SQLAlchemy 2.0 Documentation
SQLAlchemy Unified Tutorial
- Establishing Connectivity - the Engine
- Working with Transactions and the DBAPI
- Working with Database Metadata
- Working with Data
- Using INSERT Statements
- Using SELECT Statements¶
- The select() SQL Expression Construct
- Setting the COLUMNS and FROM clause
- The WHERE clause
- Explicit FROM clauses and JOINs
- ORDER BY, GROUP BY, HAVING
- Using Aliases
- Subqueries and CTEs
- Scalar and Correlated Subqueries
- UNION, UNION ALL and other set operations
- EXISTS subqueries
- Working with SQL Functions
- Data Casts and Type Coercion
- Using UPDATE and DELETE Statements
- Data Manipulation with the ORM
- Working with ORM Related Objects
- Further Reading
Project Versions
- Previous: Using INSERT Statements
- Next: Using UPDATE and DELETE Statements
- Up: Home
- On this page:
- Using SELECT Statements
- The select() SQL Expression Construct
- Setting the COLUMNS and FROM clause
- The WHERE clause
- Explicit FROM clauses and JOINs
- ORDER BY, GROUP BY, HAVING
- Using Aliases
- Subqueries and CTEs
- Scalar and Correlated Subqueries
- UNION, UNION ALL and other set operations
- EXISTS subqueries
- Working with SQL Functions
- Data Casts and Type Coercion
Using SELECT Statements¶
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() SQL Expression Construct¶
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
The following sections will discuss the SELECT construct in more detail.
Setting the COLUMNS and FROM clause¶
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
Alternatively, when using the FromClause.c
collection of any
FromClause
such as Table
, multiple columns may be specified
for a select()
by using a tuple of string names:
>>> print(select(user_table.c["name", "fullname"]))
SELECT user_account.name, user_account.fullname
FROM user_account
New in version 2.0: Added tuple-accessor capability to the
FromClause.c
collection
Selecting ORM Entities and Columns¶
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')
A highly recommended convenience method of achieving the same result as above
is to use the Session.scalars()
method to execute the statement
directly; this method will return a ScalarResult
object
that delivers the first “column” of each row at once, in this case,
instances of the User
class:
>>> user = session.scalars(select(User)).first()
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
[...] ()
>>> user
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
Selecting from Labeled SQL Expressions¶
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
See also
Ordering or Grouping by a Label - the label names we create may also be
referenced in the ORDER BY or GROUP BY clause of the Select
.
Selecting with Textual Column Expressions¶
When we construct a Select
object using the select()
function, we are normally passing to it a series of Table
and Column
objects that were defined using
table metadata, or when using the ORM we may be
sending ORM-mapped attributes that represent table columns. However,
sometimes there is also the need to manufacture arbitrary SQL blocks inside
of statements, such as constant string expressions, or just some arbitrary
SQL that’s quicker to write literally.
The text()
construct introduced at
Working with Transactions and the DBAPI can in fact be embedded into a
Select
construct directly, such as below where we manufacture
a hardcoded string literal 'some phrase'
and embed it within the
SELECT statement:
>>> from sqlalchemy import text
>>> stmt = select(text("'some phrase'"), user_table.c.name).order_by(user_table.c.name)
>>> with engine.connect() as conn:
... print(conn.execute(stmt).all())
BEGIN (implicit)
SELECT 'some phrase', user_account.name
FROM user_account ORDER BY user_account.name
[generated in ...] ()
[('some phrase', 'patrick'), ('some phrase', 'sandy'), ('some phrase', 'spongebob')]
ROLLBACK
While the text()
construct can be used in most places to inject
literal SQL phrases, more often than not we are actually dealing with textual
units that each represent an individual
column expression. In this common case we can get more functionality out of
our textual fragment using the literal_column()
construct instead. This object is similar to text()
except that
instead of representing arbitrary SQL of any form,
it explicitly represents a single “column” and can then be labeled and referred
towards in subqueries and other expressions:
>>> from sqlalchemy import literal_column
>>> stmt = select(literal_column("'some phrase'").label("p"), user_table.c.name).order_by(
... user_table.c.name
... )
>>> with engine.connect() as conn:
... for row in conn.execute(stmt):
... print(f"{row.p}, {row.name}")
BEGIN (implicit)
SELECT 'some phrase' AS p, user_account.name
FROM user_account ORDER BY user_account.name
[generated in ...] ()
some phrase, patrick
some phrase, sandy
some phrase, spongebob
ROLLBACK
Note that in both cases, when using text()
or
literal_column()
, we are writing a syntactical SQL expression, and
not a literal value. We therefore have to include whatever quoting or syntaxes
are necessary for the SQL we want to see rendered.
