The SQLAlchemy Expression Language presents a system of representing relational database structures and expressions using Python constructs. These constructs are modeled to resemble those of the underlying database as closely as possible, while providing a modicum of abstraction of the various implementation differences between database backends. While the constructs attempt to represent equivalent concepts between backends with consistent structures, they do not conceal useful concepts that are unique to particular subsets of backends. The Expression Language therefore presents a method of writing backend-neutral SQL expressions, but does not attempt to enforce that expressions are backend-neutral.
The Expression Language is in contrast to the Object Relational Mapper, which is a distinct API that builds on top of the Expression Language. Whereas the ORM, introduced in :ref:`ormtutorial_toplevel`, presents a high level and abstracted pattern of usage, which itself is an example of applied usage of the Expression Language, the Expression Language presents a system of representing the primitive constructs of the relational database directly without opinion.
While there is overlap among the usage patterns of the ORM and the Expression Language, the similarities are more superficial than they may at first appear. One approaches the structure and content of data from the perspective of a user-defined domain model which is transparently persisted and refreshed from its underlying storage model. The other approaches it from the perspective of literal schema and SQL expression representations which are explicitly composed into messages consumed individually by the database.
A successful application may be constructed using the Expression Language exclusively, though the application will need to define its own system of translating application concepts into individual database messages and from individual database result sets. Alternatively, an application constructed with the ORM may, in advanced scenarios, make occasional usage of the Expression Language directly in certain areas where specific database interactions are required.
The following tutorial is in doctest format, meaning each >>> line
represents something you can type at a Python command prompt, and the
following text represents the expected return value. The tutorial has no
prerequisites.
A quick check to verify that we are on at least version 1.4 of SQLAlchemy:
>>> import sqlalchemy
>>> sqlalchemy.__version__ # doctest: +SKIP
1.4.0
For this tutorial we will use an in-memory-only SQLite database. This is an easy way to test things without needing to have an actual database defined anywhere. To connect we use :func:`~sqlalchemy.create_engine`:
>>> from sqlalchemy import create_engine
>>> engine = create_engine('sqlite:///:memory:', echo=True)
The echo flag is a shortcut to setting up SQLAlchemy logging, which is
accomplished via Python's standard logging module. With it enabled, we'll
see all the generated SQL produced. If you are working through this tutorial
and want less output generated, set it to False. This tutorial will format
the SQL behind a popup window so it doesn't get in our way; just click the
"SQL" links to see what's being generated.
The return value of :func:`_sa.create_engine` is an instance of
:class:`_engine.Engine`, and it represents the core interface to the
database, adapted through a :term:`dialect` that handles the details
of the database and :term:`DBAPI` in use. In this case the SQLite
dialect will interpret instructions to the Python built-in sqlite3
module.
Lazy Connecting
The :class:`_engine.Engine`, when first returned by :func:`_sa.create_engine`, has not actually tried to connect to the database yet; that happens only the first time it is asked to perform a task against the database.
The first time a method like :meth:`_engine.Engine.execute` or :meth:`_engine.Engine.connect` is called, the :class:`_engine.Engine` establishes a real :term:`DBAPI` connection to the database, which is then used to emit the SQL.
.. seealso::
:ref:`database_urls` - includes examples of :func:`_sa.create_engine`
connecting to several kinds of databases with links to more information.
The SQL Expression Language constructs its expressions in most cases against table columns. In SQLAlchemy, a column is most often represented by an object called :class:`~sqlalchemy.schema.Column`, and in all cases a :class:`~sqlalchemy.schema.Column` is associated with a :class:`~sqlalchemy.schema.Table`. A collection of :class:`~sqlalchemy.schema.Table` objects and their associated child objects is referred to as database metadata. In this tutorial we will explicitly lay out several :class:`~sqlalchemy.schema.Table` objects, but note that SA can also "import" whole sets of :class:`~sqlalchemy.schema.Table` objects automatically from an existing database (this process is called table reflection).
We define our tables all within a catalog called :class:`~sqlalchemy.schema.MetaData`, using the :class:`~sqlalchemy.schema.Table` construct, which resembles regular SQL CREATE TABLE statements. We'll make two tables, one of which represents "users" in an application, and another which represents zero or more "email addresses" for each row in the "users" table:
>>> from sqlalchemy import Table, Column, Integer, String, MetaData, ForeignKey
>>> metadata = MetaData()
>>> users = Table('users', metadata,
... Column('id', Integer, primary_key=True),
... Column('name', String),
... Column('fullname', String),
... )
>>> addresses = Table('addresses', metadata,
... Column('id', Integer, primary_key=True),
... Column('user_id', None, ForeignKey('users.id')),
... Column('email_address', String, nullable=False)
... )
All about how to define :class:`~sqlalchemy.schema.Table` objects, as well as how to create them from an existing database automatically, is described in :ref:`metadata_toplevel`.
Next, to tell the :class:`~sqlalchemy.schema.MetaData` we'd actually like to
create our selection of tables for real inside the SQLite database, we use
:func:`~sqlalchemy.schema.MetaData.create_all`, passing it the engine
instance which points to our database. This will check for the presence of
each table first before creating, so it's safe to call multiple times:
{sql}>>> metadata.create_all(engine)
PRAGMA...
CREATE TABLE users (
id INTEGER NOT NULL,
name VARCHAR,
fullname VARCHAR,
PRIMARY KEY (id)
)
[...] ()
COMMIT
CREATE TABLE addresses (
id INTEGER NOT NULL,
user_id INTEGER,
email_address VARCHAR NOT NULL,
PRIMARY KEY (id),
FOREIGN KEY(user_id) REFERENCES users (id)
)
[...] ()
COMMIT
Note
Users familiar with the syntax of CREATE TABLE may notice that the VARCHAR columns were generated without a length; on SQLite and PostgreSQL, this is a valid datatype, but on others, it's not allowed. So if running this tutorial on one of those databases, and you wish to use SQLAlchemy to issue CREATE TABLE, a "length" may be provided to the :class:`~sqlalchemy.types.String` type as below:
Column('name', String(50))
The length field on :class:`~sqlalchemy.types.String`, as well as similar precision/scale fields available on :class:`~sqlalchemy.types.Integer`, :class:`~sqlalchemy.types.Numeric`, etc. are not referenced by SQLAlchemy other than when creating tables.
Additionally, Firebird and Oracle require sequences to generate new primary key identifiers, and SQLAlchemy doesn't generate or assume these without being instructed. For that, you use the :class:`~sqlalchemy.schema.Sequence` construct:
from sqlalchemy import Sequence
Column('id', Integer, Sequence('user_id_seq'), primary_key=True)
A full, foolproof :class:`~sqlalchemy.schema.Table` is therefore:
users = Table('users', metadata,
Column('id', Integer, Sequence('user_id_seq'), primary_key=True),
Column('name', String(50)),
Column('fullname', String(50)),
Column('nickname', String(50))
)
We include this more verbose :class:`_schema.Table` construct separately to highlight the difference between a minimal construct geared primarily towards in-Python usage only, versus one that will be used to emit CREATE TABLE statements on a particular set of backends with more stringent requirements.
The first SQL expression we'll create is the :class:`~sqlalchemy.sql.expression.Insert` construct, which represents an INSERT statement. This is typically created relative to its target table:
>>> ins = users.insert()
To see a sample of the SQL this construct produces, use the str()
function:
>>> str(ins) 'INSERT INTO users (id, name, fullname) VALUES (:id, :name, :fullname)'
Notice above that the INSERT statement names every column in the users
table. This can be limited by using the values() method, which establishes
the VALUES clause of the INSERT explicitly:
>>> ins = users.insert().values(name='jack', fullname='Jack Jones') >>> str(ins) 'INSERT INTO users (name, fullname) VALUES (:name, :fullname)'
Above, while the values method limited the VALUES clause to just two
columns, the actual data we placed in values didn't get rendered into the
string; instead we got named bind parameters. As it turns out, our data is
stored within our :class:`~sqlalchemy.sql.expression.Insert` construct, but it
typically only comes out when the statement is actually executed; since the
data consists of literal values, SQLAlchemy automatically generates bind
parameters for them. We can peek at this data for now by looking at the
compiled form of the statement:
>>> ins.compile().params # doctest: +SKIP
{'fullname': 'Jack Jones', 'name': 'jack'}
The interesting part of an :class:`~sqlalchemy.sql.expression.Insert` is executing it. This is performed using a database connection, which is represented by the :class:`_engine.Connection` object. To acquire a connection, we will use the :meth:`_engine.Engine.connect` method:
>>> conn = engine.connect() >>> conn <sqlalchemy.engine.base.Connection object at 0x...>
The :class:`~sqlalchemy.engine.Connection` object represents an actively checked out DBAPI connection resource. Lets feed it our :class:`~sqlalchemy.sql.expression.Insert` object and see what happens:
>>> result = conn.execute(ins)
{opensql}INSERT INTO users (name, fullname) VALUES (?, ?)
[...] ('jack', 'Jack Jones')
COMMIT
So the INSERT statement was now issued to the database. Although we got
positional "qmark" bind parameters instead of "named" bind parameters in the
output. How come ? Because when executed, the
:class:`~sqlalchemy.engine.Connection` used the SQLite dialect to
help generate the statement; when we use the str() function, the statement
isn't aware of this dialect, and falls back onto a default which uses named
parameters. We can view this manually as follows:
>>> ins.bind = engine
>>> str(ins)
'INSERT INTO users (name, fullname) VALUES (?, ?)'
What about the result variable we got when we called execute() ? As
the SQLAlchemy :class:`~sqlalchemy.engine.Connection` object references a
DBAPI connection, the result, known as a
:class:`~sqlalchemy.engine.CursorResult` object, is analogous to the DBAPI
cursor object. In the case of an INSERT, we can get important information from
it, such as the primary key values which were generated from our statement
using :attr:`_engine.CursorResult.inserted_primary_key`:
>>> result.inserted_primary_key
(1,)
The value of 1 was automatically generated by SQLite, but only because we
did not specify the id column in our
:class:`~sqlalchemy.sql.expression.Insert` statement; otherwise, our explicit
value would have been used. In either case, SQLAlchemy always knows how to get
at a newly generated primary key value, even though the method of generating
them is different across different databases; each database's
:class:`~sqlalchemy.engine.interfaces.Dialect` knows the specific steps needed to
determine the correct value (or values; note that
:attr:`_engine.CursorResult.inserted_primary_key`
returns a list so that it supports composite primary keys). Methods here
range from using cursor.lastrowid, to selecting from a database-specific
function, to using INSERT..RETURNING syntax; this all occurs transparently.
