Performing raw SQL queriesLink para este cabeçalho

When the model query APIs don’t go far enough, you can fall back to writing raw SQL. Django gives you two ways of performing raw SQL queries: you can use Manager.raw() to perform raw queries and return model instances, or you can avoid the model layer entirely and execute custom SQL directly.

Performing raw queriesLink para este cabeçalho

The raw() manager method can be used to perform raw SQL queries that return model instances:

Manager.raw(raw_query, params=None, translations=None)Link para esta definição

This method takes a raw SQL query, executes it, and returns a django.db.models.query.RawQuerySet instance. This RawQuerySet instance can be iterated over just like a normal QuerySet to provide object instances.

This is best illustrated with an example. Suppose you have the following model:

Code

class Person(models.Model):
    first_name = models.CharField(...)
    last_name = models.CharField(...)
    birth_date = models.DateField(...)

You could then execute custom SQL like so:

Code

>>> for p in Person.objects.raw('SELECT * FROM myapp_person'):
...     print(p)
John Smith
Jane Jones

Of course, this example isn’t very exciting – it’s exactly the same as running Person.objects.all(). However, raw() has a bunch of other options that make it very powerful.

Mapping query fields to model fieldsLink para este cabeçalho

raw() automatically maps fields in the query to fields on the model.

The order of fields in your query doesn’t matter. In other words, both of the following queries work identically:

Code

>>> Person.objects.raw('SELECT id, first_name, last_name, birth_date FROM myapp_person')
...
>>> Person.objects.raw('SELECT last_name, birth_date, first_name, id FROM myapp_person')
...

Matching is done by name. This means that you can use SQL’s AS clauses to map fields in the query to model fields. So if you had some other table that had Person data in it, you could easily map it into Person instances:

Code

>>> Person.objects.raw('''SELECT first AS first_name,
...                              last AS last_name,
...                              bd AS birth_date,
...                              pk AS id,
...                       FROM some_other_table''')

As long as the names match, the model instances will be created correctly.

Alternatively, you can map fields in the query to model fields using the translations argument to raw(). This is a dictionary mapping names of fields in the query to names of fields on the model. For example, the above query could also be written:

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>>> name_map = {'first': 'first_name', 'last': 'last_name', 'bd': 'birth_date', 'pk': 'id'}
>>> Person.objects.raw('SELECT * FROM some_other_table', translations=name_map)

Index lookupsLink para este cabeçalho

raw() supports indexing, so if you need only the first result you can write:

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>>> first_person = Person.objects.raw('SELECT * FROM myapp_person')[0]

However, the indexing and slicing are not performed at the database level. If you have a large number of Person objects in your database, it is more efficient to limit the query at the SQL level:

Code

>>> first_person = Person.objects.raw('SELECT * FROM myapp_person LIMIT 1')[0]

Deferring model fieldsLink para este cabeçalho

Fields may also be left out:

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>>> people = Person.objects.raw('SELECT id, first_name FROM myapp_person')

The Person objects returned by this query will be deferred model instances (see defer()). This means that the fields that are omitted from the query will be loaded on demand. For example:

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>>> for p in Person.objects.raw('SELECT id, first_name FROM myapp_person'):
...     print(p.first_name, # This will be retrieved by the original query
...           p.last_name) # This will be retrieved on demand
...
John Smith
Jane Jones

From outward appearances, this looks like the query has retrieved both the first name and last name. However, this example actually issued 3 queries. Only the first names were retrieved by the raw() query – the last names were both retrieved on demand when they were printed.

There is only one field that you can’t leave out - the primary key field. Django uses the primary key to identify model instances, so it must always be included in a raw query. An InvalidQuery exception will be raised if you forget to include the primary key.

Adding annotationsLink para este cabeçalho

You can also execute queries containing fields that aren’t defined on the model. For example, we could use PostgreSQL’s age() function to get a list of people with their ages calculated by the database:

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>>> people = Person.objects.raw('SELECT *, age(birth_date) AS age FROM myapp_person')
>>> for p in people:
...     print("%s is %s." % (p.first_name, p.age))
John is 37.
Jane is 42.
...

Passing parameters into `raw()`Link para este cabeçalho

If you need to perform parameterized queries, you can use the params argument to raw():

Code

>>> lname = 'Doe'
>>> Person.objects.raw('SELECT * FROM myapp_person WHERE last_name = %s', [lname])

params is a list or dictionary of parameters. You’ll use %s placeholders in the query string for a list, or %(key)s placeholders for a dictionary (where key is replaced by a dictionary key, of course), regardless of your database engine. Such placeholders will be replaced with parameters from the params argument.

Aviso

Do not use string formatting on raw queries!

It’s tempting to write the above query as:

Code

>>> query = 'SELECT * FROM myapp_person WHERE last_name = %s' % lname
>>> Person.objects.raw(query)

Don’t.

