SQLAlchemy 1.4 / 2.0 Tutorial
This page is part of the SQLAlchemy 1.4 / 2.0 Tutorial.
Previous: Working with Database Metadata | Next: Data Manipulation with the ORM
In Working with Transactions and the DBAPI, we learned the basics of how to
interact with the Python DBAPI and its transactional state. Then, in
Working with Database Metadata, we learned how to represent database
tables, columns, and constraints within SQLAlchemy using the
MetaData
and related objects. In this section we will combine
both concepts above to create, select and manipulate data within a relational
database. Our interaction with the database is always in terms
of a transaction, even if we’ve set our database driver to use autocommit behind the scenes.
The components of this section are as follows:
Core Insert - to get some data into the database, we introduce
and demonstrate the Core Insert
construct. INSERTs from an
ORM perspective are described in the next section
Data Manipulation with the ORM.
Selecting Data - this section will describe in detail
the Select
construct, which is the most commonly used object
in SQLAlchemy. The Select
construct emits SELECT statements
for both Core and ORM centric applications and both use cases will be
described here. Additional ORM use cases are also noted in the later
section Using Relationships in Queries as well as the
ORM Querying Guide.
Core UPDATE and DELETE - Rounding out the INSERT and SELECtion
of data, this section will describe from a Core perspective the use of the
Update
and Delete
constructs. ORM-specific
UPDATE and DELETE is similarly described in the
Data Manipulation with the ORM section.
When using Core, a SQL INSERT statement is generated using the
insert()
function - this function generates a new instance of
Insert
which represents an INSERT statement in SQL, that adds
new data into a table.
ORM Readers - The way that rows are INSERTed into the database from an ORM
perspective makes use of object-centric APIs on the Session
object known as the
unit of work process,
and is fairly different from the Core-only approach described here.
The more ORM-focused sections later starting at Inserting Rows with the ORM
subsequent to the Expression Language sections introduce this.
A simple example of Insert
illustrating the target table
and the VALUES clause at once:
>>> from sqlalchemy import insert
>>> stmt = insert(user_table).values(name='spongebob', fullname="Spongebob Squarepants")
The above stmt
variable is an instance of Insert
. Most
SQL expressions can be stringified in place as a means to see the general
form of what’s being produced:
>>> print(stmt)
INSERT INTO user_account (name, fullname) VALUES (:name, :fullname)
The stringified form is created by producing a Compiled
form
of the object which includes a database-specific string SQL representation of
the statement; we can acquire this object directly using the
ClauseElement.compile()
method:
>>> compiled = stmt.compile()
Our Insert
construct is an example of a “parameterized”
construct, illustrated previously at Sending Parameters; to
view the name
and fullname
bound parameters, these are
available from the Compiled
construct as well:
>>> compiled.params
{'name': 'spongebob', 'fullname': 'Spongebob Squarepants'}
Invoking the statement we can INSERT a row into user_table
.
The INSERT SQL as well as the bundled parameters can be seen in the
SQL logging:
>>> with engine.connect() as conn:
... result = conn.execute(stmt)
... conn.commit()
BEGIN (implicit)
INSERT INTO user_account (name, fullname) VALUES (?, ?)
[...] ('spongebob', 'Spongebob Squarepants')
COMMIT
In its simple form above, the INSERT statement does not return any rows, and if
only a single row is inserted, it will usually include the ability to return
information about column-level default values that were generated during the
INSERT of that row, most commonly an integer primary key value. In the above
case the first row in a SQLite database will normally return 1
for the
first integer primary key value, which we can acquire using the
CursorResult.inserted_primary_key
accessor:
>>> result.inserted_primary_key
(1,)
Tip
CursorResult.inserted_primary_key
returns a tuple
because a primary key may contain multiple columns. This is known as
a composite primary key. The CursorResult.inserted_primary_key
is intended to always contain the complete primary key of the record just
inserted, not just a “cursor.lastrowid” kind of value, and is also intended
to be populated regardless of whether or not “autoincrement” were used, hence
to express a complete primary key it’s a tuple.
Changed in version 1.4.8: the tuple returned by
CursorResult.inserted_primary_key
is now a named tuple
fullfilled by returning it as a Row
object.
The example above made use of the Insert.values()
method to
explicitly create the VALUES clause of the SQL INSERT statement. This method
in fact has some variants that allow for special forms such as multiple rows in
one statement and insertion of SQL expressions. However the usual way that
Insert
is used is such that the VALUES clause is generated
automatically from the parameters passed to the
Connection.execute()
method; below we INSERT two more rows to
illustrate this:
>>> with engine.connect() as conn:
... result = conn.execute(
... insert(user_table),
... [
... {"name": "sandy", "fullname": "Sandy Cheeks"},
... {"name": "patrick", "fullname": "Patrick Star"}
... ]
... )
... conn.commit()
BEGIN (implicit)
INSERT INTO user_account (name, fullname) VALUES (?, ?)
[...] (('sandy', 'Sandy Cheeks'), ('patrick', 'Patrick Star'))
COMMIT
The execution above features “executemany” form first illustrated at
Sending Multiple Parameters, however unlike when using the
text()
construct, we didn’t have to spell out any SQL.
By passing a dictionary or list of dictionaries to the Connection.execute()
method in conjunction with the Insert
construct, the
Connection
ensures that the column names which are passed
will be expressed in the VALUES clause of the Insert
construct automatically.
Deep Alchemy
Hi, welcome to the first edition of Deep Alchemy. The person on the left is known as The Alchemist, and you’ll note they are not a wizard, as the pointy hat is not sticking upwards. The Alchemist comes around to describe things that are generally more advanced and/or tricky and additionally not usually needed, but for whatever reason they feel you should know about this thing that SQLAlchemy can do.
In this edition, towards the goal of having some interesting data in the
address_table
as well, below is a more advanced example illustrating
how the Insert.values()
method may be used explicitly while at
the same time including for additional VALUES generated from the
parameters. A scalar subquery is constructed, making use of the
select()
construct introduced in the next section, and the
parameters used in the subquery are set up using an explicit bound
parameter name, established using the bindparam()
construct.
This is some slightly deeper alchemy just so that we can add related
rows without fetching the primary key identifiers from the user_table
operation into the application. Most Alchemists will simply use the ORM
which takes care of things like this for us.
>>> from sqlalchemy import select, bindparam
>>> scalar_subquery = (
... select(user_table.c.id).
... where(user_table.c.name==bindparam('username')).
... scalar_subquery()
... )
>>> with engine.connect() as conn:
... result = conn.execute(
... insert(address_table).values(user_id=scalar_subquery),
... [
... {"username": 'spongebob', "email_address": "spongebob@sqlalchemy.org"},
... {"username": 'sandy', "email_address": "sandy@sqlalchemy.org"},
... {"username": 'sandy', "email_address": "sandy@squirrelpower.org"},
... ]
... )
... conn.commit()
BEGIN (implicit)
INSERT INTO address (user_id, email_address) VALUES ((SELECT user_account.id
FROM user_account
WHERE user_account.name = ?), ?)