Our insert example above was intentionally a little drawn out to show some
various behaviors of expression language constructs. In the usual case, an
:class:`~sqlalchemy.sql.expression.Insert` statement is usually compiled
against the parameters sent to the execute() method on
:class:`~sqlalchemy.engine.Connection`, so that there's no need to use
the values keyword with :class:`~sqlalchemy.sql.expression.Insert`. Lets
create a generic :class:`~sqlalchemy.sql.expression.Insert` statement again
and use it in the "normal" way:
>>> ins = users.insert()
>>> conn.execute(ins, id=2, name='wendy', fullname='Wendy Williams')
{opensql}INSERT INTO users (id, name, fullname) VALUES (?, ?, ?)
[...] (2, 'wendy', 'Wendy Williams')
COMMIT
{stop}<sqlalchemy.engine.cursor.LegacyCursorResult object at 0x...>
Above, because we specified all three columns in the execute() method,
the compiled :class:`_expression.Insert` included all three
columns. The :class:`_expression.Insert` statement is compiled
at execution time based on the parameters we specified; if we specified fewer
parameters, the :class:`_expression.Insert` would have fewer
entries in its VALUES clause.
To issue many inserts using DBAPI's executemany() method, we can send in a
list of dictionaries each containing a distinct set of parameters to be
inserted, as we do here to add some email addresses:
>>> conn.execute(addresses.insert(), [
... {'user_id': 1, 'email_address' : 'jack@yahoo.com'},
... {'user_id': 1, 'email_address' : 'jack@msn.com'},
... {'user_id': 2, 'email_address' : 'www@www.org'},
... {'user_id': 2, 'email_address' : 'wendy@aol.com'},
... ])
{opensql}INSERT INTO addresses (user_id, email_address) VALUES (?, ?)
[...] ((1, 'jack@yahoo.com'), (1, 'jack@msn.com'), (2, 'www@www.org'), (2, 'wendy@aol.com'))
COMMIT
{stop}<sqlalchemy.engine.cursor.LegacyCursorResult object at 0x...>
Above, we again relied upon SQLite's automatic generation of primary key
identifiers for each addresses row.
When executing multiple sets of parameters, each dictionary must have the same set of keys; i.e. you cant have fewer keys in some dictionaries than others. This is because the :class:`~sqlalchemy.sql.expression.Insert` statement is compiled against the first dictionary in the list, and it's assumed that all subsequent argument dictionaries are compatible with that statement.
The "executemany" style of invocation is available for each of the :func:`_expression.insert`, :func:`_expression.update` and :func:`_expression.delete` constructs.
We began with inserts just so that our test database had some data in it. The more interesting part of the data is selecting it! We'll cover UPDATE and DELETE statements later. The primary construct used to generate SELECT statements is the :func:`_expression.select` function:
>>> from sqlalchemy.sql import select
>>> s = select(users)
>>> result = conn.execute(s)
{opensql}SELECT users.id, users.name, users.fullname
FROM users
[...] ()
Above, we issued a basic :func:`_expression.select` call, placing the users table
within the COLUMNS clause of the select, and then executing. SQLAlchemy
expanded the users table into the set of each of its columns, and also
generated a FROM clause for us.
.. versionchanged:: 1.4 The :func:`_expression.select` construct now accepts column arguments positionally, as ``select(*args)``. The previous style of ``select()`` accepting a list of column elements is now deprecated. See :ref:`change_5284`.
The result returned is again a :class:`~sqlalchemy.engine.CursorResult` object, which acts much like a DBAPI cursor, including methods such as :func:`~sqlalchemy.engine.CursorResult.fetchone` and :func:`~sqlalchemy.engine.CursorResult.fetchall`. These methods return row objects, which are provided via the :class:`.Row` class. The result object can be iterated directly in order to provide an iterator of :class:`.Row` objects:
>>> for row in result:
... print(row)
(1, u'jack', u'Jack Jones')
(2, u'wendy', u'Wendy Williams')
Above, we see that printing each :class:`.Row` produces a simple tuple-like result. The most canonical way in Python to access the values of these tuples as rows are fetched is through tuple assignment:
{sql}>>> result = conn.execute(s)
SELECT users.id, users.name, users.fullname
FROM users
[...] ()
{stop}>>> for id, name, fullname in result:
... print("name:", name, "; fullname: ", fullname)
name: jack ; fullname: Jack Jones
name: wendy ; fullname: Wendy Williams
The :class:`.Row` object actually behaves like a Python named tuple, so we may also access these attributes from the row itself using attribute access:
{sql}>>> result = conn.execute(s)
SELECT users.id, users.name, users.fullname
FROM users
[...] ()
{stop}>>> for row in result:
... print("name:", row.name, "; fullname: ", row.fullname)
name: jack ; fullname: Jack Jones
name: wendy ; fullname: Wendy Williams
To access columns via name using strings, either when the column name is progammatically generated, or contains non-ascii characters, the :attr:`.Row._mapping` view may be used that provides dictionary-like access:
{sql}>>> result = conn.execute(s)
SELECT users.id, users.name, users.fullname
FROM users
[...] ()
{stop}>>> row = result.fetchone()
>>> print("name:", row._mapping['name'], "; fullname:", row._mapping['fullname'])
name: jack ; fullname: Jack Jones
.. deprecated:: 1.4
In versions of SQLAlchemy prior to 1.4, the above access using
:attr:`.Row._mapping` would proceed against the row object itself, that
is::
row = result.fetchone()
name, fullname = row["name"], row["fullname"]
This pattern is now deprecated and will be removed in SQLAlchemy 2.0, so
that the :class:`.Row` object may now behave fully like a Python named
tuple.
.. versionchanged:: 1.4 Added :attr:`.Row._mapping` which provides for dictionary-like access to a :class:`.Row`, superseding the use of string/ column keys against the :class:`.Row` object directly.
As the :class:`.Row` is a tuple, sequence (i.e. integer or slice) access may be used as well:
>>> row = result.fetchone()
>>> print("name:", row[1], "; fullname:", row[2])
name: wendy ; fullname: Wendy Williams
A more specialized method of column access is to use the SQL construct that directly corresponds to a particular column as the mapping key; in this example, it means we would use the :class:`_schema.Column` objects selected in our SELECT directly as keys in conjunction with the :attr:`.Row._mapping` collection:
{sql}>>> for row in conn.execute(s):
... print("name:", row._mapping[users.c.name], "; fullname:", row._mapping[users.c.fullname])
SELECT users.id, users.name, users.fullname
FROM users
[...] ()
{stop}name: jack ; fullname: Jack Jones
name: wendy ; fullname: Wendy Williams
Results and Rows are changing
The :class:`.Row` class was known as RowProxy and the
:class:`_engine.CursorResult` class was known as ResultProxy, for all
SQLAlchemy versions through 1.3. In 1.4, the objects returned by
:class:`_engine.CursorResult` are actually a subclass of :class:`.Row` known as
:class:`.LegacyRow`. See :ref:`change_4710_core` for background on this
change.
The :class:`_engine.CursorResult` object features "auto-close" behavior that closes the
underlying DBAPI cursor object when all pending result rows have been
fetched. If a :class:`_engine.CursorResult` is to be discarded before such an
autoclose has occurred, it can be explicitly closed using the
:meth:`_engine.CursorResult.close` method:
>>> result.close()
If we'd like to more carefully control the columns which are placed in the
COLUMNS clause of the select, we reference individual
:class:`~sqlalchemy.schema.Column` objects from our
:class:`~sqlalchemy.schema.Table`. These are available as named attributes off
the c attribute of the :class:`~sqlalchemy.schema.Table` object:
>>> s = select(users.c.name, users.c.fullname)
{sql}>>> result = conn.execute(s)
SELECT users.name, users.fullname
FROM users
[...] ()
{stop}>>> for row in result:
... print(row)
(u'jack', u'Jack Jones')
(u'wendy', u'Wendy Williams')
Lets observe something interesting about the FROM clause. Whereas the generated statement contains two distinct sections, a "SELECT columns" part and a "FROM table" part, our :func:`_expression.select` construct only has a list containing columns. How does this work ? Let's try putting two tables into our :func:`_expression.select` statement:
{sql}>>> for row in conn.execute(select(users, addresses)):
... print(row)
SELECT users.id, users.name, users.fullname, addresses.id, addresses.user_id, addresses.email_address
FROM users, addresses
[...] ()
{stop}(1, u'jack', u'Jack Jones', 1, 1, u'jack@yahoo.com')
(1, u'jack', u'Jack Jones', 2, 1, u'jack@msn.com')
(1, u'jack', u'Jack Jones', 3, 2, u'www@www.org')
(1, u'jack', u'Jack Jones', 4, 2, u'wendy@aol.com')
(2, u'wendy', u'Wendy Williams', 1, 1, u'jack@yahoo.com')
(2, u'wendy', u'Wendy Williams', 2, 1, u'jack@msn.com')
(2, u'wendy', u'Wendy Williams', 3, 2, u'www@www.org')
(2, u'wendy', u'Wendy Williams', 4, 2, u'wendy@aol.com')
It placed both tables into the FROM clause. But also, it made a real mess.
Those who are familiar with SQL joins know that this is a Cartesian
product; each row from the users table is produced against each row from
the addresses table. So to put some sanity into this statement, we need a
WHERE clause. We do that using :meth:`_expression.Select.where`:
>>> s = select(users, addresses).where(users.c.id == addresses.c.user_id)
{sql}>>> for row in conn.execute(s):
... print(row)
SELECT users.id, users.name, users.fullname, addresses.id,
addresses.user_id, addresses.email_address
FROM users, addresses
WHERE users.id = addresses.user_id
[...] ()
{stop}(1, u'jack', u'Jack Jones', 1, 1, u'jack@yahoo.com')
(1, u'jack', u'Jack Jones', 2, 1, u'jack@msn.com')
(2, u'wendy', u'Wendy Williams', 3, 2, u'www@www.org')
(2, u'wendy', u'Wendy Williams', 4, 2, u'wendy@aol.com')
So that looks a lot better, we added an expression to our :func:`_expression.select`
which had the effect of adding WHERE users.id = addresses.user_id to our
statement, and our results were managed down so that the join of users and
addresses rows made sense. But let's look at that expression? It's using
just a Python equality operator between two different
:class:`~sqlalchemy.schema.Column` objects. It should be clear that something
is up. Saying 1 == 1 produces True, and 1 == 2 produces False, not
a WHERE clause. So lets see exactly what that expression is doing:
>>> users.c.id == addresses.c.user_id
<sqlalchemy.sql.elements.BinaryExpression object at 0x...>
Wow, surprise ! This is neither a True nor a False. Well what is it ?
>>> str(users.c.id == addresses.c.user_id)
'users.id = addresses.user_id'
As you can see, the == operator is producing an object that is very much
like the :class:`_expression.Insert` and :func:`_expression.select`
objects we've made so far, thanks to Python's __eq__() builtin; you call
str() on it and it produces SQL. By now, one can see that everything we
are working with is ultimately the same type of object. SQLAlchemy terms the
base class of all of these expressions as :class:`_expression.ColumnElement`.
