SQLAlchemy 0.6 Documentation

Release: 0.6.9 | Release Date: May 5, 2012
SQLAlchemy 0.6 Documentation » SQLAlchemy Core » SQL Expression Language Tutorial

SQL Expression Language Tutorial

SQL Expression Language Tutorial

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 Object Relational Tutorial, 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.

Version Check

A quick check to verify that we are on at least version 0.6 of SQLAlchemy:

>>> import sqlalchemy
>>> sqlalchemy.__version__ 


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 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.

Define and Create Tables

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 Column, and in all cases a Column is associated with a Table. A collection of Table objects and their associated child objects is referred to as database metadata. In this tutorial we will explicitly lay out several Table objects, but note that SA can also “import” whole sets of Table objects automatically from an existing database (this process is called table reflection).

We define our tables all within a catalog called MetaData, using the 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 addreses” 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 Table objects, as well as how to create them from an existing database automatically, is described in Schema Definition Language.

Next, to tell the MetaData we’d actually like to create our selection of tables for real inside the SQLite database, we use 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) 


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 String type as below:

Column('name', String(50))

The length field on String, as well as similar precision/scale fields available on Integer, 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 Sequence construct:

from sqlalchemy import Sequence
Column('id', Integer, Sequence('user_id_seq'), primary_key=True)

A full, foolproof 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('password', String(12))

We include this more verbose 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.

Insert Expressions

The first SQL expression we’ll create is the 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 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 
{'fullname': 'Jack Jones', 'name': 'jack'}


The interesting part of an Insert is executing it. In this tutorial, we will generally focus on the most explicit method of executing a SQL construct, and later touch upon some “shortcut” ways to do it. The engine object we created is a repository for database connections capable of issuing SQL to the database. To acquire a connection, we use the connect() method:

>>> conn = engine.connect()
>>> conn 
<sqlalchemy.engine.base.Connection object at 0x...>

The Connection object represents an actively checked out DBAPI connection resource. Lets feed it our Insert object and see what happens:

>>> result = conn.execute(ins)
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 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 Connection object references a DBAPI connection, the result, known as a ResultProxy 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:

>>> result.inserted_primary_key

The value of 1 was automatically generated by SQLite, but only because we did not specify the id column in our 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 Dialect knows the specific steps needed to determine the correct value (or values; note that inserted_primary_key returns a list so that it supports composite primary keys).

Executing Multiple Statements

Our insert example above was intentionally a little drawn out to show some various behaviors of expression language constructs. In the usual case, an Insert statement is usually compiled against the parameters sent to the execute() method on Connection, so that there’s no need to use the values keyword with Insert. Lets create a generic Insert statement again and use it in the “normal” way:

>>> ins = users.insert()
>>> conn.execute(ins, id=2, name='wendy', fullname='Wendy Williams') 
INSERT INTO users (id, name, fullname) VALUES (?, ?, ?) (2, 'wendy', 'Wendy Williams') COMMIT
<sqlalchemy.engine.base.ResultProxy object at 0x...>

Above, because we specified all three columns in the the execute() method, the compiled Insert included all three columns. The Insert statement is compiled at execution time based on the parameters we specified; if we specified fewer parameters, the 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'},
... ])
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
<sqlalchemy.engine.base.ResultProxy 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 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.

Connectionless / Implicit Execution

We’re executing our Insert using a Connection. There’s two options that allow you to not have to deal with the connection part. You can execute in the connectionless style, using the engine, which checks out from the connection pool a connection for you, performs the execute operation with that connection, and then checks the connection back into the pool upon completion of the operation:

sql>>> result = engine.execute(users.insert(), name='fred', fullname="Fred Flintstone")

and you can save even more steps than that, if you connect the Engine to the MetaData object we created earlier. When this is done, all SQL expressions which involve tables within the MetaData object will be automatically bound to the Engine. In this case, we call it implicit execution:

>>> metadata.bind = engine
sql>>> result = users.insert().execute(name="mary", fullname="Mary Contrary")

When the MetaData is bound, statements will also compile against the engine’s dialect. Since a lot of the examples here assume the default dialect, we’ll detach the engine from the metadata which we just attached:

>>> metadata.bind = None

Detailed examples of connectionless and implicit execution are available in the “Engines” chapter: Connectionless Execution, Implicit Execution.