[...] (('spongebob', 'spongebob@sqlalchemy.org'), ('sandy', 'sandy@sqlalchemy.org'),
('sandy', 'sandy@squirrelpower.org'))
COMMIT
The Insert
construct can compose
an INSERT that gets rows directly from a SELECT using the Insert.from_select()
method:
>>> select_stmt = select(user_table.c.id, user_table.c.name + "@aol.com")
>>> insert_stmt = insert(address_table).from_select(
... ["user_id", "email_address"], select_stmt
... )
>>> print(insert_stmt)
INSERT INTO address (user_id, email_address)
SELECT user_account.id, user_account.name || :name_1 AS anon_1
FROM user_account
The RETURNING clause for supported backends is used
automatically in order to retrieve the last inserted primary key value
as well as the values for server defaults. However the RETURNING clause
may also be specified explicitly using the Insert.returning()
method; in this case, the Result
object that’s returned when the statement is executed has rows which
can be fetched:
>>> insert_stmt = insert(address_table).returning(address_table.c.id, address_table.c.email_address)
>>> print(insert_stmt)
INSERT INTO address (id, user_id, email_address)
VALUES (:id, :user_id, :email_address)
RETURNING address.id, address.email_address
It can also be combined with Insert.from_select()
,
as in the example below that builds upon the example stated in
INSERT…FROM SELECT:
>>> select_stmt = select(user_table.c.id, user_table.c.name + "@aol.com")
>>> insert_stmt = insert(address_table).from_select(
... ["user_id", "email_address"], select_stmt
... )
>>> print(insert_stmt.returning(address_table.c.id, address_table.c.email_address))
INSERT INTO address (user_id, email_address)
SELECT user_account.id, user_account.name || :name_1 AS anon_1
FROM user_account RETURNING address.id, address.email_address
Tip
The RETURNING feature is also supported by UPDATE and DELETE statements,
which will be introduced later in this tutorial.
The RETURNING feature is generally 1 only
supported for statement executions that use a single set of bound
parameters; that is, it wont work with the “executemany” form introduced
at Sending Multiple Parameters. Additionally, some dialects
such as the Oracle dialect only allow RETURNING to return a single row
overall, meaning it won’t work with “INSERT..FROM SELECT” nor will it
work with multiple row Update
or Delete
forms.
See also
Insert
- in the SQL Expression API documentation
For both Core and ORM, the select()
function generates a
Select
construct which is used for all SELECT queries.
Passed to methods like Connection.execute()
in Core and
Session.execute()
in ORM, a SELECT statement is emitted in the
current transaction and the result rows available via the returned
Result
object.
ORM Readers - the content here applies equally well to both Core and ORM use and basic ORM variant use cases are mentioned here. However there are a lot more ORM-specific features available as well; these are documented at ORM Querying Guide.
The select()
construct builds up a statement in the same way
as that of insert()
, using a generative approach where
each method builds more state onto the object. Like the other SQL constructs,
it can be stringified in place:
>>> from sqlalchemy import select
>>> stmt = select(user_table).where(user_table.c.name == 'spongebob')
>>> print(stmt)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = :name_1
Also in the same manner as all other statement-level SQL constructs, to
actually run the statement we pass it to an execution method.
Since a SELECT statement returns
rows we can always iterate the result object to get Row
objects back:
>>> with engine.connect() as conn:
... for row in conn.execute(stmt):
... print(row)
BEGIN (implicit)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = ?
[...] ('spongebob',)
(1, 'spongebob', 'Spongebob Squarepants')
ROLLBACK
When using the ORM, particularly with a select()
construct that’s
composed against ORM entities, we will want to execute it using the
Session.execute()
method on the Session
; using
this approach, we continue to get Row
objects from the
result, however these rows are now capable of including
complete entities, such as instances of the User
class, as individual
elements within each row:
>>> stmt = select(User).where(User.name == 'spongebob')
>>> with Session(engine) as session:
... for row in session.execute(stmt):
... print(row)
BEGIN (implicit)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = ?
[...] ('spongebob',)
(User(id=1, name='spongebob', fullname='Spongebob Squarepants'),)
ROLLBACK
select() from a Table vs. ORM class
While the SQL generated in these examples looks the same whether we invoke
select(user_table)
or select(User)
, in the more general case
they do not necessarily render the same thing, as an ORM-mapped class
may be mapped to other kinds of “selectables” besides tables. The
select()
that’s against an ORM entity also indicates that ORM-mapped
instances should be returned in a result, which is not the case when
SELECTing from a Table
object.
The following sections will discuss the SELECT construct in more detail.
The select()
function accepts positional elements representing any
number of Column
and/or Table
expressions, as
well as a wide range of compatible objects, which are resolved into a list of SQL
expressions to be SELECTed from that will be returned as columns in the result
set. These elements also serve in simpler cases to create the FROM clause,
which is inferred from the columns and table-like expressions passed:
>>> print(select(user_table))
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
To SELECT from individual columns using a Core approach,
Column
objects are accessed from the Table.c
accessor and can be sent directly; the FROM clause will be inferred as the set
of all Table
and other FromClause
objects that
are represented by those columns:
>>> print(select(user_table.c.name, user_table.c.fullname))
SELECT user_account.name, user_account.fullname
FROM user_account
ORM entities, such our User
class as well as the column-mapped
attributes upon it such as User.name
, also participate in the SQL Expression
Language system representing tables and columns. Below illustrates an
example of SELECTing from the User
entity, which ultimately renders
in the same way as if we had used user_table
directly:
>>> print(select(User))
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
When executing a statement like the above using the ORM Session.execute()
method, there is an important difference when we select from a full entity
such as User
, as opposed to user_table
, which is that the entity
itself is returned as a single element within each row. That is, when we fetch rows from
the above statement, as there is only the User
entity in the list of
things to fetch, we get back Row
objects that have only one element, which contain
instances of the User
class:
>>> row = session.execute(select(User)).first()
BEGIN...
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
[...] ()
>>> row
(User(id=1, name='spongebob', fullname='Spongebob Squarepants'),)
The above Row
has just one element, representing the User
entity:
>>> row[0]
User(id=1, name='spongebob', fullname='Spongebob Squarepants')
Alternatively, we can select individual columns of an ORM entity as distinct
elements within result rows, by using the class-bound attributes; when these
are passed to a construct such as select()
, they are resolved into
the Column
or other SQL expression represented by each
attribute:
>>> print(select(User.name, User.fullname))
SELECT user_account.name, user_account.fullname
FROM user_account
When we invoke this statement using Session.execute()
, we now
receive rows that have individual elements per value, each corresponding
to a separate column or other SQL expression:
>>> row = session.execute(select(User.name, User.fullname)).first()
SELECT user_account.name, user_account.fullname
FROM user_account
[...] ()
>>> row
('spongebob', 'Spongebob Squarepants')
The approaches can also be mixed, as below where we SELECT the name
attribute of the User
entity as the first element of the row, and combine
it with full Address
entities in the second element:
>>> session.execute(
... select(User.name, Address).
... where(User.id==Address.user_id).
... order_by(Address.id)
... ).all()
SELECT user_account.name, address.id, address.email_address, address.user_id
FROM user_account, address
WHERE user_account.id = address.user_id ORDER BY address.id
[...] ()
[('spongebob', Address(id=1, email_address='spongebob@sqlalchemy.org')),
('sandy', Address(id=2, email_address='sandy@sqlalchemy.org')),
('sandy', Address(id=3, email_address='sandy@squirrelpower.org'))]
Approaches towards selecting ORM entities and columns as well as common methods for converting rows are discussed further at Selecting ORM Entities and Attributes.
See also
Selecting ORM Entities and Attributes - in the ORM Querying Guide
The ColumnElement.label()
method as well as the same-named method
available on ORM attributes provides a SQL label of a column or expression,
allowing it to have a specific name in a result set. This can be helpful
when referring to arbitrary SQL expressions in a result row by name:
>>> from sqlalchemy import func, cast
>>> stmt = (
... select(
... ("Username: " + user_table.c.name).label("username"),
... ).order_by(user_table.c.name)
... )
>>> with engine.connect() as conn:
... for row in conn.execute(stmt):
... print(f"{row.username}")
BEGIN (implicit)
SELECT ? || user_account.name AS username
FROM user_account ORDER BY user_account.name
[...] ('Username: ',)
Username: patrick
Username: sandy
Username: spongebob
ROLLBACK
SQLAlchemy allows us to compose SQL expressions, such as name = 'squidward'
or user_id > 10
, by making use of standard Python operators in
conjunction with
Column
and similar objects. For boolean expressions, most
Python operators such as ==
, !=
, <
, >=
etc. generate new
SQL Expression objects, rather than plain boolean True
/False
values:
>>> print(user_table.c.name == 'squidward')
user_account.name = :name_1
>>> print(address_table.c.user_id > 10)
address.user_id > :user_id_1
We can use expressions like these to generate the WHERE clause by passing
the resulting objects to the Select.where()
method:
>>> print(select(user_table).where(user_table.c.name == 'squidward'))
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = :name_1
To produce multiple expressions joined by AND, the Select.where()
method may be invoked any number of times:
>>> print(
... select(address_table.c.email_address).