Since we've stumbled upon SQLAlchemy's operator paradigm, let's go through some of its capabilities. We've seen how to equate two columns to each other:
>>> print(users.c.id == addresses.c.user_id)
users.id = addresses.user_id
If we use a literal value (a literal meaning, not a SQLAlchemy clause object), we get a bind parameter:
>>> print(users.c.id == 7)
users.id = :id_1
The 7 literal is embedded the resulting
:class:`_expression.ColumnElement`; we can use the same trick
we did with the :class:`~sqlalchemy.sql.expression.Insert` object to see it:
>>> (users.c.id == 7).compile().params
{u'id_1': 7}
Most Python operators, as it turns out, produce a SQL expression here, like equals, not equals, etc.:
>>> print(users.c.id != 7)
users.id != :id_1
>>> # None converts to IS NULL
>>> print(users.c.name == None)
users.name IS NULL
>>> # reverse works too
>>> print('fred' > users.c.name)
users.name < :name_1
If we add two integer columns together, we get an addition expression:
>>> print(users.c.id + addresses.c.id)
users.id + addresses.id
Interestingly, the type of the :class:`~sqlalchemy.schema.Column` is important!
If we use + with two string based columns (recall we put types like
:class:`~sqlalchemy.types.Integer` and :class:`~sqlalchemy.types.String` on
our :class:`~sqlalchemy.schema.Column` objects at the beginning), we get
something different:
>>> print(users.c.name + users.c.fullname)
users.name || users.fullname
Where || is the string concatenation operator used on most databases. But
not all of them. MySQL users, fear not:
>>> print((users.c.name + users.c.fullname).
... compile(bind=create_engine('mysql://'))) # doctest: +SKIP
concat(users.name, users.fullname)
The above illustrates the SQL that's generated for an
:class:`~sqlalchemy.engine.Engine` that's connected to a MySQL database;
the || operator now compiles as MySQL's concat() function.
If you have come across an operator which really isn't available, you can always use the :meth:`.Operators.op` method; this generates whatever operator you need:
>>> print(users.c.name.op('tiddlywinks')('foo'))
users.name tiddlywinks :name_1
This function can also be used to make bitwise operators explicit. For example:
somecolumn.op('&')(0xff)
is a bitwise AND of the value in somecolumn.
When using :meth:`.Operators.op`, the return type of the expression may be important, especially when the operator is used in an expression that will be sent as a result column. For this case, be sure to make the type explicit, if not what's normally expected, using :func:`.type_coerce`:
from sqlalchemy import type_coerce
expr = type_coerce(somecolumn.op('-%>')('foo'), MySpecialType())
stmt = select(expr)
For boolean operators, use the :meth:`.Operators.bool_op` method, which will ensure that the return type of the expression is handled as boolean:
somecolumn.bool_op('-->')('some value')
Here's a rundown of some of the most common operators used in both the Core expression language as well as in the ORM. Here we see expressions that are most commonly present when using the :meth:`_sql.Select.where` method, but can be used in other scenarios as well.
A listing of all the column-level operations common to all column-like objects is at :class:`.ColumnOperators`.
:meth:`equals <.ColumnOperators.__eq__>`:
statement.where(users.c.name == 'ed')
:meth:`not equals <.ColumnOperators.__ne__>`:
statement.where(users.c.name != 'ed')
:meth:`LIKE <.ColumnOperators.like>`:
statement.where(users.c.name.like('%ed%'))
Note
:meth:`.ColumnOperators.like` renders the LIKE operator, which is case insensitive on some backends, and case sensitive on others. For guaranteed case-insensitive comparisons, use :meth:`.ColumnOperators.ilike`.
:meth:`ILIKE <.ColumnOperators.ilike>` (case-insensitive LIKE):
statement.where(users.c.name.ilike('%ed%'))
Note
most backends don't support ILIKE directly. For those, the :meth:`.ColumnOperators.ilike` operator renders an expression combining LIKE with the LOWER SQL function applied to each operand.
:meth:`IN <.ColumnOperators.in_>`:
statement.where(users.c..name.in_(['ed', 'wendy', 'jack'])) # works with Select objects too: statement.where.filter(users.c.name.in_( select(users.c.name).where(users.c.name.like('%ed%')) )) # use tuple_() for composite (multi-column) queries from sqlalchemy import tuple_ statement.where( tuple_(users.c.name, users.c.nickname).\ in_([('ed', 'edsnickname'), ('wendy', 'windy')]) ):meth:`NOT IN <.ColumnOperators.notin_>`:
statement.where(~users.c.name.in_(['ed', 'wendy', 'jack']))
:meth:`IS NULL <.ColumnOperators.is_>`:
statement.where(users.c. == None) # alternatively, if pep8/linters are a concern statement.where(users.c.name.is_(None))
:meth:`IS NOT NULL <.ColumnOperators.isnot>`:
statement.where(users.c.name != None) # alternatively, if pep8/linters are a concern statement.where(users.c.name.isnot(None))
:func:`AND <.sql.expression.and_>`:
# use and_() from sqlalchemy import and_ statement.where(and_(users.c.name == 'ed', users.c.fullname == 'Ed Jones')) # or send multiple expressions to .where() statement.where(users.c.name == 'ed', users.c.fullname == 'Ed Jones') # or chain multiple where() calls statement.where(users.c.name == 'ed').where(users.c.fullname == 'Ed Jones')
Note
Make sure you use :func:`.and_` and not the Python
andoperator!
:func:`OR <.sql.expression.or_>`:
from sqlalchemy import or_ statement.where(or_(users.c.name == 'ed', users.c.name == 'wendy'))
Note
Make sure you use :func:`.or_` and not the Python
oroperator!
:meth:`MATCH <.ColumnOperators.match>`:
statement.where(users.c.name.match('wendy'))
Note
:meth:`~.ColumnOperators.match` uses a database-specific
MATCHorCONTAINSfunction; its behavior will vary by backend and is not available on some backends such as SQLite.
While :meth:`.Operators.op` is handy to get at a custom operator in a hurry, the Core supports fundamental customization and extension of the operator system at the type level. The behavior of existing operators can be modified on a per-type basis, and new operations can be defined which become available for all column expressions that are part of that particular type. See the section :ref:`types_operators` for a description.
We'd like to show off some of our operators inside of :func:`_expression.select` constructs. But we need to lump them together a little more, so let's first introduce some conjunctions. Conjunctions are those little words like AND and OR that put things together. We'll also hit upon NOT. :func:`.and_`, :func:`.or_`, and :func:`.not_` can work from the corresponding functions SQLAlchemy provides (notice we also throw in a :meth:`~.ColumnOperators.like`):
>>> from sqlalchemy.sql import and_, or_, not_
>>> print(and_(
... users.c.name.like('j%'),
... users.c.id == addresses.c.user_id,
... or_(
... addresses.c.email_address == 'wendy@aol.com',
... addresses.c.email_address == 'jack@yahoo.com'
... ),
... not_(users.c.id > 5)
... )
... )
users.name LIKE :name_1 AND users.id = addresses.user_id AND
(addresses.email_address = :email_address_1
OR addresses.email_address = :email_address_2)
AND users.id <= :id_1
And you can also use the re-jiggered bitwise AND, OR and NOT operators, although because of Python operator precedence you have to watch your parenthesis:
>>> print(users.c.name.like('j%') & (users.c.id == addresses.c.user_id) &
... (
... (addresses.c.email_address == 'wendy@aol.com') | \
... (addresses.c.email_address == 'jack@yahoo.com')
... ) \
... & ~(users.c.id>5)
... )
users.name LIKE :name_1 AND users.id = addresses.user_id AND
(addresses.email_address = :email_address_1
OR addresses.email_address = :email_address_2)
AND users.id <= :id_1
So with all of this vocabulary, let's select all users who have an email
address at AOL or MSN, whose name starts with a letter between "m" and "z",
and we'll also generate a column containing their full name combined with
their email address. We will add two new constructs to this statement,
:meth:`~.ColumnOperators.between` and :meth:`_expression.ColumnElement.label`.
:meth:`~.ColumnOperators.between` produces a BETWEEN clause, and
:meth:`_expression.ColumnElement.label` is used in a column expression to produce labels using the AS
keyword; it's recommended when selecting from expressions that otherwise would
not have a name:
>>> s = select((users.c.fullname +
... ", " + addresses.c.email_address).
... label('title')).\
... where(
... and_(
... users.c.id == addresses.c.user_id,
... users.c.name.between('m', 'z'),
... or_(
... addresses.c.email_address.like('%@aol.com'),
... addresses.c.email_address.like('%@msn.com')
... )
... )
... )
>>> conn.execute(s).fetchall()
{opensql}SELECT users.fullname || ? || addresses.email_address AS title
FROM users, addresses
WHERE users.id = addresses.user_id AND users.name BETWEEN ? AND ? AND
(addresses.email_address LIKE ? OR addresses.email_address LIKE ?)
[...] (', ', 'm', 'z', '%@aol.com', '%@msn.com')
{stop}[(u'Wendy Williams, wendy@aol.com',)]
Once again, SQLAlchemy figured out the FROM clause for our statement. In fact it will determine the FROM clause based on all of its other bits; the columns clause, the where clause, and also some other elements which we haven't covered yet, which include ORDER BY, GROUP BY, and HAVING.
A shortcut to using :func:`.and_` is to chain together multiple :meth:`_expression.Select.where` clauses. The above can also be written as:
>>> s = select((users.c.fullname +
... ", " + addresses.c.email_address).
... label('title')).\
... where(users.c.id == addresses.c.user_id).\
... where(users.c.name.between('m', 'z')).\
... where(
... or_(
... addresses.c.email_address.like('%@aol.com'),
... addresses.c.email_address.like('%@msn.com')
... )
... )
>>> conn.execute(s).fetchall()
{opensql}SELECT users.fullname || ? || addresses.email_address AS title
FROM users, addresses
WHERE users.id = addresses.user_id AND users.name BETWEEN ? AND ? AND
(addresses.email_address LIKE ? OR addresses.email_address LIKE ?)
[...] (', ', 'm', 'z', '%@aol.com', '%@msn.com')
{stop}[(u'Wendy Williams, wendy@aol.com',)]
The way that we can build up a :func:`_expression.select` construct through successive method calls is called :term:`method chaining`.
