QuerySet API reference

This document describes the details of the QuerySet API. It builds on thematerial presented in the model and databasequery guides, so you'll probably want to read andunderstand those documents before reading this one.

Throughout this reference we'll use the example Weblog models presented in the database query guide.

When QuerySets are evaluated

Internally, a QuerySet can be constructed, filtered, sliced, and generallypassed around without actually hitting the database. No database activityactually occurs until you do something to evaluate the queryset.

You can evaluate a QuerySet in the following ways:

• Iteration. A QuerySet is iterable, and it executes its databasequery the first time you iterate over it. For example, this will printthe headline of all entries in the database:

for e in Entry.objects.all():
    print(e.headline)

Note: Don't use this if all you want to do is determine if at least oneresult exists. It's more efficient to use exists().

• Slicing. As explained in Limiting QuerySets, a QuerySet canbe sliced, using Python's array-slicing syntax. Slicing an unevaluatedQuerySet usually returns another unevaluated QuerySet, but Djangowill execute the database query if you use the "step" parameter of slicesyntax, and will return a list. Slicing a QuerySet that has beenevaluated also returns a list.

Also note that even though slicing an unevaluated QuerySet returnsanother unevaluated QuerySet, modifying it further (e.g., addingmore filters, or modifying ordering) is not allowed, since that does nottranslate well into SQL and it would not have a clear meaning either.

• Pickling/Caching. See the following section for details of whatis involved when pickling QuerySets. The important thing for thepurposes of this section is that the results are read from the database.

• repr(). A QuerySet is evaluated when you call repr() on it.This is for convenience in the Python interactive interpreter, so you canimmediately see your results when using the API interactively.

• len(). A QuerySet is evaluated when you call len() on it.This, as you might expect, returns the length of the result list.

Note: If you only need to determine the number of records in the set (anddon't need the actual objects), it's much more efficient to handle a countat the database level using SQL's SELECT COUNT(*). Django provides acount() method for precisely this reason.

• list(). Force evaluation of a QuerySet by calling list() onit. For example:

entry_list = list(Entry.objects.all())

• bool(). Testing a QuerySet in a boolean context, such as usingbool(), or, and or an if statement, will cause the queryto be executed. If there is at least one result, the QuerySet isTrue, otherwise False. For example:

if Entry.objects.filter(headline="Test"):
   print("There is at least one Entry with the headline Test")

Note: If you only want to determine if at least one result exists (and don'tneed the actual objects), it's more efficient to use exists().

Pickling QuerySets

If you pickle a QuerySet, this will force all the results to be loadedinto memory prior to pickling. Pickling is usually used as a precursor tocaching and when the cached queryset is reloaded, you want the results toalready be present and ready for use (reading from the database can take sometime, defeating the purpose of caching). This means that when you unpickle aQuerySet, it contains the results at the moment it was pickled, ratherthan the results that are currently in the database.

If you only want to pickle the necessary information to recreate theQuerySet from the database at a later time, pickle the query attributeof the QuerySet. You can then recreate the original QuerySet (withoutany results loaded) using some code like this:

>>> import pickle
>>> query = pickle.loads(s)     # Assuming 's' is the pickled string.
>>> qs = MyModel.objects.all()
>>> qs.query = query            # Restore the original 'query'.

The query attribute is an opaque object. It represents the internals ofthe query construction and is not part of the public API. However, it is safe(and fully supported) to pickle and unpickle the attribute's contents asdescribed here.

You can't share pickles between versions

Pickles of QuerySets are only valid for the version of Django thatwas used to generate them. If you generate a pickle using Djangoversion N, there is no guarantee that pickle will be readable withDjango version N+1. Pickles should not be used as part of a long-termarchival strategy.

Since pickle compatibility errors can be difficult to diagnose, such assilently corrupted objects, a RuntimeWarning is raised when you try tounpickle a queryset in a Django version that is different than the one inwhich it was pickled.

QuerySet API

Here's the formal declaration of a QuerySet:

• class QuerySet(model=None, query=None, using=None)[源代码]
• Usually when you'll interact with a QuerySet you'll use it bychaining filters. To make this work, mostQuerySet methods return new querysets. These methods are covered indetail later in this section.

The QuerySet class has two public attributes you can use forintrospection:

• ordered

• True if the QuerySet is ordered — i.e. has anorder_by() clause or a default ordering on the model.False otherwise.

• db

• The database that will be used if this query is executed now.

注解

The query parameter to QuerySet exists so that specializedquery subclasses can reconstruct internal query state. The value of theparameter is an opaque representation of that query state and is notpart of a public API. To put it simply: if you need to ask, you don'tneed to use it.

Methods that return new QuerySets

Django provides a range of QuerySet refinement methods that modify eitherthe types of results returned by the QuerySet or the way its SQL query isexecuted.

filter()

• filter(**kwargs)
• Returns a new QuerySet containing objects that match the given lookupparameters.

The lookup parameters (**kwargs) should be in the format described inField lookups below. Multiple parameters are joined via AND in theunderlying SQL statement.

If you need to execute more complex queries (for example, queries with OR statements),you can use Q objects.

exclude()

• exclude(**kwargs)
• Returns a new QuerySet containing objects that do not match the givenlookup parameters.

The lookup parameters (**kwargs) should be in the format described inField lookups below. Multiple parameters are joined via AND in theunderlying SQL statement, and the whole thing is enclosed in a NOT().

This example excludes all entries whose pub_date is later than 2005-1-3AND whose headline is "Hello":

Entry.objects.exclude(pub_date__gt=datetime.date(2005, 1, 3), headline='Hello')

In SQL terms, that evaluates to:

SELECT ...
WHERE NOT (pub_date > '2005-1-3' AND headline = 'Hello')

This example excludes all entries whose pub_date is later than 2005-1-3OR whose headline is "Hello":

Entry.objects.exclude(pub_date__gt=datetime.date(2005, 1, 3)).exclude(headline='Hello')

In SQL terms, that evaluates to:

SELECT ...
WHERE NOT pub_date > '2005-1-3'
AND NOT headline = 'Hello'

Note the second example is more restrictive.

If you need to execute more complex queries (for example, queries with OR statements),you can use Q objects.

annotate()

• annotate(*args, **kwargs)
• Annotates each object in the QuerySet with the provided list of queryexpressions. An expression may be a simple value, areference to a field on the model (or any related models), or an aggregateexpression (averages, sums, etc.) that has been computed over the objects thatare related to the objects in the QuerySet.

Each argument to annotate() is an annotation that will be addedto each object in the QuerySet that is returned.

The aggregation functions that are provided by Django are describedin Aggregation Functions below.

Annotations specified using keyword arguments will use the keyword asthe alias for the annotation. Anonymous arguments will have an aliasgenerated for them based upon the name of the aggregate function andthe model field that is being aggregated. Only aggregate expressionsthat reference a single field can be anonymous arguments. Everythingelse must be a keyword argument.

For example, if you were manipulating a list of blogs, you may wantto determine how many entries have been made in each blog:

>>> from django.db.models import Count
>>> q = Blog.objects.annotate(Count('entry'))
# The name of the first blog
>>> q[0].name
'Blogasaurus'
# The number of entries on the first blog
>>> q[0].entry__count
42

The Blog model doesn't define an entry__count attribute by itself,but by using a keyword argument to specify the aggregate function, you cancontrol the name of the annotation:

>>> q = Blog.objects.annotate(number_of_entries=Count('entry'))
# The number of entries on the first blog, using the name provided
>>> q[0].number_of_entries
42

For an in-depth discussion of aggregation, see the topic guide onAggregation.

order_by()

• orderby(*fields_)
• By default, results returned by a QuerySet are ordered by the orderingtuple given by the ordering option in the model's Meta. You canoverride this on a per-QuerySet basis by using the order_by method.

Example:

Entry.objects.filter(pub_date__year=2005).order_by('-pub_date', 'headline')

The result above will be ordered by pubdate descending, then byheadline ascending. The negative sign in front of "-pub_date" indicates_descending order. Ascending order is implied. To order randomly, use "?",like so:

Entry.objects.order_by('?')

Note: order_by('?') queries may be expensive and slow, depending on thedatabase backend you're using.

To order by a field in a different model, use the same syntax as when you arequerying across model relations. That is, the name of the field, followed by adouble underscore (__), followed by the name of the field in the new model,and so on for as many models as you want to join. For example:

Entry.objects.order_by('blog__name', 'headline')

If you try to order by a field that is a relation to another model, Django willuse the default ordering on the related model, or order by the related model'sprimary key if there is no Meta.ordering specified. For example, since the Blogmodel has no default ordering specified:

Entry.objects.order_by('blog')

…is identical to:

Entry.objects.order_by('blog__id')

If Blog had ordering = ['name'], then the first queryset would beidentical to:

Entry.objects.order_by('blog__name')

You can also order by query expressions bycalling asc() or desc() on theexpression:

Entry.objects.order_by(Coalesce('summary', 'headline').desc())

asc() and desc() have arguments(nulls_first and nulls_last) that control how null values are sorted.

Be cautious when ordering by fields in related models if you are also usingdistinct(). See the note in distinct() for an explanation of howrelated model ordering can change the expected results.

注解

It is permissible to specify a multi-valued field to order the results by(for example, a ManyToManyField field, or thereverse relation of a ForeignKey field).

Consider this case:

class Event(Model):
   parent = models.ForeignKey(
       'self',
       on_delete=models.CASCADE,
       related_name='children',
   )
   date = models.DateField()
 
Event.objects.order_by('children__date')

Here, there could potentially be multiple ordering data for each Event;each Event with multiple children will be returned multiple timesinto the new QuerySet that order_by() creates. In other words,using order_by() on the QuerySet could return more items than youwere working on to begin with - which is probably neither expected noruseful.

Thus, take care when using multi-valued field to order the results. Ifyou can be sure that there will only be one ordering piece of data for eachof the items you're ordering, this approach should not present problems. Ifnot, make sure the results are what you expect.

There's no way to specify whether ordering should be case sensitive. Withrespect to case-sensitivity, Django will order results however your databasebackend normally orders them.

You can order by a field converted to lowercase withLower which will achieve case-consistentordering:

Entry.objects.order_by(Lower('headline').desc())

If you don't want any ordering to be applied to a query, not even the defaultordering, call order_by() with no parameters.

You can tell if a query is ordered or not by checking theQuerySet.ordered attribute, which will be True if theQuerySet has been ordered in any way.