... where(user_table.c.name == 'squidward').
... where(address_table.c.user_id == user_table.c.id)
... )
SELECT address.email_address
FROM address, user_account
WHERE user_account.name = :name_1 AND address.user_id = user_account.id
A single call to Select.where()
also accepts multiple expressions
with the same effect:
>>> print(
... select(address_table.c.email_address).
... where(
... user_table.c.name == 'squidward',
... address_table.c.user_id == user_table.c.id
... )
... )
SELECT address.email_address
FROM address, user_account
WHERE user_account.name = :name_1 AND address.user_id = user_account.id
“AND” and “OR” conjunctions are both available directly using the
and_()
and or_()
functions, illustrated below in terms
of ORM entities:
>>> from sqlalchemy import and_, or_
>>> print(
... select(Address.email_address).
... where(
... and_(
... or_(User.name == 'squidward', User.name == 'sandy'),
... Address.user_id == User.id
... )
... )
... )
SELECT address.email_address
FROM address, user_account
WHERE (user_account.name = :name_1 OR user_account.name = :name_2)
AND address.user_id = user_account.id
For simple “equality” comparisons against a single entity, there’s also a
popular method known as Select.filter_by()
which accepts keyword
arguments that match to column keys or ORM attribute names. It will filter
against the leftmost FROM clause or the last entity joined:
>>> print(
... select(User).filter_by(name='spongebob', fullname='Spongebob Squarepants')
... )
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = :name_1 AND user_account.fullname = :fullname_1
See also
Operator Reference - descriptions of most SQL operator functions in SQLAlchemy
As mentioned previously, the FROM clause is usually inferred
based on the expressions that we are setting in the columns
clause as well as other elements of the Select
.
If we set a single column from a particular Table
in the COLUMNS clause, it puts that Table
in the FROM
clause as well:
>>> print(select(user_table.c.name))
SELECT user_account.name
FROM user_account
If we were to put columns from two tables, then we get a comma-separated FROM clause:
>>> print(select(user_table.c.name, address_table.c.email_address))
SELECT user_account.name, address.email_address
FROM user_account, address
In order to JOIN these two tables together, we typically use one of two methods
on Select
. The first is the Select.join_from()
method, which allows us to indicate the left and right side of the JOIN
explicitly:
>>> print(
... select(user_table.c.name, address_table.c.email_address).
... join_from(user_table, address_table)
... )
SELECT user_account.name, address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id
The other is the the Select.join()
method, which indicates only the
right side of the JOIN, the left hand-side is inferred:
>>> print(
... select(user_table.c.name, address_table.c.email_address).
... join(address_table)
... )
SELECT user_account.name, address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id
The ON Clause is inferred
When using Select.join_from()
or Select.join()
, we may
observe that the ON clause of the join is also inferred for us in simple
foreign key cases. More on that in the next section.
We also have the option add elements to the FROM clause explicitly, if it is not
inferred the way we want from the columns clause. We use the
Select.select_from()
method to achieve this, as below
where we establish user_table
as the first element in the FROM
clause and Select.join()
to establish address_table
as
the second:
>>> print(
... select(address_table.c.email_address).
... select_from(user_table).join(address_table)
... )
SELECT address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id
Another example where we might want to use Select.select_from()
is if our columns clause doesn’t have enough information to provide for a
FROM clause. For example, to SELECT from the common SQL expression
count(*)
, we use a SQLAlchemy element known as sqlalchemy.sql.expression.func
to
produce the SQL count()
function:
>>> from sqlalchemy import func
>>> print (
... select(func.count('*')).select_from(user_table)
... )
SELECT count(:count_2) AS count_1
FROM user_account
See also
Controlling what to Join From - in the ORM Querying Guide -
contains additional examples and notes
regarding the interaction of Select.select_from()
and
Select.join()
.
The previous examples of JOIN illustrated that the Select
construct
can join between two tables and produce the ON clause automatically. This
occurs in those examples because the user_table
and address_table
Table
objects include a single ForeignKeyConstraint
definition which is used to form this ON clause.
If the left and right targets of the join do not have such a constraint, or
there are multiple constraints in place, we need to specify the ON clause
directly. Both Select.join()
and Select.join_from()
accept an additional argument for the ON clause, which is stated using the
same SQL Expression mechanics as we saw about in The WHERE clause:
>>> print(
... select(address_table.c.email_address).
... select_from(user_table).
... join(address_table, user_table.c.id == address_table.c.user_id)
... )
SELECT address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id
ORM Tip - there’s another way to generate the ON clause when using
ORM entities that make use of the relationship()
construct,
like the mapping set up in the previous section at
Declaring Mapped Classes.
This is a whole subject onto itself, which is introduced at length
at Using Relationships to Join.
Both the Select.join()
and Select.join_from()
methods
accept keyword arguments Select.join.isouter
and
Select.join.full
which will render LEFT OUTER JOIN
and FULL OUTER JOIN, respectively:
>>> print(
... select(user_table).join(address_table, isouter=True)
... )
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account LEFT OUTER JOIN address ON user_account.id = address.user_id
>>> print(
... select(user_table).join(address_table, full=True)
... )
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account FULL OUTER JOIN address ON user_account.id = address.user_id
There is also a method Select.outerjoin()
that is equivalent to
using .join(..., isouter=True)
.
Tip
SQL also has a “RIGHT OUTER JOIN”. SQLAlchemy doesn’t render this directly; instead, reverse the order of the tables and use “LEFT OUTER JOIN”.
The ORDER BY clause is constructed in terms
of SQL Expression constructs typically based on Column
or
similar objects. The Select.order_by()
method accepts one or
more of these expressions positionally:
>>> print(select(user_table).order_by(user_table.c.name))
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account ORDER BY user_account.name
Ascending / descending is available from the ColumnElement.asc()
and ColumnElement.desc()
modifiers, which are present
from ORM-bound attributes as well:
>>> print(select(User).order_by(User.name.asc(), User.fullname.desc()))
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account ORDER BY user_account.name ASC, user_account.fullname DESC
In SQL, aggregate functions allow column expressions across multiple rows to be aggregated together to produce a single result. Examples include counting, computing averages, as well as locating the maximum or minimum value in a set of values.
SQLAlchemy provides for SQL functions in an open-ended way using a namespace
known as func
. This is a special constructor object which
will create new instances of Function
when given the name
of a particular SQL function, which can have any name, as well as zero or
more arguments to pass to the function, which are, like in all other cases,
SQL Expression constructs. For example, to
render the SQL COUNT() function against the user_account.id
column,
we call upon the name count()
name:
>>> from sqlalchemy import func
>>> count_fn = func.count(user_table.c.id)
>>> print(count_fn)
count(user_account.id)
SQL functions are described in more detail later in this tutorial at Working with SQL Functions.
When using aggregate functions in SQL, the GROUP BY clause is essential in that it allows rows to be partitioned into groups where aggregate functions will be applied to each group individually. When requesting non-aggregated columns in the COLUMNS clause of a SELECT statement, SQL requires that these columns all be subject to a GROUP BY clause, either directly or indirectly based on a primary key association. The HAVING clause is then used in a similar manner as the WHERE clause, except that it filters out rows based on aggregated values rather than direct row contents.