Our last example really became a handful to type. Going from what one understands to be a textual SQL expression into a Python construct which groups components together in a programmatic style can be hard. That's why SQLAlchemy lets you just use strings, for those cases when the SQL is already known and there isn't a strong need for the statement to support dynamic features. The :func:`_expression.text` construct is used to compose a textual statement that is passed to the database mostly unchanged. Below, we create a :func:`_expression.text` object and execute it:
>>> from sqlalchemy.sql import text
>>> s = text(
... "SELECT users.fullname || ', ' || addresses.email_address AS title "
... "FROM users, addresses "
... "WHERE users.id = addresses.user_id "
... "AND users.name BETWEEN :x AND :y "
... "AND (addresses.email_address LIKE :e1 "
... "OR addresses.email_address LIKE :e2)")
>>> conn.execute(s, x='m', y='z', e1='%@aol.com', e2='%@msn.com').fetchall()
{opensql}SELECT users.fullname || ', ' || addresses.email_address AS title
FROM users, addresses
WHERE users.id = addresses.user_id AND users.name BETWEEN ? AND ? AND
(addresses.email_address LIKE ? OR addresses.email_address LIKE ?)
[...] ('m', 'z', '%@aol.com', '%@msn.com')
{stop}[(u'Wendy Williams, wendy@aol.com',)]
Above, we can see that bound parameters are specified in :func:`_expression.text` using the named colon format; this format is consistent regardless of database backend. To send values in for the parameters, we passed them into the :meth:`_engine.Connection.execute` method as additional arguments.
The :func:`_expression.text` construct supports pre-established bound values using the :meth:`_expression.TextClause.bindparams` method:
stmt = text("SELECT * FROM users WHERE users.name BETWEEN :x AND :y")
stmt = stmt.bindparams(x="m", y="z")
The parameters can also be explicitly typed:
stmt = stmt.bindparams(bindparam("x", type_=String), bindparam("y", type_=String))
result = conn.execute(stmt, {"x": "m", "y": "z"})
Typing for bound parameters is necessary when the type requires Python-side or special SQL-side processing provided by the datatype.
.. seealso::
:meth:`_expression.TextClause.bindparams` - full method description
We may also specify information about the result columns using the :meth:`_expression.TextClause.columns` method; this method can be used to specify the return types, based on name:
stmt = stmt.columns(id=Integer, name=String)
or it can be passed full column expressions positionally, either typed or untyped. In this case it's a good idea to list out the columns explicitly within our textual SQL, since the correlation of our column expressions to the SQL will be done positionally:
stmt = text("SELECT id, name FROM users")
stmt = stmt.columns(users.c.id, users.c.name)
When we call the :meth:`_expression.TextClause.columns` method, we get back a :class:`.TextAsFrom` object that supports the full suite of :attr:`.TextAsFrom.c` and other "selectable" operations:
j = stmt.join(addresses, stmt.c.id == addresses.c.user_id)
new_stmt = select(stmt.c.id, addresses.c.id).\
select_from(j).where(stmt.c.name == 'x')
The positional form of :meth:`_expression.TextClause.columns` is particularly useful when relating textual SQL to existing Core or ORM models, because we can use column expressions directly without worrying about name conflicts or other issues with the result column names in the textual SQL:
>>> stmt = text("SELECT users.id, addresses.id, users.id, "
... "users.name, addresses.email_address AS email "
... "FROM users JOIN addresses ON users.id=addresses.user_id "
... "WHERE users.id = 1").columns(
... users.c.id,
... addresses.c.id,
... addresses.c.user_id,
... users.c.name,
... addresses.c.email_address
... )
>>> result = conn.execute(stmt)
{opensql}SELECT users.id, addresses.id, users.id, users.name,
addresses.email_address AS email
FROM users JOIN addresses ON users.id=addresses.user_id WHERE users.id = 1
[...] ()
{stop}
Above, there's three columns in the result that are named "id", but since
we've associated these with column expressions positionally, the names aren't an issue
when the result-columns are fetched using the actual column object as a key.
Fetching the email_address column would be:
>>> row = result.fetchone() >>> row._mapping[addresses.c.email_address] 'jack@yahoo.com'
If on the other hand we used a string column key, the usual rules of
name-based matching still apply, and we'd get an ambiguous column error for
the id value:
>>> row._mapping["id"] Traceback (most recent call last): ... InvalidRequestError: Ambiguous column name 'id' in result set column descriptions
It's important to note that while accessing columns from a result set using :class:`_schema.Column` objects may seem unusual, it is in fact the only system used by the ORM, which occurs transparently beneath the facade of the :class:`~.orm.query.Query` object; in this way, the :meth:`_expression.TextClause.columns` method is typically very applicable to textual statements to be used in an ORM context. The example at :ref:`orm_tutorial_literal_sql` illustrates a simple usage.
.. versionadded:: 1.1
The :meth:`_expression.TextClause.columns` method now accepts column expressions
which will be matched positionally to a plain text SQL result set,
eliminating the need for column names to match or even be unique in the
SQL statement when matching table metadata or ORM models to textual SQL.
.. seealso::
:meth:`_expression.TextClause.columns` - full method description
:ref:`orm_tutorial_literal_sql` - integrating ORM-level queries with
:func:`_expression.text`
:func:`_expression.text` can also be used to produce fragments of SQL that can be freely within a :func:`_expression.select` object, which accepts :func:`_expression.text` objects as an argument for most of its builder functions. Below, we combine the usage of :func:`_expression.text` within a :func:`_expression.select` object. The :func:`_expression.select` construct provides the "geometry" of the statement, and the :func:`_expression.text` construct provides the textual content within this form. We can build a statement without the need to refer to any pre-established :class:`_schema.Table` metadata:
>>> s = select(
... text("users.fullname || ', ' || addresses.email_address AS title")
... ).\
... where(
... and_(
... text("users.id = addresses.user_id"),
... text("users.name BETWEEN 'm' AND 'z'"),
... text(
... "(addresses.email_address LIKE :x "
... "OR addresses.email_address LIKE :y)")
... )
... ).select_from(text('users, addresses'))
>>> conn.execute(s, x='%@aol.com', y='%@msn.com').fetchall()
{opensql}SELECT users.fullname || ', ' || addresses.email_address AS title
FROM users, addresses
WHERE users.id = addresses.user_id AND users.name BETWEEN 'm' AND 'z'
AND (addresses.email_address LIKE ? OR addresses.email_address LIKE ?)
[...] ('%@aol.com', '%@msn.com')
{stop}[(u'Wendy Williams, wendy@aol.com',)]
.. versionchanged:: 1.0.0 The :func:`_expression.select` construct emits warnings when string SQL fragments are coerced to :func:`_expression.text`, and :func:`_expression.text` should be used explicitly. See :ref:`migration_2992` for background.
Using More Specific Text with :func:`.table`, :func:`_expression.literal_column`, and :func:`_expression.column`
We can move our level of structure back in the other direction too,
by using :func:`_expression.column`, :func:`_expression.literal_column`,
and :func:`_expression.table` for some of the
key elements of our statement. Using these constructs, we can get
some more expression capabilities than if we used :func:`_expression.text`
directly, as they provide to the Core more information about how the strings
they store are to be used, but still without the need to get into full
:class:`_schema.Table` based metadata. Below, we also specify the :class:`.String`
datatype for two of the key :func:`_expression.literal_column` objects,
so that the string-specific concatenation operator becomes available.
We also use :func:`_expression.literal_column` in order to use table-qualified
expressions, e.g. users.fullname, that will be rendered as is;
using :func:`_expression.column` implies an individual column name that may
be quoted:
>>> from sqlalchemy import select, and_, text, String
>>> from sqlalchemy.sql import table, literal_column
>>> s = select(
... literal_column("users.fullname", String) +
... ', ' +
... literal_column("addresses.email_address").label("title")
... ).\
... where(
... and_(
... literal_column("users.id") == literal_column("addresses.user_id"),
... text("users.name BETWEEN 'm' AND 'z'"),
... text(
... "(addresses.email_address LIKE :x OR "
... "addresses.email_address LIKE :y)")
... )
... ).select_from(table('users')).select_from(table('addresses'))
>>> conn.execute(s, x='%@aol.com', y='%@msn.com').fetchall()
{opensql}SELECT users.fullname || ? || addresses.email_address AS anon_1
FROM users, addresses
WHERE users.id = addresses.user_id
AND users.name BETWEEN 'm' AND 'z'
AND (addresses.email_address LIKE ? OR addresses.email_address LIKE ?)
[...] (', ', '%@aol.com', '%@msn.com')
{stop}[(u'Wendy Williams, wendy@aol.com',)]
One place where we sometimes want to use a string as a shortcut is when our statement has some labeled column element that we want to refer to in a place such as the "ORDER BY" or "GROUP BY" clause; other candidates include fields within an "OVER" or "DISTINCT" clause. If we have such a label in our :func:`_expression.select` construct, we can refer to it directly by passing the string straight into :meth:`_expression.select.order_by` or :meth:`_expression.select.group_by`, among others. This will refer to the named label and also prevent the expression from being rendered twice. Label names that resolve to columns are rendered fully:
>>> from sqlalchemy import func
>>> stmt = select(
... addresses.c.user_id,
... func.count(addresses.c.id).label('num_addresses')).\
... group_by("user_id").order_by("user_id", "num_addresses")
{sql}>>> conn.execute(stmt).fetchall()
SELECT addresses.user_id, count(addresses.id) AS num_addresses
FROM addresses GROUP BY addresses.user_id ORDER BY addresses.user_id, num_addresses
[...] ()
{stop}[(1, 2), (2, 2)]
We can use modifiers like :func:`.asc` or :func:`.desc` by passing the string name:
>>> from sqlalchemy import func, desc
>>> stmt = select(
... addresses.c.user_id,
... func.count(addresses.c.id).label('num_addresses')).\
... group_by("user_id").order_by("user_id", desc("num_addresses"))
{sql}>>> conn.execute(stmt).fetchall()
SELECT addresses.user_id, count(addresses.id) AS num_addresses
FROM addresses GROUP BY addresses.user_id ORDER BY addresses.user_id, num_addresses DESC
[...] ()
{stop}[(1, 2), (2, 2)]
Note that the string feature here is very much tailored to when we have already used the :meth:`_expression.ColumnElement.label` method to create a specifically-named label. In other cases, we always want to refer to the :class:`_expression.ColumnElement` object directly so that the expression system can make the most effective choices for rendering. Below, we illustrate how using the :class:`_expression.ColumnElement` eliminates ambiguity when we want to order by a column name that appears more than once:
>>> u1a, u1b = users.alias(), users.alias()
>>> stmt = select(u1a, u1b).\
... where(u1a.c.name > u1b.c.name).\
... order_by(u1a.c.name) # using "name" here would be ambiguous
{sql}>>> conn.execute(stmt).fetchall()
SELECT users_1.id, users_1.name, users_1.fullname, users_2.id,
users_2.name, users_2.fullname
FROM users AS users_1, users AS users_2
WHERE users_1.name > users_2.name ORDER BY users_1.name
[...] ()
{stop}[(2, u'wendy', u'Wendy Williams', 1, u'jack', u'Jack Jones')]
The alias in SQL corresponds to a "renamed" version of a table or SELECT
statement, which occurs anytime you say "SELECT .. FROM sometable AS
someothername". The AS creates a new name for the table. Aliases are a key
construct as they allow any table or subquery to be referenced by a unique
name. In the case of a table, this allows the same table to be named in the
FROM clause multiple times. In the case of a SELECT statement, it provides a
parent name for the columns represented by the statement, allowing them to be
referenced relative to this name.
