:doc:`Django 数据库抽象 API ` 描述了使用 Django queries 来增删查改单个对象的方法。 然而，有时候你要获取的值需要根据一组对象聚合后才能得到。这个主题指南描述了如何使用 Django queries 来生成和返回聚合值的方法。
\n整篇指南我们将引用以下模型。这些模型用来记录多个网上书店的库存。
\nfrom django.db import models\n\nclass Author(models.Model):\n name = models.CharField(max_length=100)\n age = models.IntegerField()\n\nclass Publisher(models.Model):\n name = models.CharField(max_length=300)\n num_awards = models.IntegerField()\n\nclass Book(models.Model):\n name = models.CharField(max_length=300)\n pages = models.IntegerField()\n price = models.DecimalField(max_digits=10, decimal_places=2)\n rating = models.FloatField()\n authors = models.ManyToManyField(Author)\n publisher = models.ForeignKey(Publisher, on_delete=models.CASCADE)\n pubdate = models.DateField()\n\nclass Store(models.Model):\n name = models.CharField(max_length=300)\n books = models.ManyToManyField(Book)\n registered_users = models.PositiveIntegerField()\n下面是根据以上模型执行常见的聚合查询：
\n# Total number of books.\n>>> Book.objects.count()\n2452\n\n# Total number of books with publisher=BaloneyPress\n>>> Book.objects.filter(publisher__name='BaloneyPress').count()\n73\n\n# Average price across all books.\n>>> from django.db.models import Avg\n>>> Book.objects.all().aggregate(Avg('price'))\n{'price__avg': 34.35}\n\n# Max price across all books.\n>>> from django.db.models import Max\n>>> Book.objects.all().aggregate(Max('price'))\n{'price__max': Decimal('81.20')}\n\n# Difference between the highest priced book and the average price of all books.\n>>> from django.db.models import FloatField\n>>> Book.objects.aggregate(\n... price_diff=Max('price', output_field=FloatField()) - Avg('price'))\n{'price_diff': 46.85}\n\n# All the following queries involve traversing the Book<->Publisher\n# foreign key relationship backwards.\n\n# Each publisher, each with a count of books as a "num_books" attribute.\n>>> from django.db.models import Count\n>>> pubs = Publisher.objects.annotate(num_books=Count('book'))\n>>> pubs\n<QuerySet [<Publisher: BaloneyPress>, <Publisher: SalamiPress>, ...]>\n>>> pubs[0].num_books\n73\n\n# Each publisher, with a separate count of books with a rating above and below 5\n>>> from django.db.models import Q\n>>> above_5 = Count('book', filter=Q(book__rating__gt=5))\n>>> below_5 = Count('book', filter=Q(book__rating__lte=5))\n>>> pubs = Publisher.objects.annotate(below_5=below_5).annotate(above_5=above_5)\n>>> pubs[0].above_5\n23\n>>> pubs[0].below_5\n12\n\n# The top 5 publishers, in order by number of books.\n>>> pubs = Publisher.objects.annotate(num_books=Count('book')).order_by('-num_books')[:5]\n>>> pubs[0].num_books\n1323\nQuerySet 上生成聚合Link to this heading#Django 提供了两种生成聚合的方法。第一种方法是从整个 QuerySet 生成汇总值。比如你想要计算所有在售书的平均价格。Django 的查询语法提供了一种用来描述所有图书集合的方法：
>>> Book.objects.all()\n可以通过在 QuerySet 后添加 aggregate() 子句来计算 QuerySet 对象的汇总值。
>>> from django.db.models import Avg\n>>> Book.objects.all().aggregate(Avg('price'))\n{'price__avg': 34.35}\nThe all() is redundant in this example, so this could be simplified to:
>>> Book.objects.aggregate(Avg('price'))\n{'price__avg': 34.35}\nThe argument to the aggregate() clause describes the aggregate value that\nwe want to compute - in this case, the average of the price field on the\nBook model. A list of the aggregate functions that are available can be\nfound in the QuerySet reference.
aggregate() is a terminal clause for a QuerySet that, when invoked,\nreturns a dictionary of name-value pairs. The name is an identifier for the\naggregate value; the value is the computed aggregate. The name is\nautomatically generated from the name of the field and the aggregate function.\nIf you want to manually specify a name for the aggregate value, you can do so\nby providing that name when you specify the aggregate clause:
>>> Book.objects.aggregate(average_price=Avg('price'))\n{'average_price': 34.35}\nIf you want to generate more than one aggregate, you just add another\nargument to the aggregate() clause. So, if we also wanted to know\nthe maximum and minimum price of all books, we would issue the query:
>>> from django.db.models import Avg, Max, Min\n>>> Book.objects.aggregate(Avg('price'), Max('price'), Min('price'))\n{'price__avg': 34.35, 'price__max': Decimal('81.20'), 'price__min': Decimal('12.99')}\nQuerySetLink to this heading#The second way to generate summary values is to generate an independent\nsummary for each object in a QuerySet. For example, if you are retrieving\na list of books, you may want to know how many authors contributed to\neach book. Each Book has a many-to-many relationship with the Author; we\nwant to summarize this relationship for each book in the QuerySet.