SQLAlchemy provides for these two clauses using the Select.group_by()
and Select.having()
methods. Below we illustrate selecting
user name fields as well as count of addresses, for those users that have more
than one address:
>>> with engine.connect() as conn:
... result = conn.execute(
... select(User.name, func.count(Address.id).label("count")).
... join(Address).
... group_by(User.name).
... having(func.count(Address.id) > 1)
... )
... print(result.all())
BEGIN (implicit)
SELECT user_account.name, count(address.id) AS count
FROM user_account JOIN address ON user_account.id = address.user_id GROUP BY user_account.name
HAVING count(address.id) > ?
[...] (1,)
[('sandy', 2)]
ROLLBACK
An important technique, in particular on some database backends, is the ability
to ORDER BY or GROUP BY an expression that is already stated in the columns
clause, without re-stating the expression in the ORDER BY or GROUP BY clause
and instead using the column name or labeled name from the COLUMNS clause.
This form is available by passing the string text of the name to the
Select.order_by()
or Select.group_by()
method. The text
passed is not rendered directly; instead, the name given to an expression
in the columns clause and rendered as that expression name in context, raising an
error if no match is found. The unary modifiers
asc()
and desc()
may also be used in this form:
>>> from sqlalchemy import func, desc
>>> stmt = select(
... Address.user_id,
... func.count(Address.id).label('num_addresses')).\
... group_by("user_id").order_by("user_id", desc("num_addresses"))
>>> print(stmt)
SELECT address.user_id, count(address.id) AS num_addresses
FROM address GROUP BY address.user_id ORDER BY address.user_id, num_addresses DESC
Now that we are selecting from multiple tables and using joins, we quickly run into the case where we need to refer to the same table mutiple times in the FROM clause of a statement. We accomplish this using SQL aliases, which are a syntax that supplies an alternative name to a table or subquery from which it can be referred towards in the statement.
In the SQLAlchemy Expression Language, these “names” are instead represented by
FromClause
objects known as the Alias
construct,
which is constructed in Core using the FromClause.alias()
method. An Alias
construct is just like a Table
construct in that it also has a namespace of Column
objects within the Alias.c
collection. The SELECT statement
below for example returns all unique pairs of user names:
>>> user_alias_1 = user_table.alias()
>>> user_alias_2 = user_table.alias()
>>> print(
... select(user_alias_1.c.name, user_alias_2.c.name).
... join_from(user_alias_1, user_alias_2, user_alias_1.c.id > user_alias_2.c.id)
... )
SELECT user_account_1.name, user_account_2.name AS name_1
FROM user_account AS user_account_1
JOIN user_account AS user_account_2 ON user_account_1.id > user_account_2.id
The ORM equivalent of the FromClause.alias()
method is the
ORM aliased()
function, which may be applied to an entity
such as User
and Address
. This produces a Alias
object
internally that’s against the original mapped Table
object,
while maintaining ORM functionality. The SELECT below selects from the
User
entity all objects that include two particular email addresses:
>>> from sqlalchemy.orm import aliased
>>> address_alias_1 = aliased(Address)
>>> address_alias_2 = aliased(Address)
>>> print(
... select(User).
... join_from(User, address_alias_1).
... where(address_alias_1.email_address == 'patrick@aol.com').
... join_from(User, address_alias_2).
... where(address_alias_2.email_address == 'patrick@gmail.com')
... )
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
JOIN address AS address_1 ON user_account.id = address_1.user_id
JOIN address AS address_2 ON user_account.id = address_2.user_id
WHERE address_1.email_address = :email_address_1
AND address_2.email_address = :email_address_2
Tip
As mentioned in Setting the ON Clause, the ORM provides
for another way to join using the relationship()
construct.
The above example using aliases is demonstrated using relationship()
at Joining between Aliased targets.
A subquery in SQL is a SELECT statement that is rendered within parenthesis and placed within the context of an enclosing statement, typically a SELECT statement but not necessarily.
This section will cover a so-called “non-scalar” subquery, which is typically placed in the FROM clause of an enclosing SELECT. We will also cover the Common Table Expression or CTE, which is used in a similar way as a subquery, but includes additional features.
SQLAlchemy uses the Subquery
object to represent a subquery and
the CTE
to represent a CTE, usually obtained from the
Select.subquery()
and Select.cte()
methods, respectively.
Either object can be used as a FROM element inside of a larger
select()
construct.
We can construct a Subquery
that will select an aggregate count
of rows from the address
table (aggregate functions and GROUP BY were
introduced previously at Aggregate functions with GROUP BY / HAVING):
>>> subq = select(
... func.count(address_table.c.id).label("count"),
... address_table.c.user_id
... ).group_by(address_table.c.user_id).subquery()
Stringifying the subquery by itself without it being embedded inside of another
Select
or other statement produces the plain SELECT statement
without any enclosing parenthesis:
>>> print(subq)
SELECT count(address.id) AS count, address.user_id
FROM address GROUP BY address.user_id
The Subquery
object behaves like any other FROM object such
as a Table
, notably that it includes a Subquery.c
namespace of the columns which it selects. We can use this namespace to
refer to both the user_id
column as well as our custom labeled
count
expression:
>>> print(select(subq.c.user_id, subq.c.count))
SELECT anon_1.user_id, anon_1.count
FROM (SELECT count(address.id) AS count, address.user_id AS user_id
FROM address GROUP BY address.user_id) AS anon_1
With a selection of rows contained within the subq
object, we can apply
the object to a larger Select
that will join the data to
the user_account
table:
>>> stmt = select(
... user_table.c.name,
... user_table.c.fullname,
... subq.c.count
... ).join_from(user_table, subq)
>>> print(stmt)
SELECT user_account.name, user_account.fullname, anon_1.count
FROM user_account JOIN (SELECT count(address.id) AS count, address.user_id AS user_id
FROM address GROUP BY address.user_id) AS anon_1 ON user_account.id = anon_1.user_id
In order to join from user_account
to address
, we made use of the
Select.join_from()
method. As has been illustrated previously, the
ON clause of this join was again inferred based on foreign key constraints.
Even though a SQL subquery does not itself have any constraints, SQLAlchemy can
act upon constraints represented on the columns by determining that the
subq.c.user_id
column is derived from the address_table.c.user_id
column, which does express a foreign key relationship back to the
user_table.c.id
column which is then used to generate the ON clause.
Usage of the CTE
construct in SQLAlchemy is virtually
the same as how the Subquery
construct is used. By changing
the invocation of the Select.subquery()
method to use
Select.cte()
instead, we can use the resulting object as a FROM
element in the same way, but the SQL rendered is the very different common
table expression syntax:
>>> subq = select(
... func.count(address_table.c.id).label("count"),
... address_table.c.user_id
... ).group_by(address_table.c.user_id).cte()
>>> stmt = select(
... user_table.c.name,
... user_table.c.fullname,
... subq.c.count
... ).join_from(user_table, subq)
>>> print(stmt)
WITH anon_1 AS
(SELECT count(address.id) AS count, address.user_id AS user_id
FROM address GROUP BY address.user_id)
SELECT user_account.name, user_account.fullname, anon_1.count
FROM user_account JOIN anon_1 ON user_account.id = anon_1.user_id
The CTE
construct also features the ability to be used
in a “recursive” style, and may in more elaborate cases be composed from the
RETURNING clause of an INSERT, UPDATE or DELETE statement. The docstring
for CTE
includes details on these additional patterns.
In both cases, the subquery and CTE were named at the SQL level using an
“anonymous” name. In the Python code, we don’t need to provide these names
at all. The object identity of the Subquery
or CTE
instances serves as the syntactical identity of the object when rendered.
A name that will be rendered in the SQL can be provided by passing it as the
first argument of the Select.subquery()
or Select.cte()
methods.