Each order_by() call will clear any previous ordering. For example, thisquery will be ordered by pub_date and not headline:

Entry.objects.order_by('headline').order_by('pub_date')

警告

Ordering is not a free operation. Each field you add to the orderingincurs a cost to your database. Each foreign key you add willimplicitly include all of its default orderings as well.

If a query doesn't have an ordering specified, results are returned fromthe database in an unspecified order. A particular ordering is guaranteedonly when ordering by a set of fields that uniquely identify each object inthe results. For example, if a name field isn't unique, ordering by itwon't guarantee objects with the same name always appear in the same order.

reverse()

• reverse()
• Use the reverse() method to reverse the order in which a queryset'selements are returned. Calling reverse() a second time restores theordering back to the normal direction.

To retrieve the "last" five items in a queryset, you could do this:

my_queryset.reverse()[:5]

Note that this is not quite the same as slicing from the end of a sequence inPython. The above example will return the last item first, then thepenultimate item and so on. If we had a Python sequence and looked atseq[-5:], we would see the fifth-last item first. Django doesn't supportthat mode of access (slicing from the end), because it's not possible to do itefficiently in SQL.

Also, note that reverse() should generally only be called on a QuerySetwhich has a defined ordering (e.g., when querying against a model which definesa default ordering, or when using order_by()). If no such ordering isdefined for a given QuerySet, calling reverse() on it has no realeffect (the ordering was undefined prior to calling reverse(), and willremain undefined afterward).

distinct()

• distinct(*fields)
• Returns a new QuerySet that uses SELECT DISTINCT in its SQL query. Thiseliminates duplicate rows from the query results.

By default, a QuerySet will not eliminate duplicate rows. In practice, thisis rarely a problem, because simple queries such as Blog.objects.all()don't introduce the possibility of duplicate result rows. However, if yourquery spans multiple tables, it's possible to get duplicate results when aQuerySet is evaluated. That's when you'd use distinct().

注解

Any fields used in an order_by() call are included in the SQLSELECT columns. This can sometimes lead to unexpected results when usedin conjunction with distinct(). If you order by fields from a relatedmodel, those fields will be added to the selected columns and they may makeotherwise duplicate rows appear to be distinct. Since the extra columnsdon't appear in the returned results (they are only there to supportordering), it sometimes looks like non-distinct results are being returned.

Similarly, if you use a values() query to restrict the columnsselected, the columns used in any order_by() (or default modelordering) will still be involved and may affect uniqueness of the results.

The moral here is that if you are using distinct() be careful aboutordering by related models. Similarly, when using distinct() andvalues() together, be careful when ordering by fields not in thevalues() call.

On PostgreSQL only, you can pass positional arguments (*fields) in order tospecify the names of fields to which the DISTINCT should apply. Thistranslates to a SELECT DISTINCT ON SQL query. Here's the difference. For anormal distinct() call, the database compares each field in each row whendetermining which rows are distinct. For a distinct() call with specifiedfield names, the database will only compare the specified field names.

注解

When you specify field names, you must provide an order_by() in theQuerySet, and the fields in order_by() must start with the fields indistinct(), in the same order.

For example, SELECT DISTINCT ON (a) gives you the first row for eachvalue in column a. If you don't specify an order, you'll get somearbitrary row.

Examples (those after the first will only work on PostgreSQL):

>>> Author.objects.distinct()
[...]
 
>>> Entry.objects.order_by('pub_date').distinct('pub_date')
[...]
 
>>> Entry.objects.order_by('blog').distinct('blog')
[...]
 
>>> Entry.objects.order_by('author', 'pub_date').distinct('author', 'pub_date')
[...]
 
>>> Entry.objects.order_by('blog__name', 'mod_date').distinct('blog__name', 'mod_date')
[...]
 
>>> Entry.objects.order_by('author', 'pub_date').distinct('author')
[...]

注解

Keep in mind that order_by() uses any default related model orderingthat has been defined. You might have to explicitly order by the relation_id or referenced field to make sure the DISTINCT ON expressionsmatch those at the beginning of the ORDER BY clause. For example, ifthe Blog model defined an ordering byname:

Entry.objects.order_by('blog').distinct('blog')

…wouldn't work because the query would be ordered by blogname thusmismatching the DISTINCT ON expression. You'd have to explicitly orderby the relation id_ field (blog_id in this case) or the referencedone (blog__pk) to make sure both expressions match.

values()

• values(*fields, **expressions)
• Returns a QuerySet that returns dictionaries, rather than model instances,when used as an iterable.

Each of those dictionaries represents an object, with the keys corresponding tothe attribute names of model objects.

This example compares the dictionaries of values() with the normal modelobjects:

# This list contains a Blog object.
>>> Blog.objects.filter(name__startswith='Beatles')
<QuerySet [<Blog: Beatles Blog>]>
 
# This list contains a dictionary.
>>> Blog.objects.filter(name__startswith='Beatles').values()
<QuerySet [{'id': 1, 'name': 'Beatles Blog', 'tagline': 'All the latest Beatles news.'}]>

The values() method takes optional positional arguments, *fields, whichspecify field names to which the SELECT should be limited. If you specifythe fields, each dictionary will contain only the field keys/values for thefields you specify. If you don't specify the fields, each dictionary willcontain a key and value for every field in the database table.

Example:

>>> Blog.objects.values()
<QuerySet [{'id': 1, 'name': 'Beatles Blog', 'tagline': 'All the latest Beatles news.'}]>
>>> Blog.objects.values('id', 'name')
<QuerySet [{'id': 1, 'name': 'Beatles Blog'}]>

The values() method also takes optional keyword arguments,**expressions, which are passed through to annotate():

>>> from django.db.models.functions import Lower
>>> Blog.objects.values(lower_name=Lower('name'))
<QuerySet [{'lower_name': 'beatles blog'}]>

You can use built-in and custom lookups inordering. For example:

>>> from django.db.models import CharField
>>> from django.db.models.functions import Lower
>>> CharField.register_lookup(Lower)
>>> Blog.objects.values('name__lower')
<QuerySet [{'name__lower': 'beatles blog'}]>

Changed in Django 2.1:
Support for lookups was added.

An aggregate within a values() clause is applied before other argumentswithin the same values() clause. If you need to group by another value,add it to an earlier values() clause instead. For example:

>>> from django.db.models import Count
>>> Blog.objects.values('entry__authors', entries=Count('entry'))
<QuerySet [{'entry__authors': 1, 'entries': 20}, {'entry__authors': 1, 'entries': 13}]>
>>> Blog.objects.values('entry__authors').annotate(entries=Count('entry'))
<QuerySet [{'entry__authors': 1, 'entries': 33}]>

A few subtleties that are worth mentioning:

• If you have a field called foo that is aForeignKey, the default values() callwill return a dictionary key called foo_id, since this is the nameof the hidden model attribute that stores the actual value (the fooattribute refers to the related model). When you are callingvalues() and passing in field names, you can pass in either fooor foo_id and you will get back the same thing (the dictionary keywill match the field name you passed in).

For example:

>>> Entry.objects.values()
<QuerySet [{'blog_id': 1, 'headline': 'First Entry', ...}, ...]>
 
>>> Entry.objects.values('blog')
<QuerySet [{'blog': 1}, ...]>
 
>>> Entry.objects.values('blog_id')
<QuerySet [{'blog_id': 1}, ...]>

• When using values() together with distinct(), be aware thatordering can affect the results. See the note in distinct() fordetails.

• If you use a values() clause after an extra() call,any fields defined by a select argument in the extra() mustbe explicitly included in the values() call. Any extra() callmade after a values() call will have its extra selected fieldsignored.

• Calling only() and defer() after values() doesn't makesense, so doing so will raise a NotImplementedError.

• Combining transforms and aggregates requires the use of two annotate()calls, either explicitly or as keyword arguments to values(). As above,if the transform has been registered on the relevant field type the firstannotate() can be omitted, thus the following examples are equivalent:

>>> from django.db.models import CharField, Count
>>> from django.db.models.functions import Lower
>>> CharField.register_lookup(Lower)
>>> Blog.objects.values('entry__authors__name__lower').annotate(entries=Count('entry'))
<QuerySet [{'entry__authors__name__lower': 'test author', 'entries': 33}]>
>>> Blog.objects.values(
...     entry__authors__name__lower=Lower('entry__authors__name')
... ).annotate(entries=Count('entry'))
<QuerySet [{'entry__authors__name__lower': 'test author', 'entries': 33}]>
>>> Blog.objects.annotate(
...     entry__authors__name__lower=Lower('entry__authors__name')
... ).values('entry__authors__name__lower').annotate(entries=Count('entry'))
<QuerySet [{'entry__authors__name__lower': 'test author', 'entries': 33}]>

It is useful when you know you're only going to need values from a small numberof the available fields and you won't need the functionality of a modelinstance object. It's more efficient to select only the fields you need to use.

Finally, note that you can call filter(), order_by(), etc. after thevalues() call, that means that these two calls are identical:

Blog.objects.values().order_by('id')
Blog.objects.order_by('id').values()

The people who made Django prefer to put all the SQL-affecting methods first,followed (optionally) by any output-affecting methods (such as values()),but it doesn't really matter. This is your chance to really flaunt yourindividualism.

You can also refer to fields on related models with reverse relations throughOneToOneField, ForeignKey and ManyToManyField attributes:

>>> Blog.objects.values('name', 'entry__headline')
<QuerySet [{'name': 'My blog', 'entry__headline': 'An entry'},
     {'name': 'My blog', 'entry__headline': 'Another entry'}, ...]>

警告

Because ManyToManyField attributes and reverserelations can have multiple related rows, including these can have amultiplier effect on the size of your result set. This will be especiallypronounced if you include multiple such fields in your values() query,in which case all possible combinations will be returned.

values_list()

• valueslist(*fields, _flat=False, named=False)
• This is similar to values() except that instead of returning dictionaries,it returns tuples when iterated over. Each tuple contains the value from therespective field or expression passed into the values_list() call — so thefirst item is the first field, etc. For example:

>>> Entry.objects.values_list('id', 'headline')
<QuerySet [(1, 'First entry'), ...]>
>>> from django.db.models.functions import Lower
>>> Entry.objects.values_list('id', Lower('headline'))
<QuerySet [(1, 'first entry'), ...]>

If you only pass in a single field, you can also pass in the flatparameter. If True, this will mean the returned results are single values,rather than one-tuples. An example should make the difference clearer:

>>> Entry.objects.values_list('id').order_by('id')
<QuerySet[(1,), (2,), (3,), ...]>
 
>>> Entry.objects.values_list('id', flat=True).order_by('id')
<QuerySet [1, 2, 3, ...]>

It is an error to pass in flat when there is more than one field.

