=================
Query Expressions
=================

.. currentmodule:: django.db.models

Query expressions describe a value or a computation that can be used as part of
a filter, an annotation, or an aggregation. There are a number of built-in
expressions (documented below) that can be used to help you write queries.
Expressions can be combined, or in some cases nested, to form more complex
computations.

Supported arithmetic
====================

Django supports addition, subtraction, multiplication, division, modulo
arithmetic, and the power operator on query expressions, using Python constants,
variables, and even other expressions.

.. versionadded:: 1.7

    Support for the power operator ``**`` was added.

Some examples
=============

.. versionchanged:: 1.8

    Some of the examples rely on functionality that is new in Django 1.8.

.. code-block:: python

    # Find companies that have more employees than chairs.
    Company.objects.filter(num_employees__gt=F('num_chairs'))

    # Find companies that have at least twice as many employees
    # as chairs. Both the querysets below are equivalent.
    Company.objects.filter(num_employees__gt=F('num_chairs') * 2)
    Company.objects.filter(
        num_employees__gt=F('num_chairs') + F('num_chairs'))

    # How many chairs are needed for each company to seat all employees?
    >>> company = Company.objects.filter(
    ...    num_employees__gt=F('num_chairs')).annotate(
    ...    chairs_needed=F('num_employees') - F('num_chairs')).first()
    >>> company.num_employees
    120
    >>> company.num_chairs
    50
    >>> company.chairs_needed
    70

    # Annotate models with an aggregated value. Both forms
    # below are equivalent.
    Company.objects.annotate(num_products=Count('products'))
    Company.objects.annotate(num_products=Count(F('products')))

    # Aggregates can contain complex computations also
    Company.objects.annotate(num_offerings=Count(F('products') + F('services')))


Built-in Expressions
====================

``F()`` expressions
-------------------

.. class:: F

An ``F()`` object represents the value of a model field or annotated column. It
makes it possible to refer to model field values and perform  database
operations using them without actually having to pull them out of the  database
into Python memory.

Instead, Django uses the ``F()`` object to generate a SQL expression that
describes the required operation at the database level.

This is easiest to understand through an example. Normally, one might do
something like this::

    # Tintin filed a news story!
    reporter = Reporters.objects.get(name='Tintin')
    reporter.stories_filed += 1
    reporter.save()

Here, we have pulled the value of ``reporter.stories_filed`` from the database
into memory and manipulated it using familiar Python operators, and then saved
the object back to the database. But instead we could also have done::

    from django.db.models import F
    reporter = Reporters.objects.get(name='Tintin')
    reporter.stories_filed = F('stories_filed') + 1
    reporter.save()

Although ``reporter.stories_filed = F('stories_filed') + 1`` looks like a
normal Python assignment of value to an instance attribute, in fact it's an SQL
construct describing an operation on the database.

When Django encounters an instance of ``F()``, it overrides the standard Python
operators to create an encapsulated SQL expression; in this case, one which
instructs the database to increment the database field represented by
``reporter.stories_filed``.

Whatever value is or was on ``reporter.stories_filed``, Python never gets to
know about it - it is dealt with entirely by the database. All Python does,
through Django's ``F()`` class, is create the SQL syntax to refer to the field
and describe the operation.

.. note::

   In order to access the new value that has been saved in this way, the object
   will need to be reloaded::

       reporter = Reporters.objects.get(pk=reporter.pk)

As well as being used in operations on single instances as above, ``F()`` can
be used on ``QuerySets`` of object instances, with ``update()``. This reduces
the two queries we were using above - the ``get()`` and the
:meth:`~Model.save()` - to just one::

    reporter = Reporters.objects.filter(name='Tintin')
    reporter.update(stories_filed=F('stories_filed') + 1)

We can also use :meth:`~django.db.models.query.QuerySet.update()` to increment
the field value on multiple objects - which could be very much faster than
pulling them all into Python from the database, looping over them, incrementing
the field value of each one, and saving each one back to the database::

    Reporter.objects.all().update(stories_filed=F('stories_filed) + 1)

``F()`` therefore can offer performance advantages by:

* getting the database, rather than Python, to do work
* reducing the number of queries some operations require

.. _avoiding-race-conditions-using-f:

Avoiding race conditions using ``F()``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Another useful benefit of ``F()`` is that having the database - rather than
Python - update a field's value avoids a *race condition*.