See also
Select.subquery()
- further detail on subqueries
Select.cte()
- examples for CTE including how to use
RECURSIVE as well as DML-oriented CTEs
In the ORM, the aliased()
construct may be used to associate an ORM
entity, such as our User
or Address
class, with any FromClause
concept that represents a source of rows. The preceding section
ORM Entity Aliases illustrates using aliased()
to associate the mapped class with an Alias
of its
mapped Table
. Here we illustrate aliased()
doing the same
thing against both a Subquery
as well as a CTE
generated against a Select
construct, that ultimately derives
from that same mapped Table
.
Below is an example of applying aliased()
to the Subquery
construct, so that ORM entities can be extracted from its rows. The result
shows a series of User
and Address
objects, where the data for
each Address
object ultimately came from a subquery against the
address
table rather than that table directly:
>>> subq = select(Address).where(~Address.email_address.like('%@aol.com')).subquery()
>>> address_subq = aliased(Address, subq)
>>> stmt = select(User, address_subq).join_from(User, address_subq).order_by(User.id, address_subq.id)
>>> with Session(engine) as session:
... for user, address in session.execute(stmt):
... print(f"{user} {address}")
BEGIN (implicit)
SELECT user_account.id, user_account.name, user_account.fullname,
anon_1.id AS id_1, anon_1.email_address, anon_1.user_id
FROM user_account JOIN
(SELECT address.id AS id, address.email_address AS email_address, address.user_id AS user_id
FROM address
WHERE address.email_address NOT LIKE ?) AS anon_1 ON user_account.id = anon_1.user_id
ORDER BY user_account.id, anon_1.id
[...] ('%@aol.com',)
User(id=1, name='spongebob', fullname='Spongebob Squarepants') Address(id=1, email_address='spongebob@sqlalchemy.org')
User(id=2, name='sandy', fullname='Sandy Cheeks') Address(id=2, email_address='sandy@sqlalchemy.org')
User(id=2, name='sandy', fullname='Sandy Cheeks') Address(id=3, email_address='sandy@squirrelpower.org')
ROLLBACK
Another example follows, which is exactly the same except it makes use of the
CTE
construct instead:
>>> cte = select(Address).where(~Address.email_address.like('%@aol.com')).cte()
>>> address_cte = aliased(Address, cte)
>>> stmt = select(User, address_cte).join_from(User, address_cte).order_by(User.id, address_cte.id)
>>> with Session(engine) as session:
... for user, address in session.execute(stmt):
... print(f"{user} {address}")
BEGIN (implicit)
WITH anon_1 AS
(SELECT address.id AS id, address.email_address AS email_address, address.user_id AS user_id
FROM address
WHERE address.email_address NOT LIKE ?)
SELECT user_account.id, user_account.name, user_account.fullname,
anon_1.id AS id_1, anon_1.email_address, anon_1.user_id
FROM user_account
JOIN anon_1 ON user_account.id = anon_1.user_id
ORDER BY user_account.id, anon_1.id
[...] ('%@aol.com',)
User(id=1, name='spongebob', fullname='Spongebob Squarepants') Address(id=1, email_address='spongebob@sqlalchemy.org')
User(id=2, name='sandy', fullname='Sandy Cheeks') Address(id=2, email_address='sandy@sqlalchemy.org')
User(id=2, name='sandy', fullname='Sandy Cheeks') Address(id=3, email_address='sandy@squirrelpower.org')
ROLLBACK
A scalar subquery is a subquery that returns exactly zero or one row and exactly one column. The subquery is then used in the COLUMNS or WHERE clause of an enclosing SELECT statement and is different than a regular subquery in that it is not used in the FROM clause. A correlated subquery is a scalar subquery that refers to a table in the enclosing SELECT statement.
SQLAlchemy represents the scalar subquery using the
ScalarSelect
construct, which is part of the
ColumnElement
expression hierarchy, in contrast to the regular
subquery which is represented by the Subquery
construct, which is
in the FromClause
hierarchy.
Scalar subqueries are often, but not necessarily, used with aggregate functions,
introduced previously at Aggregate functions with GROUP BY / HAVING. A scalar
subquery is indicated explicitly by making use of the Select.scalar_subquery()
method as below. It’s default string form when stringified by itself
renders as an ordinary SELECT statement that is selecting from two tables:
>>> subq = select(func.count(address_table.c.id)).\
... where(user_table.c.id == address_table.c.user_id).\
... scalar_subquery()
>>> print(subq)
(SELECT count(address.id) AS count_1
FROM address, user_account
WHERE user_account.id = address.user_id)
The above subq
object now falls within the ColumnElement
SQL expression hierarchy, in that it may be used like any other column
expression:
>>> print(subq == 5)
(SELECT count(address.id) AS count_1
FROM address, user_account
WHERE user_account.id = address.user_id) = :param_1
Although the scalar subquery by itself renders both user_account
and
address
in its FROM clause when stringified by itself, when embedding it
into an enclosing select()
construct that deals with the
user_account
table, the user_account
table is automatically
correlated, meaning it does not render in the FROM clause of the subquery:
>>> stmt = select(user_table.c.name, subq.label("address_count"))
>>> print(stmt)
SELECT user_account.name, (SELECT count(address.id) AS count_1
FROM address
WHERE user_account.id = address.user_id) AS address_count
FROM user_account
Simple correlated subqueries will usually do the right thing that’s desired. However, in the case where the correlation is ambiguous, SQLAlchemy will let us know that more clarity is needed:
>>> stmt = select(
... user_table.c.name,
... address_table.c.email_address,
... subq.label("address_count")
... ).\
... join_from(user_table, address_table).\
... order_by(user_table.c.id, address_table.c.id)
>>> print(stmt)
Traceback (most recent call last):
...
InvalidRequestError: Select statement '<... Select object at ...>' returned
no FROM clauses due to auto-correlation; specify correlate(<tables>) to
control correlation manually.
To specify that the user_table
is the one we seek to correlate we specify
this using the ScalarSelect.correlate()
or
ScalarSelect.correlate_except()
methods:
>>> subq = select(func.count(address_table.c.id)).\
... where(user_table.c.id == address_table.c.user_id).\
... scalar_subquery().correlate(user_table)
The statement then can return the data for this column like any other:
>>> with engine.connect() as conn:
... result = conn.execute(
... select(
... user_table.c.name,
... address_table.c.email_address,
... subq.label("address_count")
... ).
... join_from(user_table, address_table).
... order_by(user_table.c.id, address_table.c.id)
... )
... print(result.all())
BEGIN (implicit)
SELECT user_account.name, address.email_address, (SELECT count(address.id) AS count_1
FROM address
WHERE user_account.id = address.user_id) AS address_count
FROM user_account JOIN address ON user_account.id = address.user_id ORDER BY user_account.id, address.id
[...] ()
[('spongebob', 'spongebob@sqlalchemy.org', 1), ('sandy', 'sandy@sqlalchemy.org', 2),
('sandy', 'sandy@squirrelpower.org', 2)]
ROLLBACK
The SQL EXISTS keyword is an operator that is used with scalar subqueries to return a boolean true or false depending on if
the SELECT statement would return a row. SQLAlchemy includes a variant of the
ScalarSelect
object called Exists
, which will
generate an EXISTS subquery and is most conveniently generated using the
SelectBase.exists()
method. Below we produce an EXISTS so that we
can return user_account
rows that have more than one related row in
address
:
>>> subq = (
... select(func.count(address_table.c.id)).
... where(user_table.c.id == address_table.c.user_id).
... group_by(address_table.c.user_id).
... having(func.count(address_table.c.id) > 1)
... ).exists()
>>> with engine.connect() as conn:
... result = conn.execute(
... select(user_table.c.name).where(subq)
... )
... print(result.all())
BEGIN (implicit)
SELECT user_account.name
FROM user_account
WHERE EXISTS (SELECT count(address.id) AS count_1
FROM address
WHERE user_account.id = address.user_id GROUP BY address.user_id
HAVING count(address.id) > ?)