Per-object summaries can be generated using the annotate() clause.\nWhen an annotate() clause is specified, each object in the QuerySet\nwill be annotated with the specified values.
The syntax for these annotations is identical to that used for the\naggregate() clause. Each argument to annotate() describes an\naggregate that is to be calculated. For example, to annotate books with\nthe number of authors:
# Build an annotated queryset\n>>> from django.db.models import Count\n>>> q = Book.objects.annotate(Count('authors'))\n# Interrogate the first object in the queryset\n>>> q[0]\n<Book: The Definitive Guide to Django>\n>>> q[0].authors__count\n2\n# Interrogate the second object in the queryset\n>>> q[1]\n<Book: Practical Django Projects>\n>>> q[1].authors__count\n1\nAs with aggregate(), the name for the annotation is automatically derived\nfrom the name of the aggregate function and the name of the field being\naggregated. You can override this default name by providing an alias when you\nspecify the annotation:
>>> q = Book.objects.annotate(num_authors=Count('authors'))\n>>> q[0].num_authors\n2\n>>> q[1].num_authors\n1\nUnlike aggregate(), annotate() is not a terminal clause. The output\nof the annotate() clause is a QuerySet; this QuerySet can be\nmodified using any other QuerySet operation, including filter(),\norder_by(), or even additional calls to annotate().
Combining multiple aggregations with annotate() will yield the wrong\nresults because joins are used\ninstead of subqueries:
>>> book = Book.objects.first()\n>>> book.authors.count()\n2\n>>> book.store_set.count()\n3\n>>> q = Book.objects.annotate(Count('authors'), Count('store'))\n>>> q[0].authors__count\n6\n>>> q[0].store__count\n6\nFor most aggregates, there is no way to avoid this problem, however, the\nCount aggregate has a distinct parameter that\nmay help:
>>> q = Book.objects.annotate(Count('authors', distinct=True), Count('store', distinct=True))\n>>> q[0].authors__count\n2\n>>> q[0].store__count\n3\nSo far, we have dealt with aggregates over fields that belong to the\nmodel being queried. However, sometimes the value you want to aggregate\nwill belong to a model that is related to the model you are querying.
\nWhen specifying the field to be aggregated in an aggregate function, Django\nwill allow you to use the same double underscore notation that is used when referring to related fields in\nfilters. Django will then handle any table joins that are required to retrieve\nand aggregate the related value.
\nFor example, to find the price range of books offered in each store,\nyou could use the annotation:
\n>>> from django.db.models import Max, Min\n>>> Store.objects.annotate(min_price=Min('books__price'), max_price=Max('books__price'))\nThis tells Django to retrieve the Store model, join (through the\nmany-to-many relationship) with the Book model, and aggregate on the\nprice field of the book model to produce a minimum and maximum value.
The same rules apply to the aggregate() clause. If you wanted to\nknow the lowest and highest price of any book that is available for sale\nin any of the stores, you could use the aggregate:
>>> Store.objects.aggregate(min_price=Min('books__price'), max_price=Max('books__price'))\nJoin chains can be as deep as you require. For example, to extract the\nage of the youngest author of any book available for sale, you could\nissue the query:
\n>>> Store.objects.aggregate(youngest_age=Min('books__authors__age'))\nIn a way similar to Lookups that span relationships, aggregations and\nannotations on fields of models or models that are related to the one you are\nquerying can include traversing "reverse" relationships. The lowercase name\nof related models and double-underscores are used here too.
\nFor example, we can ask for all publishers, annotated with their respective\ntotal book stock counters (note how we use 'book' to specify the\nPublisher -> Book reverse foreign key hop):
>>> from django.db.models import Avg, Count, Min, Sum\n>>> Publisher.objects.annotate(Count('book'))\n(Every Publisher in the resulting QuerySet will have an extra attribute\ncalled book__count.)
We can also ask for the oldest book of any of those managed by every publisher:
\n>>> Publisher.objects.aggregate(oldest_pubdate=Min('book__pubdate'))\n(The resulting dictionary will have a key called 'oldest_pubdate'. If no\nsuch alias were specified, it would be the rather long 'book__pubdate__min'.)