In SQLAlchemy, any :class:`_schema.Table` or other :class:`_expression.FromClause` based selectable can be turned into an alias using :meth:`_expression.FromClause.alias` method, which produces an :class:`_expression.Alias` construct. :class:`_expression.Alias` is a :class:`_expression.FromClause` object that refers to a mapping of :class:`_schema.Column` objects via its :attr:`_expression.FromClause.c` collection, and can be used within the FROM clause of any subsequent SELECT statement, by referring to its column elements in the columns or WHERE clause of the statement, or through explicit placement in the FROM clause, either directly or within a join.
As an example, suppose we know that our user jack has two particular email
addresses. How can we locate jack based on the combination of those two
addresses? To accomplish this, we'd use a join to the addresses table,
once for each address. We create two :class:`_expression.Alias` constructs against
addresses, and then use them both within a :func:`_expression.select` construct:
>>> a1 = addresses.alias()
>>> a2 = addresses.alias()
>>> s = select(users).\
... where(and_(
... users.c.id == a1.c.user_id,
... users.c.id == a2.c.user_id,
... a1.c.email_address == 'jack@msn.com',
... a2.c.email_address == 'jack@yahoo.com'
... ))
>>> conn.execute(s).fetchall()
{opensql}SELECT users.id, users.name, users.fullname
FROM users, addresses AS addresses_1, addresses AS addresses_2
WHERE users.id = addresses_1.user_id
AND users.id = addresses_2.user_id
AND addresses_1.email_address = ?
AND addresses_2.email_address = ?
[...] ('jack@msn.com', 'jack@yahoo.com')
{stop}[(1, u'jack', u'Jack Jones')]
Note that the :class:`_expression.Alias` construct generated the names addresses_1 and
addresses_2 in the final SQL result. The generation of these names is determined
by the position of the construct within the statement. If we created a query using
only the second a2 alias, the name would come out as addresses_1. The
generation of the names is also deterministic, meaning the same SQLAlchemy
statement construct will produce the identical SQL string each time it is
rendered for a particular dialect.
Since on the outside, we refer to the alias using the :class:`_expression.Alias` construct itself, we don't need to be concerned about the generated name. However, for the purposes of debugging, it can be specified by passing a string name to the :meth:`_expression.FromClause.alias` method:
>>> a1 = addresses.alias('a1')
SELECT-oriented constructs which extend from :class:`_expression.SelectBase` may be turned into aliased subqueries using the :meth:`_expression.SelectBase.subquery` method, which produces a :class:`.Subquery` construct; for ease of use, there is also a :meth:`_expression.SelectBase.alias` method that is synonymous with :meth:`_expression.SelectBase.subquery`. Like :class:`_expression.Alias`, :class:`.Subquery` is also a :class:`_expression.FromClause` object that may be part of any enclosing SELECT using the same techniques one would use for a :class:`_expression.Alias`.
We can self-join the users table back to the :func:`_expression.select` we've created
by making :class:`.Subquery` of the entire statement:
>>> address_subq = s.subquery()
>>> s = select(users.c.name).where(users.c.id == address_subq.c.id)
>>> conn.execute(s).fetchall()
{opensql}SELECT users.name
FROM users,
(SELECT users.id AS id, users.name AS name, users.fullname AS fullname
FROM users, addresses AS addresses_1, addresses AS addresses_2
WHERE users.id = addresses_1.user_id AND users.id = addresses_2.user_id
AND addresses_1.email_address = ?
AND addresses_2.email_address = ?) AS anon_1
WHERE users.id = anon_1.id
[...] ('jack@msn.com', 'jack@yahoo.com')
{stop}[(u'jack',)]
.. versionchanged:: 1.4 Added the :class:`.Subquery` object and created more of a separation between an "alias" of a FROM clause and a named subquery of a SELECT. See :ref:`change_4617`.
We're halfway along to being able to construct any SELECT expression. The next cornerstone of the SELECT is the JOIN expression. We've already been doing joins in our examples, by just placing two tables in either the columns clause or the where clause of the :func:`_expression.select` construct. But if we want to make a real "JOIN" or "OUTERJOIN" construct, we use the :meth:`_expression.FromClause.join` and :meth:`_expression.FromClause.outerjoin` methods, most commonly accessed from the left table in the join:
>>> print(users.join(addresses))
users JOIN addresses ON users.id = addresses.user_id
The alert reader will see more surprises; SQLAlchemy figured out how to JOIN
the two tables ! The ON condition of the join, as it's called, was
automatically generated based on the :class:`~sqlalchemy.schema.ForeignKey`
object which we placed on the addresses table way at the beginning of this
tutorial. Already the join() construct is looking like a much better way
to join tables.
Of course you can join on whatever expression you want, such as if we want to join on all users who use the same name in their email address as their username:
>>> print(users.join(addresses,
... addresses.c.email_address.like(users.c.name + '%')
... )
... )
users JOIN addresses ON addresses.email_address LIKE users.name || :name_1
When we create a :func:`_expression.select` construct, SQLAlchemy looks around at the tables we've mentioned and then places them in the FROM clause of the statement. When we use JOINs however, we know what FROM clause we want, so here we make use of the :meth:`_expression.Select.select_from` method:
>>> s = select(users.c.fullname).select_from(
... users.join(addresses,
... addresses.c.email_address.like(users.c.name + '%'))
... )
{sql}>>> conn.execute(s).fetchall()
SELECT users.fullname
FROM users JOIN addresses ON addresses.email_address LIKE users.name || ?
[...] ('%',)
{stop}[(u'Jack Jones',), (u'Jack Jones',), (u'Wendy Williams',)]
The :meth:`_expression.FromClause.outerjoin` method creates LEFT OUTER JOIN constructs,
and is used in the same way as :meth:`_expression.FromClause.join`:
>>> s = select(users.c.fullname).select_from(users.outerjoin(addresses))
>>> print(s)
SELECT users.fullname
FROM users
LEFT OUTER JOIN addresses ON users.id = addresses.user_id
That's the output outerjoin() produces, unless, of course, you're stuck in
a gig using Oracle prior to version 9, and you've set up your engine (which
would be using OracleDialect) to use Oracle-specific SQL:
>>> from sqlalchemy.dialects.oracle import dialect as OracleDialect
>>> print(s.compile(dialect=OracleDialect(use_ansi=False)))
SELECT users.fullname
FROM users, addresses
WHERE users.id = addresses.user_id(+)
If you don't know what that SQL means, don't worry ! The secret tribe of Oracle DBAs don't want their black magic being found out ;).
.. seealso::
:func:`_expression.join`
:func:`_expression.outerjoin`
:class:`_expression.Join`
Common table expressions are now supported by every major database, including modern MySQL, MariaDB, SQLite, PostgreSQL, Oracle and MS SQL Server. SQLAlchemy supports this construct via the :class:`_expression.CTE` object, which one typically acquires using the :meth:`_expression.Select.cte` method on a :class:`_expression.Select` construct:
>>> users_cte = select(users.c.id, users.c.name).where(users.c.name == 'wendy').cte()
>>> stmt = select(addresses).where(addresses.c.user_id == users_cte.c.id).order_by(addresses.c.id)
>>> conn.execute(stmt).fetchall()
{opensql}WITH anon_1 AS
(SELECT users.id AS id, users.name AS name
FROM users
WHERE users.name = ?)
SELECT addresses.id, addresses.user_id, addresses.email_address
FROM addresses, anon_1
WHERE addresses.user_id = anon_1.id ORDER BY addresses.id
[...] ('wendy',)
{stop}[(3, 2, 'www@www.org'), (4, 2, 'wendy@aol.com')]
The CTE construct is a great way to provide a source of rows that is semantically similar to using a subquery, but with a much simpler format where the source of rows is neatly tucked away at the top of the query where it can be referenced anywhere in the main statement like a regular table.
When we construct a :class:`_expression.CTE` object, we make use of it like any other table in the statement. However instead of being added to the FROM clause as a subquery, it comes out on top, which has the additional benefit of not causing surprise cartesian products.
The RECURSIVE format of CTE is available when one uses the :paramref:`_expression.Select.cte.recursive` parameter. A recursive CTE typically requires that we are linking to ourselves as an alias. The general form of this kind of operation involves a UNION of the original CTE against itself. Noting that our example tables are not well suited to producing an actually useful query with this feature, this form looks like:
>>> users_cte = select(users.c.id, users.c.name).cte(recursive=True)
>>> users_recursive = users_cte.alias()
>>> users_cte = users_cte.union(select(users.c.id, users.c.name).where(users.c.id > users_recursive.c.id))
>>> stmt = select(addresses).where(addresses.c.user_id == users_cte.c.id).order_by(addresses.c.id)
>>> conn.execute(stmt).fetchall()
{opensql}WITH RECURSIVE anon_1(id, name) AS
(SELECT users.id AS id, users.name AS name
FROM users UNION SELECT users.id AS id, users.name AS name
FROM users, anon_1 AS anon_2
WHERE users.id > anon_2.id)
SELECT addresses.id, addresses.user_id, addresses.email_address
FROM addresses, anon_1
WHERE addresses.user_id = anon_1.id ORDER BY addresses.id
[...] ()
{stop}[(1, 1, 'jack@yahoo.com'), (2, 1, 'jack@msn.com'), (3, 2, 'www@www.org'), (4, 2, 'wendy@aol.com')]
The concepts of creating SQL expressions have been introduced. What's left are more variants of the same themes. So now we'll catalog the rest of the important things we'll need to know.
Throughout all these examples, SQLAlchemy is busy creating bind parameters wherever literal expressions occur. You can also specify your own bind parameters with your own names, and use the same statement repeatedly. The :func:`.bindparam` construct is used to produce a bound parameter with a given name. While SQLAlchemy always refers to bound parameters by name on the API side, the database dialect converts to the appropriate named or positional style at execution time, as here where it converts to positional for SQLite:
>>> from sqlalchemy.sql import bindparam
>>> s = users.select().where(users.c.name == bindparam('username'))
{sql}>>> conn.execute(s, {"username": "wendy"}).fetchall()
SELECT users.id, users.name, users.fullname
FROM users
WHERE users.name = ?