[...] (1,)
[('sandy',)]
ROLLBACK
The EXISTS construct is more often than not used as a negation, e.g. NOT EXISTS,
as it provides a SQL-efficient form of locating rows for which a related
table has no rows. Below we select user names that have no email addresses;
note the binary negation operator (~
) used inside the second WHERE
clause:
>>> subq = (
... select(address_table.c.id).
... where(user_table.c.id == address_table.c.user_id)
... ).exists()
>>> with engine.connect() as conn:
... result = conn.execute(
... select(user_table.c.name).where(~subq)
... )
... print(result.all())
BEGIN (implicit)
SELECT user_account.name
FROM user_account
WHERE NOT (EXISTS (SELECT address.id
FROM address
WHERE user_account.id = address.user_id))
[...] ()
[('patrick',)]
ROLLBACK
First introduced earlier in this section at
Aggregate functions with GROUP BY / HAVING, the func
object serves as a
factory for creating new Function
objects, which when used
in a construct like select()
, produce a SQL function display,
typically consisting of a name, some parenthesis (although not always), and
possibly some arguments. Examples of typical SQL functions include:
the count()
function, an aggregate function which counts how many
rows are returned:
>>> print(select(func.count()).select_from(user_table))
SELECT count(*) AS count_1
FROM user_account
the lower()
function, a string function that converts a string to lower
case:
>>> print(select(func.lower("A String With Much UPPERCASE")))
SELECT lower(:lower_2) AS lower_1
the now()
function, which provides for the current date and time; as this
is a common function, SQLAlchemy knows how to render this differently for each
backend, in the case of SQLite using the CURRENT_TIMESTAMP function:
>>> stmt = select(func.now())
>>> with engine.connect() as conn:
... result = conn.execute(stmt)
... print(result.all())
BEGIN (implicit)
SELECT CURRENT_TIMESTAMP AS now_1
[...] ()
[(datetime.datetime(...),)]
ROLLBACK
As most database backends feature dozens if not hundreds of different SQL
functions, func
tries to be as liberal as possible in what it
accepts. Any name that is accessed from this namespace is automatically
considered to be a SQL function that will render in a generic way:
>>> print(select(func.some_crazy_function(user_table.c.name, 17)))
SELECT some_crazy_function(user_account.name, :some_crazy_function_2) AS some_crazy_function_1
FROM user_account
At the same time, a relatively small set of extremely common SQL functions such
as count
, now
, max
,
concat
include pre-packaged versions of themselves which
provide for proper typing information as well as backend-specific SQL
generation in some cases. The example below contrasts the SQL generation
that occurs for the PostgreSQL dialect compared to the Oracle dialect for
the now
function:
>>> from sqlalchemy.dialects import postgresql
>>> print(select(func.now()).compile(dialect=postgresql.dialect()))
SELECT now() AS now_1
>>> from sqlalchemy.dialects import oracle
>>> print(select(func.now()).compile(dialect=oracle.dialect()))
SELECT CURRENT_TIMESTAMP AS now_1 FROM DUAL
As functions are column expressions, they also have SQL datatypes that describe the data type of a generated SQL expression. We refer to these types here as “SQL return types”, in reference to the type of SQL value that is returned by the function in the context of a database-side SQL expression, as opposed to the “return type” of a Python function.
The SQL return type of any SQL function may be accessed, typically for
debugging purposes, by referring to the Function.type
attribute:
>>> func.now().type
DateTime()
These SQL return types are significant when making
use of the function expression in the context of a larger expression; that is,
math operators will work better when the datatype of the expression is
something like Integer
or Numeric
, JSON
accessors in order to work need to be using a type such as
JSON
. Certain classes of functions return entire rows
instead of column values, where there is a need to refer to specific columns;
such functions are referred towards
as table valued functions.
The SQL return type of the function may also be significant when executing a
statement and getting rows back, for those cases where SQLAlchemy has to apply
result-set processing. A prime example of this are date-related functions on
SQLite, where SQLAlchemy’s DateTime
and related datatypes take
on the role of converting from string values to Python datetime()
objects
as result rows are received.
To apply a specific type to a function we’re creating, we pass it using the
Function.type_
parameter; the type argument may be
either a TypeEngine
class or an instance. In the example
below we pass the JSON
class to generate the PostgreSQL
json_object()
function, noting that the SQL return type will be of
type JSON:
>>> from sqlalchemy import JSON
>>> function_expr = func.json_object('{a, 1, b, "def", c, 3.5}', type_=JSON)
By creating our JSON function with the JSON
datatype, the
SQL expression object takes on JSON-related features, such as that of accessing
elements:
>>> stmt = select(function_expr["def"])
>>> print(stmt)
SELECT json_object(:json_object_1)[:json_object_2] AS anon_1
For common aggregate functions like count
,
max
, min
as well as a very small number
of date functions like now
and string functions like
concat
, the SQL return type is set up appropriately,
sometimes based on usage. The max
function and similar
aggregate filtering functions will set up the SQL return type based on the
argument given:
>>> m1 = func.max(Column("some_int", Integer))
>>> m1.type
Integer()
>>> m2 = func.max(Column("some_str", String))
>>> m2.type
String()
Date and time functions typically correspond to SQL expressions described by
DateTime
, Date
or Time
:
>>> func.now().type
DateTime()
>>> func.current_date().type
Date()
A known string function such as concat
will know that a SQL expression would be of type String
:
>>> func.concat("x", "y").type
String()
However, for the vast majority of SQL functions, SQLAlchemy does not have them
explicitly present in its very small list of known functions. For example,
while there is typically no issue using SQL functions func.lower()
and func.upper()
to convert the casing of strings, SQLAlchemy doesn’t
actually know about these functions, so they have a “null” SQL return type:
>>> func.upper("lowercase").type
NullType()
For simple functions like upper
and lower
, the issue is not usually
significant, as string values may be received from the database without any
special type handling on the SQLAlchemy side, and SQLAlchemy’s type
coercion rules can often correctly guess intent as well; the Python +
operator for example will be correctly interpreted as the string concatenation
operator based on looking at both sides of the expression:
>>> print(select(func.upper("lowercase") + " suffix"))
SELECT upper(:upper_1) || :upper_2 AS anon_1
Overall, the scenario where the
Function.type_
parameter is likely necessary is:
the function is not already a SQLAlchemy built-in function; this can be
evidenced by creating the function and observing the Function.type
attribute, that is:
>>> func.count().type
Integer()
vs.:
>>> func.json_object('{"a", "b"}').type
NullType()
Function-aware expression support is needed; this most typically refers to
special operators related to datatypes such as JSON
or
ARRAY
Result value processing is needed, which may include types such as
DateTime
, Boolean
, Enum
,
or again special datatypes such as JSON
,
ARRAY
.
A window function is a special use of a SQL aggregate function which calculates
the aggregate value over the rows being returned in a group as the individual
result rows are processed. Whereas a function like MAX()
will give you
the highest value of a column within a set of rows, using the same function
as a “window function” will given you the highest value for each row,
as of that row.
In SQL, window functions allow one to specify the rows over which the function should be applied, a “partition” value which considers the window over different sub-sets of rows, and an “order by” expression which importantly indicates the order in which rows should be applied to the aggregate function.
In SQLAlchemy, all SQL functions generated by the func
namespace
include a method FunctionElement.over()
which
grants the window function, or “OVER”, syntax; the construct produced
is the Over
construct.