If two Python threads execute the code in the first example above, one thread
could retrieve, increment, and save a field's value after the other has
retrieved it from the database. The value that the second thread saves will be
based on the original value; the work of the first thread will simply be lost.

If the database is responsible for updating the field, the process is more
robust: it will only ever update the field based on the value of the field in
the database when the :meth:`~Model.save()` or ``update()`` is executed, rather
than based on its value when the instance was retrieved.

Using ``F()`` in filters
~~~~~~~~~~~~~~~~~~~~~~~~

``F()`` is also very useful in ``QuerySet`` filters, where they make it
possible to filter a set of objects against criteria based on their field
values, rather than on Python values.

This is documented in :ref:`using F() expressions in queries
<using-f-expressions-in-filters>`.


.. _func-expressions:

``Func()`` expressions
----------------------

.. versionadded:: 1.8

``Func()`` expressions are the base type of all expressions that involve
database functions like ``COALESCE`` and ``LOWER``, or aggregates like ``SUM``.
They can be used directly::

    queryset.annotate(field_lower=Func(F('field'), function='LOWER'))

or they can be used to build a library of database functions::

    class Lower(Func):
        function = 'LOWER'

    queryset.annotate(field_lower=Lower(F('field')))

But both cases will result in a queryset where each model is annotated with an
extra attribute ``field_lower`` produced, roughly, from the following SQL::

    SELECT
        ...
        LOWER("app_label"."field") as "field_lower"

The ``Func`` API is as follows:

.. class:: Func(*expressions, **extra)

    .. attribute:: function

        A class attribute describing the function that will be generated.
        Specifically, the ``function`` will be interpolated as the ``function``
        placeholder within :attr:`template`. Defaults to ``None``.

    .. attribute:: template

        A class attribute, as a format string, that describes the SQL that is
        generated for this function. Defaults to
        ``'%(function)s(%(expressions)s)'``.

    .. attribute:: arg_joiner

        A class attribute that denotes the character used to join the list of
        ``expressions`` together. Defaults to ``', '``.

The ``*expressions`` argument is a list of positional expressions that the
function will be applied to. The expressions will be converted to strings,
joined together with ``arg_joiner``, and then interpolated into the ``template``
as the ``expressions`` placeholder.

The ``**extra`` kwargs are ``key=value`` pairs that can be interpolated
into the ``template`` attribute. Note that the keywords ``function`` and
``template`` can be used to replace the ``function`` and ``template``
attributes respectively, without having to define your own class.
``output_field`` can be used to define the expected return type.

``Aggregate()`` expressions
---------------------------

An aggregate expression is a special case of a :ref:`Func() expression
<func-expressions>` that informs the query that a ``GROUP BY`` clause
is required. All of the :ref:`aggregate functions <aggregation-functions>`,
like ``Sum()`` and ``Count()``, inherit from ``Aggregate()``.

Since ``Aggregate``\s are expressions and wrap expressions, you can represent
some complex computations::

    Company.objects.annotate(
        managers_required=(Count('num_employees') / 4) + Count('num_managers'))

The ``Aggregate`` API is as follows:

.. class:: Aggregate(expression, output_field=None, **extra)

    .. attribute:: template

        A class attribute, as a format string, that describes the SQL that is
        generated for this aggregate. Defaults to
        ``'%(function)s( %(expressions)s )'``.

    .. attribute:: function

        A class attribute describing the aggregate function that will be
        generated. Specifically, the ``function`` will be interpolated as the
        ``function`` placeholder within :attr:`template`. Defaults to ``None``.

The ``expression`` argument can be the name of a field on the model, or another
expression. It will be converted to a string and used as the ``expressions``
placeholder within the ``template``.

The ``output_field`` argument requires a model field instance, like
``IntegerField()`` or ``BooleanField()``, into which Django will load the value
after it's retrieved from the database. Usually no arguments are needed when
instantiating the model field as any arguments relating to data validation
(``max_length``, ``max_digits``, etc.) will not be enforced on the expression's
output value.

Note that ``output_field`` is only required when Django is unable to determine
what field type the result should be. Complex expressions that mix field types
should define the desired ``output_field``. For example, adding an