The WHERE clause¶
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
Explicit FROM clauses and JOINs¶
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 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
We also have the option to 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
Setting the leftmost FROM clause in a join - in the ORM Querying Guide -
contains additional examples and notes
regarding the interaction of Select.select_from()
and
Select.join()
.
Setting the ON Clause¶
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.
OUTER and FULL 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”.
ORDER BY, GROUP BY, HAVING¶
The SELECT SQL statement includes a clause called ORDER BY which is used to return the selected rows within a given ordering.
The GROUP BY clause is constructed similarly to the ORDER BY clause, and has the purpose of sub-dividing the selected rows into specific groups upon which aggregate functions may be invoked. The HAVING clause is usually used with GROUP BY and is of a similar form to the WHERE clause, except that it’s applied to the aggregated functions used within groups.
ORDER BY¶
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.fullname.desc()))
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account ORDER BY user_account.fullname DESC
The above statement will yield rows that are sorted by the
user_account.fullname
column in descending order.
Aggregate functions with GROUP BY / HAVING¶
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 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
Ordering or Grouping by a Label¶
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
Using Aliases¶
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 multiple 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 referenced 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
ORM Entity Aliases¶
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 Using Relationship to join between aliased targets.
Subqueries and CTEs¶
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.
Common Table Expressions (CTEs)¶
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
ORM Entity Subqueries/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_obj = select(Address).where(~Address.email_address.like("%@aol.com")).cte()
>>> address_cte = aliased(Address, cte_obj)
>>> 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
See also
Selecting Entities from Subqueries - in the ORM Querying Guide
Scalar and Correlated Subqueries¶
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
LATERAL correlation¶
LATERAL correlation is a special sub-category of SQL correlation which allows a selectable unit to refer to another selectable unit within a single FROM clause. This is an extremely special use case which, while part of the SQL standard, is only known to be supported by recent versions of PostgreSQL.
Normally, if a SELECT statement refers to
table1 JOIN (SELECT ...) AS subquery
in its FROM clause, the subquery
on the right side may not refer to the “table1” expression from the left side;
correlation may only refer to a table that is part of another SELECT that
entirely encloses this SELECT. The LATERAL keyword allows us to turn this
behavior around and allow correlation from the right side JOIN.
SQLAlchemy supports this feature using the Select.lateral()
method, which creates an object known as Lateral
. Lateral
is in the same family as Subquery
and Alias
, but also
includes correlation behavior when the construct is added to the FROM clause of
an enclosing SELECT. The following example illustrates a SQL query that makes
use of LATERAL, selecting the “user account / count of email address” data as
was discussed in the previous section:
>>> subq = (
... select(
... func.count(address_table.c.id).label("address_count"),
... address_table.c.email_address,
... address_table.c.user_id,
... )
... .where(user_table.c.id == address_table.c.user_id)
... .lateral()
... )
>>> stmt = (
... select(user_table.c.name, subq.c.address_count, subq.c.email_address)
... .join_from(user_table, subq)
... .order_by(user_table.c.id, subq.c.email_address)
... )
>>> print(stmt)
SELECT user_account.name, anon_1.address_count, anon_1.email_address
FROM user_account
JOIN LATERAL (SELECT count(address.id) AS address_count,
address.email_address AS email_address, address.user_id AS user_id
FROM address
WHERE user_account.id = address.user_id) AS anon_1
ON user_account.id = anon_1.user_id
ORDER BY user_account.id, anon_1.email_address
Above, the right side of the JOIN is a subquery that correlates to the
user_account
table that’s on the left side of the join.
When using Select.lateral()
, the behavior of
Select.correlate()
and
Select.correlate_except()
methods is applied to the
Lateral
construct as well.
UNION, UNION ALL and other set operations¶
In SQL, SELECT statements can be merged together using the UNION or UNION ALL SQL operation, which produces the set of all rows produced by one or more statements together. Other set operations such as INTERSECT [ALL] and EXCEPT [ALL] are also possible.
SQLAlchemy’s Select
construct supports compositions of this
nature using functions like union()
, intersect()
and
except_()
, and the “all” counterparts union_all()
,
intersect_all()
and except_all()
. These functions all
accept an arbitrary number of sub-selectables, which are typically
Select
constructs but may also be an existing composition.
