Django's database API is the other half of the model API discussed in Appendix A. Once you've defined a model, you'll use this API any time you need to access the database. You've seen examples of this API in use throughout the book; this appendix explains all the various options in detail.
Like the model APIs discussed in Appendix A, though these APIs are considered very stable, the Django developers consistently add new shortcuts and conveniences. It's a good idea to always check the latest documentation online, available at http://docs.djangoproject.com/.
Throughout this reference, we'll refer to the following models, which might form a simple blog application:
from django.db import models
class Blog(models.Model):
name = models.CharField(max_length=100)
tagline = models.TextField()
def __unicode__(self):
return self.name
class Author(models.Model):
name = models.CharField(max_length=50)
email = models.EmailField()
def __unicode__(self):
return self.name
class Entry(models.Model):
blog = models.ForeignKey(Blog)
headline = models.CharField(max_length=255)
body_text = models.TextField()
pub_date = models.DateTimeField()
authors = models.ManyToManyField(Author)
def __unicode__(self):
return self.headline
To create an object, instantiate it using keyword arguments to the model class, and
then call save() to save it to the database:
>>> from mysite.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.
To create an object and save it all in one step, see the create manager
method.
When you save an object, Django performs the following steps:
Emit a pre_save signal. This provides a notification that an object is about to be saved. You can register a listener that will be invoked whenever this signal is emitted. Check the online documentation for more on signals.
Preprocess the data. Each field on the object is asked to perform any automated data modification that the field may need to perform.
Most fields do no preprocessing -- the field data is kept as is. Preprocessing is only used on fields that have special behavior, like file fields.
Prepare the data for the database. Each field is asked to provide its current value in a data type that can be written to the database.
Most fields require no data preparation. Simple data types, such as integers and strings, are "ready to write" as a Python object. However, more complex data types often require some modification. For example,
DateFieldsuse a Pythondatetimeobject to store data. Databases don't storedatetimeobjects, so the field value must be converted into an ISO-compliant date string for insertion into the database.Insert the data into the database. The preprocessed, prepared data is then composed into an SQL statement for insertion into the database.
Emit a post_save signal. As with the
pre_savesignal, this is used to provide notification that an object has been successfully saved.
For convenience, each model is given an autoincrementing primary key field
named id unless you explicitly specify primary_key=True on a field (see
the section titled "AutoField" in Appendix A).
If your model has an AutoField, that autoincremented value will be
calculated and saved as an attribute on your object the first time you call
save():
>>> b2 = Blog(name='Cheddar Talk', tagline='Thoughts on cheese.') >>> b2.id # Returns None, because b doesn't have an ID yet. None >>> b2.save() >>> b2.id # Returns the ID of your new object. 14
There's no way to tell what the value of an ID will be before you call
save(), because that value is calculated by your database, not by Django.
If a model has an AutoField but you want to define a new object's ID
explicitly when saving, just define it explicitly before saving, rather than
relying on the autoassignment of the ID:
>>> b3 = Blog(id=3, name='Cheddar Talk', tagline='Thoughts on cheese.') >>> b3.id 3 >>> b3.save() >>> b3.id 3
If you assign auto-primary-key values manually, make sure not to use an already existing primary key value! If you create a new object with an explicit primary key value that already exists in the database, Django will assume you're changing the existing record rather than creating a new one.
Given the preceding 'Cheddar Talk' blog example, this example would override the
previous record in the database:
>>> b4 = Blog(id=3, name='Not Cheddar', tagline='Anything but cheese.') >>> b4.save() # Overrides the previous blog with ID=3!
Explicitly specifying auto-primary-key values is mostly useful for bulk-saving objects, when you're confident you won't have primary key collision.
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. Again, Django
doesn't hit the database until you explicitly call save().
How Django Knows When to UPDATE and When to INSERT
You may have noticed that Django database objects use the same save() method
for creating and changing objects. Django abstracts the need to use
INSERT or UPDATE SQL statements. Specifically, when you call
save(), Django follows this algorithm:
- If the object's primary key attribute is set to a value that evaluates
to
True(i.e., a value other thanNoneor the empty string), Django executes aSELECTquery to determine whether a record with the given primary key already exists. - If the record with the given primary key does already exist, Django
executes an
UPDATEquery. - If the object's primary key attribute is not set, or if it's set but
a record doesn't exist, Django executes an
INSERT.
Because of this, you should be careful not to specify a primary key value explicitly when saving new objects if you cannot guarantee the primary key value is unused.