``IntegerField()`` and a ``FloatField()`` together should probably have
``output_field=FloatField()`` defined.

.. versionchanged:: 1.8

    ``output_field`` is a new parameter.

The ``**extra`` kwargs are ``key=value`` pairs that can be interpolated
into the ``template`` attribute.

.. versionadded:: 1.8

    Aggregate functions can now use arithmetic and reference multiple
    model fields in a single function.

Creating your own Aggregate Functions
-------------------------------------

Creating your own aggregate is extremely easy. At a minimum, you need
to define ``function``, but you can also completely customize the
SQL that is generated. Here's a brief example::

    class Count(Aggregate):
        # supports COUNT(distinct field)
        function = 'COUNT'
        template = '%(function)s(%(distinct)s%(expressions)s)'

        def __init__(self, expression, distinct=False, **extra):
            super(Count, self).__init__(
                expression,
                distinct='DISTINCT ' if distinct else '',
                output_field=IntegerField(),
                **extra)


``Value()`` expressions
-----------------------

.. class:: Value(value, output_field=None)


A ``Value()`` object represents the smallest possible component of an
expression: a simple value. When you need to represent the value of an integer,
boolean, or string within an expression, you can wrap that value within a
``Value()``.

You will rarely need to use ``Value()`` directly. When you write the expression
``F('field') + 1``, Django implicitly wraps the ``1`` in a ``Value()``,
allowing simple values to be used in more complex expressions.

The ``value`` argument describes the value to be included in the expression,
such as ``1``, ``True``, or ``None``. Django knows how to convert these Python
values into their corresponding database type.

The ``output_field`` argument should be a model field instance, like
``IntegerField()`` or ``BooleanField()``, into which Django will load the value
after it's retrieved from the database. Usually no arguments are needed when
instantiating the model field as any arguments relating to data validation
(``max_length``, ``max_digits``, etc.) will not be enforced on the expression's
output value.

Technical Information
=====================

Below you'll find technical implementation details that may be useful to
library authors. The technical API and examples below will help with
creating generic query expressions that can extend the built-in functionality
that Django provides.

Expression API
--------------

Query expressions implement the :ref:`query expression API <query-expression>`,
but also expose a number of extra methods and attributes listed below. All
query expressions must inherit from ``ExpressionNode()`` or a relevant
subclass.

When a query expression wraps another expression, it is responsible for
calling the appropriate methods on the wrapped expression.

.. class:: ExpressionNode

    .. attribute:: contains_aggregate

        Tells Django that this expression contains an aggregate and that a
        ``GROUP BY`` clause needs to be added to the query.

    .. method:: resolve_expression(query=None, allow_joins=True, reuse=None, summarize=False)

        Provides the chance to do any pre-processing or validation of
        the expression before it's added to the query. ``resolve_expression()``
        must also be called on any nested expressions. A ``copy()`` of ``self``
        should be returned with any necessary transformations.

        ``query`` is the backend query implementation.

        ``allow_joins`` is a boolean that allows or denies the use of
        joins in the query.

        ``reuse`` is a set of reusable joins for multi-join scenarios.

        ``summarize`` is a boolean that, when ``True``, signals that the
        query being computed is a terminal aggregate query.

    .. method:: get_source_expressions()

        Returns an ordered list of inner expressions. For example::

          >>> Sum(F('foo')).get_source_expressions()
          [F('foo')]

    .. method:: set_source_expressions(expressions)

        Takes a list of expressions and stores them such that
        ``get_source_expressions()`` can return them.

    .. method:: relabeled_clone(change_map)

        Returns a clone (copy) of ``self``, with any column aliases relabeled.
        Column aliases are renamed when subqueries are created.
        ``relabeled_clone()`` should also be called on any nested expressions
        and assigned to the clone.

        ``change_map`` is a dictionary mapping old aliases to new aliases.

        Example::

          def relabeled_clone(self, change_map):
              clone = copy.copy(self)
              clone.expression = self.expression.relabeled_clone(change_map)
              return clone

    .. method:: convert_value(self, value, connection)

        A hook allowing the expression to coerce ``value`` into a more