A common function used with window functions is the row_number()
function
which simply counts rows. We may partition this row count against user name to
number the email addresses of individual users:
>>> stmt = select(
... func.row_number().over(partition_by=user_table.c.name),
... user_table.c.name,
... address_table.c.email_address
... ).select_from(user_table).join(address_table)
>>> with engine.connect() as conn:
... result = conn.execute(stmt)
... print(result.all())
BEGIN (implicit)
SELECT row_number() OVER (PARTITION BY user_account.name) AS anon_1,
user_account.name, address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id
[...] ()
[(1, 'sandy', 'sandy@sqlalchemy.org'), (2, 'sandy', 'sandy@squirrelpower.org'), (1, 'spongebob', 'spongebob@sqlalchemy.org')]
ROLLBACK
Above, the FunctionElement.over.partition_by
parameter
is used so that the PARTITION BY
clause is rendered within the OVER clause.
We also may make use of the ORDER BY
clause using FunctionElement.over.order_by
:
>>> stmt = select(
... func.count().over(order_by=user_table.c.name),
... user_table.c.name,
... address_table.c.email_address).select_from(user_table).join(address_table)
>>> with engine.connect() as conn:
... result = conn.execute(stmt)
... print(result.all())
BEGIN (implicit)
SELECT count(*) OVER (ORDER BY user_account.name) AS anon_1,
user_account.name, address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id
[...] ()
[(2, 'sandy', 'sandy@sqlalchemy.org'), (2, 'sandy', 'sandy@squirrelpower.org'), (3, 'spongebob', 'spongebob@sqlalchemy.org')]
ROLLBACK
Further options for window functions include usage of ranges; see
over()
for more examples.
Tip
It’s important to note that the FunctionElement.over()
method only applies to those SQL functions which are in fact aggregate
functions; while the Over
construct will happily render itself
for any SQL function given, the database will reject the expression if the
function itself is not a SQL aggregate function.
The “WITHIN GROUP” SQL syntax is used in conjunction with an “ordered set”
or a “hypothetical set” aggregate
function. Common “ordered set” functions include percentile_cont()
and rank()
. SQLAlchemy includes built in implementations
rank
, dense_rank
,
mode
, percentile_cont
and
percentile_disc
which include a FunctionElement.within_group()
method:
>>> print(
... func.unnest(
... func.percentile_disc([0.25,0.5,0.75,1]).within_group(user_table.c.name)
... )
... )
unnest(percentile_disc(:percentile_disc_1) WITHIN GROUP (ORDER BY user_account.name))
“FILTER” is supported by some backends to limit the range of an aggregate function to a
particular subset of rows compared to the total range of rows returned, available
using the FunctionElement.filter()
method:
>>> stmt = select(
... func.count(address_table.c.email_address).filter(user_table.c.name == 'sandy'),
... func.count(address_table.c.email_address).filter(user_table.c.name == 'spongebob')
... ).select_from(user_table).join(address_table)
>>> with engine.connect() as conn:
... result = conn.execute(stmt)
... print(result.all())
BEGIN (implicit)
SELECT count(address.email_address) FILTER (WHERE user_account.name = ?) AS anon_1,
count(address.email_address) FILTER (WHERE user_account.name = ?) AS anon_2
FROM user_account JOIN address ON user_account.id = address.user_id
[...] ('sandy', 'spongebob')
[(2, 1)]
ROLLBACK
Table-valued SQL functions support a scalar representation that contains named
sub-elements. Often used for JSON and ARRAY-oriented functions as well as
functions like generate_series()
, the table-valued function is specified in
the FROM clause, and is then referred towards as a table, or sometimes even as
a column. Functions of this form are prominent within the PostgreSQL database,
however some forms of table valued functions are also supported by SQLite,
Oracle, and SQL Server.
See also
Table values, Table and Column valued functions, Row and Tuple objects - in the PostgreSQL documentation.
While many databases support table valued and other special forms, PostgreSQL tends to be where there is the most demand for these features. See this section for additional examples of PostgreSQL syntaxes as well as additional features.
SQLAlchemy provides the FunctionElement.table_valued()
method
as the basic “table valued function” construct, which will convert a
func
object into a FROM clause containing a series of named
columns, based on string names passed positionally. This returns a
TableValuedAlias
object, which is a function-enabled
Alias
construct that may be used as any other FROM clause as
introduced at Using Aliases. Below we illustrate the
json_each()
function, which while common on PostgreSQL is also supported by
modern versions of SQLite:
>>> onetwothree = func.json_each('["one", "two", "three"]').table_valued("value")
>>> stmt = select(onetwothree).where(onetwothree.c.value.in_(["two", "three"]))
>>> with engine.connect() as conn:
... result = conn.execute(stmt)
... print(result.all())
BEGIN (implicit)
SELECT anon_1.value
FROM json_each(?) AS anon_1
WHERE anon_1.value IN (?, ?)
[...] ('["one", "two", "three"]', 'two', 'three')
[('two',), ('three',)]
ROLLBACK
Above, we used the json_each()
JSON function supported by SQLite and
PostgreSQL to generate a table valued expression with a single column referred
towards as value
, and then selected two of its three rows.
See also
Table-Valued Functions - in the PostgreSQL documentation - this section will detail additional syntaxes such as special column derivations and “WITH ORDINALITY” that are known to work with PostgreSQL.
A special syntax supported by PostgreSQL and Oracle is that of referring
towards a function in the FROM clause, which then delivers itself as a
single column in the columns clause of a SELECT statement or other column
expression context. PostgreSQL makes great use of this syntax for such
functions as json_array_elements()
, json_object_keys()
,
json_each_text()
, json_each()
, etc.
SQLAlchemy refers to this as a “column valued” function and is available
by applying the FunctionElement.column_valued()
modifier
to a Function
construct:
>>> from sqlalchemy import select, func
>>> stmt = select(func.json_array_elements('["one", "two"]').column_valued("x"))
>>> print(stmt)
SELECT x
FROM json_array_elements(:json_array_elements_1) AS x
The “column valued” form is also supported by the Oracle dialect, where it is usable for custom SQL functions:
>>> from sqlalchemy.dialects import oracle
>>> stmt = select(func.scalar_strings(5).column_valued("s"))
>>> print(stmt.compile(dialect=oracle.dialect()))
SELECT COLUMN_VALUE s
FROM TABLE (scalar_strings(:scalar_strings_1)) s
See also
Column Valued Functions - in the PostgreSQL documentation.
So far we’ve covered Insert
, so that we can get some data into
our database, and then spent a lot of time on Select
which
handles the broad range of usage patterns used for retrieving data from the
database. In this section we will cover the Update
and
Delete
constructs, which are used to modify existing rows
as well as delete existing rows. This section will cover these constructs
from a Core-centric perspective.
ORM Readers - As was the case mentioned at Core Insert,
the Update
and Delete
operations when used with
the ORM are usually invoked internally from the Session
object as part of the unit of work process.
However, unlike Insert
, the Update
and
Delete
constructs can also be used directly with the ORM,
using a pattern known as “ORM-enabled update and delete”; for this reason,
familiarity with these constructs is useful for ORM use. Both styles of
use are discussed in the sections Updating ORM Objects and
Deleting ORM Objects.
The update()
function generates a new instance of
Update
which represents an UPDATE statement in SQL, that will
update existing data in a table.
Like the insert()
construct, there is a “traditional” form of
update()
, which emits UPDATE against a single table at a time and
does not return any rows. However some backends support an UPDATE statement
that may modify multiple tables at once, and the UPDATE statement also
supports RETURNING such that columns contained in matched rows may be returned
in the result set.
A basic UPDATE looks like:
>>> from sqlalchemy import update
>>> stmt = (
... update(user_table).where(user_table.c.name == 'patrick').
... values(fullname='Patrick the Star')
... )
>>> print(stmt)
UPDATE user_account SET fullname=:fullname WHERE user_account.name = :name_1
The Update.values()
method controls the contents of the SET elements
of the UPDATE statement. This is the same method shared by the Insert
construct. Parameters can normally be passed using the column names as
keyword arguments.
