTML forms in the browsable API. This is because the data they return does not include all the field information that would allow each field to be rendered into a suitable HTML input.
To implement a read-only serializer using the BaseSerializer class, we just need to override the .to_representation() method. Let's take a look at an example using a simple Django model:
class HighScore(models.Model):
created = models.DateTimeField(auto_now_add=True)
player_name = models.CharField(max_length=10)
score = models.IntegerField()
It's simple to create a read-only serializer for converting HighScore instances into primitive data types.
class HighScoreSerializer(serializers.BaseSerializer):
def to_representation(self, instance):
return {
'score': instance.score,
'player_name': instance.player_name
}
We can now use this class to serialize single HighScore instances:
@api_view(['GET'])
def high_score(request, pk):
instance = HighScore.objects.get(pk=pk)
serializer = HighScoreSerializer(instance)
return Response(serializer.data)
Or use it to serialize multiple instances:
@api_view(['GET'])
def all_high_scores(request):
queryset = HighScore.objects.order_by('-score')
serializer = HighScoreSerializer(queryset, many=True)
return Response(serializer.data)
To create a read-write serializer we first need to implement a .to_internal_value() method. This method returns the validated values that will be used to construct the object instance, and may raise a serializers.ValidationError if the supplied data is in an incorrect format.
Once you've implemented .to_internal_value(), the basic validation API will be available on the serializer, and you will be able to use .is_valid(), .validated_data and .errors.
If you want to also support .save() you'll need to also implement either or both of the .create() and .update() methods.
Here's a complete example of our previous HighScoreSerializer, that's been updated to support both read and write operations.
class HighScoreSerializer(serializers.BaseSerializer):
def to_internal_value(self, data):
score = data.get('score')
player_name = data.get('player_name')
# Perform the data validation.
if not score:
raise serializers.ValidationError({
'score': 'This field is required.'
})
if not player_name:
raise serializers.ValidationError({
'player_name': 'This field is required.'
})
if len(player_name) > 10:
raise serializers.ValidationError({
'player_name': 'May not be more than 10 characters.'
})
# Return the validated values. This will be available as
# the `.validated_data` property.
return {
'score': int(score),
'player_name': player_name
}
def to_representation(self, instance):
return {
'score': instance.score,
'player_name': instance.player_name
}
def create(self, validated_data):
return HighScore.objects.create(**validated_data)
The BaseSerializer class is also useful if you want to implement new generic serializer classes for dealing with particular serialization styles, or for integrating with alternative storage backends.
The following class is an example of a generic serializer that can handle coercing arbitrary complex objects into primitive representations.
class ObjectSerializer(serializers.BaseSerializer):
"""
A read-only serializer that coerces arbitrary complex objects
into primitive representations.
"""
def to_representation(self, instance):
output = {}
for attribute_name in dir(instance):
attribute = getattr(instance, attribute_name)
if attribute_name.startswith('_'):
# Ignore private attributes.
pass
elif hasattr(attribute, '__call__'):
# Ignore methods and other callables.
pass
elif isinstance(attribute, (str, int, bool, float, type(None))):
# Primitive types can be passed through unmodified.
output[attribute_name] = attribute
elif isinstance(attribute, list):
# Recursively deal with items in lists.
output[attribute_name] = [
self.to_representation(item) for item in attribute
]
elif isinstance(attribute, dict):
# Recursively deal with items in dictionaries.
output[attribute_name] = {
str(key): self.to_representation(value)
for key, value in attribute.items()
}
else:
# Force anything else to its string representation.
output[attribute_name] = str(attribute)
return output
If you need to alter the serialization or deserialization behavior of a serializer class, you can do so by overriding the .to_representation() or .to_internal_value() methods.
Some reasons this might be useful include...
- Adding new behavior for new serializer base classes.
- Modifying the behavior slightly for an existing class.
- Improving serialization performance for a frequently accessed API endpoint that returns lots of data.
The signatures for these methods are as follows:
Takes the object instance that requires serialization, and should return a primitive representation. Typically this means returning a structure of built-in Python datatypes. The exact types that can be handled will depend on the render classes you have configured for your API.
May be overridden in order to modify the representation style. For example:
def to_representation(self, instance):
"""Convert `username` to lowercase."""
ret = super().to_representation(instance)
ret['username'] = ret['username'].lower()
return ret
Takes the unvalidated incoming data as input and should return the validated data that will be made available as serializer.validated_data. The return value will also be passed to the .create() or .update() methods if .save() is called on the serializer class.
If any of the validation fails, then the method should raise a serializers.ValidationError(errors). The errors argument should be a dictionary mapping field names (or settings.NON_FIELD_ERRORS_KEY) to a list of error messages. If you don't need to alter deserialization behavior and instead want to provide object-level validation, it's recommended that you instead override the .validate() method.
The data argument passed to this method will normally be the value of request.data, so the datatype it provides will depend on the parser classes you have configured for your API.
Similar to Django forms, you can extend and reuse serializers through inheritance. This allows you to declare a common set of fields or methods on a parent class that can then be used in a number of serializers. For example,
class MyBaseSerializer(Serializer):
my_field = serializers.CharField()
def validate_my_field(self, value):
...
class MySerializer(MyBaseSerializer):
...
