Making queries

Making queries

Once you’ve created your data models, Django automatically gives you a database-abstraction API that lets you create, retrieve, update and delete objects. This document explains how to use this API. Refer to the data model reference for full details of all the various model lookup options.

Throughout this guide (and in the reference), we’ll refer to the following models, which comprise a blog application:

from datetime import date

from django.db import models

class Blog(models.Model):
    name = models.CharField(max_length=100)
    tagline = models.TextField()

    def __str__(self):
        return self.name

class Author(models.Model):
    name = models.CharField(max_length=200)
    email = models.EmailField()

    def __str__(self):
        return self.name

class Entry(models.Model):
    blog = models.ForeignKey(Blog, on_delete=models.CASCADE)
    headline = models.CharField(max_length=255)
    body_text = models.TextField()
    pub_date = models.DateField()
    mod_date = models.DateField(default=date.today)
    authors = models.ManyToManyField(Author)
    number_of_comments = models.IntegerField(default=0)
    number_of_pingbacks = models.IntegerField(default=0)
    rating = models.IntegerField(default=5)

    def __str__(self):
        return self.headline

Creating objects

To represent database-table data in Python objects, Django uses an intuitive system: A model class represents a database table, and an instance of that class represents a particular record in the database table.

To create an object, instantiate it using keyword arguments to the model class, then call save() to save it to the database.

Assuming models live in a file mysite/blog/models.py, here’s an example:

>>> from blog.models import Blog
>>> b = Blog(name="Beatles Blog", tagline="All the latest Beatles news.")
>>> b.save()

This performs an INSERT SQL statement behind the scenes. Django doesn’t hit the database until you explicitly call save().

The save() method has no return value.

See also

save() takes a number of advanced options not described here. See the documentation for save() for complete details.

To create and save an object in a single step, use the create() method.

Saving changes to objects

To save changes to an object that’s already in the database, use save().

Given a Blog instance b5 that has already been saved to the database, this example changes its name and updates its record in the database:

>>> b5.name = "New name"
>>> b5.save()

This performs an UPDATE SQL statement behind the scenes. Django doesn’t hit the database until you explicitly call save().

Saving ForeignKey and ManyToManyField fields

Updating a ForeignKey field works exactly the same way as saving a normal field – assign an object of the right type to the field in question. This example updates the blog attribute of an Entry instance entry, assuming appropriate instances of Entry and Blog are already saved to the database (so we can retrieve them below):

>>> from blog.models import Blog, Entry
>>> entry = Entry.objects.get(pk=1)
>>> cheese_blog = Blog.objects.get(name="Cheddar Talk")
>>> entry.blog = cheese_blog
>>> entry.save()

Updating a ManyToManyField works a little differently – use the add() method on the field to add a record to the relation. This example adds the Author instance joe to the entry object:

>>> from blog.models import Author
>>> joe = Author.objects.create(name="Joe")
>>> entry.authors.add(joe)

To add multiple records to a ManyToManyField in one go, include multiple arguments in the call to add(), like this:

>>> john = Author.objects.create(name="John")
>>> paul = Author.objects.create(name="Paul")
>>> george = Author.objects.create(name="George")
>>> ringo = Author.objects.create(name="Ringo")
>>> entry.authors.add(john, paul, george, ringo)

Django will complain if you try to assign or add an object of the wrong type.

Retrieving objects

To retrieve objects from your database, construct a QuerySet via a Manager on your model class.

A QuerySet represents a collection of objects from your database. It can have zero, one or many filters. Filters narrow down the query results based on the given parameters. In SQL terms, a QuerySet equates to a SELECT statement, and a filter is a limiting clause such as WHERE or LIMIT.

You get a QuerySet by using your model’s Manager. Each model has at least one Manager, and it’s called objects by default. Access it directly via the model class, like so:

>>> Blog.objects
<django.db.models.manager.Manager object at ...>
>>> b = Blog(name="Foo", tagline="Bar")
>>> b.objects
Traceback:
    ...
AttributeError: "Manager isn't accessible via Blog instances."

Note

Managers are accessible only via model classes, rather than from model instances, to enforce a separation between “table-level” operations and “record-level” operations.

The Manager is the main source of QuerySets for a model. For example, Blog.objects.all() returns a QuerySet that contains all Blog objects in the database.

Retrieving all objects

The simplest way to retrieve objects from a table is to get all of them. To do this, use the all() method on a Manager:

>>> all_entries = Entry.objects.all()

The all() method returns a QuerySet of all the objects in the database.

