JSONField

MySQL 5.7 comes with a JSON data type that stores JSON in a way that is queryable and updatable in place. This is ideal for data that varies widely, or very sparse columns, or just for storing API responses that you don’t have time to turn into the relational format.

Docs: MySQL.

Django-MySQL supports the JSON data type and related functions through JSONField plus some JSON database functions.

class JSONField(**kwargs)

A field for storing JSON. The Python data type may be either str (unicode on Python 2), int, float, dict, or list - basically anything that is supported by json.dumps. There is no restriction between these types - this may be surprising if you expect it to just store JSON objects/dicts.

So for example, the following all work:

mymodel.myfield = "a string"
mymodel.myfield = 1
mymodel.myfield = 0.3
mymodel.myfield = ["a", "list"]
mymodel.myfield = {"a": "dict"}

This field requires Django 1.8+ and MySQL 5.7+. Both requirements are checked by the field and you’ll get sensible errors for them when Django’s checks run if you’re not up to date on either.

Warning

If you give the field a default, ensure it’s a callable, such as a function, or the dict or list classes themselves. Incorrectly using a mutable object, such as default={}, creates a single object that is shared between all instances of the field. There’s a field check that errors if a plain list or dict instance is used for default, so there is some protection against this.

The correct way to provide a rich default like {'foo': 'bar'} is to define a module level function that returns it, so it can be serialized in migrations. For example:

def my_default():
    return {'foo': 'bar'}

class MyModel(Model):
    attrs = JSONField(default=my_default)

JSONFields in Forms

By default this uses the simple Django-MySQL form field JSONField, which simply displays the JSON in an HTML <textarea>.

Querying JSONField

You can query by object keys as well as array positions. In cases where names collide with existing lookups, you might want to use the JSONExtract database function.

Warning

Most of the standard lookups don’t make sense for JSONField and so have been made to fail with NotImplementedError. There is scope for making some of them work in the future, but it’s non-trivial. Only the lookups documented below work.

Also be careful with the key lookups. Since any string could be a key in a JSON object, any lookup name other than the standard ones or those listed below will be interpreted as a key lookup. No errors are raised. Be extra careful for typing mistakes, and always check your queries, e.g. myfield__eaxct as a typo of myfield__exact will not do what the author intended!

We’ll use the following example model:

from django_mysql.models import JSONField, Model

class ShopItem(Model):
    name = models.CharField(max_length=200)
    attrs = JSONField()

    def __str__(self):  # __unicode__ on Python 2
        return self.name

Exact Lookups

To query based on an exact match, just use an object of any JSON type.

For example:

>>> ShopItem.objects.create(name='Gruyère', attrs={'smelliness': 5})
>>> ShopItem.objects.create(name='Feta', attrs={'smelliness': 3, 'crumbliness': 10})
>>> ShopItem.objects.create(name='Hack', attrs=[1, 'arbitrary', 'data'])

>>> ShopItem.objects.filter(attrs={'smelliness': 5})
[<ShopItem: Gruyère>]
>>> ShopItem.objects.filter(attrs__exact={'smelliness': 3, 'crumbliness': 10})
[<ShopItem: Feta>]
>>> ShopItem.objects.filter(attrs=[1, 'arbitrary', 'data'])
[<ShopItem: Hack>]

Ordering Lookups

MySQL defines an ordering on JSON objects - see the docs for more details. The ordering rules can make sense for some types (e.g. strings, arrays), however they can also be confusing if your data is of mixed types, so be careful. You can use the ordering by querying with Django’s built-in gt, gte, lt, and lte lookups.