Like Django's Model and ModelForm classes, the inner Meta class on serializers does not implicitly inherit from it's parents' inner Meta classes. If you want the Meta class to inherit from a parent class you must do so explicitly. For example:
class AccountSerializer(MyBaseSerializer):
class Meta(MyBaseSerializer.Meta):
model = Account
Typically we would recommend not using inheritance on inner Meta classes, but instead declaring all options explicitly.
Additionally, the following caveats apply to serializer inheritance:
-
Normal Python name resolution rules apply. If you have multiple base classes that declare a
Metainner class, only the first one will be used. This means the child’sMeta, if it exists, otherwise theMetaof the first parent, etc. -
It’s possible to declaratively remove a
Fieldinherited from a parent class by setting the name to beNoneon the subclass.class MyBaseSerializer(ModelSerializer): my_field = serializers.CharField() class MySerializer(MyBaseSerializer): my_field = NoneHowever, you can only use this technique to opt out from a field defined declaratively by a parent class; it won’t prevent the
ModelSerializerfrom generating a default field. To opt-out from default fields, see Specifying which fields to include.
Once a serializer has been initialized, the dictionary of fields that are set on the serializer may be accessed using the .fields attribute. Accessing and modifying this attribute allows you to dynamically modify the serializer.
Modifying the fields argument directly allows you to do interesting things such as changing the arguments on serializer fields at runtime, rather than at the point of declaring the serializer.
For example, if you wanted to be able to set which fields should be used by a serializer at the point of initializing it, you could create a serializer class like so:
class DynamicFieldsModelSerializer(serializers.ModelSerializer):
"""
A ModelSerializer that takes an additional `fields` argument that
controls which fields should be displayed.
"""
def __init__(self, *args, **kwargs):
# Don't pass the 'fields' arg up to the superclass
fields = kwargs.pop('fields', None)
# Instantiate the superclass normally
super().__init__(*args, **kwargs)
if fields is not None:
# Drop any fields that are not specified in the `fields` argument.
allowed = set(fields)
existing = set(self.fields)
for field_name in existing - allowed:
self.fields.pop(field_name)
This would then allow you to do the following:
>>> class UserSerializer(DynamicFieldsModelSerializer):
>>> class Meta:
>>> model = User
>>> fields = ['id', 'username', 'email']
>>>
>>> print(UserSerializer(user))
{'id': 2, 'username': 'jonwatts', 'email': '[email protected]'}
>>>
>>> print(UserSerializer(user, fields=('id', 'email')))
{'id': 2, 'email': '[email protected]'}
REST framework 2 provided an API to allow developers to override how a ModelSerializer class would automatically generate the default set of fields.
This API included the .get_field(), .get_pk_field() and other methods.
Because the serializers have been fundamentally redesigned with 3.0 this API no longer exists. You can still modify the fields that get created but you'll need to refer to the source code, and be aware that if the changes you make are against private bits of API then they may be subject to change.
The following third party packages are also available.
The django-rest-marshmallow package provides an alternative implementation for serializers, using the python marshmallow library. It exposes the same API as the REST framework serializers, and can be used as a drop-in replacement in some use-cases.
The serpy package is an alternative implementation for serializers that is built for speed. Serpy serializes complex datatypes to simple native types. The native types can be easily converted to JSON or any other format needed.
The django-rest-framework-mongoengine package provides a MongoEngineModelSerializer serializer class that supports using MongoDB as the storage layer for Django REST framework.
The django-rest-framework-gis package provides a GeoFeatureModelSerializer serializer class that supports GeoJSON both for read and write operations.
The django-rest-framework-hstore package provides an HStoreSerializer to support django-hstore DictionaryField model field and its schema-mode feature.
The dynamic-rest package extends the ModelSerializer and ModelViewSet interfaces, adding API query parameters for filtering, sorting, and including / excluding all fields and relationships defined by your serializers.
The drf-dynamic-fields package provides a mixin to dynamically limit the fields per serializer to a subset specified by an URL parameter.
The drf-flex-fields package extends the ModelSerializer and ModelViewSet to provide commonly used functionality for dynamically setting fields and expanding primitive fields to nested models, both from URL parameters and your serializer class definitions.
The django-rest-framework-serializer-extensions package provides a collection of tools to DRY up your serializers, by allowing fields to be defined on a per-view/request basis. Fields can be whitelisted, blacklisted and child serializers can be optionally expanded.
The html-json-forms package provides an algorithm and serializer for processing <form> submissions per the (inactive) HTML JSON Form specification. The serializer facilitates processing of arbitrarily nested JSON structures within HTML. For example, <input name="items[0][id]" value="5"> will be interpreted as {"items": [{"id": "5"}]}.
DRF-Base64 provides a set of field and model serializers that handles the upload of base64-encoded files.
djangorestframework-queryfields allows API clients to specify which fields will be sent in the response via inclusion/exclusion query parameters.
The drf-writable-nested package provides writable nested model serializer which allows to create/update models with nested related data.
The drf-encrypt-content package helps you encrypt your data, serialized through ModelSerializer. It also contains some helper functions. Which helps you to encrypt your data.