You can pass named=True to get results as anamedtuple():

>>> Entry.objects.values_list('id', 'headline', named=True)
<QuerySet [Row(id=1, headline='First entry'), ...]>

Using a named tuple may make use of the results more readable, at the expenseof a small performance penalty for transforming the results into a named tuple.

If you don't pass any values to values_list(), it will return all thefields in the model, in the order they were declared.

A common need is to get a specific field value of a certain model instance. Toachieve that, use values_list() followed by a get() call:

>>> Entry.objects.values_list('headline', flat=True).get(pk=1)
'First entry'

values() and values_list() are both intended as optimizations for aspecific use case: retrieving a subset of data without the overhead of creatinga model instance. This metaphor falls apart when dealing with many-to-many andother multivalued relations (such as the one-to-many relation of a reverseforeign key) because the "one row, one object" assumption doesn't hold.

For example, notice the behavior when querying across aManyToManyField:

>>> Author.objects.values_list('name', 'entry__headline')
<QuerySet [('Noam Chomsky', 'Impressions of Gaza'),
 ('George Orwell', 'Why Socialists Do Not Believe in Fun'),
 ('George Orwell', 'In Defence of English Cooking'),
 ('Don Quixote', None)]>

Authors with multiple entries appear multiple times and authors without anyentries have None for the entry headline.

Similarly, when querying a reverse foreign key, None appears for entriesnot having any author:

>>> Entry.objects.values_list('authors')
<QuerySet [('Noam Chomsky',), ('George Orwell',), (None,)]>

dates()

• dates(field, kind, order='ASC')
• Returns a QuerySet that evaluates to a list of datetime.dateobjects representing all available dates of a particular kind within thecontents of the QuerySet.

field should be the name of a DateField of your model.kind should be either "year", "month", "week", or "day".Each datetime.date object in the result list is "truncated" to thegiven type.

• "year" returns a list of all distinct year values for the field.
• "month" returns a list of all distinct year/month values for thefield.
• "week" returns a list of all distinct year/week values for the field. Alldates will be a Monday.
• "day" returns a list of all distinct year/month/day values for thefield.
  order, which defaults to 'ASC', should be either 'ASC' or'DESC'. This specifies how to order the results.

Examples:

>>> Entry.objects.dates('pub_date', 'year')
[datetime.date(2005, 1, 1)]
>>> Entry.objects.dates('pub_date', 'month')
[datetime.date(2005, 2, 1), datetime.date(2005, 3, 1)]
>>> Entry.objects.dates('pub_date', 'week')
[datetime.date(2005, 2, 14), datetime.date(2005, 3, 14)]
>>> Entry.objects.dates('pub_date', 'day')
[datetime.date(2005, 2, 20), datetime.date(2005, 3, 20)]
>>> Entry.objects.dates('pub_date', 'day', order='DESC')
[datetime.date(2005, 3, 20), datetime.date(2005, 2, 20)]
>>> Entry.objects.filter(headline__contains='Lennon').dates('pub_date', 'day')
[datetime.date(2005, 3, 20)]

Changed in Django 2.1:
"week" support was added.

datetimes()

• datetimes(field_name, kind, order='ASC', tzinfo=None)
• Returns a QuerySet that evaluates to a list of datetime.datetimeobjects representing all available dates of a particular kind within thecontents of the QuerySet.

field_name should be the name of a DateTimeField of your model.

kind should be either "year", "month", "week", "day","hour", "minute", or "second". Each datetime.datetimeobject in the result list is "truncated" to the given type.

order, which defaults to 'ASC', should be either 'ASC' or'DESC'. This specifies how to order the results.

tzinfo defines the time zone to which datetimes are converted prior totruncation. Indeed, a given datetime has different representations dependingon the time zone in use. This parameter must be a datetime.tzinfoobject. If it's None, Django uses the current time zone. It has no effect when USE_TZ isFalse.

Changed in Django 2.1:
"week" support was added.

注解

This function performs time zone conversions directly in the database.As a consequence, your database must be able to interpret the value oftzinfo.tzname(None). This translates into the following requirements:

• SQLite: no requirements. Conversions are performed in Python with pytz(installed when you install Django).
• PostgreSQL: no requirements (see Time Zones).
• Oracle: no requirements (see Choosing a Time Zone File).
• MySQL: load the time zone tables with mysql_tzinfo_to_sql.

none()

• none()
• Calling none() will create a queryset that never returns any objects and noquery will be executed when accessing the results. A qs.none() querysetis an instance of EmptyQuerySet.

Examples:

>>> Entry.objects.none()
<QuerySet []>
>>> from django.db.models.query import EmptyQuerySet
>>> isinstance(Entry.objects.none(), EmptyQuerySet)
True

all()

• all()
• Returns a copy of the current QuerySet (or QuerySet subclass). Thiscan be useful in situations where you might want to pass in either a modelmanager or a QuerySet and do further filtering on the result. After callingall() on either object, you'll definitely have a QuerySet to work with.

When a QuerySet is evaluated, ittypically caches its results. If the data in the database might have changedsince a QuerySet was evaluated, you can get updated results for the samequery by calling all() on a previously evaluated QuerySet.

union()

• union(*other_qs, all=False)
• Uses SQL's UNION operator to combine the results of two or moreQuerySets. For example:

>>> qs1.union(qs2, qs3)

The UNION operator selects only distinct values by default. To allowduplicate values, use the all=True argument.

union(), intersection(), and difference() return model instancesof the type of the first QuerySet even if the arguments are QuerySetsof other models. Passing different models works as long as the SELECT listis the same in all QuerySets (at least the types, the names don't matteras long as the types in the same order). In such cases, you must use the columnnames from the first QuerySet in QuerySet methods applied to theresulting QuerySet. For example:

>>> qs1 = Author.objects.values_list('name')
>>> qs2 = Entry.objects.values_list('headline')
>>> qs1.union(qs2).order_by('name')

In addition, only LIMIT, OFFSET, COUNT(*), ORDER BY, andspecifying columns (i.e. slicing, count(), order_by(), andvalues()/values_list()) are allowed on the resultingQuerySet. Further, databases place restrictions on what operations areallowed in the combined queries. For example, most databases don't allowLIMIT or OFFSET in the combined queries.

intersection()

• intersection(*other_qs)
• Uses SQL's INTERSECT operator to return the shared elements of two or moreQuerySets. For example:

>>> qs1.intersection(qs2, qs3)

See union() for some restrictions.

difference()

• difference(*other_qs)
• Uses SQL's EXCEPT operator to keep only elements present in theQuerySet but not in some other QuerySets. For example:

>>> qs1.difference(qs2, qs3)

See union() for some restrictions.

select_related()

• selectrelated(*fields_)
• Returns a QuerySet that will "follow" foreign-key relationships, selectingadditional related-object data when it executes its query. This is aperformance booster which results in a single more complex query but meanslater use of foreign-key relationships won't require database queries.

The following examples illustrate the difference between plain lookups andselect_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('blog').get(id=5)
 
# Doesn't hit the database, because e.blog has been prepopulated
# in the previous query.
b = e.blog

You can use select_related() with any queryset of objects:

from django.utils import timezone
 
# Find all the blogs with entries scheduled to be published in the future.
blogs = set()
 
for e in Entry.objects.filter(pub_date__gt=timezone.now()).select_related('blog'):
    # Without select_related(), this would make a database query for each
    # loop iteration in order to fetch the related blog for each entry.
    blogs.add(e.blog)

The order of filter() and select_related() chaining isn't important.These querysets are equivalent:

Entry.objects.filter(pub_date__gt=timezone.now()).select_related('blog')
Entry.objects.select_related('blog').filter(pub_date__gt=timezone.now())

You can follow foreign keys in a similar way to querying them. If you have thefollowing models:

from django.db import models
 
class City(models.Model):
    # ...
    pass
 
class Person(models.Model):
    # ...
    hometown = models.ForeignKey(
        City,
        on_delete=models.SET_NULL,
        blank=True,
        null=True,
    )
 
class Book(models.Model):
    # ...
    author = models.ForeignKey(Person, on_delete=models.CASCADE)

… then a call to Book.objects.selectrelated('author__hometown').get(id=4)will cache the related Person _and the related City:

# Hits the database with joins to the author and hometown tables.
b = Book.objects.select_related('author__hometown').get(id=4)
p = b.author         # Doesn't hit the database.
c = p.hometown       # Doesn't hit the database.
 
# Without select_related()...
b = Book.objects.get(id=4)  # Hits the database.
p = b.author         # Hits the database.
c = p.hometown       # Hits the database.

You can refer to any ForeignKey orOneToOneField relation in the list of fieldspassed to select_related().

You can also refer to the reverse direction of aOneToOneField in the list of fields passed toselect_related — that is, you can traverse aOneToOneField back to the object on which the fieldis defined. Instead of specifying the field name, use the related_name for the field on the related object.

There may be some situations where you wish to call select_related() with alot of related objects, or where you don't know all of the relations. In thesecases it is possible to call select_related() with no arguments. This willfollow all non-null foreign keys it can find - nullable foreign keys must bespecified. This is not recommended in most cases as it is likely to make theunderlying query more complex, and return more data, than is actually needed.

If you need to clear the list of related fields added by past calls ofselect_related on a QuerySet, you can pass None as a parameter:

>>> without_relations = queryset.select_related(None)

Chaining select_related calls works in a similar way to other methods -that is that select_related('foo', 'bar') is equivalent toselect_related('foo').select_related('bar').

prefetch_related()

• prefetchrelated(*lookups_)
• Returns a QuerySet that will automatically retrieve, in a single batch,related objects for each of the specified lookups.

This has a similar purpose to select_related, in that both are designed tostop the deluge of database queries that is caused by accessing related objects,but the strategy is quite different.

select_related works by creating an SQL join and including the fields of therelated object in the SELECT statement. For this reason, select_relatedgets the related objects in the same database query. However, to avoid the muchlarger result set that would result from joining across a 'many' relationship,select_related is limited to single-valued relationships - foreign key andone-to-one.

prefetch_related, on the other hand, does a separate lookup for eachrelationship, and does the 'joining' in Python. This allows it to prefetchmany-to-many and many-to-one objects, which cannot be done usingselect_related, in addition to the foreign key and one-to-one relationshipsthat are supported by select_related. It also supports prefetching ofGenericRelation andGenericForeignKey, however, itmust be restricted to a homogeneous set of results. For example, prefetchingobjects referenced by a GenericForeignKey is only supported if the queryis restricted to one ContentType.