Retrieving specific objects with filters

The QuerySet returned by all() describes all objects in the database table. Usually, though, you’ll need to select only a subset of the complete set of objects.

To create such a subset, you refine the initial QuerySet, adding filter conditions. The two most common ways to refine a QuerySet are:

filter(**kwargs)

Returns a new QuerySet containing objects that match the given lookup parameters.

exclude(**kwargs)

Returns a new QuerySet containing objects that do not match the given lookup parameters.

The lookup parameters (**kwargs in the above function definitions) should be in the format described in Field lookups below.

For example, to get a QuerySet of blog entries from the year 2006, use filter() like so:

Entry.objects.filter(pub_date__year=2006)

With the default manager class, it is the same as:

Entry.objects.all().filter(pub_date__year=2006)

Chaining filters

The result of refining a QuerySet is itself a QuerySet, so it’s possible to chain refinements together. For example:

>>> Entry.objects.filter(headline__startswith="What").exclude(
...     pub_date__gte=datetime.date.today()
... ).filter(pub_date__gte=datetime.date(2005, 1, 30))

This takes the initial QuerySet of all entries in the database, adds a filter, then an exclusion, then another filter. The final result is a QuerySet containing all entries with a headline that starts with “What”, that were published between January 30, 2005, and the current day.

Filtered QuerySets are unique

Each time you refine a QuerySet, you get a brand-new QuerySet that is in no way bound to the previous QuerySet. Each refinement creates a separate and distinct QuerySet that can be stored, used and reused.

Example:

>>> q1 = Entry.objects.filter(headline__startswith="What")
>>> q2 = q1.exclude(pub_date__gte=datetime.date.today())
>>> q3 = q1.filter(pub_date__gte=datetime.date.today())

These three QuerySets are separate. The first is a base QuerySet containing all entries that contain a headline starting with “What”. The second is a subset of the first, with an additional criteria that excludes records whose pub_date is today or in the future. The third is a subset of the first, with an additional criteria that selects only the records whose pub_date is today or in the future. The initial QuerySet (q1) is unaffected by the refinement process.

QuerySets are lazy

QuerySets are lazy – the act of creating a QuerySet doesn’t involve any database activity. You can stack filters together all day long, and Django won’t actually run the query until the QuerySet is evaluated. Take a look at this example:

>>> q = Entry.objects.filter(headline__startswith="What")
>>> q = q.filter(pub_date__lte=datetime.date.today())
>>> q = q.exclude(body_text__icontains="food")
>>> print(q)

Though this looks like three database hits, in fact it hits the database only once, at the last line (print(q)). In general, the results of a QuerySet aren’t fetched from the database until you “ask” for them. When you do, the QuerySet is evaluated by accessing the database. For more details on exactly when evaluation takes place, see When QuerySets are evaluated.

Retrieving a single object with get()

filter() will always give you a QuerySet, even if only a single object matches the query - in this case, it will be a QuerySet containing a single element.

If you know there is only one object that matches your query, you can use the get() method on a Manager which returns the object directly:

>>> one_entry = Entry.objects.get(pk=1)

You can use any query expression with get(), just like with filter() - again, see Field lookups below.

Note that there is a difference between using get(), and using filter() with a slice of [0]. If there are no results that match the query, get() will raise a DoesNotExist exception. This exception is an attribute of the model class that the query is being performed on - so in the code above, if there is no Entry object with a primary key of 1, Django will raise Entry.DoesNotExist.

Similarly, Django will complain if more than one item matches the get() query. In this case, it will raise MultipleObjectsReturned, which again is an attribute of the model class itself.

Other QuerySet methods

Most of the time you’ll use all(), get(), filter() and exclude() when you need to look up objects from the database. However, that’s far from all there is; see the QuerySet API Reference for a complete list of all the various QuerySet methods.

Limiting QuerySets

Use a subset of Python’s array-slicing syntax to limit your QuerySet to a certain number of results. This is the equivalent of SQL’s LIMIT and OFFSET clauses.

For example, this returns the first 5 objects (LIMIT 5):

>>> Entry.objects.all()[:5]

This returns the sixth through tenth objects (OFFSET 5 LIMIT 5):

>>> Entry.objects.all()[5:10]

Negative indexing (i.e. Entry.objects.all()[-1]) is not supported.