[...] ('wendy',)
{stop}[(2, u'wendy', u'Wendy Williams')]
Another important aspect of :func:`.bindparam` is that it may be assigned a type. The type of the bind parameter will determine its behavior within expressions and also how the data bound to it is processed before being sent off to the database:
>>> s = users.select().where(users.c.name.like(bindparam('username', type_=String) + text("'%'")))
{sql}>>> conn.execute(s, {"username": "wendy"}).fetchall()
SELECT users.id, users.name, users.fullname
FROM users
WHERE users.name LIKE ? || '%'
[...] ('wendy',)
{stop}[(2, u'wendy', u'Wendy Williams')]
:func:`.bindparam` constructs of the same name can also be used multiple times, where only a single named value is needed in the execute parameters:
>>> s = select(users, addresses).\
... where(
... or_(
... users.c.name.like(
... bindparam('name', type_=String) + text("'%'")),
... addresses.c.email_address.like(
... bindparam('name', type_=String) + text("'@%'"))
... )
... ).\
... select_from(users.outerjoin(addresses)).\
... order_by(addresses.c.id)
{sql}>>> conn.execute(s, {"name": "jack"}).fetchall()
SELECT users.id, users.name, users.fullname, addresses.id,
addresses.user_id, addresses.email_address
FROM users LEFT OUTER JOIN addresses ON users.id = addresses.user_id
WHERE users.name LIKE ? || '%' OR addresses.email_address LIKE ? || '@%'
ORDER BY addresses.id
[...] ('jack', 'jack')
{stop}[(1, u'jack', u'Jack Jones', 1, 1, u'jack@yahoo.com'), (1, u'jack', u'Jack Jones', 2, 1, u'jack@msn.com')]
.. seealso::
:func:`.bindparam`
SQL functions are created using the :data:`~.expression.func` keyword, which generates functions using attribute access:
>>> from sqlalchemy.sql import func
>>> print(func.now())
now()
>>> print(func.concat('x', 'y'))
concat(:concat_1, :concat_2)
By "generates", we mean that any SQL function is created based on the word you choose:
>>> print(func.xyz_my_goofy_function()) xyz_my_goofy_function()
Certain function names are known by SQLAlchemy, allowing special behavioral rules to be applied. Some for example are "ANSI" functions, which mean they don't get the parenthesis added after them, such as CURRENT_TIMESTAMP:
>>> print(func.current_timestamp())
CURRENT_TIMESTAMP
A function, like any other column expression, has a type, which indicates the type of expression as well as how SQLAlchemy will interpret result columns that are returned from this expression. The default type used for an arbitrary function name derived from :attr:`.func` is simply a "null" datatype. However, in order for the column expression generated by the function to have type-specific operator behavior as well as result-set behaviors, such as date and numeric coercions, the type may need to be specified explicitly:
stmt = select(func.date(some_table.c.date_string, type_=Date))
Functions are most typically used in the columns clause of a select statement,
and can also be labeled as well as given a type. Labeling a function is
recommended so that the result can be targeted in a result row based on a
string name, and assigning it a type is required when you need result-set
processing to occur, such as for Unicode conversion and date conversions.
Below, we use the result function scalar() to just read the first column
of the first row and then close the result; the label, even though present, is
not important in this case:
>>> conn.execute(
... select(
... func.max(addresses.c.email_address, type_=String).
... label('maxemail')
... )
... ).scalar()
{opensql}SELECT max(addresses.email_address) AS maxemail
FROM addresses
[...] ()
{stop}u'www@www.org'
Databases such as PostgreSQL and Oracle which support functions that return
whole result sets can be assembled into selectable units, which can be used in
statements. Such as, a database function calculate() which takes the
parameters x and y, and returns three columns which we'd like to name
q, z and r, we can construct using "lexical" column objects as
well as bind parameters:
>>> from sqlalchemy.sql import column
>>> calculate = select(column('q'), column('z'), column('r')).\
... select_from(
... func.calculate(
... bindparam('x'),
... bindparam('y')
... )
... )
>>> calc = calculate.alias()
>>> print(select(users).where(users.c.id > calc.c.z))
SELECT users.id, users.name, users.fullname
FROM users, (SELECT q, z, r
FROM calculate(:x, :y)) AS anon_1
WHERE users.id > anon_1.z
If we wanted to use our calculate statement twice with different bind
parameters, the :func:`~sqlalchemy.sql.expression.ClauseElement.unique_params`
function will create copies for us, and mark the bind parameters as "unique"
so that conflicting names are isolated. Note we also make two separate aliases
of our selectable:
>>> calc1 = calculate.alias('c1').unique_params(x=17, y=45)
>>> calc2 = calculate.alias('c2').unique_params(x=5, y=12)
>>> s = select(users).\
... where(users.c.id.between(calc1.c.z, calc2.c.z))
>>> print(s)
SELECT users.id, users.name, users.fullname
FROM users,
(SELECT q, z, r FROM calculate(:x_1, :y_1)) AS c1,
(SELECT q, z, r FROM calculate(:x_2, :y_2)) AS c2
WHERE users.id BETWEEN c1.z AND c2.z
>>> s.compile().params # doctest: +SKIP
{u'x_2': 5, u'y_2': 12, u'y_1': 45, u'x_1': 17}
.. seealso::
:data:`.func`
Any :class:`.FunctionElement`, including functions generated by :data:`~.expression.func`, can be turned into a "window function", that is an OVER clause, using the :meth:`.FunctionElement.over` method:
>>> s = select( ... users.c.id, ... func.row_number().over(order_by=users.c.name) ... ) >>> print(s) SELECT users.id, row_number() OVER (ORDER BY users.name) AS anon_1 FROM users
:meth:`.FunctionElement.over` also supports range specification using either the :paramref:`.expression.over.rows` or :paramref:`.expression.over.range` parameters:
>>> s = select( ... users.c.id, ... func.row_number().over( ... order_by=users.c.name, ... rows=(-2, None)) ... ) >>> print(s) SELECT users.id, row_number() OVER (ORDER BY users.name ROWS BETWEEN :param_1 PRECEDING AND UNBOUNDED FOLLOWING) AS anon_1 FROM users
:paramref:`.expression.over.rows` and :paramref:`.expression.over.range` each
accept a two-tuple which contains a combination of negative and positive
integers for ranges, zero to indicate "CURRENT ROW" and None to
indicate "UNBOUNDED". See the examples at :func:`.over` for more detail.
.. versionadded:: 1.1 support for "rows" and "range" specification for window functions
.. seealso::
:func:`.over`
:meth:`.FunctionElement.over`
In SQL, we often need to indicate the datatype of an element explicitly, or we need to convert between one datatype and another within a SQL statement. The CAST SQL function performs this. In SQLAlchemy, the :func:`.cast` function renders the SQL CAST keyword. It accepts a column expression and a data type object as arguments:
>>> from sqlalchemy import cast
>>> s = select(cast(users.c.id, String))
>>> conn.execute(s).fetchall()
{opensql}SELECT CAST(users.id AS VARCHAR) AS id
FROM users
[...] ()
{stop}[('1',), ('2',)]
The :func:`.cast` function is used not just when converting between datatypes, but also in cases where the database needs to know that some particular value should be considered to be of a particular datatype within an expression.
The :func:`.cast` function also tells SQLAlchemy itself that an expression
should be treated as a particular type as well. The datatype of an expression
directly impacts the behavior of Python operators upon that object, such as how
the + operator may indicate integer addition or string concatenation, and
it also impacts how a literal Python value is transformed or handled before
being passed to the database as well as how result values of that expression
should be transformed or handled.
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 :func:`.type_coerce` which is closely related to
:func:`.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. :func:`.type_coerce` is particularly important when dealing
with the :class:`_types.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 :func:`.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 :func:`.type_coerce` to indicate that our Python dictionary
should be treated as :class:`_types.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.
Unions come in two flavors, UNION and UNION ALL, which are available via module level functions :func:`_expression.union` and :func:`_expression.union_all`:
>>> from sqlalchemy.sql import union
>>> u = union(
... addresses.select().
... where(addresses.c.email_address == 'foo@bar.com'),
... addresses.select().
... where(addresses.c.email_address.like('%@yahoo.com')),
... ).order_by(addresses.c.email_address)
{sql}>>> conn.execute(u).fetchall()
SELECT addresses.id, addresses.user_id, addresses.email_address
FROM addresses
WHERE addresses.email_address = ?
UNION
SELECT addresses.id, addresses.user_id, addresses.email_address
FROM addresses
WHERE addresses.email_address LIKE ? ORDER BY email_address
[...] ('foo@bar.com', '%@yahoo.com')
{stop}[(1, 1, u'jack@yahoo.com')]
Also available, though not supported on all databases, are :func:`_expression.intersect`, :func:`_expression.intersect_all`, :func:`_expression.except_`, and :func:`_expression.except_all`:
>>> from sqlalchemy.sql import except_
>>> u = except_(
... addresses.select().
... where(addresses.c.email_address.like('%@%.com')),
... addresses.select().
... where(addresses.c.email_address.like('%@msn.com'))
... )
{sql}>>> conn.execute(u).fetchall()
SELECT addresses.id, addresses.user_id, addresses.email_address
FROM addresses
WHERE addresses.email_address LIKE ?
EXCEPT
SELECT addresses.id, addresses.user_id, addresses.email_address
FROM addresses
WHERE addresses.email_address LIKE ?
[...] ('%@%.com', '%@msn.com')
{stop}[(1, 1, u'jack@yahoo.com'), (4, 2, u'wendy@aol.com')]
A common issue with so-called "compound" selectables arises due to the fact
that they nest with parenthesis. SQLite in particular doesn't like a statement
that starts with parenthesis. So when nesting a "compound" inside a
"compound", it's often necessary to apply .subquery().select() to the first
element of the outermost compound, if that element is also a compound. For
example, to nest a "union" and a "select" inside of "except_", SQLite will
want the "union" to be stated as a subquery:
>>> u = except_(
... union(
... addresses.select().
... where(addresses.c.email_address.like('%@yahoo.com')),
... addresses.select().
... where(addresses.c.email_address.like('%@msn.com'))
... ).subquery().select(), # apply subquery here
... addresses.select().where(addresses.c.email_address.like('%@msn.com'))
... )
{sql}>>> conn.execute(u).fetchall()
SELECT anon_1.id, anon_1.user_id, anon_1.email_address
FROM (SELECT addresses.id AS id, addresses.user_id AS user_id,
addresses.email_address AS email_address
FROM addresses
WHERE addresses.email_address LIKE ?
UNION
SELECT addresses.id AS id,
addresses.user_id AS user_id,
addresses.email_address AS email_address
FROM addresses
WHERE addresses.email_address LIKE ?) AS anon_1
EXCEPT
SELECT addresses.id, addresses.user_id, addresses.email_address
FROM addresses
WHERE addresses.email_address LIKE ?