For example, suppose you have these models:

from django.db import models
 
class Topping(models.Model):
    name = models.CharField(max_length=30)
 
class Pizza(models.Model):
    name = models.CharField(max_length=50)
    toppings = models.ManyToManyField(Topping)
 
    def __str__(self):
        return "%s (%s)" % (
            self.name,
            ", ".join(topping.name for topping in self.toppings.all()),
        )

and run:

>>> Pizza.objects.all()
["Hawaiian (ham, pineapple)", "Seafood (prawns, smoked salmon)"...

The problem with this is that every time Pizza.str() asks forself.toppings.all() it has to query the database, soPizza.objects.all() will run a query on the Toppings table for everyitem in the Pizza QuerySet.

We can reduce to just two queries using prefetch_related:

>>> Pizza.objects.all().prefetch_related('toppings')

This implies a self.toppings.all() for each Pizza; now each timeself.toppings.all() is called, instead of having to go to the database forthe items, it will find them in a prefetched QuerySet cache that waspopulated in a single query.

That is, all the relevant toppings will have been fetched in a single query,and used to make QuerySets that have a pre-filled cache of the relevantresults; these QuerySets are then used in the self.toppings.all() calls.

The additional queries in prefetch_related() are executed after theQuerySet has begun to be evaluated and the primary query has been executed.

If you have an iterable of model instances, you can prefetch related attributeson those instances using the prefetch_related_objects()function.

Note that the result cache of the primary QuerySet and all specified relatedobjects will then be fully loaded into memory. This changes the typicalbehavior of QuerySets, which normally try to avoid loading all objects intomemory before they are needed, even after a query has been executed in thedatabase.

注解

Remember that, as always with QuerySets, any subsequent chained methodswhich imply a different database query will ignore previously cachedresults, and retrieve data using a fresh database query. So, if you writethe following:

>>> pizzas = Pizza.objects.prefetch_related('toppings')
>>> [list(pizza.toppings.filter(spicy=True)) for pizza in pizzas]

…then the fact that pizza.toppings.all() has been prefetched will nothelp you. The prefetch_related('toppings') impliedpizza.toppings.all(), but pizza.toppings.filter() is a new anddifferent query. The prefetched cache can't help here; in fact it hurtsperformance, since you have done a database query that you haven't used. Souse this feature with caution!

Also, if you call the database-altering methodsadd(),remove(),clear() orset(), onrelated managers,any prefetched cache for the relation will be cleared.

You can also use the normal join syntax to do related fields of relatedfields. Suppose we have an additional model to the example above:

class Restaurant(models.Model):
    pizzas = models.ManyToManyField(Pizza, related_name='restaurants')
    best_pizza = models.ForeignKey(Pizza, related_name='championed_by', on_delete=models.CASCADE)

The following are all legal:

>>> Restaurant.objects.prefetch_related('pizzas__toppings')

This will prefetch all pizzas belonging to restaurants, and all toppingsbelonging to those pizzas. This will result in a total of 3 database queries -one for the restaurants, one for the pizzas, and one for the toppings.

>>> Restaurant.objects.prefetch_related('best_pizza__toppings')

This will fetch the best pizza and all the toppings for the best pizza for eachrestaurant. This will be done in 3 database queries - one for the restaurants,one for the 'best pizzas', and one for the toppings.

Of course, the best_pizza relationship could also be fetched usingselect_related to reduce the query count to 2:

>>> Restaurant.objects.select_related('best_pizza').prefetch_related('best_pizza__toppings')

Since the prefetch is executed after the main query (which includes the joinsneeded by select_related), it is able to detect that the best_pizzaobjects have already been fetched, and it will skip fetching them again.

Chaining prefetch_related calls will accumulate the lookups that areprefetched. To clear any prefetch_related behavior, pass None as aparameter:

>>> non_prefetched = qs.prefetch_related(None)

One difference to note when using prefetch_related is that objects createdby a query can be shared between the different objects that they are related toi.e. a single Python model instance can appear at more than one point in thetree of objects that are returned. This will normally happen with foreign keyrelationships. Typically this behavior will not be a problem, and will in factsave both memory and CPU time.

While prefetch_related supports prefetching GenericForeignKeyrelationships, the number of queries will depend on the data. Since aGenericForeignKey can reference data in multiple tables, one query per tablereferenced is needed, rather than one query for all the items. There could beadditional queries on the ContentType table if the relevant rows have notalready been fetched.

prefetch_related in most cases will be implemented using an SQL query thatuses the 'IN' operator. This means that for a large QuerySet a large 'IN' clausecould be generated, which, depending on the database, might have performanceproblems of its own when it comes to parsing or executing the SQL query. Alwaysprofile for your use case!

Note that if you use iterator() to run the query, prefetch_related()calls will be ignored since these two optimizations do not make sense together.

You can use the Prefetch object to further controlthe prefetch operation.

In its simplest form Prefetch is equivalent to the traditional string basedlookups:

>>> from django.db.models import Prefetch
>>> Restaurant.objects.prefetch_related(Prefetch('pizzas__toppings'))

You can provide a custom queryset with the optional queryset argument.This can be used to change the default ordering of the queryset:

>>> Restaurant.objects.prefetch_related(
...     Prefetch('pizzas__toppings', queryset=Toppings.objects.order_by('name')))

Or to call select_related() whenapplicable to reduce the number of queries even further:

>>> Pizza.objects.prefetch_related(
...     Prefetch('restaurants', queryset=Restaurant.objects.select_related('best_pizza')))

You can also assign the prefetched result to a custom attribute with the optionalto_attr argument. The result will be stored directly in a list.

This allows prefetching the same relation multiple times with a differentQuerySet; for instance:

>>> vegetarian_pizzas = Pizza.objects.filter(vegetarian=True)
>>> Restaurant.objects.prefetch_related(
...     Prefetch('pizzas', to_attr='menu'),
...     Prefetch('pizzas', queryset=vegetarian_pizzas, to_attr='vegetarian_menu'))

Lookups created with custom to_attr can still be traversed as usual by otherlookups:

>>> vegetarian_pizzas = Pizza.objects.filter(vegetarian=True)
>>> Restaurant.objects.prefetch_related(
...     Prefetch('pizzas', queryset=vegetarian_pizzas, to_attr='vegetarian_menu'),
...     'vegetarian_menu__toppings')

Using to_attr is recommended when filtering down the prefetch result as it isless ambiguous than storing a filtered result in the related manager's cache:

>>> queryset = Pizza.objects.filter(vegetarian=True)
>>>
>>> # Recommended:
>>> restaurants = Restaurant.objects.prefetch_related(
...     Prefetch('pizzas', queryset=queryset, to_attr='vegetarian_pizzas'))
>>> vegetarian_pizzas = restaurants[0].vegetarian_pizzas
>>>
>>> # Not recommended:
>>> restaurants = Restaurant.objects.prefetch_related(
...     Prefetch('pizzas', queryset=queryset))
>>> vegetarian_pizzas = restaurants[0].pizzas.all()

Custom prefetching also works with single related relations likeforward ForeignKey or OneToOneField. Generally you'll want to useselect_related() for these relations, but there are a number of caseswhere prefetching with a custom QuerySet is useful:

• You want to use a QuerySet that performs further prefetchingon related models.

• You want to prefetch only a subset of the related objects.

• You want to use performance optimization techniques likedeferred fields:

>>> queryset = Pizza.objects.only('name')
>>>
>>> restaurants = Restaurant.objects.prefetch_related(
...     Prefetch('best_pizza', queryset=queryset))

注解

The ordering of lookups matters.

Take the following examples:

>>> prefetch_related('pizzas__toppings', 'pizzas')

This works even though it's unordered because 'pizzas__toppings'already contains all the needed information, therefore the second argument'pizzas' is actually redundant.

>>> prefetch_related('pizzas__toppings', Prefetch('pizzas', queryset=Pizza.objects.all()))

This will raise a ValueError because of the attempt to redefine thequeryset of a previously seen lookup. Note that an implicit queryset wascreated to traverse 'pizzas' as part of the 'pizzas__toppings'lookup.

>>> prefetch_related('pizza_list__toppings', Prefetch('pizzas', to_attr='pizza_list'))

This will trigger an AttributeError because 'pizza_list' doesn't exist yetwhen 'pizza_list__toppings' is being processed.

This consideration is not limited to the use of Prefetch objects. Someadvanced techniques may require that the lookups be performed in aspecific order to avoid creating extra queries; therefore it's recommendedto always carefully order prefetch_related arguments.

extra()

• extra(select=None, where=None, params=None, tables=None, order_by=None, select_params=None)
• Sometimes, the Django query syntax by itself can't easily express a complexWHERE clause. For these edge cases, Django provides the extra()QuerySet modifier — a hook for injecting specific clauses into the SQLgenerated by a QuerySet.

Use this method as a last resort

This is an old API that we aim to deprecate at some point in the future.Use it only if you cannot express your query using other queryset methods.If you do need to use it, please file a ticket using the QuerySet.extrakeywordwith your use case (please check the list of existing tickets first) sothat we can enhance the QuerySet API to allow removing extra(). We areno longer improving or fixing bugs for this method.

For example, this use of extra():

>>> qs.extra(
...     select={'val': "select col from sometable where othercol = %s"},
...     select_params=(someparam,),
... )

is equivalent to:

>>> qs.annotate(val=RawSQL("select col from sometable where othercol = %s", (someparam,)))

The main benefit of using RawSQL isthat you can set output_field if needed. The main downside is that ifyou refer to some table alias of the queryset in the raw SQL, then it ispossible that Django might change that alias (for example, when thequeryset is used as a subquery in yet another query).

警告

You should be very careful whenever you use extra(). Every time you useit, you should escape any parameters that the user can control by usingparams in order to protect against SQL injection attacks.

You also must not quote placeholders in the SQL string. This example isvulnerable to SQL injection because of the quotes around %s:

"select col from sometable where othercol = '%s'"  # unsafe!

You can read more about how Django's SQL injection protection works.