Generally, slicing a QuerySet returns a new QuerySet – it doesn’t evaluate the query. An exception is if you use the “step” parameter of Python slice syntax. For example, this would actually execute the query in order to return a list of every second object of the first 10:

>>> Entry.objects.all()[:10:2]

Further filtering or ordering of a sliced queryset is prohibited due to the ambiguous nature of how that might work.

To retrieve a single object rather than a list (e.g. SELECT foo FROM bar LIMIT 1), use an index instead of a slice. For example, this returns the first Entry in the database, after ordering entries alphabetically by headline:

>>> Entry.objects.order_by("headline")[0]

This is roughly equivalent to:

>>> Entry.objects.order_by("headline")[0:1].get()

Note, however, that the first of these will raise IndexError while the second will raise DoesNotExist if no objects match the given criteria. See get() for more details.

Field lookups

Field lookups are how you specify the meat of an SQL WHERE clause. They’re specified as keyword arguments to the QuerySet methods filter(), exclude() and get().

Basic lookups keyword arguments take the form field__lookuptype=value. (That’s a double-underscore). For example:

>>> Entry.objects.filter(pub_date__lte="2006-01-01")

translates (roughly) into the following SQL:

SELECT * FROM blog_entry WHERE pub_date <= '2006-01-01';

How this is possible

Python has the ability to define functions that accept arbitrary name-value arguments whose names and values are evaluated at runtime. For more information, see Keyword Arguments in the official Python tutorial.

The field specified in a lookup has to be the name of a model field. There’s one exception though, in case of a ForeignKey you can specify the field name suffixed with _id. In this case, the value parameter is expected to contain the raw value of the foreign model’s primary key. For example:

>>> Entry.objects.filter(blog_id=4)

If you pass an invalid keyword argument, a lookup function will raise TypeError.

The database API supports about two dozen lookup types; a complete reference can be found in the field lookup reference. To give you a taste of what’s available, here’s some of the more common lookups you’ll probably use:

exact

An “exact” match. For example:

>>> Entry.objects.get(headline__exact="Cat bites dog")

Would generate SQL along these lines:

SELECT ... WHERE headline = 'Cat bites dog';

If you don’t provide a lookup type – that is, if your keyword argument doesn’t contain a double underscore – the lookup type is assumed to be exact.

For example, the following two statements are equivalent:

>>> Blog.objects.get(id__exact=14)  # Explicit form
>>> Blog.objects.get(id=14)  # __exact is implied

This is for convenience, because exact lookups are the common case.

iexact

A case-insensitive match. So, the query:

>>> Blog.objects.get(name__iexact="beatles blog")

Would match a Blog titled "Beatles Blog", "beatles blog", or even "BeAtlES blOG".

contains

Case-sensitive containment test. For example:

Entry.objects.get(headline__contains="Lennon")

Roughly translates to this SQL:

SELECT ... WHERE headline LIKE '%Lennon%';

Note this will match the headline 'Today Lennon honored' but not 'today lennon honored'.

There’s also a case-insensitive version, icontains.

startswith, endswith

Starts-with and ends-with search, respectively. There are also case-insensitive versions called istartswith and iendswith.

Again, this only scratches the surface. A complete reference can be found in the field lookup reference.

Lookups that span relationships

Django offers a powerful and intuitive way to “follow” relationships in lookups, taking care of the SQL JOINs for you automatically, behind the scenes. To span a relationship, use the field name of related fields across models, separated by double underscores, until you get to the field you want.

This example retrieves all Entry objects with a Blog whose name is 'Beatles Blog':

>>> Entry.objects.filter(blog__name="Beatles Blog")

This spanning can be as deep as you’d like.

It works backwards, too. While it can be customized, by default you refer to a “reverse” relationship in a lookup using the lowercase name of the model.

This example retrieves all Blog objects which have at least one Entry whose headline contains 'Lennon':

>>> Blog.objects.filter(entry__headline__contains="Lennon")

If you are filtering across multiple relationships and one of the intermediate models doesn’t have a value that meets the filter condition, Django will treat it as if there is an empty (all values are NULL), but valid, object there. All this means is that no error will be raised. For example, in this filter:

Blog.objects.filter(entry__authors__name="Lennon")

(if there was a related Author model), if there was no author associated with an entry, it would be treated as if there was also no name attached, rather than raising an error because of the missing author. Usually this is exactly what you want to have happen. The only case where it might be confusing is if you are using isnull. Thus:

Blog.objects.filter(entry__authors__name__isnull=True)

will return Blog objects that have an empty name on the author and also those which have an empty author on the entry. If you don’t want those latter objects, you could write:

Blog.objects.filter(entry__authors__isnull=False, entry__authors__name__isnull=True)

Spanning multi-valued relationships

When spanning a ManyToManyField or a reverse ForeignKey (such as from Blog to Entry), filtering on multiple attributes raises the question of whether to require each attribute to coincide in the same related object. We might seek blogs that have an entry from 2008 with “Lennon” in its headline, or we might seek blogs that merely have any entry from 2008 as well as some newer or older entry with “Lennon” in its headline.