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 select() function:

>>> from sqlalchemy.sql import select
>>> s = select([users])
>>> result = conn.execute(s)  
SELECT users.id, users.name, users.fullname FROM users ()

Above, we issued a basic 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. The result returned is again a ResultProxy object, which acts much like a DBAPI cursor, including methods such as fetchone() and fetchall(). The easiest way to get rows from it is to just iterate:

>>> for row in result:
...     print row
(1, u'jack', u'Jack Jones')
(2, u'wendy', u'Wendy Williams')
(3, u'fred', u'Fred Flintstone')
(4, u'mary', u'Mary Contrary')

Above, we see that printing each row produces a simple tuple-like result. We have more options at accessing the data in each row. One very common way is through dictionary access, using the string names of columns:

sql>>> result = conn.execute(s)  
>>> row = result.fetchone()
>>> print "name:", row['name'], "; fullname:", row['fullname']
name: jack ; fullname: Jack Jones

Integer indexes work as well:

>>> row = result.fetchone()
>>> print "name:", row[1], "; fullname:", row[2]
name: wendy ; fullname: Wendy Williams

But another way, whose usefulness will become apparent later on, is to use the Column objects directly as keys:

sql>>> for row in conn.execute(s):  
...     print "name:", row[users.c.name], "; fullname:", row[users.c.fullname]
name: jack ; fullname: Jack Jones
name: wendy ; fullname: Wendy Williams
name: fred ; fullname: Fred Flintstone
name: mary ; fullname: Mary Contrary

Result sets which have pending rows remaining should be explicitly closed before discarding. While the cursor and connection resources referenced by the ResultProxy will be respectively closed and returned to the connection pool when the object is garbage collected, it’s better to make it explicit as some database APIs are very picky about such things:

>>> 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 Column objects from our Table. These are available as named attributes off the c attribute of the Table object:

>>> s = select([users.c.name, users.c.fullname])
sql>>> result = conn.execute(s)  
>>> for row in result:  
...     print row
(u'jack', u'Jack Jones')
(u'wendy', u'Wendy Williams')
(u'fred', u'Fred Flintstone')
(u'mary', u'Mary Contrary')

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 select() construct only has a list containing columns. How does this work ? Let’s try putting two tables into our select() statement:

sql>>> for row in conn.execute(select([users, addresses])):
...     print row  
(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')
(3, u'fred', u'Fred Flintstone', 1, 1, u'jack@yahoo.com')
(3, u'fred', u'Fred Flintstone', 2, 1, u'jack@msn.com')
(3, u'fred', u'Fred Flintstone', 3, 2, u'www@www.org')
(3, u'fred', u'Fred Flintstone', 4, 2, u'wendy@aol.com')
(4, u'mary', u'Mary Contrary', 1, 1, u'jack@yahoo.com')
(4, u'mary', u'Mary Contrary', 2, 1, u'jack@msn.com')
(4, u'mary', u'Mary Contrary', 3, 2, u'www@www.org')
(4, u'mary', u'Mary Contrary', 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. Which brings us to the second argument of select():

>>> s = select([users, addresses], users.c.id==addresses.c.user_id)
sql>>> for row in conn.execute(s):
...     print row  
(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 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 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.expression._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 Insert and 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 sqlalchemy.sql.ClauseElement.


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 in ClauseElement; we can use the same trick we did with the 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 Column is important ! If we use + with two string based columns (recall we put types like Integer and String on our 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://'))
concat(users.name, users.fullname)

The above illustrates the SQL that’s generated for an 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 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:


is a bitwise AND of the value in somecolumn.


We’d like to show off some of our operators inside of 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. AND, OR and NOT can work from the corresponding functions SQLAlchemy provides (notice we also throw in a 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, between() and label(). between() produces a BETWEEN clause, and 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')],
...        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')
...           )
...        )
...    )
>>> print conn.execute(s).fetchall() 
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')
[(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.