By definition, these extra lookups may not be portable to different databaseengines (because you're explicitly writing SQL code) and violate the DRYprinciple, so you should avoid them if possible.

Specify one or more of params, select, where or tables. Noneof the arguments is required, but you should use at least one of them.

• select

The select argument lets you put extra fields in the SELECTclause. It should be a dictionary mapping attribute names to SQLclauses to use to calculate that attribute.

Example:

Entry.objects.extra(select={'is_recent': "pub_date > '2006-01-01'"})

As a result, each Entry object will have an extra attribute,is_recent, a boolean representing whether the entry's pub_dateis greater than Jan. 1, 2006.

Django inserts the given SQL snippet directly into the SELECTstatement, so the resulting SQL of the above example would be somethinglike:

SELECT blog_entry.*, (pub_date > '2006-01-01') AS is_recent
FROM blog_entry;

The next example is more advanced; it does a subquery to give eachresulting Blog object an entry_count attribute, an integer countof associated Entry objects:

Blog.objects.extra(
    select={
        'entry_count': 'SELECT COUNT(*) FROM blog_entry WHERE blog_entry.blog_id = blog_blog.id'
    },
)

In this particular case, we're exploiting the fact that the query willalready contain the blog_blog table in its FROM clause.

The resulting SQL of the above example would be:

SELECT blog_blog.*, (SELECT COUNT(*) FROM blog_entry WHERE blog_entry.blog_id = blog_blog.id) AS entry_count
FROM blog_blog;

Note that the parentheses required by most database engines aroundsubqueries are not required in Django's select clauses. Also notethat some database backends, such as some MySQL versions, don't supportsubqueries.

In some rare cases, you might wish to pass parameters to the SQLfragments in extra(select=…). For this purpose, use theselect_params parameter. Since select_params is a sequence andthe select attribute is a dictionary, some care is required so thatthe parameters are matched up correctly with the extra select pieces.In this situation, you should use a collections.OrderedDict forthe select value, not just a normal Python dictionary.

This will work, for example:

Blog.objects.extra(
    select=OrderedDict([('a', '%s'), ('b', '%s')]),
    select_params=('one', 'two'))

If you need to use a literal %s inside your select string, usethe sequence %%s.

• where / tables

You can define explicit SQL WHERE clauses — perhaps to performnon-explicit joins — by using where. You can manually add tables tothe SQL FROM clause by using tables.

where and tables both take a list of strings. All whereparameters are "AND"ed to any other search criteria.

Example:

Entry.objects.extra(where=["foo='a' OR bar = 'a'", "baz = 'a'"])

…translates (roughly) into the following SQL:

SELECT * FROM blog_entry WHERE (foo='a' OR bar='a') AND (baz='a')

Be careful when using the tables parameter if you're specifyingtables that are already used in the query. When you add extra tablesvia the tables parameter, Django assumes you want that tableincluded an extra time, if it is already included. That creates aproblem, since the table name will then be given an alias. If a tableappears multiple times in an SQL statement, the second and subsequentoccurrences must use aliases so the database can tell them apart. Ifyou're referring to the extra table you added in the extra whereparameter this is going to cause errors.

Normally you'll only be adding extra tables that don't already appearin the query. However, if the case outlined above does occur, there area few solutions. First, see if you can get by without including theextra table and use the one already in the query. If that isn'tpossible, put your extra() call at the front of the querysetconstruction so that your table is the first use of that table.Finally, if all else fails, look at the query produced and rewrite yourwhere addition to use the alias given to your extra table. Thealias will be the same each time you construct the queryset in the sameway, so you can rely upon the alias name to not change.

• order_by

If you need to order the resulting queryset using some of the newfields or tables you have included via extra() use the order_byparameter to extra() and pass in a sequence of strings. Thesestrings should either be model fields (as in the normalorder_by() method on querysets), of the formtable_name.column_name or an alias for a column that you specifiedin the select parameter to extra().

For example:

q = Entry.objects.extra(select={'is_recent': "pub_date > '2006-01-01'"})
q = q.extra(order_by = ['-is_recent'])

This would sort all the items for which is_recent is true to thefront of the result set (True sorts before False in adescending ordering).

This shows, by the way, that you can make multiple calls to extra()and it will behave as you expect (adding new constraints each time).

• params

The where parameter described above may use standard Pythondatabase string placeholders — '%s' to indicate parameters thedatabase engine should automatically quote. The params argument isa list of any extra parameters to be substituted.

Example:

Entry.objects.extra(where=['headline=%s'], params=['Lennon'])

Always use params instead of embedding values directly intowhere because params will ensure values are quoted correctlyaccording to your particular backend. For example, quotes will beescaped correctly.

Bad:

Entry.objects.extra(where=["headline='Lennon'"])

Good:

Entry.objects.extra(where=['headline=%s'], params=['Lennon'])

警告

If you are performing queries on MySQL, note that MySQL's silent type coercionmay cause unexpected results when mixing types. If you query on a stringtype column, but with an integer value, MySQL will coerce the types of all valuesin the table to an integer before performing the comparison. For example, if yourtable contains the values 'abc', 'def' and you query for WHERE mycolumn=0,both rows will match. To prevent this, perform the correct typecastingbefore using the value in a query.

defer()

• defer(*fields)
• In some complex data-modeling situations, your models might contain a lot offields, some of which could contain a lot of data (for example, text fields),or require expensive processing to convert them to Python objects. If you areusing the results of a queryset in some situation where you don't knowif you need those particular fields when you initially fetch the data, you cantell Django not to retrieve them from the database.

This is done by passing the names of the fields to not load to defer():

Entry.objects.defer("headline", "body")

A queryset that has deferred fields will still return model instances. Eachdeferred field will be retrieved from the database if you access that field(one at a time, not all the deferred fields at once).

You can make multiple calls to defer(). Each call adds new fields to thedeferred set:

# Defers both the body and headline fields.
Entry.objects.defer("body").filter(rating=5).defer("headline")

The order in which fields are added to the deferred set does not matter.Calling defer() with a field name that has already been deferred isharmless (the field will still be deferred).

You can defer loading of fields in related models (if the related models areloading via select_related()) by using the standard double-underscorenotation to separate related fields:

Blog.objects.select_related().defer("entry__headline", "entry__body")

If you want to clear the set of deferred fields, pass None as a parameterto defer():

# Load all fields immediately.
my_queryset.defer(None)

Some fields in a model won't be deferred, even if you ask for them. You cannever defer the loading of the primary key. If you are usingselect_related() to retrieve related models, you shouldn't defer theloading of the field that connects from the primary model to the relatedone, doing so will result in an error.

注解

The defer() method (and its cousin, only(), below) are only foradvanced use-cases. They provide an optimization for when you have analyzedyour queries closely and understand exactly what information you need andhave measured that the difference between returning the fields you need andthe full set of fields for the model will be significant.

Even if you think you are in the advanced use-case situation, only usedefer() when you cannot, at queryset load time, determine if you will needthe extra fields or not. If you are frequently loading and using aparticular subset of your data, the best choice you can make is tonormalize your models and put the non-loaded data into a separate model(and database table). If the columns must stay in the one table for somereason, create a model with Meta.managed = False (see themanaged attribute documentation)containing just the fields you normally need to load and use that where youmight otherwise call defer(). This makes your code more explicit to thereader, is slightly faster and consumes a little less memory in the Pythonprocess.

For example, both of these models use the same underlying database table:

class CommonlyUsedModel(models.Model):
    f1 = models.CharField(max_length=10)
 
    class Meta:
        managed = False
        db_table = 'app_largetable'
 
class ManagedModel(models.Model):
    f1 = models.CharField(max_length=10)
    f2 = models.CharField(max_length=10)
 
    class Meta:
        db_table = 'app_largetable'
 
# Two equivalent QuerySets:
CommonlyUsedModel.objects.all()
ManagedModel.objects.all().defer('f2')

If many fields need to be duplicated in the unmanaged model, it may be bestto create an abstract model with the shared fields and then have theunmanaged and managed models inherit from the abstract model.

注解

When calling save() for instances withdeferred fields, only the loaded fields will be saved. Seesave() for more details.

only()

• only(*fields)
• The only() method is more or less the opposite of defer(). You callit with the fields that should not be deferred when retrieving a model. Ifyou have a model where almost all the fields need to be deferred, usingonly() to specify the complementary set of fields can result in simplercode.

Suppose you have a model with fields name, age and biography. Thefollowing two querysets are the same, in terms of deferred fields:

Person.objects.defer("age", "biography")
Person.objects.only("name")

Whenever you call only() it replaces the set of fields to loadimmediately. The method's name is mnemonic: only those fields are loadedimmediately; the remainder are deferred. Thus, successive calls to only()result in only the final fields being considered:

# This will defer all fields except the headline.
Entry.objects.only("body", "rating").only("headline")

Since defer() acts incrementally (adding fields to the deferred list), youcan combine calls to only() and defer() and things will behavelogically:

# Final result is that everything except "headline" is deferred.
Entry.objects.only("headline", "body").defer("body")
 
# Final result loads headline and body immediately (only() replaces any
# existing set of fields).
Entry.objects.defer("body").only("headline", "body")

All of the cautions in the note for the defer() documentation apply toonly() as well. Use it cautiously and only after exhausting your otheroptions.

Using only() and omitting a field requested using select_related()is an error as well.

注解

When calling save() for instances withdeferred fields, only the loaded fields will be saved. Seesave() for more details.

using()

• using(alias)
• This method is for controlling which database the QuerySet will beevaluated against if you are using more than one database. The only argumentthis method takes is the alias of a database, as defined inDATABASES.

For example:

# queries the database with the 'default' alias.
>>> Entry.objects.all()
 
# queries the database with the 'backup' alias
>>> Entry.objects.using('backup')

select_for_update()

• selectfor_update(_nowait=False, skip_locked=False, of=())
• Returns a queryset that will lock rows until the end of the transaction,generating a SELECT … FOR UPDATE SQL statement on supported databases.

For example:

from django.db import transaction
 
entries = Entry.objects.select_for_update().filter(author=request.user)
with transaction.atomic():
    for entry in entries:
        ...

When the queryset is evaluated (for entry in entries in this case), allmatched entries will be locked until the end of the transaction block, meaningthat other transactions will be prevented from changing or acquiring locks onthem.