To select all blogs containing at least one entry from 2008 having “Lennon” in its headline (the same entry satisfying both conditions), we would write:

Blog.objects.filter(entry__headline__contains="Lennon", entry__pub_date__year=2008)

Otherwise, to perform a more permissive query selecting any blogs with merely some entry with “Lennon” in its headline and some entry from 2008, we would write:

Blog.objects.filter(entry__headline__contains="Lennon").filter(
    entry__pub_date__year=2008
)

Suppose there is only one blog that has both entries containing “Lennon” and entries from 2008, but that none of the entries from 2008 contained “Lennon”. The first query would not return any blogs, but the second query would return that one blog. (This is because the entries selected by the second filter may or may not be the same as the entries in the first filter. We are filtering the Blog items with each filter statement, not the Entry items.) In short, if each condition needs to match the same related object, then each should be contained in a single filter() call.

Note

As the second (more permissive) query chains multiple filters, it performs multiple joins to the primary model, potentially yielding duplicates.

>>> from datetime import date
>>> beatles = Blog.objects.create(name="Beatles Blog")
>>> pop = Blog.objects.create(name="Pop Music Blog")
>>> Entry.objects.create(
...     blog=beatles,
...     headline="New Lennon Biography",
...     pub_date=date(2008, 6, 1),
... )
<Entry: New Lennon Biography>
>>> Entry.objects.create(
...     blog=beatles,
...     headline="New Lennon Biography in Paperback",
...     pub_date=date(2009, 6, 1),
... )
<Entry: New Lennon Biography in Paperback>
>>> Entry.objects.create(
...     blog=pop,
...     headline="Best Albums of 2008",
...     pub_date=date(2008, 12, 15),
... )
<Entry: Best Albums of 2008>
>>> Entry.objects.create(
...     blog=pop,
...     headline="Lennon Would Have Loved Hip Hop",
...     pub_date=date(2020, 4, 1),
... )
<Entry: Lennon Would Have Loved Hip Hop>
>>> Blog.objects.filter(
...     entry__headline__contains="Lennon",
...     entry__pub_date__year=2008,
... )
<QuerySet [<Blog: Beatles Blog>]>
>>> Blog.objects.filter(
...     entry__headline__contains="Lennon",
... ).filter(
...     entry__pub_date__year=2008,
... )
<QuerySet [<Blog: Beatles Blog>, <Blog: Beatles Blog>, <Blog: Pop Music Blog]>

Note

The behavior of filter() for queries that span multi-value relationships, as described above, is not implemented equivalently for exclude(). Instead, the conditions in a single exclude() call will not necessarily refer to the same item.

For example, the following query would exclude blogs that contain both entries with “Lennon” in the headline and entries published in 2008:

Blog.objects.exclude(
    entry__headline__contains="Lennon",
    entry__pub_date__year=2008,
)

However, unlike the behavior when using filter(), this will not limit blogs based on entries that satisfy both conditions. In order to do that, i.e. to select all blogs that do not contain entries published with “Lennon” that were published in 2008, you need to make two queries:

Blog.objects.exclude(
    entry__in=Entry.objects.filter(
        headline__contains="Lennon",
        pub_date__year=2008,
    ),
)

Filters can reference fields on the model

In the examples given so far, we have constructed filters that compare the value of a model field with a constant. But what if you want to compare the value of a model field with another field on the same model?

Django provides F expressions to allow such comparisons. Instances of F() act as a reference to a model field within a query. These references can then be used in query filters to compare the values of two different fields on the same model instance.