Using the params argument completely protects you from SQL injection attacks, a common exploit where attackers inject arbitrary SQL into your database. If you use string interpolation, sooner or later you’ll fall victim to SQL injection. As long as you remember to always use the params argument you’ll be protected.

Executing custom SQL directlyLink para este cabeçalho

Sometimes even Manager.raw() isn’t quite enough: you might need to perform queries that don’t map cleanly to models, or directly execute UPDATE, INSERT, or DELETE queries.

In these cases, you can always access the database directly, routing around the model layer entirely.

The object django.db.connection represents the default database connection. To use the database connection, call connection.cursor() to get a cursor object. Then, call cursor.execute(sql, [params]) to execute the SQL and cursor.fetchone() or cursor.fetchall() to return the resulting rows.

Por exemplo:

Code

from django.db import connection

def my_custom_sql(self):
    cursor = connection.cursor()

    cursor.execute("UPDATE bar SET foo = 1 WHERE baz = %s", [self.baz])

    cursor.execute("SELECT foo FROM bar WHERE baz = %s", [self.baz])
    row = cursor.fetchone()

    return row

Note that if you want to include literal percent signs in the query, you have to double them in the case you are passing parameters:

Code

cursor.execute("SELECT foo FROM bar WHERE baz = '30%'")
cursor.execute("SELECT foo FROM bar WHERE baz = '30%%' AND id = %s", [self.id])

If you are using more than one database, you can use django.db.connections to obtain the connection (and cursor) for a specific database. django.db.connections is a dictionary-like object that allows you to retrieve a specific connection using its alias:

Code

from django.db import connections
cursor = connections['my_db_alias'].cursor()
# Your code here...

By default, the Python DB API will return results without their field names, which means you end up with a list of values, rather than a dict. At a small performance and memory cost, you can return results as a dict by using something like this:

Code

def dictfetchall(cursor):
    "Return all rows from a cursor as a dict"
    columns = [col[0] for col in cursor.description]
    return [
        dict(zip(columns, row))
        for row in cursor.fetchall()
    ]

Another option is to use collections.namedtuple() from the Python standard library. A namedtuple is a tuple-like object that has fields accessible by attribute lookup; it’s also indexable and iterable. Results are immutable and accessible by field names or indices, which might be useful:

Code

from collections import namedtuple

def namedtuplefetchall(cursor):
    "Return all rows from a cursor as a namedtuple"
    desc = cursor.description
    nt_result = namedtuple('Result', [col[0] for col in desc])
    return [nt_result(*row) for row in cursor.fetchall()]

Here is an example of the difference between the three:

Code

>>> cursor.execute("SELECT id, parent_id FROM test LIMIT 2");
>>> cursor.fetchall()
((54360982, None), (54360880, None))

>>> cursor.execute("SELECT id, parent_id FROM test LIMIT 2");
>>> dictfetchall(cursor)
[{'parent_id': None, 'id': 54360982}, {'parent_id': None, 'id': 54360880}]

>>> cursor.execute("SELECT id, parent_id FROM test LIMIT 2");
>>> results = namedtuplefetchall(cursor)
>>> results
[Result(id=54360982, parent_id=None), Result(id=54360880, parent_id=None)]
>>> results[0].id
54360982
>>> results[0][0]
54360982

Conexões e cursoresLink para este cabeçalho

connection and cursor mostly implement the standard Python DB-API described in PEP 249 — except when it comes to transaction handling.

If you’re not familiar with the Python DB-API, note that the SQL statement in cursor.execute() uses placeholders, "%s", rather than adding parameters directly within the SQL. If you use this technique, the underlying database library will automatically escape your parameters as necessary.

Also note that Django expects the "%s" placeholder, not the "?" placeholder, which is used by the SQLite Python bindings. This is for the sake of consistency and sanity.

Using a cursor as a context manager:

Code

with connection.cursor() as c:
    c.execute(...)

is equivalent to:

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c = connection.cursor()
try:
    c.execute(...)
finally:
    c.close()

Performing raw SQL queriesLink para este cabeçalho#

Performing raw queriesLink para este cabeçalho#

Mapping query fields to model fieldsLink para este cabeçalho#

Index lookupsLink para este cabeçalho#

Deferring model fieldsLink para este cabeçalho#

Adding annotationsLink para este cabeçalho#

Passing parameters into raw()Link para este cabeçalho#

Executing custom SQL directlyLink para este cabeçalho#

Conexões e cursoresLink para este cabeçalho#

Performing raw SQL queriesLink para este cabeçalho

Performing raw queriesLink para este cabeçalho

Mapping query fields to model fieldsLink para este cabeçalho

Index lookupsLink para este cabeçalho

Deferring model fieldsLink para este cabeçalho

Adding annotationsLink para este cabeçalho

Passing parameters into `raw()`Link para este cabeçalho

Executing custom SQL directlyLink para este cabeçalho

Conexões e cursoresLink para este cabeçalho