Usually, if another transaction has already acquired a lock on one of theselected rows, the query will block until the lock is released. If this isnot the behavior you want, call select_for_update(nowait=True). This willmake the call non-blocking. If a conflicting lock is already acquired byanother transaction, DatabaseError will be raised when thequeryset is evaluated. You can also ignore locked rows by usingselect_for_update(skip_locked=True) instead. The nowait andskip_locked are mutually exclusive and attempts to callselect_for_update() with both options enabled will result in aValueError.

By default, select_for_update() locks all rows that are selected by thequery. For example, rows of related objects specified in select_related()are locked in addition to rows of the queryset's model. If this isn't desired,specify the related objects you want to lock in select_for_update(of=(…))using the same fields syntax as select_related(). Use the value 'self'to refer to the queryset's model.

You can't use select_for_update() on nullable relations:

>>> Person.objects.select_related('hometown').select_for_update()
Traceback (most recent call last):
...
django.db.utils.NotSupportedError: FOR UPDATE cannot be applied to the nullable side of an outer join

To avoid that restriction, you can exclude null objects if you don't care aboutthem:

>>> Person.objects.select_related('hometown').select_for_update().exclude(hometown=None)
<QuerySet [<Person: ...)>, ...]>

Currently, the postgresql, oracle, and mysql databasebackends support select_for_update(). However, MySQL doesn't support thenowait, skip_locked, and of arguments.

Passing nowait=True, skip_locked=True, or of toselect_for_update() using database backends that do not support theseoptions, such as MySQL, raises a NotSupportedError. Thisprevents code from unexpectedly blocking.

Evaluating a queryset with select_for_update() in autocommit mode onbackends which support SELECT … FOR UPDATE is aTransactionManagementError error because therows are not locked in that case. If allowed, this would facilitate datacorruption and could easily be caused by calling code that expects to be run ina transaction outside of one.

Using select_for_update() on backends which do not supportSELECT … FOR UPDATE (such as SQLite) will have no effect.SELECT … FOR UPDATE will not be added to the query, and an error isn'traised if select_for_update() is used in autocommit mode.

警告

Although select_for_update() normally fails in autocommit mode, sinceTestCase automatically wraps each test in atransaction, calling select_for_update() in a TestCase even outsidean atomic() block will (perhaps unexpectedly)pass without raising a TransactionManagementError. To properly testselect_for_update() you should useTransactionTestCase.

Certain expressions may not be supported

PostgreSQL doesn't support select_for_update() withWindow expressions.

raw()

• raw(raw_query, params=None, translations=None)
• Takes a raw SQL query, executes it, and returns adjango.db.models.query.RawQuerySet instance. This RawQuerySet instancecan be iterated over just like an normal QuerySet to provide object instances.

See the Performing raw SQL queries for more information.

警告

raw() always triggers a new query and doesn't account for previousfiltering. As such, it should generally be called from the Manager orfrom a fresh QuerySet instance.

Operators that return new QuerySets

Combined querysets must use the same model.

AND (&)

Combines two QuerySets using the SQL AND operator.

The following are equivalent:

Model.objects.filter(x=1) & Model.objects.filter(y=2)
Model.objects.filter(x=1, y=2)
from django.db.models import Q
Model.objects.filter(Q(x=1) & Q(y=2))

SQL equivalent:

SELECT ... WHERE x=1 AND y=2

OR (|)

Combines two QuerySets using the SQL OR operator.

The following are equivalent:

Model.objects.filter(x=1) | Model.objects.filter(y=2)
from django.db.models import Q
Model.objects.filter(Q(x=1) | Q(y=2))

SQL equivalent:

SELECT ... WHERE x=1 OR y=2

Methods that do not return QuerySets

The following QuerySet methods evaluate the QuerySet and returnsomething other than a QuerySet.

These methods do not use a cache (see Caching and QuerySets). Rather,they query the database each time they're called.

get()

• get(**kwargs)
• Returns the object matching the given lookup parameters, which should be inthe format described in Field lookups.

get() raises MultipleObjectsReturned if morethan one object was found. TheMultipleObjectsReturned exception is anattribute of the model class.

get() raises a DoesNotExist exception if anobject wasn't found for the given parameters. This exception is an attributeof the model class. Example:

Entry.objects.get(id='foo') # raises Entry.DoesNotExist

The DoesNotExist exception inherits fromdjango.core.exceptions.ObjectDoesNotExist, so you can target multipleDoesNotExist exceptions. Example:

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.")

If you expect a queryset to return one row, you can use get() without anyarguments to return the object for that row:

entry = Entry.objects.filter(...).exclude(...).get()

create()

• create(**kwargs)
• A convenience method for creating an object and saving it all in one step. Thus:

p = Person.objects.create(first_name="Bruce", last_name="Springsteen")

and:

p = Person(first_name="Bruce", last_name="Springsteen")
p.save(force_insert=True)

are equivalent.

The force_insert parameter is documentedelsewhere, but all it means is that a new object will always be created.Normally you won't need to worry about this. However, if your model contains amanual primary key value that you set and if that value already exists in thedatabase, a call to create() will fail with anIntegrityError since primary keys must be unique. Beprepared to handle the exception if you are using manual primary keys.

get_or_create()

• getor_create(_defaults=None, **kwargs)
• A convenience method for looking up an object with the given kwargs (may beempty if your model has defaults for all fields), creating one if necessary.

Returns a tuple of (object, created), where object is the retrieved orcreated object and created is a boolean specifying whether a new object wascreated.

This is meant as a shortcut to boilerplatish code. 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 goes up.The above 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 getor_create() — _except an optional onecalled defaults — will be used in a get() call. If an object isfound, get_or_create() returns a tuple of that object and False.

You can specify more complex conditions for the retrieved object by chainingget_or_create() with filter() and using Q objects. For example, to retrieve Robert or Bob Marley if eitherexists, and create the latter otherwise:

from django.db.models import Q
 
obj, created = Person.objects.filter(
    Q(first_name='Bob') | Q(first_name='Robert'),
).get_or_create(last_name='Marley', defaults={'first_name': 'Bob'})

If multiple objects are found, get_or_create() raisesMultipleObjectsReturned. If an object is _not_found, get_or_create() will instantiate and save a new object, returning atuple of the new object and True. The new object will be created roughlyaccording to this algorithm:

params = {k: v for k, v in kwargs.items() if '__' not in k}
params.update({k: v() if callable(v) else v for k, v in defaults.items()})
obj = self.model(**params)
obj.save()

In English, that means start with any non-'defaults' keyword argument thatdoesn't contain a double underscore (which would indicate a non-exact lookup).Then add the contents of defaults, overriding any keys if necessary, anduse the result as the keyword arguments to the model class. If there are anycallables in defaults, evaluate them. As hinted at above, this is asimplification of the algorithm that is used, but it contains all the pertinentdetails. The internal implementation has some more error-checking than this andhandles some extra edge-conditions; if you're interested, read the code.

If you have a field named defaults and want to use it as an exact lookup inget_or_create(), just use 'defaults__exact', like so:

Foo.objects.get_or_create(defaults__exact='bar', defaults={'defaults': 'baz'})

The get_or_create() method has similar error behavior to create()when you're using manually specified primary keys. If an object needs to becreated and the key already exists in the database, anIntegrityError will be raised.

This method is atomic assuming correct usage, correct database configuration,and correct behavior of the underlying database. However, if uniqueness is notenforced at the database level for the kwargs used in a get_or_createcall (see unique orunique_together), this method is prone to arace-condition which can result in multiple rows with the same parameters beinginserted simultaneously.

If you are using MySQL, be sure to use the READ COMMITTED isolation levelrather than REPEATABLE READ (the default), otherwise you may see caseswhere get_or_create will raise an IntegrityError but theobject won't appear in a subsequent get()call.

Finally, a word on using get_or_create() in Django views. Please make sureto use it only in POST requests unless you have a good reason not to.GET requests shouldn't have any effect on data. Instead, use POSTwhenever a request to a page has a side effect on your data. For more, seeSafe methods in the HTTP spec.

警告

You can use get_or_create() through ManyToManyFieldattributes and reverse relations. In that case you will restrict the queriesinside the context of that relation. That could lead you to some integrityproblems if you don't use it consistently.

Being the following models:

class Chapter(models.Model):
    title = models.CharField(max_length=255, unique=True)
 
class Book(models.Model):
    title = models.CharField(max_length=256)
    chapters = models.ManyToManyField(Chapter)

You can use get_or_create() through Book's chapters field, but it onlyfetches inside the context of that book:

>>> book = Book.objects.create(title="Ulysses")
>>> book.chapters.get_or_create(title="Telemachus")
(<Chapter: Telemachus>, True)
>>> book.chapters.get_or_create(title="Telemachus")
(<Chapter: Telemachus>, False)
>>> Chapter.objects.create(title="Chapter 1")
<Chapter: Chapter 1>
>>> book.chapters.get_or_create(title="Chapter 1")
# Raises IntegrityError

This is happening because it's trying to get or create "Chapter 1" through thebook "Ulysses", but it can't do any of them: the relation can't fetch thatchapter because it isn't related to that book, but it can't create it eitherbecause title field should be unique.

update_or_create()

• updateor_create(_defaults=None, **kwargs)
• A convenience method for updating an object with the given kwargs, creatinga new one if necessary. The defaults is a dictionary of (field, value)pairs used to update the object. The values in defaults can be callables.

Returns a tuple of (object, created), where object is the created orupdated object and created is a boolean specifying whether a new object wascreated.

The update_or_create method tries to fetch an object from database based onthe given kwargs. If a match is found, it updates the fields passed in thedefaults dictionary.

This is meant as a shortcut to boilerplatish code. For example:

defaults = {'first_name': 'Bob'}
try:
    obj = Person.objects.get(first_name='John', last_name='Lennon')
    for key, value in defaults.items():
        setattr(obj, key, value)
    obj.save()
except Person.DoesNotExist:
    new_values = {'first_name': 'John', 'last_name': 'Lennon'}
    new_values.update(defaults)
    obj = Person(**new_values)
    obj.save()

This pattern gets quite unwieldy as the number of fields in a model goes up.The above example can be rewritten using update_or_create() like so:

obj, created = Person.objects.update_or_create(
    first_name='John', last_name='Lennon',
    defaults={'first_name': 'Bob'},
)

For detailed description how names passed in kwargs are resolved seeget_or_create().

As described above in get_or_create(), this method is prone to arace-condition which can result in multiple rows being inserted simultaneouslyif uniqueness is not enforced at the database level.