[...] ('%@yahoo.com', '%@msn.com', '%@msn.com')
{stop}[(1, 1, u'jack@yahoo.com')]
.. seealso::
:func:`_expression.union`
:func:`_expression.union_all`
:func:`_expression.intersect`
:func:`_expression.intersect_all`
:func:`.except_`
:func:`_expression.except_all`
UNION and other set constructs have a special case when it comes to ordering
the results. As the UNION consists of several SELECT statements, to ORDER the
whole result usually requires that an ORDER BY clause refer to column names but
not specific tables. As in the previous examples, we used
.order_by(addresses.c.email_address) but SQLAlchemy rendered the ORDER BY
without using the table name. A generalized way to apply ORDER BY to a union
is also to refer to the :attr:`_selectable.CompoundSelect.selected_columns` collection in
order to access the column expressions which are synonymous with the columns
selected from the first SELECT; the SQLAlchemy compiler will ensure these will
be rendered without table names:
>>> u = union(
... addresses.select().
... where(addresses.c.email_address == 'foo@bar.com'),
... addresses.select().
... where(addresses.c.email_address.like('%@yahoo.com')),
... )
>>> u = u.order_by(u.selected_columns.email_address)
>>> print(u)
SELECT addresses.id, addresses.user_id, addresses.email_address
FROM addresses
WHERE addresses.email_address = :email_address_1
UNION SELECT addresses.id, addresses.user_id, addresses.email_address
FROM addresses
WHERE addresses.email_address LIKE :email_address_2 ORDER BY email_address
A scalar select is a SELECT that returns exactly one row and one column. It can then be used as a column expression. A scalar select is often a :term:`correlated subquery`, which relies upon the enclosing SELECT statement in order to acquire at least one of its FROM clauses.
The :func:`_expression.select` construct can be modified to act as a column expression by calling either the :meth:`_expression.SelectBase.scalar_subquery` or :meth:`_expression.SelectBase.label` method:
>>> subq = select(func.count(addresses.c.id)).\
... where(users.c.id == addresses.c.user_id).\
... scalar_subquery()
The above construct is now a :class:`_expression.ScalarSelect` object, which is an adapter around the original :class:`.~expression.Select` object; it participates within the :class:`_expression.ColumnElement` family of expression constructs. We can place this construct the same as any other column within another :func:`_expression.select`:
>>> conn.execute(select(users.c.name, subq)).fetchall()
{opensql}SELECT users.name, (SELECT count(addresses.id) AS count_1
FROM addresses
WHERE users.id = addresses.user_id) AS anon_1
FROM users
[...] ()
{stop}[(u'jack', 2), (u'wendy', 2)]
To apply a non-anonymous column name to our scalar select, we create it using :meth:`_expression.SelectBase.label` instead:
>>> subq = select(func.count(addresses.c.id)).\
... where(users.c.id == addresses.c.user_id).\
... label("address_count")
>>> conn.execute(select(users.c.name, subq)).fetchall()
{opensql}SELECT users.name, (SELECT count(addresses.id) AS count_1
FROM addresses
WHERE users.id = addresses.user_id) AS address_count
FROM users
[...] ()
{stop}[(u'jack', 2), (u'wendy', 2)]
.. seealso::
:meth:`_expression.Select.scalar_subquery`
:meth:`_expression.Select.label`
In the examples on :ref:`scalar_selects`, the FROM clause of each embedded
select did not contain the users table in its FROM clause. This is because
SQLAlchemy automatically :term:`correlates` embedded FROM objects to that
of an enclosing query, if present, and if the inner SELECT statement would
still have at least one FROM clause of its own. For example:
>>> stmt = select(addresses.c.user_id).\
... where(addresses.c.user_id == users.c.id).\
... where(addresses.c.email_address == 'jack@yahoo.com')
>>> enclosing_stmt = select(users.c.name).\
... where(users.c.id == stmt.scalar_subquery())
>>> conn.execute(enclosing_stmt).fetchall()
{opensql}SELECT users.name
FROM users
WHERE users.id = (SELECT addresses.user_id
FROM addresses
WHERE addresses.user_id = users.id
AND addresses.email_address = ?)
[...] ('jack@yahoo.com',)
{stop}[(u'jack',)]
Auto-correlation will usually do what's expected, however it can also be controlled.
For example, if we wanted a statement to correlate only to the addresses table
but not the users table, even if both were present in the enclosing SELECT,
we use the :meth:`_expression.Select.correlate` method to specify those FROM clauses that
may be correlated:
>>> stmt = select(users.c.id).\
... where(users.c.id == addresses.c.user_id).\
... where(users.c.name == 'jack').\
... correlate(addresses)
>>> enclosing_stmt = select(
... users.c.name, addresses.c.email_address).\
... select_from(users.join(addresses)).\
... where(users.c.id == stmt.scalar_subquery())
>>> conn.execute(enclosing_stmt).fetchall()
{opensql}SELECT users.name, addresses.email_address
FROM users JOIN addresses ON users.id = addresses.user_id
WHERE users.id = (SELECT users.id
FROM users
WHERE users.id = addresses.user_id AND users.name = ?)
[...] ('jack',)
{stop}[(u'jack', u'jack@yahoo.com'), (u'jack', u'jack@msn.com')]
To entirely disable a statement from correlating, we can pass None
as the argument:
>>> stmt = select(users.c.id).\
... where(users.c.name == 'wendy').\
... correlate(None)
>>> enclosing_stmt = select(users.c.name).\
... where(users.c.id == stmt.scalar_subquery())
>>> conn.execute(enclosing_stmt).fetchall()
{opensql}SELECT users.name
FROM users
WHERE users.id = (SELECT users.id
FROM users
WHERE users.name = ?)
[...] ('wendy',)
{stop}[(u'wendy',)]
We can also control correlation via exclusion, using the :meth:`_expression.Select.correlate_except`
method. Such as, we can write our SELECT for the users table
by telling it to correlate all FROM clauses except for users:
>>> stmt = select(users.c.id).\
... where(users.c.id == addresses.c.user_id).\
... where(users.c.name == 'jack').\
... correlate_except(users)
>>> enclosing_stmt = select(
... users.c.name, addresses.c.email_address).\
... select_from(users.join(addresses)).\
... where(users.c.id == stmt.scalar_subquery())
>>> conn.execute(enclosing_stmt).fetchall()
{opensql}SELECT users.name, addresses.email_address
FROM users JOIN addresses ON users.id = addresses.user_id
WHERE users.id = (SELECT users.id
FROM users
WHERE users.id = addresses.user_id AND users.name = ?)
[...] ('jack',)
{stop}[(u'jack', u'jack@yahoo.com'), (u'jack', u'jack@msn.com')]
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 (some 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, allowing an expression such as:
SELECT people.people_id, people.age, people.name
FROM people JOIN LATERAL (SELECT books.book_id AS book_id
FROM books WHERE books.owner_id = people.people_id)
AS book_subq ON trueWhere above, the right side of the JOIN contains a subquery that refers not just to the "books" table but also the "people" table, correlating to the left side of the JOIN. SQLAlchemy Core supports a statement like the above using the :meth:`_expression.Select.lateral` method as follows:
>>> from sqlalchemy import table, column, select, true
>>> people = table('people', column('people_id'), column('age'), column('name'))
>>> books = table('books', column('book_id'), column('owner_id'))
>>> subq = select(books.c.book_id).\
... where(books.c.owner_id == people.c.people_id).lateral("book_subq")
>>> print(select(people).select_from(people.join(subq, true())))
SELECT people.people_id, people.age, people.name
FROM people JOIN LATERAL (SELECT books.book_id AS book_id
FROM books WHERE books.owner_id = people.people_id)
AS book_subq ON true
Above, we can see that the :meth:`_expression.Select.lateral` method acts a lot like the :meth:`_expression.Select.alias` method, including that we can specify an optional name. However the construct is the :class:`_expression.Lateral` construct instead of an :class:`_expression.Alias` which provides for the LATERAL keyword as well as special instructions to allow correlation from inside the FROM clause of the enclosing statement.
The :meth:`_expression.Select.lateral` method interacts normally with the :meth:`_expression.Select.correlate` and :meth:`_expression.Select.correlate_except` methods, except that the correlation rules also apply to any other tables present in the enclosing statement's FROM clause. Correlation is "automatic" to these tables by default, is explicit if the table is specified to :meth:`_expression.Select.correlate`, and is explicit to all tables except those specified to :meth:`_expression.Select.correlate_except`.
.. versionadded:: 1.1
Support for the LATERAL keyword and lateral correlation.
.. seealso::
:class:`_expression.Lateral`
:meth:`_expression.Select.lateral`
Ordering is done by passing column expressions to the :meth:`_expression.SelectBase.order_by` method:
>>> stmt = select(users.c.name).order_by(users.c.name)
>>> conn.execute(stmt).fetchall()
{opensql}SELECT users.name
FROM users ORDER BY users.name
[...] ()
{stop}[(u'jack',), (u'wendy',)]
Ascending or descending can be controlled using the :meth:`_expression.ColumnElement.asc` and :meth:`_expression.ColumnElement.desc` modifiers:
>>> stmt = select(users.c.name).order_by(users.c.name.desc())
>>> conn.execute(stmt).fetchall()
{opensql}SELECT users.name
FROM users ORDER BY users.name DESC
[...] ()
{stop}[(u'wendy',), (u'jack',)]
Grouping refers to the GROUP BY clause, and is usually used in conjunction with aggregate functions to establish groups of rows to be aggregated. This is provided via the :meth:`_expression.SelectBase.group_by` method:
>>> stmt = select(users.c.name, func.count(addresses.c.id)).\
... select_from(users.join(addresses)).\
... group_by(users.c.name)
>>> conn.execute(stmt).fetchall()
{opensql}SELECT users.name, count(addresses.id) AS count_1
FROM users JOIN addresses
ON users.id = addresses.user_id
GROUP BY users.name
[...] ()
{stop}[(u'jack', 2), (u'wendy', 2)]
HAVING can be used to filter results on an aggregate value, after GROUP BY has been applied. It's available here via the :meth:`_expression.Select.having` method:
>>> stmt = select(users.c.name, func.count(addresses.c.id)).\
... select_from(users.join(addresses)).\
... group_by(users.c.name).\
... having(func.length(users.c.name) > 4)
>>> conn.execute(stmt).fetchall()
{opensql}SELECT users.name, count(addresses.id) AS count_1
FROM users JOIN addresses
ON users.id = addresses.user_id
GROUP BY users.name
HAVING length(users.name) > ?
[...] (4,)
{stop}[(u'wendy', 2)]
A common system of dealing with duplicates in composed SELECT statements is the DISTINCT modifier. A simple DISTINCT clause can be added using the :meth:`_expression.Select.distinct` method:
>>> stmt = select(users.c.name).\
... where(addresses.c.email_address.