Using Text

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 too. The text() construct represents any textual statement. To use bind parameters with text(), always use the named colon format. Such as below, we create a text() and execute it, feeding in the bind parameters to the execute() method:

>>> 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)
...        """)
sql>>> print conn.execute(s, x='m', y='z', e1='%@aol.com', e2='%@msn.com').fetchall() 
[(u'Wendy Williams, wendy@aol.com',)]

To gain a “hybrid” approach, the select() construct accepts strings for most of its arguments. Below we combine the usage of strings with our constructed select() object, by using the select() object to structure the statement, and strings to provide all the content within the structure. For this example, SQLAlchemy is not given any Column or Table objects in any of its expressions, so it cannot generate a FROM clause. So we also give it the from_obj keyword argument, which is a list of ClauseElements (or strings) to be placed within the FROM clause:

>>> s = select(["users.fullname || ', ' || addresses.email_address AS title"],
...        and_(
...            "users.id = addresses.user_id",
...             "users.name BETWEEN 'm' AND 'z'",
...             "(addresses.email_address LIKE :x OR addresses.email_address LIKE :y)"
...        ),
...         from_obj=['users', 'addresses']
...    )
sql>>> print conn.execute(s, x='%@aol.com', y='%@msn.com').fetchall() 
[(u'Wendy Williams, wendy@aol.com',)]

Going from constructed SQL to text, we lose some capabilities. We lose the capability for SQLAlchemy to compile our expression to a specific target database; above, our expression won’t work with MySQL since it has no || construct. It also becomes more tedious for SQLAlchemy to be made aware of the datatypes in use; for example, if our bind parameters required UTF-8 encoding before going in, or conversion from a Python datetime into a string (as is required with SQLite), we would have to add extra information to our text() construct. Similar issues arise on the result set side, where SQLAlchemy also performs type-specific data conversion in some cases; still more information can be added to text() to work around this. But what we really lose from our statement is the ability to manipulate it, transform it, and analyze it. These features are critical when using the ORM, which makes heavy usage of relational transformations. To show off what we mean, we’ll first introduce the ALIAS construct and the JOIN construct, just so we have some juicier bits to play with.

Using Aliases

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 Table, select() construct, or other selectable can be turned into an alias using the FromClause.alias() method, which produces a Alias construct. 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 Alias constructs against addresses, and then use them both within a select() construct:

>>> a1 = addresses.alias()
>>> a2 = addresses.alias()
>>> s = select([users], 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'
...   ))
sql>>> print conn.execute(s).fetchall()  
[(1, u'jack', u'Jack Jones')]

Note that the 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 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 FromClause.alias() method:

>>> a1 = addresses.alias('a1')

Aliases can of course be used for anything which you can SELECT from, including SELECT statements themselves. We can self-join the users table back to the select() we’ve created by making an alias of the entire statement. The correlate(None) directive is to avoid SQLAlchemy’s attempt to “correlate” the inner users table with the outer one:

>>> a1 = s.correlate(None).alias()
>>> s = select([users.c.name], users.c.id==a1.c.id)
sql>>> print conn.execute(s).fetchall()  

Using Joins

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 select() construct. But if we want to make a real “JOIN” or “OUTERJOIN” construct, we use the join() and 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 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 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 usage of the from_obj keyword argument:

>>> s = select([users.c.fullname], from_obj=[
...    users.join(addresses, addresses.c.email_address.like(users.c.name + '%'))
...    ])
sql>>> print conn.execute(s).fetchall()  
[(u'Jack Jones',), (u'Jack Jones',), (u'Wendy Williams',)]

The outerjoin() function just creates LEFT OUTER JOIN constructs. It’s used just like join():

>>> s = select([users.c.fullname], from_obj=[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 ;).

Intro to Generative Selects

We’ve now gained the ability to construct very sophisticated statements. We can use all kinds of operators, table constructs, text, joins, and aliases. The point of all of this, as mentioned earlier, is not that it’s an “easier” or “better” way to write SQL than just writing a SQL statement yourself; the point is that it’s better for writing programmatically generated SQL which can be morphed and adapted as needed in automated scenarios.

To support this, the select() construct we’ve been working with supports piecemeal construction, in addition to the “all at once” method we’ve been doing. Suppose you’re writing a search function, which receives criterion and then must construct a select from it. To accomplish this, upon each criterion encountered, you apply “generative” criterion to an existing select() construct with new elements, one at a time. We start with a basic select() constructed with the shortcut method available on the users table:

>>> query = users.select()
>>> print query  
SELECT users.id, users.name, users.fullname
FROM users

We encounter search criterion of “name=’jack’”. So we apply WHERE criterion stating such:

>>> query = query.where(users.c.name=='jack')

Next, we encounter that they’d like the results in descending order by full name. We apply ORDER BY, using an extra modifier desc:

>>> query = query.order_by(users.c.fullname.desc())

We also come across that they’d like only users who have an address at MSN. A quick way to tack this on is by using an EXISTS clause, which we correlate to the users table in the enclosing SELECT:

>>> from sqlalchemy.sql import exists
>>> query = query.where(
...    exists([addresses.c.id],
...        and_(addresses.c.user_id==users.c.id, addresses.c.email_address.like('%@msn.com'))
...    ).correlate(users))

And finally, the application also wants to see the listing of email addresses at once; so to save queries, we outerjoin the addresses table (using an outer join so that users with no addresses come back as well; since we’re programmatic, we might not have kept track that we used an EXISTS clause against the addresses table too...). Additionally, since the users and addresses table both have a column named id, let’s isolate their names from each other in the COLUMNS clause by using labels:

>>> query = query.column(addresses).select_from(users.outerjoin(addresses)).apply_labels()

Let’s bake for .0001 seconds and see what rises:

>>> conn.execute(query).fetchall()  
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname, addresses.id AS addresses_id, addresses.user_id AS addresses_user_id, addresses.email_address AS addresses_email_address FROM users LEFT OUTER JOIN addresses ON users.id = addresses.user_id WHERE users.name = ? AND (EXISTS (SELECT addresses.id FROM addresses WHERE addresses.user_id = users.id AND addresses.email_address LIKE ?)) ORDER BY users.fullname DESC ('jack', '%@msn.com')
[(1, u'jack', u'Jack Jones', 1, 1, u'jack@yahoo.com'), (1, u'jack', u'Jack Jones', 2, 1, u'jack@msn.com')]

The generative approach is about starting small, adding one thing at a time, to arrive with a full statement.

Transforming a Statement

We’ve seen how methods like Select.where() and _SelectBase.order_by() are part of the so-called Generative family of methods on the select() construct, where one select() copies itself to return a new one with modifications. SQL constructs also support another form of generative behavior which is the transformation. This is an advanced technique that most core applications won’t use directly; however, it is a system which the ORM relies on heavily, and can be useful for any system that deals with generalized behavior of Core SQL constructs.

Using a transformation we can take our users/addresses query and replace all occurrences of addresses with an alias of itself. That is, anywhere that addresses is referred to in the original query, the new query will refer to addresses_1, which is selected as addresses AS addresses_1. The FromClause.replace_selectable() method can achieve this:

>>> a1 = addresses.alias()
>>> query = query.replace_selectable(addresses, a1)
>>> print query  
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname, addresses_1.id AS addresses_1_id, addresses_1.user_id AS addresses_1_user_id, addresses_1.email_address AS addresses_1_email_address FROM users LEFT OUTER JOIN addresses AS addresses_1 ON users.id = addresses_1.user_id WHERE users.name = :name_1 AND (EXISTS (SELECT addresses_1.id FROM addresses AS addresses_1 WHERE addresses_1.user_id = users.id AND addresses_1.email_address LIKE :email_address_1)) ORDER BY users.fullname DESC

For a query such as the above, we can access the columns referred to by the a1 alias in a result set using the Column objects present directly on a1:

sql>>> for row in conn.execute(query):
...     print "Name:", row[users.c.name], "; Email Address", row[a1.c.email_address]  
Name: jack ; Email Address jack@yahoo.com
Name: jack ; Email Address jack@msn.com

Everything Else

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.

Bind Parameter Objects

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 database dialect converts to the appropriate named or positional style, as here where it converts to positional for SQLite:

>>> from sqlalchemy.sql import bindparam
>>> s = users.select(users.c.name==bindparam('username'))
sql>>> conn.execute(s, username='wendy').fetchall() 
[(2, u'wendy', u'Wendy Williams')]

Another important aspect of bind parameters is that they 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(users.c.name.like(bindparam('username', type_=String) + text("'%'")))
sql>>> conn.execute(s, username='wendy').fetchall() 
[(2, u'wendy', u'Wendy Williams')]

Bind parameters 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],
...    users.c.name.like(bindparam('name', type_=String) + text("'%'")) |
...    addresses.c.email_address.like(bindparam('name', type_=String) + text("'@%'")),
...    from_obj=[users.outerjoin(addresses)])
sql>>> conn.execute(s, name='jack').fetchall() 
[(1, u'jack', u'Jack Jones', 1, 1, u'jack@yahoo.com'), (1, u'jack', u'Jack Jones', 2, 1, u'jack@msn.com')]


SQL functions are created using the func keyword, which generates functions using attribute access:

>>> from sqlalchemy.sql import func
>>> print func.now()