Updating ForeignKey fields works exactly the same way; simply assign an
object of the right type to the field in question:
>>> joe = Author.objects.create(name="Joe") >>> entry.author = joe >>> entry.save()
Django will complain if you try to assign an object of the wrong type.
Throughout the book you've seen objects retrieved using code like the following:
>>> blogs = Blog.objects.filter(author__name__contains="Joe")
There are quite a few "moving parts" behind the scenes here: when you
retrieve objects from the database, you're actually constructing a QuerySet
using the model's Manager. This QuerySet knows how to execute SQL and
return the requested objects.
Appendix A looked at both of these objects from a model-definition point of view; now we'll look at how they operate.
A QuerySet represents a collection of objects from your database. It can
have zero, one, or many filters -- criteria that narrow down the collection
based on given parameters. In SQL terms, a QuerySet equates to a SELECT
statement, and a filter is a WHERE.
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 0x137d00d>
Managers are accessible only via model classes, rather than from model
instances, to enforce a separation between "table-level" operations and
"record-level" operations:
>>> b = Blog(name='Foo', tagline='Bar') >>> b.objects Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: Manager isn't accessible via Blog instances.
The Manager is the main source of QuerySets for a model. It acts as a
"root" QuerySet that describes all objects in the model's database table.
For example, Blog.objects is the initial QuerySet that contains all
Blog objects in the database.
Each QuerySet contains a cache, to minimize database access. It's important
to understand how it works, in order 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, simply save the QuerySet and reuse it:
queryset = Poll.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.
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:
>>> Entry.objects.all()
The all() method returns a QuerySet of all the objects in the database.
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. You'll usually do this using the filter() and/or exclude()
methods:
>>> y2006 = Entry.objects.filter(pub_date__year=2006) >>> not2006 = Entry.objects.exclude(pub_date__year=2006)
filter() and exclude() both take field lookup arguments, which are
discussed in detail shortly.
The result of refining a QuerySet is itself a QuerySet, so it's
possible to chain refinements together, for example:
>>> qs = Entry.objects.filter(headline__startswith='What') >>> qs = qs.exclude(pub_date__gte=datetime.datetime.now()) >>> qs = qs.filter(pub_date__gte=datetime.datetime(2005, 1, 1))
This takes the initial QuerySet of all entries in the database, adds a
filter, then an exclusion, and then another filter. The final result is a
QuerySet containing all entries with a headline that starts with "What"
that were published between January 1, 2005, and the current day.
It's important to point out here that QuerySets are lazy -- the act of creating
a QuerySet doesn't involve any database activity. In fact, the three preceding lines
don't make any database calls; you can chain filters together all day
long and Django won't actually run the query until the QuerySet is
evaluated.
You can evaluate a QuerySet in any following ways:
Iterating: A
QuerySetis iterable, and it executes its database query the first time you iterate over it. For example, the followingQuerySetisn't evaluated until it's iterated over in theforloop:qs = Entry.objects.filter(pub_date__year=2006) qs = qs.filter(headline__icontains="bill") for e in qs: print e.headlineThis prints all headlines from 2006 that contain "bill" but causes only one database hit.
Printing it: A
QuerySetis evaluated when you callrepr()on it. This is for convenience in the Python interactive interpreter, so you can immediately see your results when using the API interactively.Slicing: As explained in the upcoming "Limiting QuerySets" section, a
QuerySetcan be sliced using Python's array-slicing syntax. Usually slicing aQuerySetreturns another (unevaluated)``QuerySet``, but Django will execute the database query if you use the "step" parameter of slice syntax.Converting to a list: You can force evaluation of a
QuerySetby callinglist()on it, for example:>>> entry_list = list(Entry.objects.all())
Be warned, though, that this could have a large memory overhead, because Django will load each element of the list into memory. In contrast, iterating over a
QuerySetwill take advantage of your database to load data and instantiate objects only as you need them.
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:
q1 = Entry.objects.filter(headline__startswith="What") q2 = q1.exclude(pub_date__gte=datetime.now()) q3 = q1.filter(pub_date__gte=datetime.now())
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 criterion that excludes
records whose pub_date is greater than now. The third is a subset of the
first, with an additional criterion that selects only the records whose
pub_date is greater than now. The initial QuerySet (q1) is
unaffected by the refinement process.