Like get_or_create() and create(), if you're using manuallyspecified primary keys and an object needs to be created but the key alreadyexists in the database, an IntegrityError is raised.

bulk_create()

• bulkcreate(_objs, batch_size=None, ignore_conflicts=False)
• This method inserts the provided list of objects into the database in anefficient manner (generally only 1 query, no matter how many objects thereare):

>>> Entry.objects.bulk_create([
...     Entry(headline='This is a test'),
...     Entry(headline='This is only a test'),
... ])

This has a number of caveats though:

• The model's save() method will not be called, and the pre_save andpost_save signals will not be sent.

• It does not work with child models in a multi-table inheritance scenario.

• If the model's primary key is an AutoField itdoes not retrieve and set the primary key attribute, as save() does,unless the database backend supports it (currently PostgreSQL).

• It does not work with many-to-many relationships.

• It casts objs to a list, which fully evaluates objs if it's agenerator. The cast allows inspecting all objects so that any objects with amanually set primary key can be inserted first. If you want to insert objectsin batches without evaluating the entire generator at once, you can use thistechnique as long as the objects don't have any manually set primary keys:

from itertools import islice
 
batch_size = 100
objs = (Entry(headline='Test %s' % i) for i in range(1000))
while True:
    batch = list(islice(objs, batch_size))
    if not batch:
        break
    Entry.objects.bulk_create(batch, batch_size)

The batch_size parameter controls how many objects are created in a singlequery. The default is to create all objects in one batch, except for SQLitewhere the default is such that at most 999 variables per query are used.

On databases that support it (all except PostgreSQL < 9.5 and Oracle), settingthe ignore_conflicts parameter to True tells the database to ignorefailure to insert any rows that fail constraints such as duplicate uniquevalues. Enabling this parameter disables setting the primary key on each modelinstance (if the database normally supports it).

Changed in Django 2.2:
The ignore_conflicts parameter was added.

bulk_update()

New in Django 2.2:

• bulkupdate(_objs, fields, batch_size=None)
• This method efficiently updates the given fields on the provided modelinstances, generally with one query:

>>> objs = [
...    Entry.objects.create(headline='Entry 1'),
...    Entry.objects.create(headline='Entry 2'),
... ]
>>> objs[0].headline = 'This is entry 1'
>>> objs[1].headline = 'This is entry 2'
>>> Entry.objects.bulk_update(objs, ['headline'])

QuerySet.update() is used to save the changes, so this is more efficientthan iterating through the list of models and calling save() on each ofthem, but it has a few caveats:

• You cannot update the model's primary key.
• Each model's save() method isn't called, and thepre_save andpost_save signals aren't sent.
• If updating a large number of columns in a large number of rows, the SQLgenerated can be very large. Avoid this by specifying a suitablebatch_size.
• Updating fields defined on multi-table inheritance ancestors will incur anextra query per ancestor.
• If objs contains duplicates, only the first one is updated.
  The batch_size parameter controls how many objects are saved in a singlequery. The default is to update all objects in one batch, except for SQLiteand Oracle which have restrictions on the number of variables used in a query.

count()

• count()
• Returns an integer representing the number of objects in the database matchingthe QuerySet.

Example:

# Returns the total number of entries in the database.
Entry.objects.count()
 
# Returns the number of entries whose headline contains 'Lennon'
Entry.objects.filter(headline__contains='Lennon').count()

A count() call performs a SELECT COUNT(*) behind the scenes, so youshould always use count() rather than loading all of the record into Pythonobjects and calling len() on the result (unless you need to load theobjects into memory anyway, in which case len() will be faster).

Note that if you want the number of items in a QuerySet and are alsoretrieving model instances from it (for example, by iterating over it), it'sprobably more efficient to use len(queryset) which won't cause an extradatabase query like count() would.

in_bulk()

• inbulk(_id_list=None, field_name='pk')
• Takes a list of field values (id_list) and the field_name for thosevalues, and returns a dictionary mapping each value to an instance of theobject with the given field value. If id_list isn't provided, all objectsin the queryset are returned. field_name must be a unique field, and itdefaults to the primary key.

Example:

>>> Blog.objects.in_bulk([1])
{1: <Blog: Beatles Blog>}
>>> Blog.objects.in_bulk([1, 2])
{1: <Blog: Beatles Blog>, 2: <Blog: Cheddar Talk>}
>>> Blog.objects.in_bulk([])
{}
>>> Blog.objects.in_bulk()
{1: <Blog: Beatles Blog>, 2: <Blog: Cheddar Talk>, 3: <Blog: Django Weblog>}
>>> Blog.objects.in_bulk(['beatles_blog'], field_name='slug')
{'beatles_blog': <Blog: Beatles Blog>}

If you pass in_bulk() an empty list, you'll get an empty dictionary.

iterator()

• iterator(chunk_size=2000)
• Evaluates the QuerySet (by performing the query) and returns an iterator(see PEP 234) over the results. A QuerySet typically caches its resultsinternally so that repeated evaluations do not result in additional queries. Incontrast, iterator() will read results directly, without doing any cachingat the QuerySet level (internally, the default iterator calls iterator()and caches the return value). For a QuerySet which returns a large number ofobjects that you only need to access once, this can result in betterperformance and a significant reduction in memory.

Note that using iterator() on a QuerySet which has already beenevaluated will force it to evaluate again, repeating the query.

Also, use of iterator() causes previous prefetch_related() calls to beignored since these two optimizations do not make sense together.

Depending on the database backend, query results will either be loaded all atonce or streamed from the database using server-side cursors.

With server-side cursors

Oracle and PostgreSQL use server-sidecursors to stream results from the database without loading the entire resultset into memory.

The Oracle database driver always uses server-side cursors.

With server-side cursors, the chunk_size parameter specifies the number ofresults to cache at the database driver level. Fetching bigger chunksdiminishes the number of round trips between the database driver and thedatabase, at the expense of memory.

On PostgreSQL, server-side cursors will only be used when theDISABLE_SERVER_SIDE_CURSORSsetting is False. Read Transaction pooling and server-side cursors ifyou're using a connection pooler configured in transaction pooling mode. Whenserver-side cursors are disabled, the behavior is the same as databases thatdon't support server-side cursors.

Without server-side cursors

MySQL doesn't support streaming results, hence the Python database driver loadsthe entire result set into memory. The result set is then transformed intoPython row objects by the database adapter using the fetchmany() methoddefined in PEP 249.

SQLite can fetch results in batches using fetchmany(), but since SQLitedoesn't provide isolation between queries within a connection, be careful whenwriting to the table being iterated over. See Isolation when using QuerySet.iterator() formore information.

The chunk_size parameter controls the size of batches Django retrieves fromthe database driver. Larger batches decrease the overhead of communicating withthe database driver at the expense of a slight increase in memory consumption.

The default value of chunk_size, 2000, comes from a calculation on thepsycopg mailing list:

Assuming rows of 10-20 columns with a mix of textual and numeric data, 2000is going to fetch less than 100KB of data, which seems a good compromisebetween the number of rows transferred and the data discarded if the loopis exited early.

Changed in Django 2.2:
Support for result streaming on SQLite was added.

latest()

• latest(*fields)
• Returns the latest object in the table based on the given field(s).

This example returns the latest Entry in the table, according to thepub_date field:

Entry.objects.latest('pub_date')

You can also choose the latest based on several fields. For example, to selectthe Entry with the earliest expire_date when two entries have the samepub_date:

Entry.objects.latest('pub_date', '-expire_date')

The negative sign in '-expiredate' means to sort expire_date in_descending order. Since latest() gets the last result, the Entry withthe earliest expire_date is selected.

If your model's Meta specifiesget_latest_by, you can omit any arguments toearliest() or latest(). The fields specified inget_latest_by will be used by default.

Like get(), earliest() and latest() raiseDoesNotExist if there is no object with thegiven parameters.

Note that earliest() and latest() exist purely for convenience andreadability.

earliest() and latest() may return instances with null dates.

Since ordering is delegated to the database, results on fields that allownull values may be ordered differently if you use different databases. Forexample, PostgreSQL and MySQL sort null values as if they are higher thannon-null values, while SQLite does the opposite.

You may want to filter out null values:

Entry.objects.filter(pub_date__isnull=False).latest('pub_date')

earliest()

• earliest(*fields)
• Works otherwise like latest() exceptthe direction is changed.

first()

• first()
• Returns the first object matched by the queryset, or None if thereis no matching object. If the QuerySet has no ordering defined, then thequeryset is automatically ordered by the primary key. This can affectaggregation results as described in Interaction with default ordering or order_by().

Example:

p = Article.objects.order_by('title', 'pub_date').first()

Note that first() is a convenience method, the following code sample isequivalent to the above example:

try:
    p = Article.objects.order_by('title', 'pub_date')[0]
except IndexError:
    p = None

last()

• last()
• Works like first(), but returns the last object in the queryset.

aggregate()

• aggregate(*args, **kwargs)
• Returns a dictionary of aggregate values (averages, sums, etc.) calculated overthe QuerySet. Each argument to aggregate() specifies a value that willbe included in the dictionary that is returned.

The aggregation functions that are provided by Django are described inAggregation Functions below. Since aggregates are also queryexpressions, you may combine aggregates with otheraggregates or values to create complex aggregates.

Aggregates specified using keyword arguments will use the keyword as the namefor the annotation. Anonymous arguments will have a name generated for thembased upon the name of the aggregate function and the model field that is beingaggregated. Complex aggregates cannot use anonymous arguments and must specifya keyword argument as an alias.

For example, when you are working with blog entries, you may want to know thenumber of authors that have contributed blog entries:

>>> from django.db.models import Count
>>> q = Blog.objects.aggregate(Count('entry'))
{'entry__count': 16}

By using a keyword argument to specify the aggregate function, you cancontrol the name of the aggregation value that is returned:

>>> q = Blog.objects.aggregate(number_of_entries=Count('entry'))
{'number_of_entries': 16}

For an in-depth discussion of aggregation, see the topic guide onAggregation.

exists()

• exists()
• Returns True if the QuerySet contains any results, and Falseif not. This tries to perform the query in the simplest and fastest waypossible, but it does execute nearly the same query as a normalQuerySet query.

exists() is useful for searches relating to bothobject membership in a QuerySet and to the existence of any objects ina QuerySet, particularly in the context of a large QuerySet.