The construct produced by these functions is the CompoundSelect
,
which is used in the same manner as the Select
construct, except
that it has fewer methods. The CompoundSelect
produced by
union_all()
for example may be invoked directly using
Connection.execute()
:
>>> from sqlalchemy import union_all
>>> stmt1 = select(user_table).where(user_table.c.name == "sandy")
>>> stmt2 = select(user_table).where(user_table.c.name == "spongebob")
>>> u = union_all(stmt1, stmt2)
>>> with engine.connect() as conn:
... result = conn.execute(u)
... print(result.all())
BEGIN (implicit)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = ?
UNION ALL SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = ?
[generated in ...] ('sandy', 'spongebob')
[(2, 'sandy', 'Sandy Cheeks'), (1, 'spongebob', 'Spongebob Squarepants')]
ROLLBACK
To use a CompoundSelect
as a subquery, just like Select
it provides a SelectBase.subquery()
method which will produce a
Subquery
object with a FromClause.c
collection that may be referenced in an enclosing select()
:
>>> u_subq = u.subquery()
>>> stmt = (
... select(u_subq.c.name, address_table.c.email_address)
... .join_from(address_table, u_subq)
... .order_by(u_subq.c.name, address_table.c.email_address)
... )
>>> with engine.connect() as conn:
... result = conn.execute(stmt)
... print(result.all())
BEGIN (implicit)
SELECT anon_1.name, address.email_address
FROM address JOIN
(SELECT user_account.id AS id, user_account.name AS name, user_account.fullname AS fullname
FROM user_account
WHERE user_account.name = ?
UNION ALL
SELECT user_account.id AS id, user_account.name AS name, user_account.fullname AS fullname
FROM user_account
WHERE user_account.name = ?)
AS anon_1 ON anon_1.id = address.user_id
ORDER BY anon_1.name, address.email_address
[generated in ...] ('sandy', 'spongebob')
[('sandy', 'sandy@sqlalchemy.org'), ('sandy', 'sandy@squirrelpower.org'), ('spongebob', 'spongebob@sqlalchemy.org')]
ROLLBACK
Selecting ORM Entities from Unions¶
The preceding examples illustrated how to construct a UNION given two
Table
objects, to then return database rows. If we wanted
to use a UNION or other set operation to select rows that we then receive
as ORM objects, there are two approaches that may be used. In both cases,
we first construct a select()
or CompoundSelect
object that represents the SELECT / UNION / etc statement we want to
execute; this statement should be composed against the target
ORM entities or their underlying mapped Table
objects:
>>> stmt1 = select(User).where(User.name == "sandy")
>>> stmt2 = select(User).where(User.name == "spongebob")
>>> u = union_all(stmt1, stmt2)
For a simple SELECT with UNION that is not already nested inside of a
subquery, these
can often be used in an ORM object fetching context by using the
Select.from_statement()
method. With this approach, the UNION
statement represents the entire query; no additional
criteria can be added after Select.from_statement()
is used:
>>> orm_stmt = select(User).from_statement(u)
>>> with Session(engine) as session:
... for obj in session.execute(orm_stmt).scalars():
... print(obj)
BEGIN (implicit)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = ? UNION ALL SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = ?
[generated in ...] ('sandy', 'spongebob')
User(id=2, name='sandy', fullname='Sandy Cheeks')
User(id=1, name='spongebob', fullname='Spongebob Squarepants')
ROLLBACK
To use a UNION or other set-related construct as an entity-related component in
in a more flexible manner, the CompoundSelect
construct may be
organized into a subquery using CompoundSelect.subquery()
, which
then links to ORM objects using the aliased()
function. This works
in the same way introduced at ORM Entity Subqueries/CTEs, to first
create an ad-hoc “mapping” of our desired entity to the subquery, then
selecting from that new entity as though it were any other mapped class.