Use 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 five entries (LIMIT 5):
>>> Entry.objects.all()[:5]
This returns the sixth through tenth entries (OFFSET 5 LIMIT 5):
>>> Entry.objects.all()[5:10]
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 ten:
>>> Entry.objects.all()[:10:2]
To retrieve a single object rather than a list (e.g., SELECT foo FROM bar
LIMIT 1), use a simple 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 the following:
>>> 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.
Django provides a range of QuerySet refinement methods that modify either
the types of results returned by the QuerySet or the way its SQL query is
executed. These methods are described in the sections that follow. Some of the
methods take field lookup arguments, which are discussed in detail a bit later
on.
Returns a new QuerySet containing objects that match the given lookup
parameters.
Returns a new QuerySet containing objects that do not match the given
lookup parameters.
By default, results returned by a QuerySet are ordered by the ordering
tuple given by the ordering option in the model's metadata (see Appendix A). You can
override this for a particular query using the order_by() method:
>> Entry.objects.filter(pub_date__year=2005).order_by('-pub_date', 'headline')
This result will be ordered by pub_date descending, then by
headline ascending. The negative sign in front of "-pub_date" indicates
descending order. Ascending order is assumed if the - is absent. To order
randomly, use "?", like so:
>>> Entry.objects.order_by('?')
Ordering randomly incurs a performance penalty, though, so you shouldn't use it for anything with heavy load.
If no ordering is specified in a model's class Meta and a QuerySet from
that model doesn't include order_by(), then ordering will be undefined and
may differ from query to query.
Returns a new QuerySet that uses SELECT DISTINCT in its SQL query. This
eliminates duplicate rows from the query results.
By default, a QuerySet will not eliminate duplicate rows. In practice, this
is rarely a problem, because simple queries such as Blog.objects.all() don't
introduce the possibility of duplicate result rows.
However, if your query spans multiple tables, it's possible to get duplicate
results when a QuerySet is evaluated. That's when you'd use distinct().
Returns a special QuerySet that evaluates to a list of dictionaries instead
of model-instance objects. Each of those dictionaries represents an object, with
the keys corresponding to the attribute names of model objects:
# This list contains a Blog object.
>>> Blog.objects.filter(name__startswith='Beatles')
[Beatles Blog]
# This list contains a dictionary.
>>> Blog.objects.filter(name__startswith='Beatles').values()
[{'id': 1, 'name': 'Beatles Blog', 'tagline': 'All the latest Beatles news.'}]
values() takes optional positional arguments, *fields, which specify
field names to which the SELECT should be limited. If you specify the
fields, each dictionary will contain only the field keys/values for the fields
you specify. If you don't specify the fields, each dictionary will contain a
key and value for every field in the database table:
>>> Blog.objects.values()
[{'id': 1, 'name': 'Beatles Blog', 'tagline': 'All the latest Beatles news.'}],
>>> Blog.objects.values('id', 'name')
[{'id': 1, 'name': 'Beatles Blog'}]
This method is useful when you know you're only going to need values from a small number of the available fields and you won't need the functionality of a model instance object. It's more efficient to select only the fields you need to use.
Returns a special QuerySet that evaluates to a list of datetime.datetime
objects representing all available dates of a particular kind within the
contents of the QuerySet.
The field argument must be the name of a DateField or DateTimeField
of your model. The kind argument must be either "year", "month", or
"day". Each datetime.datetime object in the result list is "truncated"
to the given type:
"year"returns a list of all distinct year values for the field."month"returns a list of all distinct year/month values for the field."day"returns a list of all distinct year/month/day values for the field.
order, which defaults to 'ASC', should be either 'ASC' or
'DESC'. This specifies how to order the results.
Here are a few examples:
>>> Entry.objects.dates('pub_date', 'year')
[datetime.datetime(2005, 1, 1)]
>>> Entry.objects.dates('pub_date', 'month')
[datetime.datetime(2005, 2, 1), datetime.datetime(2005, 3, 1)]
>>> Entry.objects.dates('pub_date', 'day')
[datetime.datetime(2005, 2, 20), datetime.datetime(2005, 3, 20)]
>>> Entry.objects.dates('pub_date', 'day', order='DESC')
[datetime.datetime(2005, 3, 20), datetime.datetime(2005, 2, 20)]
>>> Entry.objects.filter(headline__contains='Lennon').dates('pub_date', 'day')
[datetime.datetime(2005, 3, 20)]
Returns a QuerySet that will automatically "follow" foreign key
relationships, selecting that additional related-object data when it executes
its query. This is a performance booster that results in (sometimes much)
larger queries but means later use of foreign key relationships won't require
database queries.