For example:

>>> ShopItem.objects.create(name='Cheshire', attrs=['Dense', 'Crumbly'])
>>> ShopItem.objects.create(name='Double Gloucester', attrs=['Semi-hard'])

>>> ShopItem.objects.filter(attrs__gt=['Dense', 'Crumbly'])
[<ShopItem: Double Gloucester>]
>>> ShopItem.objects.filter(attrs__lte=['ZZZ'])
[<ShopItem: Cheshire>, <ShopItem: Double Gloucester>]

Key, Index, and Path Lookups

To query based on a given dictionary key, use that key as the lookup name:

>>> ShopItem.objects.create(name='Gruyère', attrs={
    'smelliness': 5,
    'origin': {
        'country': 'Switzerland',
    }
    'certifications': ['Swiss AOC', 'Swiss AOP'],
})
>>> ShopItem.objects.create(name='Feta', attrs={'smelliness': 3, 'crumbliness': 10})

>>> ShopItem.objects.filter(attrs__smelliness=3)
[<ShopItem: Feta>]

Multiple keys can be chained together to form a path lookup:

>>> ShopItem.objects.filter(attrs__origin__country='Switzerland')
[<ShopItem: Gruyère>]

If the key is an integer, it will be interpreted as an index lookup in an array:

>>> ShopItem.objects.filter(attrs__certifications__0='Swiss AOC')
[<ShopItem: Gruyère>]

If the key you wish to query is not valid for a Python keyword argument (e.g. it contains unicode characters), or it clashes with the name of another field lookup, use the JSONExtract database function to fetch it.

Key Presence Lookups

To query to check if an object has a given key, use the has_key lookup:

# Find all ShopItems with a hardness rating
>>> ShopItem.objects.filter(attrs__has_key='hardness')
[]
# Find all ShopItems missing certification information
>>> ShopItem.objects.exclude(attrs__has_key='certifications')
[<ShopItem: Feta>]

To check if an object has several keys, use the has_keys lookup with a list of keys:

# Find all ShopItems with both origin and certification information
>>> ShopItem.objects.filter(attrs__has_keys=['origin', 'certifications'])
[<ShopItem: Gruyère>]

To find objects with one of several keys, use the has_any_keys lookup with a list of keys:

# Find all ShopItems with either a smelliness or a hardness rating
>>> ShopItem.objects.filter(attrs__has_any_keys=['smelliness', 'hardness'])
[<ShopItem: Gruyère>, <ShopItem: Feta>]

Length Lookup

This is very similar to the functions:JSONLength database function. You can use it to filter based upon the length of the JSON documents in the field, using the MySQL JSON_LENGTH function.

As per the MySQL documentation, the length of a document is determined as follows:

  • The length of a scalar is 1.
  • The length of an array is the number of array elements.
  • The length of an object is the number of object members.
  • The length does not count the length of nested arrays or objects.

Docs: MySQL.

For example:

# Find all the ShopItems with nothing in 'attrs'
>>> ShopItems.objects.filter(attrs__length=0)
[]
# Find all the ShopItems with >50 keys in 'attrs'
>>> ShopItems.objects.filter(attrs__length__gt=50)
[<ShopItem: Incredible Cheese>]

Containment Lookups

The contains lookup is overriden on JSONField to support the MySQL JSON_CONTAINS function. This allows you to search, for example, JSON objects that contain at least a given set of key-value pairs. Additionally you can do the inverse with contained_by, i.e. find values where the objects are contained by a given value.

The definition of containment is, as per the MySQL docs:

  • A candidate scalar is contained in a target scalar if and only if they are comparable and are equal. Two scalar values are comparable if they have the same JSON_TYPE() types, with the exception that values of types INTEGER and DECIMAL are also comparable to each other.
  • A candidate array is contained in a target array if and only if every element in the candidate is contained in some element of the target.
  • A candidate nonarray is contained in a target array if and only if the candidate is contained in some element of the target.
  • A candidate object is contained in a target object if and only if for each key in the candidate there is a key with the same name in the target and the value associated with the candidate key is contained in the value associated with the target key.

Docs: MySQL.

For example:

# Find all ShopItems with a crumbliness of 10 and a smelliness of 5
>>> ShopItems.objects.filter(attrs__contains={
    'crumbliness': 10,
    'smelliness': 5,
})
[<ShopItem: Feta>]

# Find all ShopItems that have either 0 properties, or 1 or more of the given properties
>>> ShopItems.objects.filter(attrs__contained_by={
    'crumbliness': 10,
    'hardness': 1,
    'smelliness': 5,
})
[<ShopItem: Feta>]