... contains(users.c.name)).\
... distinct()
>>> conn.execute(stmt).fetchall()
{opensql}SELECT DISTINCT users.name
FROM users, addresses
WHERE (addresses.email_address LIKE '%' || users.name || '%')
[...] ()
{stop}[(u'jack',), (u'wendy',)]
Most database backends support a system of limiting how many rows are returned, and the majority also feature a means of starting to return rows after a given "offset". While common backends like PostgreSQL, MySQL and SQLite support LIMIT and OFFSET keywords, other backends need to refer to more esoteric features such as "window functions" and row ids to achieve the same effect. The :meth:`_expression.Select.limit` and :meth:`_expression.Select.offset` methods provide an easy abstraction into the current backend's methodology:
>>> stmt = select(users.c.name, addresses.c.email_address).\
... select_from(users.join(addresses)).\
... limit(1).offset(1)
>>> conn.execute(stmt).fetchall()
{opensql}SELECT users.name, addresses.email_address
FROM users JOIN addresses ON users.id = addresses.user_id
LIMIT ? OFFSET ?
[...] (1, 1)
{stop}[(u'jack', u'jack@msn.com')]
We've seen :meth:`_expression.TableClause.insert` demonstrated earlier in this tutorial. Where :meth:`_expression.TableClause.insert` produces INSERT, the :meth:`_expression.TableClause.update` method produces UPDATE. Both of these constructs feature a method called :meth:`~.ValuesBase.values` which specifies the VALUES or SET clause of the statement.
The :meth:`~.ValuesBase.values` method accommodates any column expression as a value:
>>> stmt = users.update().\
... values(fullname="Fullname: " + users.c.name)
>>> conn.execute(stmt)
{opensql}UPDATE users SET fullname=(? || users.name)
[...] ('Fullname: ',)
COMMIT
{stop}<sqlalchemy.engine.cursor.LegacyCursorResult object at 0x...>
When using :meth:`_expression.TableClause.insert` or :meth:`_expression.TableClause.update` in an "execute many" context, we may also want to specify named bound parameters which we can refer to in the argument list. The two constructs will automatically generate bound placeholders for any column names passed in the dictionaries sent to :meth:`_engine.Connection.execute` at execution time. However, if we wish to use explicitly targeted named parameters with composed expressions, we need to use the :func:`_expression.bindparam` construct. When using :func:`_expression.bindparam` with :meth:`_expression.TableClause.insert` or :meth:`_expression.TableClause.update`, the names of the table's columns themselves are reserved for the "automatic" generation of bind names. We can combine the usage of implicitly available bind names and explicitly named parameters as in the example below:
>>> stmt = users.insert().\
... values(name=bindparam('_name') + " .. name")
>>> conn.execute(stmt, [
... {'id':4, '_name':'name1'},
... {'id':5, '_name':'name2'},
... {'id':6, '_name':'name3'},
... ])
{opensql}INSERT INTO users (id, name) VALUES (?, (? || ?))
[...] ((4, 'name1', ' .. name'), (5, 'name2', ' .. name'), (6, 'name3', ' .. name'))
COMMIT
<sqlalchemy.engine.cursor.LegacyCursorResult object at 0x...>
An UPDATE statement is emitted using the :meth:`_expression.TableClause.update` construct. This works much like an INSERT, except there is an additional WHERE clause that can be specified:
>>> stmt = users.update().\
... where(users.c.name == 'jack').\
... values(name='ed')
>>> conn.execute(stmt)
{opensql}UPDATE users SET name=? WHERE users.name = ?
[...] ('ed', 'jack')
COMMIT
{stop}<sqlalchemy.engine.cursor.LegacyCursorResult object at 0x...>
When using :meth:`_expression.TableClause.update` in an "executemany" context, we may wish to also use explicitly named bound parameters in the WHERE clause. Again, :func:`_expression.bindparam` is the construct used to achieve this:
>>> stmt = users.update().\
... where(users.c.name == bindparam('oldname')).\
... values(name=bindparam('newname'))
>>> conn.execute(stmt, [
... {'oldname':'jack', 'newname':'ed'},
... {'oldname':'wendy', 'newname':'mary'},
... {'oldname':'jim', 'newname':'jake'},
... ])
{opensql}UPDATE users SET name=? WHERE users.name = ?
[...] (('ed', 'jack'), ('mary', 'wendy'), ('jake', 'jim'))
COMMIT
{stop}<sqlalchemy.engine.cursor.LegacyCursorResult object at 0x...>
A correlated update lets you update a table using selection from another table, or the same table; the SELECT statement is passed as a scalar subquery using :meth:`_expression.Select.scalar_subquery`:
>>> stmt = select(addresses.c.email_address).\
... where(addresses.c.user_id == users.c.id).\
... limit(1)
>>> conn.execute(users.update().values(fullname=stmt.scalar_subquery()))
{opensql}UPDATE users SET fullname=(SELECT addresses.email_address
FROM addresses
WHERE addresses.user_id = users.id
LIMIT ? OFFSET ?)
[...] (1, 0)
COMMIT
{stop}<sqlalchemy.engine.cursor.LegacyCursorResult object at 0x...>
The PostgreSQL, Microsoft SQL Server, and MySQL backends all support UPDATE statements that refer to multiple tables. For PG and MSSQL, this is the "UPDATE FROM" syntax, which updates one table at a time, but can reference additional tables in an additional "FROM" clause that can then be referenced in the WHERE clause directly. On MySQL, multiple tables can be embedded into a single UPDATE statement separated by a comma. The SQLAlchemy :func:`_expression.update` construct supports both of these modes implicitly, by specifying multiple tables in the WHERE clause:
stmt = users.update().\
values(name='ed wood').\
where(users.c.id == addresses.c.id).\
where(addresses.c.email_address.startswith('ed%'))
conn.execute(stmt)
The resulting SQL from the above statement would render as:
UPDATE users SET name=:name FROM addresses WHERE users.id = addresses.id AND addresses.email_address LIKE :email_address_1 || '%'
When using MySQL, columns from each table can be assigned to in the SET clause directly, using the dictionary form passed to :meth:`_expression.Update.values`:
stmt = users.update().\
values({
users.c.name:'ed wood',
addresses.c.email_address:'ed.wood@foo.com'
}).\
where(users.c.id == addresses.c.id).\
where(addresses.c.email_address.startswith('ed%'))
The tables are referenced explicitly in the SET clause:
UPDATE users, addresses SET addresses.email_address=%s,
users.name=%s WHERE users.id = addresses.id
AND addresses.email_address LIKE concat(%s, '%')
When the construct is used on a non-supporting database, the compiler
will raise NotImplementedError. For convenience, when a statement
is printed as a string without specification of a dialect, the "string SQL"
compiler will be invoked which provides a non-working SQL representation of the
construct.
The default behavior of the :func:`_expression.update` construct when rendering the SET clauses is to render them using the column ordering given in the originating :class:`_schema.Table` object. This is an important behavior, since it means that the rendering of a particular UPDATE statement with particular columns will be rendered the same each time, which has an impact on query caching systems that rely on the form of the statement, either client side or server side. Since the parameters themselves are passed to the :meth:`_expression.Update.values` method as Python dictionary keys, there is no other fixed ordering available.
However in some cases, the order of parameters rendered in the SET clause of an UPDATE statement may need to be explicitly stated. The main example of this is when using MySQL and providing updates to column values based on that of other column values. The end result of the following statement:
UPDATE some_table SET x = y + 10, y = 20
Will have a different result than:
UPDATE some_table SET y = 20, x = y + 10
This because on MySQL, the individual SET clauses are fully evaluated on a per-value basis, as opposed to on a per-row basis, and as each SET clause is evaluated, the values embedded in the row are changing.
To suit this specific use case, the :meth:`_expression.update.ordered_values` method may be used. When using this method, we supply a series of 2-tuples as the argument to the method:
stmt = some_table.update().\
ordered_values((some_table.c.y, 20), (some_table.c.x, some_table.c.y + 10))
The series of 2-tuples is essentially the same structure as a Python dictionary, except that it explicitly suggests a specific ordering. Using the above form, we are assured that the "y" column's SET clause will render first, then the "x" column's SET clause.
.. versionchanged:: 1.4 Added the :meth:`_expression.Update.ordered_values` method which supersedes the :paramref:`_expression.update.preserve_parameter_order` flag that will be removed in SQLAlchemy 2.0.
.. seealso::
:ref:`mysql_insert_on_duplicate_key_update` - background on the MySQL
``ON DUPLICATE KEY UPDATE`` clause and how to support parameter ordering.
Finally, a delete. This is accomplished easily enough using the :meth:`_expression.TableClause.delete` construct:
>>> conn.execute(addresses.delete())
{opensql}DELETE FROM addresses
[...] ()
COMMIT
{stop}<sqlalchemy.engine.cursor.LegacyCursorResult object at 0x...>
>>> conn.execute(users.delete().where(users.c.name > 'm'))
{opensql}DELETE FROM users WHERE users.name > ?
[...] ('m',)
COMMIT
{stop}<sqlalchemy.engine.cursor.LegacyCursorResult object at 0x...>
.. versionadded:: 1.2
The PostgreSQL, Microsoft SQL Server, and MySQL backends all support DELETE statements that refer to multiple tables within the WHERE criteria. For PG and MySQL, this is the "DELETE USING" syntax, and for SQL Server, it's a "DELETE FROM" that refers to more than one table. The SQLAlchemy :func:`_expression.delete` construct supports both of these modes implicitly, by specifying multiple tables in the WHERE clause:
stmt = users.delete().\
where(users.c.id == addresses.c.id).\
where(addresses.c.email_address.startswith('ed%'))
conn.execute(stmt)
On a PostgreSQL backend, the resulting SQL from the above statement would render as:
DELETE FROM users USING addresses WHERE users.id = addresses.id AND (addresses.email_address LIKE %(email_address_1)s || '%%')
When the construct is used on a non-supporting database, the compiler
will raise NotImplementedError. For convenience, when a statement
is printed as a string without specification of a dialect, the "string SQL"
compiler will be invoked which provides a non-working SQL representation of the
construct.
Both of :meth:`_expression.TableClause.update` and :meth:`_expression.TableClause.delete` are associated with matched row counts. This is a number indicating the number of rows that were matched by the WHERE clause. Note that by "matched", this includes rows where no UPDATE actually took place. The value is available as :attr:`_engine.CursorResult.rowcount`:
>>> result = conn.execute(users.delete())
{opensql}DELETE FROM users
[...] ()
COMMIT
{stop}>>> result.rowcount
1
Expression Language Reference: :ref:`expression_api_toplevel`
Database Metadata Reference: :ref:`metadata_toplevel`
Engine Reference: :doc:`/core/engines`
Connection Reference: :ref:`connections_toplevel`
Types Reference: :ref:`types_toplevel`