The following examples illustrate the difference between plain lookups and
select_related() lookups. Here's standard lookup:
# Hits the database. >>> e = Entry.objects.get(id=5) # Hits the database again to get the related Blog object. >>> b = e.blog
And here's select_related lookup:
# Hits the database. >>> e = Entry.objects.select_related().get(id=5) # Doesn't hit the database, because e.blog has been prepopulated # in the previous query. >>> b = e.blog
select_related() follows foreign keys as far as possible. If you have the
following models:
class City(models.Model):
# ...
class Person(models.Model):
# ...
hometown = models.ForeignKey(City)
class Book(models.Model):
# ...
author = models.ForeignKey(Person)
then a call to Book.objects.select_related().get(id=4) will cache the
related Person and the related City:
>>> b = Book.objects.select_related().get(id=4) >>> p = b.author # Doesn't hit the database. >>> c = p.hometown # Doesn't hit the database. >>> b = Book.objects.get(id=4) # No select_related() in this example. >>> p = b.author # Hits the database. >>> c = p.hometown # Hits the database.
Note that select_related() does not follow foreign keys that have
null=True.
Usually, using select_related() can vastly improve performance because your
application can avoid many database calls. However, in situations with deeply nested
sets of relationships, select_related() can sometimes end up following "too
many" relations and can generate queries so large that they end up being slow.
The following QuerySet methods evaluate the QuerySet and return
something other than a QuerySet -- a single object, value, and so forth.
Returns the object matching the given lookup parameters, which should be in the
format described in the "Field Lookups" section. This raises AssertionError if
more than one object was found.
get() raises a DoesNotExist exception if an object wasn't found for the
given parameters. The DoesNotExist exception is an attribute of the model
class, for example:
>>> Entry.objects.get(id='foo') # raises Entry.DoesNotExist
The DoesNotExist exception inherits from
django.core.exceptions.ObjectDoesNotExist, so you can target multiple
DoesNotExist exceptions:
>>> from django.core.exceptions import ObjectDoesNotExist >>> try: ... e = Entry.objects.get(id=3) ... b = Blog.objects.get(id=1) ... except ObjectDoesNotExist: ... print "Either the entry or blog doesn't exist."
This is a convenience method for creating an object and saving it all in one step. It lets you compress two common steps:
>>> p = Person(first_name="Bruce", last_name="Springsteen") >>> p.save()
into a single line:
>>> p = Person.objects.create(first_name="Bruce", last_name="Springsteen")
This is a convenience method for looking up an object and creating one if it doesn't
exist. It returns a tuple of (object, created), where object is the retrieved or
created object and created is a Boolean specifying whether a new object was
created.
This method is meant as a shortcut to boilerplate code and is mostly useful for data-import scripts. For example:
try:
obj = Person.objects.get(first_name='John', last_name='Lennon')
except Person.DoesNotExist:
obj = Person(first_name='John', last_name='Lennon', birthday=date(1940, 10, 9))
obj.save()
This pattern gets quite unwieldy as the number of fields in a model increases. The
previous example can be rewritten using get_or_create() like so:
obj, created = Person.objects.get_or_create(
first_name = 'John',
last_name = 'Lennon',
defaults = {'birthday': date(1940, 10, 9)}
)
Any keyword arguments passed to get_or_create() -- except an optional one
called defaults -- will be used in a get() call. If an object is found,
get_or_create() returns a tuple of that object and False. If an object
is not found, get_or_create() will instantiate and save a new object,
returning a tuple of the new object and True. The new object will be created
according to this algorithm:
defaults = kwargs.pop('defaults', {})
params = dict([(k, v) for k, v in kwargs.items() if '__' not in k])
params.update(defaults)
obj = self.model(**params)
obj.save()
In English, that means start with any non-'defaults' keyword argument that
doesn't contain a double underscore (which would indicate a nonexact lookup).
Then add the contents of defaults, overriding any keys if necessary, and
use the result as the keyword arguments to the model class.
If you have a field named defaults and want to use it as an exact lookup in
get_or_create(), just use 'defaults__exact' like so:
Foo.objects.get_or_create(
defaults__exact = 'bar',
defaults={'defaults': 'bar'}
)
Note
As mentioned earlier, get_or_create() is mostly useful in scripts that
need to parse data and create new records if existing ones aren't available.