This doesn't apply just to foreign keys. It also works with many-to-many\nrelations. For example, we can ask for every author, annotated with the total\nnumber of pages considering all the books the author has (co-)authored (note how we\nuse 'book' to specify the Author -> Book reverse many-to-many hop):
>>> Author.objects.annotate(total_pages=Sum('book__pages'))\n(Every Author in the resulting QuerySet will have an extra attribute\ncalled total_pages. If no such alias were specified, it would be the rather\nlong book__pages__sum.)
Or ask for the average rating of all the books written by author(s) we have on\nfile:
\n>>> Author.objects.aggregate(average_rating=Avg('book__rating'))\n(The resulting dictionary will have a key called 'average_rating'. If no\nsuch alias were specified, it would be the rather long 'book__rating__avg'.)
QuerySet clausesLink to this heading#filter() and exclude()Link to this heading#Aggregates can also participate in filters. Any filter() (or\nexclude()) applied to normal model fields will have the effect of\nconstraining the objects that are considered for aggregation.
When used with an annotate() clause, a filter has the effect of\nconstraining the objects for which an annotation is calculated. For example,\nyou can generate an annotated list of all books that have a title starting\nwith "Django" using the query:
>>> from django.db.models import Avg, Count\n>>> Book.objects.filter(name__startswith="Django").annotate(num_authors=Count('authors'))\nWhen used with an aggregate() clause, a filter has the effect of\nconstraining the objects over which the aggregate is calculated.\nFor example, you can generate the average price of all books with a\ntitle that starts with "Django" using the query:
>>> Book.objects.filter(name__startswith="Django").aggregate(Avg('price'))\nAnnotated values can also be filtered. The alias for the annotation can be\nused in filter() and exclude() clauses in the same way as any other\nmodel field.
For example, to generate a list of books that have more than one author,\nyou can issue the query:
\n>>> Book.objects.annotate(num_authors=Count('authors')).filter(num_authors__gt=1)\nThis query generates an annotated result set, and then generates a filter\nbased upon that annotation.
\nIf you need two annotations with two separate filters you can use the\nfilter argument with any aggregate. For example, to generate a list of\nauthors with a count of highly rated books:
>>> highly_rated = Count('books', filter=Q(books__rating__gte=7))\n>>> Author.objects.annotate(num_books=Count('books'), highly_rated_books=highly_rated)\nEach Author in the result set will have the num_books and\nhighly_rated_books attributes.
annotate() and filter() clausesLink to this heading#When developing a complex query that involves both annotate() and\nfilter() clauses, pay particular attention to the order in which the\nclauses are applied to the QuerySet.
When an annotate() clause is applied to a query, the annotation is computed\nover the state of the query up to the point where the annotation is requested.\nThe practical implication of this is that filter() and annotate() are\nnot commutative operations.
Given:
\nPublisher A has two books with ratings 4 and 5.
Publisher B has two books with ratings 1 and 4.
Publisher C has one book with rating 1.
Here's an example with the Count aggregate:
>>> a, b = Publisher.objects.annotate(num_books=Count('book', distinct=True)).filter(book__rating__gt=3.0)\n>>> a, a.num_books\n(<Publisher: A>, 2)\n>>> b, b.num_books\n(<Publisher: B>, 2)\n\n>>> a, b = Publisher.objects.filter(book__rating__gt=3.0).annotate(num_books=Count('book'))\n>>> a, a.num_books\n(<Publisher: A>, 2)\n>>> b, b.num_books\n(<Publisher: B>, 1)\nBoth queries return a list of publishers that have at least one book with a\nrating exceeding 3.0, hence publisher C is excluded.
\nIn the first query, the annotation precedes the filter, so the filter has no\neffect on the annotation. distinct=True is required to avoid a query\nbug.
The second query counts the number of books that have a rating exceeding 3.0\nfor each publisher. The filter precedes the annotation, so the filter\nconstrains the objects considered when calculating the annotation.
\nHere's another example with the Avg aggregate:
>>> a, b = Publisher.objects.annotate(avg_rating=Avg('book__rating')).filter(book__rating__gt=3.0)\n>>> a, a.avg_rating\n(<Publisher: A>, 4.5) # (5+4)/2\n>>> b, b.avg_rating\n(<Publisher: B>, 2.5) # (1+4)/2\n\n>>> a, b = Publisher.objects.filter(book__rating__gt=3.0).annotate(avg_rating=Avg('book__rating'))\n>>> a, a.avg_rating\n(<Publisher: A>, 4.5) # (5+4)/2\n>>> b, b.avg_rating\n(<Publisher: B>, 4.0) # 4/1 (book with rating 1 excluded)\nThe first query asks for the average rating of all a publisher's books for\npublisher's that have at least one book with a rating exceeding 3.0. The second\nquery asks for the average of a publisher's book's ratings for only those\nratings exceeding 3.0.