UPDATE supports all the major SQL forms of UPDATE, including updates against expressions,
where we can make use of Column
expressions:
>>> stmt = (
... update(user_table).
... values(fullname="Username: " + user_table.c.name)
... )
>>> print(stmt)
UPDATE user_account SET fullname=(:name_1 || user_account.name)
To support UPDATE in an “executemany” context, where many parameter sets will
be invoked against the same statement, the bindparam()
construct may be used to set up bound parameters; these replace the places
that literal values would normally go:
>>> from sqlalchemy import bindparam
>>> stmt = (
... update(user_table).
... where(user_table.c.name == bindparam('oldname')).
... values(name=bindparam('newname'))
... )
>>> with engine.begin() as conn:
... conn.execute(
... stmt,
... [
... {'oldname':'jack', 'newname':'ed'},
... {'oldname':'wendy', 'newname':'mary'},
... {'oldname':'jim', 'newname':'jake'},
... ]
... )
BEGIN (implicit)
UPDATE user_account SET name=? WHERE user_account.name = ?
[...] (('ed', 'jack'), ('mary', 'wendy'), ('jake', 'jim'))
<sqlalchemy.engine.cursor.CursorResult object at 0x...>
COMMIT
Other techniques which may be applied to UPDATE include:
An UPDATE statement can make use of rows in other tables by using a correlated subquery. A subquery may be used anywhere a column expression might be placed:
>>> scalar_subq = (
... select(address_table.c.email_address).
... where(address_table.c.user_id == user_table.c.id).
... order_by(address_table.c.id).
... limit(1).
... scalar_subquery()
... )
>>> update_stmt = update(user_table).values(fullname=scalar_subq)
>>> print(update_stmt)
UPDATE user_account SET fullname=(SELECT address.email_address
FROM address
WHERE address.user_id = user_account.id ORDER BY address.id
LIMIT :param_1)
Some databases such as PostgreSQL and MySQL support a syntax “UPDATE FROM” where additional tables may be stated directly in a special FROM clause. This syntax will be generated implicitly when additional tables are located in the WHERE clause of the statement:
>>> update_stmt = (
... update(user_table).
... where(user_table.c.id == address_table.c.user_id).
... where(address_table.c.email_address == 'patrick@aol.com').
... values(fullname='Pat')
... )
>>> print(update_stmt)
UPDATE user_account SET fullname=:fullname FROM address
WHERE user_account.id = address.user_id AND address.email_address = :email_address_1
There is also a MySQL specific syntax that can UPDATE multiple tables. This
requires we refer to Table
objects in the VALUES clause in
order to refer to additional tables:
>>> update_stmt = (
... update(user_table).
... where(user_table.c.id == address_table.c.user_id).
... where(address_table.c.email_address == 'patrick@aol.com').
... values(
... {
... user_table.c.fullname: "Pat",
... address_table.c.email_address: "pat@aol.com"
... }
... )
... )
>>> from sqlalchemy.dialects import mysql
>>> print(update_stmt.compile(dialect=mysql.dialect()))
UPDATE user_account, address
SET address.email_address=%s, user_account.fullname=%s
WHERE user_account.id = address.user_id AND address.email_address = %s
Another MySQL-only behavior is that the order of parameters in the SET clause
of an UPDATE actually impacts the evaluation of each expression. For this use
case, the Update.ordered_values()
method accepts a sequence of
tuples so that this order may be controlled 2:
>>> update_stmt = (
... update(some_table).
... ordered_values(
... (some_table.c.y, 20),
... (some_table.c.x, some_table.c.y + 10)
... )
... )
>>> print(update_stmt)
UPDATE some_table SET y=:y, x=(some_table.y + :y_1)
While Python dictionaries are
guaranteed to be insert ordered
as of Python 3.7, the
Update.ordered_values()
method still provides an additional
measure of clarity of intent when it is essential that the SET clause
of a MySQL UPDATE statement proceed in a specific way.
The delete()
function generates a new instance of
Delete
which represents an DELETE statement in SQL, that will
delete rows from a table.
The delete()
statement from an API perspective is very similar to
that of the update()
construct, traditionally returning no rows but
allowing for a RETURNING variant on some database backends.
>>> from sqlalchemy import delete
>>> stmt = delete(user_table).where(user_table.c.name == 'patrick')
>>> print(stmt)
DELETE FROM user_account WHERE user_account.name = :name_1
Like Update
, Delete
supports the use of correlated
subqueries in the WHERE clause as well as backend-specific multiple table
syntaxes, such as DELETE FROM..USING
on MySQL:
>>> delete_stmt = (
... delete(user_table).
... where(user_table.c.id == address_table.c.user_id).
... where(address_table.c.email_address == 'patrick@aol.com')
... )
>>> from sqlalchemy.dialects import mysql
>>> print(delete_stmt.compile(dialect=mysql.dialect()))
DELETE FROM user_account USING user_account, address
WHERE user_account.id = address.user_id AND address.email_address = %s
Both Update
and Delete
support the ability to
return the number of rows matched after the statement proceeds, for statements
that are invoked using Core Connection
, i.e.
Connection.execute()
. Per the caveats mentioned below, this value
is available from the CursorResult.rowcount
attribute:
>>> with engine.begin() as conn:
... result = conn.execute(
... update(user_table).
... values(fullname="Patrick McStar").
... where(user_table.c.name == 'patrick')
... )
... print(result.rowcount)
BEGIN (implicit)
UPDATE user_account SET fullname=? WHERE user_account.name = ?
[...] ('Patrick McStar', 'patrick')
1
COMMIT
Tip
The CursorResult
class is a subclass of
Result
which contains additional attributes that are
specific to the DBAPI cursor
object. An instance of this subclass is
returned when a statement is invoked via the
Connection.execute()
method. When using the ORM, the
Session.execute()
method returns an object of this type for
all INSERT, UPDATE, and DELETE statements.
Facts about CursorResult.rowcount
:
The value returned is the number of rows matched by the WHERE clause of the statement. It does not matter if the row were actually modified or not.
CursorResult.rowcount
is not necessarily available for an UPDATE
or DELETE statement that uses RETURNING.
For an executemany execution,
CursorResult.rowcount
may not be available either, which depends
highly on the DBAPI module in use as well as configured options. The
attribute CursorResult.supports_sane_multi_rowcount
indicates
if this value will be available for the current backend in use.
Some drivers, particularly third party dialects for non-relational databases,
may not support CursorResult.rowcount
at all. The
CursorResult.supports_sane_rowcount
will indicate this.
“rowcount” is used by the ORM unit of work process to validate that an UPDATE or DELETE statement matched the expected number of rows, and is also essential for the ORM versioning feature documented at Configuring a Version Counter.
Like the Insert
construct, Update
and Delete
also support the RETURNING clause which is added by using the
Update.returning()
and Delete.returning()
methods.
When these methods are used on a backend that supports RETURNING, selected
columns from all rows that match the WHERE criteria of the statement
will be returned in the Result
object as rows that can
be iterated:
>>> update_stmt = (
... update(user_table).where(user_table.c.name == 'patrick').
... values(fullname='Patrick the Star').
... returning(user_table.c.id, user_table.c.name)
... )
>>> print(update_stmt)
UPDATE user_account SET fullname=:fullname
WHERE user_account.name = :name_1
RETURNING user_account.id, user_account.name
>>> delete_stmt = (
... delete(user_table).where(user_table.c.name == 'patrick').
... returning(user_table.c.id, user_table.c.name)
... )
>>> print(delete_stmt)
DELETE FROM user_account
WHERE user_account.name = :name_1
RETURNING user_account.id, user_account.name
See also
API documentation for UPDATE / DELETE:
ORM-enabled UPDATE and DELETE:
SQLAlchemy 1.4 / 2.0 Tutorial
Next Tutorial Section: Data Manipulation with the ORM
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