But if you need to use get_or_create() in a view, please make sure to
use it only in POST requests unless you have a good reason not to.
GET requests shouldn't have any effect on data; use POST whenever a
request to a page has a side effect on your data.
Returns an integer representing the number of objects in the database matching
the QuerySet. count() never raises exceptions. Here's an example:
# Returns the total number of entries in the database. >>> Entry.objects.count() 4 # Returns the number of entries whose headline contains 'Lennon' >>> Entry.objects.filter(headline__contains='Lennon').count() 1
count() performs a SELECT COUNT(*) behind the scenes, so you should
always use count() rather than loading all of the records into Python objects
and calling len() on the result.
Depending on which database you're using (e.g., PostgreSQL or MySQL),
count() may return a long integer instead of a normal Python integer. This
is an underlying implementation quirk that shouldn't pose any real-world
problems.
Takes a list of primary key values and returns a dictionary mapping each primary key value to an instance of the object with the given ID, for example:
>>> Blog.objects.in_bulk([1])
{1: Beatles Blog}
>>> Blog.objects.in_bulk([1, 2])
{1: Beatles Blog, 2: Cheddar Talk}
>>> Blog.objects.in_bulk([])
{}
IDs of objects that don't exist are silently dropped from the result dictionary.
If you pass in_bulk() an empty list, you'll get an empty dictionary.
Returns the latest object in the table, by date, using the field_name
provided as the date field. This example returns the latest Entry in the
table, according to the pub_date field:
>>> Entry.objects.latest('pub_date')
If your model's Meta specifies get_latest_by, you can leave off the
field_name argument to latest(). Django will use the field specified in
get_latest_by by default.
Like get(), latest() raises DoesNotExist if an object doesn't exist
with the given parameters.
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 lookup keyword arguments take the form field__lookuptype=value
(note the 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';
If you pass an invalid keyword argument, a lookup function will raise
TypeError.
The supported lookup types follow.
Performs an exact match:
>>> Entry.objects.get(headline__exact="Man bites dog")
This matches any object with the exact headline "Man 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.
Performs a case-insensitive exact match:
>>> Blog.objects.get(name__iexact='beatles blog')
This will match 'Beatles Blog', 'beatles blog',
'BeAtLes BLoG', and so forth.
Performs a case-sensitive containment test:
Entry.objects.get(headline__contains='Lennon')
This will match the headline 'Today Lennon honored' but not
'today lennon honored'.
SQLite doesn't support case-sensitive LIKE statements; when using
SQLite,``contains`` acts like icontains.
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, just 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 '%\%%';
The same goes for underscores. Both percentage signs and underscores are handled for you transparently.
Performs a case-insensitive containment test:
>>> Entry.objects.get(headline__icontains='Lennon')
Unlike contains, icontains will match 'today lennon honored'.
These represent greater than, greater than or equal to, less than, and less than or equal to:
>>> Entry.objects.filter(id__gt=4) >>> Entry.objects.filter(id__lt=15) >>> Entry.objects.filter(id__gte=0)
These queries return any object with an ID greater than 4, an ID less than 15, and an ID greater than or equal to 1, respectively.
You'll usually use these on numeric fields. Be careful with character fields since character order isn't always what you'd expect (i.e., the string "4" sorts after the string "10").
Filters where a value is on a given list:
Entry.objects.filter(id__in=[1, 3, 4])
This returns all objects with the ID 1, 3, or 4.
Performs a case-sensitive starts-with:
>>> Entry.objects.filter(headline__startswith='Will')
This will return the headlines "Will he run?" and "Willbur named judge", but not "Who is Will?" or "will found in crypt".
Performs a case-insensitive starts-with:
>>> Entry.objects.filter(headline__istartswith='will')
This will return the headlines "Will he run?", "Willbur named judge", and "will found in crypt", but not "Who is Will?"
Perform case-sensitive and case-insensitive ends-with:
>>> Entry.objects.filter(headline__endswith='cats') >>> Entry.objects.filter(headline__iendswith='cats')
Similar to startswith and istartswith.
Performs an inclusive range check:
>>> start_date = datetime.date(2005, 1, 1) >>> end_date = datetime.date(2005, 3, 31) >>> Entry.objects.filter(pub_date__range=(start_date, end_date))
You can use range anywhere you can use BETWEEN in SQL -- for dates,
numbers, and even characters.