\nIt's difficult to intuit how the ORM will translate complex querysets into SQL\nqueries so when in doubt, inspect the SQL with str(queryset.query) and\nwrite plenty of tests.
order_by()Link to this heading#Annotations can be used as a basis for ordering. When you\ndefine an order_by() clause, the aggregates you provide can reference\nany alias defined as part of an annotate() clause in the query.
For example, to order a QuerySet of books by the number of authors\nthat have contributed to the book, you could use the following query:
>>> Book.objects.annotate(num_authors=Count('authors')).order_by('num_authors')\nvalues()Link to this heading#通常，注解值会添加到每个对象上，即一个被注解的 QuerySet 将会为初始 QuerySet 的每个对象返回一个结果集。然而，当使用 values() 子句来对结果集进行约束时，生成注解值的方法会稍有不同。不是在原始 QuerySet 中对每个对象添加注解并返回，而是根据定义在 values() 子句中的字段组合先对结果进行分组，再对每个单独的分组进行注解，这个注解值是根据分组中所有的对象计算得到的。
下面是一个关于作者的查询例子，查询每个作者所著书的平均评分：
\n>>> Author.objects.annotate(average_rating=Avg('book__rating'))\n这段代码返回的是数据库中的所有作者及其所著书的平均评分。
\n但是如果你使用 values() 子句，结果会稍有不同：
>>> Author.objects.values('name').annotate(average_rating=Avg('book__rating'))\n在这个例子中，作者会按名字分组，所以你只能得到不重名的作者分组的注解值。这意味着如果你有两个作者同名，那么他们原本各自的查询结果将被合并到同一个结果中；两个作者的所有评分都将被计算为一个平均分。
\nannotate() 和 values() 的顺序Link to this heading#和使用 filter() 一样，作用于某个查询的 annotate() 和 values() 子句的顺序非常重要。如果 values() 子句在 annotate() 之前，就会根据 values() 子句产生的分组来计算注解。
然而如果 annotate() 子句在 values() 之前，就会根据整个查询集生成注解。这种情况下，values() 子句只能限制输出的字段。
举个例子，如果我们颠倒上个例子中 values() 和 annotate() 的顺序：
>>> Author.objects.annotate(average_rating=Avg('book__rating')).values('name', 'average_rating')\n这段代码将为每个作者添加一个唯一注解，但只有作者姓名和 average_rating 注解会返回在输出结果中。
You should also note that average_rating has been explicitly included\nin the list of values to be returned. This is required because of the\nordering of the values() and annotate() clause.
If the values() clause precedes the annotate() clause, any annotations\nwill be automatically added to the result set. However, if the values()\nclause is applied after the annotate() clause, you need to explicitly\ninclude the aggregate column.
order_by()Link to this heading#Fields that are mentioned in the order_by() part of a queryset (or which\nare used in the default ordering on a model) are used when selecting the\noutput data, even if they are not otherwise specified in the values()\ncall. These extra fields are used to group "like" results together and they\ncan make otherwise identical result rows appear to be separate. This shows up,\nparticularly, when counting things.
By way of example, suppose you have a model like this:
\nfrom django.db import models\n\nclass Item(models.Model):\n name = models.CharField(max_length=10)\n data = models.IntegerField()\n\n class Meta:\n ordering = ["name"]\nThe important part here is the default ordering on the name field. If you\nwant to count how many times each distinct data value appears, you might\ntry this:
# Warning: not quite correct!\nItem.objects.values("data").annotate(Count("id"))\n...which will group the Item objects by their common data values and\nthen count the number of id values in each group. Except that it won't\nquite work. The default ordering by name will also play a part in the\ngrouping, so this query will group by distinct (data, name) pairs, which\nisn't what you want. Instead, you should construct this queryset:
Item.objects.values("data").annotate(Count("id")).order_by()\n...clearing any ordering in the query. You could also order by, say, data\nwithout any harmful effects, since that is already playing a role in the\nquery.
This behavior is the same as that noted in the queryset documentation for\ndistinct() and the general rule is the\nsame: normally you won't want extra columns playing a part in the result, so\nclear out the ordering, or at least make sure it's restricted only to those\nfields you also select in a values() call.
You can also generate an aggregate on the result of an annotation. When you\ndefine an aggregate() clause, the aggregates you provide can reference\nany alias defined as part of an annotate() clause in the query.
For example, if you wanted to calculate the average number of authors per\nbook you first annotate the set of books with the author count, then\naggregate that author count, referencing the annotation field:
\n>>> from django.db.models import Avg, Count\n>>> Book.objects.annotate(num_authors=Count('authors')).aggregate(Avg('num_authors'))\n{'num_authors__avg': 1.66}\n