For example, to find a list of all blog entries that have had more comments than pingbacks, we construct an F() object to reference the pingback count, and use that F() object in the query:

>>> from django.db.models import F
>>> Entry.objects.filter(number_of_comments__gt=F("number_of_pingbacks"))

Django supports the use of addition, subtraction, multiplication, division, modulo, and power arithmetic with F() objects, both with constants and with other F() objects. To find all the blog entries with more than twice as many comments as pingbacks, we modify the query:

>>> Entry.objects.filter(number_of_comments__gt=F("number_of_pingbacks") * 2)

To find all the entries where the rating of the entry is less than the sum of the pingback count and comment count, we would issue the query:

>>> Entry.objects.filter(rating__lt=F("number_of_comments") + F("number_of_pingbacks"))

You can also use the double underscore notation to span relationships in an F() object. An F() object with a double underscore will introduce any joins needed to access the related object. For example, to retrieve all the entries where the author’s name is the same as the blog name, we could issue the query:

>>> Entry.objects.filter(authors__name=F("blog__name"))

For date and date/time fields, you can add or subtract a timedelta object. The following would return all entries that were modified more than 3 days after they were published:

>>> from datetime import timedelta
>>> Entry.objects.filter(mod_date__gt=F("pub_date") + timedelta(days=3))

The F() objects support bitwise operations by .bitand(), .bitor(), .bitxor(), .bitrightshift(), and .bitleftshift(). For example:

>>> F("somefield").bitand(16)

Oracle

Oracle doesn’t support bitwise XOR operation.

Expressions can reference transforms

Django supports using transforms in expressions.

For example, to find all Entry objects published in the same year as they were last modified:

>>> from django.db.models import F
>>> Entry.objects.filter(pub_date__year=F("mod_date__year"))

To find the earliest year an entry was published, we can issue the query:

>>> from django.db.models import Min
>>> Entry.objects.aggregate(first_published_year=Min("pub_date__year"))

This example finds the value of the highest rated entry and the total number of comments on all entries for each year:

>>> from django.db.models import OuterRef, Subquery, Sum
>>> Entry.objects.values("pub_date__year").annotate(
...     top_rating=Subquery(
...         Entry.objects.filter(
...             pub_date__year=OuterRef("pub_date__year"),
...         )
...         .order_by("-rating")
...         .values("rating")[:1]
...     ),
...     total_comments=Sum("number_of_comments"),
... )

The pk lookup shortcut

For convenience, Django provides a pk lookup shortcut, which stands for “primary key”.

In the example Blog model, the primary key is the id field, so these three statements are equivalent:

>>> Blog.objects.get(id__exact=14)  # Explicit form
>>> Blog.objects.get(id=14)  # __exact is implied
>>> Blog.objects.get(pk=14)  # pk implies id__exact

The use of pk isn’t limited to __exact queries – any query term can be combined with pk to perform a query on the primary key of a model:

## Get blogs entries with id 1, 4 and 7
>>> Blog.objects.filter(pk__in=[1, 4, 7])

## Get all blog entries with id > 14
>>> Blog.objects.filter(pk__gt=14)

pk lookups also work across joins. For example, these three statements are equivalent:

>>> Entry.objects.filter(blog__id__exact=3)  # Explicit form
>>> Entry.objects.filter(blog__id=3)  # __exact is implied
>>> Entry.objects.filter(blog__pk=3)  # __pk implies __id__exact

Escaping percent signs and underscores in LIKE statements

The field lookups that equate to LIKE SQL statements (iexact, contains, icontains, startswith, istartswith, endswith and iendswith) will automatically escape the two special characters used in LIKE statements – the percent sign and the underscore. (In a LIKE statement, the percent sign signifies a multiple-character wildcard and the underscore signifies a single-character wildcard.)

This means things should work intuitively, so the abstraction doesn’t leak. For example, to retrieve all the entries that contain a percent sign, use the percent sign as any other character:

>>> Entry.objects.filter(headline__contains="%")

Django takes care of the quoting for you; the resulting SQL will look something like this:

SELECT ... WHERE headline LIKE '%\%%';

Same goes for underscores. Both percentage signs and underscores are handled for you transparently.

Caching and QuerySets

Each QuerySet contains a cache to minimize database access. Understanding how it works will allow you to write the most efficient code.

In a newly created QuerySet, the cache is empty. The first time a QuerySet is evaluated – and, hence, a database query happens – Django saves the query results in the QuerySet’s cache and returns the results that have been explicitly requested (e.g., the next element, if the QuerySet is being iterated over). Subsequent evaluations of the QuerySet reuse the cached results.

Keep this caching behavior in mind, because it may bite you if you don’t use your QuerySets correctly. For example, the following will create two QuerySets, evaluate them, and throw them away:

>>> print([e.headline for e in Entry.objects.all()])
>>> print([e.pub_date for e in Entry.objects.all()])

That means the same database query will be executed twice, effectively doubling your database load. Also, there’s a possibility the two lists may not include the same database records, because an Entry may have been added or deleted in the split second between the two requests.