For date/datetime fields, perform exact year, month, or day matches:
# Return all entries published in 2005 >>>Entry.objects.filter(pub_date__year=2005) # Return all entries published in December >>> Entry.objects.filter(pub_date__month=12) # Return all entries published on the 3rd of the month >>> Entry.objects.filter(pub_date__day=3) # Combination: return all entries on Christmas of any year >>> Entry.objects.filter(pub_date__month=12, pub_date_day=25)
Takes either True or False, which correspond to SQL queries of
IS NULL and IS NOT NULL, respectively:
>>> Entry.objects.filter(pub_date__isnull=True)
A Boolean full-text search that takes advantage of full-text indexing. This is like
contains but is significantly faster due to full-text indexing.
Note this is available only in MySQL and requires direct manipulation of the database to add the full-text index.
For convenience, Django provides a pk lookup type, 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
The point of pk is to give you a generic way to refer to the primary key in
cases where you're not sure whether the model's primary key is called id.
Keyword argument queries -- in filter() and so on -- are ANDed together. If
you need to execute more complex queries (e.g., queries with OR
statements), you can use Q objects.
A Q object (django.db.models.Q) is an object used to encapsulate a
collection of keyword arguments. These keyword arguments are specified as in
the "Field Lookups" section.
For example, this Q object encapsulates a single LIKE query:
Q(question__startswith='What')
Q objects can be combined using the & and | operators. When an
operator is used on two Q objects, it yields a new Q object. For example,
this statement yields a single Q object that represents the
OR of two "question__startswith" queries:
Q(question__startswith='Who') | Q(question__startswith='What')
This is equivalent to the following SQL WHERE clause:
WHERE question LIKE 'Who%' OR question LIKE 'What%'
You can compose statements of arbitrary complexity by combining Q objects
with the & and | operators. You can also use parenthetical grouping.
Each lookup function that takes keyword arguments (e.g., filter(),
exclude(), get()) can also be passed one or more Q objects as
positional (not-named) arguments. If you provide multiple Q object
arguments to a lookup function, the arguments will be ANDed together, for
example:
Poll.objects.get(
Q(question__startswith='Who'),
Q(pub_date=date(2005, 5, 2)) | Q(pub_date=date(2005, 5, 6))
)
roughly translates into the following SQL:
SELECT * from polls WHERE question LIKE 'Who%'
AND (pub_date = '2005-05-02' OR pub_date = '2005-05-06')
Lookup functions can mix the use of Q objects and keyword arguments. All
arguments provided to a lookup function (be they keyword arguments or Q
objects) are ANDed together. However, if a Q object is provided, it must
precede the definition of any keyword arguments. For example, the following:
Poll.objects.get(
Q(pub_date=date(2005, 5, 2)) | Q(pub_date=date(2005, 5, 6)),
question__startswith='Who')
would be a valid query, equivalent to the previous example, but this:
# INVALID QUERY
Poll.objects.get(
question__startswith='Who',
Q(pub_date=date(2005, 5, 2)) | Q(pub_date=date(2005, 5, 6)))
would not be valid.
You can find some examples online at http://www.djangoproject.com/documentation/models/or_lookups/.
When you define a relationship in a model (i.e., a ForeignKey,
OneToOneField, or ManyToManyField), instances of that model will have
a convenient API to access the related object(s).
For example, an Entry object e can get its associated Blog object by
accessing the blog attribute e.blog.
Django also creates API accessors for the "other" side of the relationship --
the link from the related model to the model that defines the relationship.
For example, a Blog object b has access to a list of all related
Entry objects via the entry_set attribute: b.entry_set.all().
All examples in this section use the sample Blog, Author, and Entry
models defined at the start of the appendix.
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, just 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__exact='Beatles Blog')
This spanning can be as deep as you'd like.
It works backward, too. To refer to a "reverse" relationship, just use the lowercase name of the model.
This example retrieves all Blog objects that have at least one Entry
whose headline contains 'Lennon':
>>> Blog.objects.filter(entry__headline__contains='Lennon')
If a model has a ForeignKey, instances of that model will have access to
the related (foreign) object via a simple attribute of the model, for example:
e = Entry.objects.get(id=2) e.blog # Returns the related Blog object.