In the example below, we are able to add additional criteria such as ORDER BY
outside of the UNION itself, as we can filter or order by the columns exported
by the subquery:
>>> user_alias = aliased(User, u.subquery())
>>> orm_stmt = select(user_alias).order_by(user_alias.id)
>>> with Session(engine) as session:
... for obj in session.execute(orm_stmt).scalars():
... print(obj)
BEGIN (implicit)
SELECT anon_1.id, anon_1.name, anon_1.fullname
FROM (SELECT user_account.id AS id, user_account.name AS name, user_account.fullname AS fullname
FROM user_account
WHERE user_account.name = ? UNION ALL SELECT user_account.id AS id, user_account.name AS name, user_account.fullname AS fullname
FROM user_account
WHERE user_account.name = ?) AS anon_1 ORDER BY anon_1.id
[generated in ...] ('sandy', 'spongebob')
User(id=1, name='spongebob', fullname='Spongebob Squarepants')
User(id=2, name='sandy', fullname='Sandy Cheeks')
ROLLBACK
See also
Selecting Entities from UNIONs and other set operations - in the ORM Querying Guide
EXISTS subqueries¶
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
Working with SQL Functions¶
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_accountthe
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_1the
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
Functions Have Return Types¶
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; this will be pre-configured for a select few of extremely
common SQL functions, but for most SQL functions is the “null” datatype
if not otherwise specified:
>>> # pre-configured SQL function (only a few dozen of these)
>>> func.now().type
DateTime()
>>> # arbitrary SQL function (all other SQL functions)
>>> func.run_some_calculation().type
NullType()
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 known
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
Built-in Functions Have Pre-Configured Return Types¶
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
orARRAY
Result value processing is needed, which may include types such as
DateTime
,Boolean
,Enum
, or again special datatypes such asJSON
,ARRAY
.
Advanced SQL Function Techniques¶
The following subsections illustrate more things that can be done with SQL functions. While these techniques are less common and more advanced than basic SQL function use, they nonetheless are extremely popular, largely as a result of PostgreSQL’s emphasis on more complex function forms, including table- and column-valued forms that are popular with JSON data.
Using Window Functions¶
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.
Special Modifiers WITHIN GROUP, FILTER¶
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 Functions¶
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 referenced 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)
... 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.
Column Valued Functions - Table Valued Function as a Scalar Column¶
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 s.COLUMN_VALUE
FROM TABLE (scalar_strings(:scalar_strings_1)) s
See also
Column Valued Functions - in the PostgreSQL documentation.
Data Casts and Type Coercion¶
In SQL, we often need to indicate the datatype of an expression explicitly,
either to tell the database what type is expected in an otherwise ambiguous
expression, or in some cases when we want to convert the implied datatype
of a SQL expression into something else. The SQL CAST keyword is used for
this task, which in SQLAlchemy is provided by the cast()
function.
This function accepts a column expression and a data type
object as arguments, as demonstrated below where we produce a SQL expression
CAST(user_account.id AS VARCHAR)
from the user_table.c.id
column
object:
>>> from sqlalchemy import cast
>>> stmt = select(cast(user_table.c.id, String))
>>> with engine.connect() as conn:
... result = conn.execute(stmt)
... result.all()
BEGIN (implicit)
SELECT CAST(user_account.id AS VARCHAR) AS id
FROM user_account
[...] ()
[('1',), ('2',), ('3',)]
ROLLBACK
The cast()
function not only renders the SQL CAST syntax, it also
produces a SQLAlchemy column expression that will act as the given datatype on
the Python side as well. A string expression that is cast()
to
JSON
will gain JSON subscript and comparison operators, for example:
>>> from sqlalchemy import JSON
>>> print(cast("{'a': 'b'}", JSON)["a"])
CAST(:param_1 AS JSON)[:param_2]
type_coerce() - a Python-only “cast”¶
Sometimes there is the need to have SQLAlchemy know the datatype of an
expression, for all the reasons mentioned above, but to not render the CAST
expression itself on the SQL side, where it may interfere with a SQL operation
that already works without it. For this fairly common use case there is
another function type_coerce()
which is closely related to
cast()
, in that it sets up a Python expression as having a specific SQL
database type, but does not render the CAST
keyword or datatype on the
database side. type_coerce()
is particularly important when dealing
with the JSON
datatype, which typically has an intricate
relationship with string-oriented datatypes on different platforms and
may not even be an explicit datatype, such as on SQLite and MariaDB.
Below, we use type_coerce()
to deliver a Python structure as a JSON
string into one of MySQL’s JSON functions:
>>> import json
>>> from sqlalchemy import JSON
>>> from sqlalchemy import type_coerce
>>> from sqlalchemy.dialects import mysql
>>> s = select(type_coerce({"some_key": {"foo": "bar"}}, JSON)["some_key"])
>>> print(s.compile(dialect=mysql.dialect()))
SELECT JSON_EXTRACT(%s, %s) AS anon_1
Above, MySQL’s JSON_EXTRACT
SQL function was invoked
because we used type_coerce()
to indicate that our Python dictionary
should be treated as JSON
. The Python __getitem__
operator, ['some_key']
in this case, became available as a result and
allowed a JSON_EXTRACT
path expression (not shown, however in this
case it would ultimately be '$."some_key"'
) to be rendered.
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