The most efficient method of finding whether a model with a unique field(e.g. primary_key) is a member of a QuerySet is:

entry = Entry.objects.get(pk=123)
if some_queryset.filter(pk=entry.pk).exists():
    print("Entry contained in queryset")

Which will be faster than the following which requires evaluating and iteratingthrough the entire queryset:

if entry in some_queryset:
   print("Entry contained in QuerySet")

And to find whether a queryset contains any items:

if some_queryset.exists():
    print("There is at least one object in some_queryset")

Which will be faster than:

if some_queryset:
    print("There is at least one object in some_queryset")

… but not by a large degree (hence needing a large queryset for efficiencygains).

Additionally, if a some_queryset has not yet been evaluated, but you knowthat it will be at some point, then using some_queryset.exists() will domore overall work (one query for the existence check plus an extra one to laterretrieve the results) than simply using bool(some_queryset), whichretrieves the results and then checks if any were returned.

update()

• update(**kwargs)
• Performs an SQL update query for the specified fields, and returnsthe number of rows matched (which may not be equal to the number of rowsupdated if some rows already have the new value).

For example, to turn comments off for all blog entries published in 2010,you could do this:

>>> Entry.objects.filter(pub_date__year=2010).update(comments_on=False)

(This assumes your Entry model has fields pub_date and comments_on.)

You can update multiple fields — there's no limit on how many. For example,here we update the comments_on and headline fields:

>>> Entry.objects.filter(pub_date__year=2010).update(comments_on=False, headline='This is old')

The update() method is applied instantly, and the only restriction on theQuerySet that is updated is that it can only update columns in themodel's main table, not on related models. You can't do this, for example:

>>> Entry.objects.update(blog__name='foo') # Won't work!

Filtering based on related fields is still possible, though:

>>> Entry.objects.filter(blog__id=1).update(comments_on=True)

You cannot call update() on a QuerySet that has had a slice takenor can otherwise no longer be filtered.

The update() method returns the number of affected rows:

>>> Entry.objects.filter(id=64).update(comments_on=True)
1
 
>>> Entry.objects.filter(slug='nonexistent-slug').update(comments_on=True)
0
 
>>> Entry.objects.filter(pub_date__year=2010).update(comments_on=False)
132

If you're just updating a record and don't need to do anything with the modelobject, the most efficient approach is to call update(), rather thanloading the model object into memory. For example, instead of doing this:

e = Entry.objects.get(id=10)
e.comments_on = False
e.save()

…do this:

Entry.objects.filter(id=10).update(comments_on=False)

Using update() also prevents a race condition wherein something mightchange in your database in the short period of time between loading the objectand calling save().

Finally, realize that update() does an update at the SQL level and, thus,does not call any save() methods on your models, nor does it emit thepre_save orpost_save signals (which are a consequence ofcalling Model.save()). If you want toupdate a bunch of records for a model that has a customsave() method, loop over them and callsave(), like this:

for e in Entry.objects.filter(pub_date__year=2010):
    e.comments_on = False
    e.save()

delete()

• delete()
• Performs an SQL delete query on all rows in the QuerySet andreturns the number of objects deleted and a dictionary with the number ofdeletions per object type.

The delete() is applied instantly. You cannot call delete() on aQuerySet that has had a slice taken or can otherwise no longer befiltered.

For example, to delete all the entries in a particular blog:

>>> b = Blog.objects.get(pk=1)
 
# Delete all the entries belonging to this Blog.
>>> Entry.objects.filter(blog=b).delete()
(4, {'weblog.Entry': 2, 'weblog.Entry_authors': 2})

By default, Django's ForeignKey emulates the SQLconstraint ON DELETE CASCADE — in other words, any objects with foreignkeys pointing at the objects to be deleted will be deleted along with them.For example:

>>> blogs = Blog.objects.all()
 
# This will delete all Blogs and all of their Entry objects.
>>> blogs.delete()
(5, {'weblog.Blog': 1, 'weblog.Entry': 2, 'weblog.Entry_authors': 2})

This cascade behavior is customizable via theon_delete argument to theForeignKey.

The delete() method does a bulk delete and does not call any delete()methods on your models. It does, however, emit thepre_delete andpost_delete signals for all deleted objects(including cascaded deletions).

Django needs to fetch objects into memory to send signals and handle cascades.However, if there are no cascades and no signals, then Django may take afast-path and delete objects without fetching into memory. For largedeletes this can result in significantly reduced memory usage. The amount ofexecuted queries can be reduced, too.

ForeignKeys which are set to on_deleteDO_NOTHING do not prevent taking the fast-path in deletion.

Note that the queries generated in object deletion is an implementationdetail subject to change.

as_manager()

• classmethod as_manager()
• Class method that returns an instance of Managerwith a copy of the QuerySet’s methods. SeeCreating a manager with QuerySet methods for more details.

explain()

New in Django 2.1:

• explain(format=None, **options)
• Returns a string of the QuerySet’s execution plan, which details how thedatabase would execute the query, including any indexes or joins that would beused. Knowing these details may help you improve the performance of slowqueries.

For example, when using PostgreSQL:

>>> print(Blog.objects.filter(title='My Blog').explain())
Seq Scan on blog  (cost=0.00..35.50 rows=10 width=12)
  Filter: (title = 'My Blog'::bpchar)

The output differs significantly between databases.

explain() is supported by all built-in database backends except Oraclebecause an implementation there isn't straightforward.

The format parameter changes the output format from the databases's default,usually text-based. PostgreSQL supports 'TEXT', 'JSON', 'YAML', and'XML'. MySQL supports 'TEXT' (also called 'TRADITIONAL') and'JSON'.

Some databases accept flags that can return more information about the query.Pass these flags as keyword arguments. For example, when using PostgreSQL:

>>> print(Blog.objects.filter(title='My Blog').explain(verbose=True))
Seq Scan on public.blog  (cost=0.00..35.50 rows=10 width=12) (actual time=0.004..0.004 rows=10 loops=1)
  Output: id, title
  Filter: (blog.title = 'My Blog'::bpchar)
Planning time: 0.064 ms
Execution time: 0.058 ms

On some databases, flags may cause the query to be executed which could haveadverse effects on your database. For example, PostgreSQL's ANALYZE flagcould result in changes to data if there are triggers or if a function iscalled, even for a SELECT query.

Field lookups

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

For an introduction, see models and database queries documentation.

Django's built-in lookups are listed below. It is also possible to writecustom lookups for model fields.

As a convenience when no lookup type is provided (like inEntry.objects.get(id=14)) the lookup type is assumed to be exact.

exact

Exact match. If the value provided for comparison is None, it will beinterpreted as an SQL NULL (see isnull for more details).

Examples:

Entry.objects.get(id__exact=14)
Entry.objects.get(id__exact=None)

SQL equivalents:

SELECT ... WHERE id = 14;
SELECT ... WHERE id IS NULL;

MySQL comparisons

In MySQL, a database table's "collation" setting determines whetherexact comparisons are case-sensitive. This is a database setting, _not_a Django setting. It's possible to configure your MySQL tables to usecase-sensitive comparisons, but some trade-offs are involved. For moreinformation about this, see the collation sectionin the databases documentation.

iexact

Case-insensitive exact match. If the value provided for comparison is None,it will be interpreted as an SQL NULL (see isnull for moredetails).

Example:

Blog.objects.get(name__iexact='beatles blog')
Blog.objects.get(name__iexact=None)

SQL equivalents:

SELECT ... WHERE name ILIKE 'beatles blog';
SELECT ... WHERE name IS NULL;

Note the first query will match 'Beatles Blog', 'beatles blog','BeAtLes BLoG', etc.

SQLite users

When using the SQLite backend and non-ASCII strings, bear in mind thedatabase note about string comparisons.SQLite does not do case-insensitive matching for non-ASCII strings.

contains

Case-sensitive containment test.

Example:

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

SQL equivalent:

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

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

SQLite users

SQLite doesn't support case-sensitive LIKE statements; containsacts like icontains for SQLite. See the database note for more information.

icontains

Case-insensitive containment test.

Example:

Entry.objects.get(headline__icontains='Lennon')

SQL equivalent:

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

SQLite users

When using the SQLite backend and non-ASCII strings, bear in mind thedatabase note about string comparisons.

in

In a given iterable; often a list, tuple, or queryset. It's not a common usecase, but strings (being iterables) are accepted.

Examples:

Entry.objects.filter(id__in=[1, 3, 4])
Entry.objects.filter(headline__in='abc')

SQL equivalents:

SELECT ... WHERE id IN (1, 3, 4);
SELECT ... WHERE headline IN ('a', 'b', 'c');

You can also use a queryset to dynamically evaluate the list of valuesinstead of providing a list of literal values:

inner_qs = Blog.objects.filter(name__contains='Cheddar')
entries = Entry.objects.filter(blog__in=inner_qs)

This queryset will be evaluated as subselect statement:

SELECT ... WHERE blog.id IN (SELECT id FROM ... WHERE NAME LIKE '%Cheddar%')

If you pass in a QuerySet resulting from values() or values_list()as the value to an __in lookup, you need to ensure you are only extractingone field in the result. For example, this will work (filtering on the blognames):

inner_qs = Blog.objects.filter(name__contains='Ch').values('name')
entries = Entry.objects.filter(blog__name__in=inner_qs)

This example will raise an exception, since the inner query is trying toextract two field values, where only one is expected:

# Bad code! Will raise a TypeError.
inner_qs = Blog.objects.filter(name__contains='Ch').values('name', 'id')
entries = Entry.objects.filter(blog__name__in=inner_qs)

Performance considerations

Be cautious about using nested queries and understand your databaseserver's performance characteristics (if in doubt, benchmark!). Somedatabase backends, most notably MySQL, don't optimize nested queries verywell. It is more efficient, in those cases, to extract a list of valuesand then pass that into the second query. That is, execute two queriesinstead of one:

values = Blog.objects.filter(
        name__contains='Cheddar').values_list('pk', flat=True)
entries = Entry.objects.filter(blog__in=list(values))

Note the list() call around the Blog QuerySet to force execution ofthe first query. Without it, a nested query would be executed, becauseQuerySets are lazy.

gt

Greater than.

Example:

Entry.objects.filter(id__gt=4)

SQL equivalent:

SELECT ... WHERE id > 4;

gte

Greater than or equal to.

lt

Less than.

lte

Less than or equal to.

startswith

Case-sensitive starts-with.

Example:

Entry.objects.filter(headline__startswith='Lennon')