You can get and set via a foreign key attribute. As you may expect, changes to
the foreign key aren't saved to the database until you call save(), for example:
e = Entry.objects.get(id=2) e.blog = some_blog e.save()
If a ForeignKey field has null=True set (i.e., it allows NULL
values), you can set it to NULL by assigning None to it and saving:
e = Entry.objects.get(id=2) e.blog = None e.save() # "UPDATE blog_entry SET blog_id = NULL ...;"
Forward access to one-to-many relationships is cached the first time the related object is accessed. Subsequent accesses to the foreign key on the same object instance are cached, for example:
e = Entry.objects.get(id=2) print e.blog # Hits the database to retrieve the associated Blog. print e.blog # Doesn't hit the database; uses cached version.
Note that the select_related() QuerySet method recursively prepopulates
the cache of all one-to-many relationships ahead of time:
e = Entry.objects.select_related().get(id=2) print e.blog # Doesn't hit the database; uses cached version. print e.blog # Doesn't hit the database; uses cached version.
select_related() is documented in the "QuerySet Methods That Return New
QuerySets" section.
Foreign key relationships are automatically symmetrical -- a reverse
relationship is inferred from the presence of a ForeignKey pointing to
another model.
If a model has a ForeignKey, instances of the foreign key model will have
access to a Manager that returns all instances of the first model that
relate to that object. By default, this Manager is named FOO_set, where
FOO is the source model name, lowercased. This Manager returns
QuerySets, which can be filtered and manipulated as described in the
"Retrieving Objects" section.
Here's an example:
b = Blog.objects.get(id=1) b.entry_set.all() # Returns all Entry objects related to Blog. # b.entry_set is a Manager that returns QuerySets. b.entry_set.filter(headline__contains='Lennon') b.entry_set.count()
You can override the FOO_set name by setting the related_name
parameter in the ForeignKey() definition. For example, if the Entry
model was altered to blog = ForeignKey(Blog, related_name='entries'), the
preceding example code would look like this:
b = Blog.objects.get(id=1) b.entries.all() # Returns all Entry objects related to Blog. # b.entries is a Manager that returns QuerySets. b.entries.filter(headline__contains='Lennon') b.entries.count()
related_name is particularly useful if a model has two foreign keys to the
same second model.
You cannot access a reverse ForeignKey Manager from the class; it must
be accessed from an instance:
Blog.entry_set # Raises AttributeError: "Manager must be accessed via instance".
In addition to the QuerySet methods defined in the "Retrieving Objects" section,
the ForeignKey Manager has these additional methods:
add(obj1, obj2, ...): Adds the specified model objects to the related object set, for example:b = Blog.objects.get(id=1) e = Entry.objects.get(id=234) b.entry_set.add(e) # Associates Entry e with Blog b.
create(**kwargs): Creates a new object, saves it, and puts it in the related object set. It returns the newly created object:b = Blog.objects.get(id=1) e = b.entry_set.create(headline='Hello', body_text='Hi', pub_date=datetime.date(2005, 1, 1)) # No need to call e.save() at this point -- it's already been saved.
This is equivalent to (but much simpler than) the following:
b = Blog.objects.get(id=1) e = Entry(blog=b, headline='Hello', body_text='Hi', pub_date=datetime.date(2005, 1, 1)) e.save()
Note that there's no need to specify the keyword argument of the model that defines the relationship. In the preceding example, we don't pass the parameter
blogtocreate(). Django figures out that the newEntryobject'sblogfield should be set tob.remove(obj1, obj2, ...): Removes the specified model objects from the related object set:b = Blog.objects.get(id=1) e = Entry.objects.get(id=234) b.entry_set.remove(e) # Disassociates Entry e from Blog b.
In order to prevent database inconsistency, this method only exists on
ForeignKeyobjects wherenull=True. If the related field can't be set toNone(NULL), then an object can't be removed from a relation without being added to another. In the preceding example, removingefromb.entry_set()is equivalent to doinge.blog = None, and because theblogForeignKeydoesn't havenull=True, this is invalid.clear(): Removes all objects from the related object set:b = Blog.objects.get(id=1) b.entry_set.clear()
Note this doesn't delete the related objects -- it just disassociates them.
Just like
remove(),clear()is only available onForeignKey``s where ``null=True.
To assign the members of a related set in one fell swoop, just assign to it from any iterable object, for example:
b = Blog.objects.get(id=1) b.entry_set = [e1, e2]
If the clear() method is available, any pre-existing objects will be
removed from the entry_set before all objects in the iterable (in this
case, a list) are added to the set. If the clear() method is not
available, all objects in the iterable will be added without removing any
existing elements.
Each "reverse" operation described in this section has an immediate effect on the database. Every addition, creation, and deletion is immediately and automatically saved to the database.