        appropriate type.

    .. method:: refs_aggregate(existing_aggregates)

        Returns a tuple containing the ``(aggregate, lookup_path)`` of the
        first aggregate that this expression (or any nested expression)
        references, or ``(False, ())`` if no aggregate is referenced.
        For example::

            queryset.filter(num_chairs__gt=F('sum__employees'))

        The ``F()`` expression here references a previous ``Sum()``
        computation which means that this filter expression should be
        added to the ``HAVING`` clause rather than the ``WHERE`` clause.

        In the majority of cases, returning the result of ``refs_aggregate``
        on any nested expression should be appropriate, as the necessary
        built-in expressions will return the correct values.

    .. method:: get_group_by_cols()

        Responsible for returning the list of columns references by
        this expression. ``get_group_by_cols()`` should be called on any
        nested expressions. ``F()`` objects, in particular, hold a reference
        to a column.

Writing your own Query Expressions
----------------------------------

You can write your own query expression classes that use, and can integrate
with, other query expressions. Let's step through an example by writing an
implementation of the ``COALESCE`` SQL function, without using the built-in
:ref:`Func() expressions <func-expressions>`.

The ``COALESCE`` SQL function is defined as taking a list of columns or
values. It will return the first column or value that isn't ``NULL``.

We'll start by defining the template to be used for SQL generation and
an ``__init__()`` method to set some attributes::

  import copy
  from django.db.models import ExpressionNode

  class Coalesce(ExpressionNode):
      template = 'COALESCE( %(expressions)s )'

      def __init__(self, expressions, output_field, **extra):
        super(Coalesce, self).__init__(output_field=output_field)
        if len(expressions) < 2:
            raise ValueError('expressions must have at least 2 elements')
        for expression in expressions:
            if not hasattr(expression, 'resolve_expression'):
                raise TypeError('%r is not an Expression' % expression)
        self.expressions = expressions
        self.extra = extra

We do some basic validation on the parameters, including requiring at least
2 columns or values, and ensuring they are expressions. We are requiring
``output_field`` here so that Django knows what kind of model field to assign
the eventual result to.

Now we implement the pre-processing and validation. Since we do not have
any of our own validation at this point, we just delegate to the nested
expressions::

    def resolve_expression(self, query=None, allow_joins=True, reuse=None, summarize=False):
        c = self.copy()
        c.is_summary = summarize
        for pos, expression in enumerate(self.expressions):
            c.expressions[pos] = expression.resolve_expression(query, allow_joins, reuse, summarize)
        return c

Next, we write the method responsible for generating the SQL::

    def as_sql(self, compiler, connection):
        sql_expressions, sql_params = [], []
        for expression in self.expressions:
            sql, params = compiler.compile(expression)
            sql_expressions.append(sql)
            sql_params.extend(params)
        self.extra['expressions'] = ','.join(sql_expressions)
        return self.template % self.extra, sql_params

    def as_oracle(self, compiler, connection):
        """
        Example of vendor specific handling (Oracle in this case).
        Let's make the function name lowercase.
        """
        self.template = 'coalesce( %(expressions)s )'
        return self.as_sql(compiler, connection)

We generate the SQL for each of the ``expressions`` by using the
``compiler.compile()`` method, and join the result together with commas.
Then the template is filled out with our data and the SQL and parameters
are returned.

We've also defined a custom implementation that is specific to the Oracle
backend. The ``as_oracle()`` function will be called instead of ``as_sql()``
if the Oracle backend is in use.

Finally, we implement the rest of the methods that allow our query expression
to play nice with other query expressions::

    def get_source_expressions(self):
        return self.expressions

    def set_source_expressions(expressions):
        self.expressions = expressions

Let's see how it works::

    >>> qs = Company.objects.annotate(
    ...    tagline=Coalesce([
    ...        F('motto'),
    ...        F('ticker_name'),
    ...        F('description'),
    ...        Value('No Tagline')
    ...        ], output_field=CharField()))
    >>> for c in qs:
    ...     print("%s: %s" % (c.name, c.tagline))
    ...
    Google: Do No Evil
    Apple: AAPL
    Yahoo: Internet Company
    Django Software Foundation: No Tagline