To avoid this problem, save the QuerySet and reuse it:

>>> queryset = Entry.objects.all()
>>> print([p.headline for p in queryset])  # Evaluate the query set.
>>> print([p.pub_date for p in queryset])  # Reuse the cache from the evaluation.

When QuerySets are not cached

Querysets do not always cache their results. When evaluating only part of the queryset, the cache is checked, but if it is not populated then the items returned by the subsequent query are not cached. Specifically, this means that limiting the queryset using an array slice or an index will not populate the cache.

For example, repeatedly getting a certain index in a queryset object will query the database each time:

>>> queryset = Entry.objects.all()
>>> print(queryset[5])  # Queries the database
>>> print(queryset[5])  # Queries the database again

However, if the entire queryset has already been evaluated, the cache will be checked instead:

>>> queryset = Entry.objects.all()
>>> [entry for entry in queryset]  # Queries the database
>>> print(queryset[5])  # Uses cache
>>> print(queryset[5])  # Uses cache

Here are some examples of other actions that will result in the entire queryset being evaluated and therefore populate the cache:

>>> [entry for entry in queryset]
>>> bool(queryset)
>>> entry in queryset
>>> list(queryset)

Note

Simply printing the queryset will not populate the cache. This is because the call to __repr__() only returns a slice of the entire queryset.

Asynchronous queries

If you are writing asynchronous views or code, you cannot use the ORM for queries in quite the way we have described above, as you cannot call blocking synchronous code from asynchronous code - it will block up the event loop (or, more likely, Django will notice and raise a SynchronousOnlyOperation to stop that from happening).

Fortunately, you can do many queries using Django’s asynchronous query APIs. Every method that might block - such as get() or delete() - has an asynchronous variant (aget() or adelete()), and when you iterate over results, you can use asynchronous iteration (async for) instead.

Query iteration

The default way of iterating over a query - with for - will result in a blocking database query behind the scenes as Django loads the results at iteration time. To fix this, you can swap to async for:

async for entry in Authors.objects.filter(name__startswith="A"):
    ...

Be aware that you also can’t do other things that might iterate over the queryset, such as wrapping list() around it to force its evaluation (you can use async for in a comprehension, if you want it).

Because QuerySet methods like filter() and exclude() do not actually run the query - they set up the queryset to run when it’s iterated over - you can use those freely in asynchronous code. For a guide to which methods can keep being used like this, and which have asynchronous versions, read the next section.

QuerySet and manager methods

Some methods on managers and querysets - like get() and first() - force execution of the queryset and are blocking. Some, like filter() and exclude(), don’t force execution and so are safe to run from asynchronous code. But how are you supposed to tell the difference?

While you could poke around and see if there is an a-prefixed version of the method (for example, we have aget() but not afilter()), there is a more logical way - look up what kind of method it is in the QuerySet reference.

In there, you’ll find the methods on QuerySets grouped into two sections:

• Methods that return new querysets : These are the non-blocking ones, and don’t have asynchronous versions. You’re free to use these in any situation, though read the notes on defer() and only() before you use them.
• Methods that do not return querysets : These are the blocking ones, and have asynchronous versions - the asynchronous name for each is noted in its documentation, though our standard pattern is to add an a prefix.

Using this distinction, you can work out when you need to use asynchronous versions, and when you don’t. For example, here’s a valid asynchronous query:

user = await User.objects.filter(username=my_input).afirst()

filter() returns a queryset, and so it’s fine to keep chaining it inside an asynchronous environment, whereas first() evaluates and returns a model instance - thus, we change to afirst(), and use await at the front of the whole expression in order to call it in an asynchronous-friendly way.

Note

If you forget to put the await part in, you may see errors like “coroutine object has no attribute x” or “ <coroutine …>” strings in place of your model instances. If you ever see these, you are missing an await somewhere to turn that coroutine into a real value.

Transactions

Transactions are not currently supported with asynchronous queries and updates. You will find that trying to use one raises SynchronousOnlyOperation.

If you wish to use a transaction, we suggest you write your ORM code inside a separate, synchronous function and then call that using sync_to_async - see Asynchronous support for more.

Querying JSONField

Lookups implementation is different in JSONField, mainly due to the existence of key transformations. To demonstrate, we will use the following example model:

from django.db import models

class Dog(models.Model):
    name = models.CharField(max_length=200)
    data = models.JSONField(null=True)

    def __str__(self):
        return self.name

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