Both ends of a many-to-many relationship get automatic API access to the other end. The API works just as a "reverse" one-to-many relationship (described in the previous section).
The only difference is in the attribute naming: the model that defines the
ManyToManyField uses the attribute name of that field itself, whereas the
"reverse" model uses the lowercased model name of the original model, plus
'_set' (just like reverse one-to-many relationships).
An example makes this concept easier to understand:
e = Entry.objects.get(id=3) e.authors.all() # Returns all Author objects for this Entry. e.authors.count() e.authors.filter(name__contains='John') a = Author.objects.get(id=5) a.entry_set.all() # Returns all Entry objects for this Author.
Like ForeignKey, ManyToManyField can specify related_name. In the
preceding example, if the ManyToManyField in Entry had specified
related_name='entries', then each Author instance would have an
entries attribute instead of entry_set.
How Are the Backward Relationships Possible?
Other object-relational mappers require you to define relationships on both sides. The Django developers believe this is a violation of the DRY (Don't Repeat Yourself) principle, so Django requires you to define the relationship on only one end. But how is this possible, given that a model class doesn't know which other model classes are related to it until those other model classes are loaded?
The answer lies in the INSTALLED_APPS setting. The first time any model
is loaded, Django iterates over every model in INSTALLED_APPS and
creates the backward relationships in memory as needed. Essentially, one of
the functions of INSTALLED_APPS is to tell Django the entire model
domain.
Queries involving related objects follow the same rules as queries involving normal value fields. When specifying the value for a query to match, you may use either an object instance itself or the primary key value for the object.
For example, if you have a Blog object b with id=5, the following
three queries would be identical:
Entry.objects.filter(blog=b) # Query using object instance Entry.objects.filter(blog=b.id) # Query using id from instance Entry.objects.filter(blog=5) # Query using id directly
The delete method, conveniently, is named delete(). This method immediately
deletes the object and has no return value:
e.delete()
You can also delete objects in bulk. Every QuerySet has a delete()
method, which deletes all members of that QuerySet. For example, this
deletes all Entry objects with a pub_date year of 2005:
Entry.objects.filter(pub_date__year=2005).delete()
When Django deletes an object, it emulates the behavior of the SQL
constraint ON DELETE CASCADE -- in other words, any objects that
had foreign keys pointing at the object to be deleted will be deleted
along with it, for example:
b = Blog.objects.get(pk=1) # This will delete the Blog and all of its Entry objects. b.delete()
Note that delete() is the only QuerySet method that is not exposed on a
Manager itself. This is a safety mechanism to prevent you from accidentally
requesting Entry.objects.delete() and deleting all the entries. If you
do want to delete all the objects, then you have to explicitly request a
complete query set:
Entry.objects.all().delete()
As you develop views, you will discover a number of common idioms in the
way you use the database API. Django encodes some of these idioms as
shortcuts that can be used to simplify the process of writing views. These
functions are in the django.shortcuts module.
One common idiom to use get() and raise Http404 if the
object doesn't exist. This idiom is captured by get_object_or_404().
This function takes a Django model as its first argument and an
arbitrary number of keyword arguments, which it passes to the default
manager's get() function. It raises Http404 if the object doesn't
exist, for example:
# Get the Entry with a primary key of 3 e = get_object_or_404(Entry, pk=3)
When you provide a model to this shortcut function, the default manager
is used to execute the underlying get() query. If you don't want to
use the default manager, or if you want to search a list of related objects,
you can provide get_object_or_404() with a Manager object instead:
# Get the author of blog instance e with a name of 'Fred' a = get_object_or_404(e.authors, name='Fred') # Use a custom manager 'recent_entries' in the search for an # entry with a primary key of 3 e = get_object_or_404(Entry.recent_entries, pk=3)
get_list_or_404 behaves the same way as get_object_or_404(),
except that it uses filter() instead of get(). It raises
Http404 if the list is empty.
If you find yourself needing to write an SQL query that is too complex for Django's database mapper to handle, you can fall back into raw SQL statement mode.
The preferred way to do this is by giving your model custom methods or custom manager methods that execute queries. Although there's nothing in Django that requires database queries to live in the model layer, this approach keeps all your data access logic in one place, which is smart from a code organization standpoint. For instructions, see Appendix A.
Finally, it's important to note that the Django database layer is merely an interface to your database. You can access your database via other tools, programming languages, or database frameworks -- there's nothing Django-specific about your database.