>>> print func.concat('x', 'y')
concat(:param_1, :param_2)

By “generates”, we mean that any SQL function is created based on the word you choose:

>>> print func.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()

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:

>>> print conn.execute(
...     select([func.max(addresses.c.email_address, type_=String).label('maxemail')])
... ).scalar() 
SELECT max(addresses.email_address) AS maxemail FROM addresses ()

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')],
...     from_obj=[func.calculate(bindparam('x'), bindparam('y'))])

>>> print select([users], users.c.id > calculate.c.z) 
SELECT users.id, users.name, users.fullname
FROM users, (SELECT q, z, r
FROM calculate(:x, :y))
WHERE users.id > z

If we wanted to use our calculate statement twice with different bind parameters, the 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:

>>> s = select([users], users.c.id.between(
...    calculate.alias('c1').unique_params(x=17, y=45).c.z,
...    calculate.alias('c2').unique_params(x=5, y=12).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
{u'x_2': 5, u'y_2': 12, u'y_1': 45, u'x_1': 17}

See also sqlalchemy.sql.expression.func.

Unions and Other Set Operations

Unions come in two flavors, UNION and UNION ALL, which are available via module level functions:

>>> from sqlalchemy.sql import union
>>> u = union(
...     addresses.select(addresses.c.email_address=='foo@bar.com'),
...    addresses.select(addresses.c.email_address.like('%@yahoo.com')),
... ).order_by(addresses.c.email_address)

sql>>> print conn.execute(u).fetchall() 
[(1, 1, u'jack@yahoo.com')]

Also available, though not supported on all databases, are intersect(), intersect_all(), except_(), and except_all():

>>> from sqlalchemy.sql import except_
>>> u = except_(
...    addresses.select(addresses.c.email_address.like('%@%.com')),
...    addresses.select(addresses.c.email_address.like('%@msn.com'))
... )

sql>>> print conn.execute(u).fetchall() 
[(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 .alias().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(addresses.c.email_address.like('%@yahoo.com')),
...         addresses.select(addresses.c.email_address.like('%@msn.com'))
...     ).alias().select(),   # apply subquery here
...    addresses.select(addresses.c.email_address.like('%@msn.com'))
... )
sql>>> print conn.execute(u).fetchall()   
[(1, 1, u'jack@yahoo.com')]

Scalar Selects

To embed a SELECT in a column expression, use as_scalar():

sql>>> print conn.execute(select([   
...       users.c.name,
...       select([func.count(addresses.c.id)], users.c.id==addresses.c.user_id).as_scalar()
...    ])).fetchall()
[(u'jack', 2), (u'wendy', 2), (u'fred', 0), (u'mary', 0)]

Alternatively, applying a label() to a select evaluates it as a scalar as well:

sql>>> print conn.execute(select([    
...       users.c.name,
...       select([func.count(addresses.c.id)], users.c.id==addresses.c.user_id).label('address_count')
...    ])).fetchall()
[(u'jack', 2), (u'wendy', 2), (u'fred', 0), (u'mary', 0)]

Correlated Subqueries

Notice in the examples on “scalar selects”, the FROM clause of each embedded select did not contain the users table in its FROM clause. This is because SQLAlchemy automatically attempts to correlate embedded FROM objects to that of an enclosing query. To disable this, or to specify explicit FROM clauses to be correlated, use correlate():

>>> s = select([users.c.name], users.c.id==select([users.c.id]).correlate(None))
>>> print s 
SELECT users.name
FROM users
WHERE users.id = (SELECT users.id
FROM users)

>>> s = select([users.c.name, addresses.c.email_address], users.c.id==
...        select([users.c.id], users.c.id==addresses.c.user_id).correlate(addresses)
...    )
>>> print s 
SELECT users.name, addresses.email_address
FROM users, addresses
WHERE users.id = (SELECT users.id
FROM users
WHERE users.id = addresses.user_id)

Ordering, Grouping, Limiting, Offset...ing...

The select() function can take keyword arguments order_by, group_by (as well as having), limit, and offset. There’s also distinct=True. These are all also available as generative functions. order_by() expressions can use the modifiers asc() or desc() to indicate ascending or descending.

>>> s = select([addresses.c.user_id, func.count(addresses.c.id)]).\
...     group_by(addresses.c.user_id).having(func.count(addresses.c.id)>1)
sql>>> print conn.execute(s).fetchall() 
[(1, 2), (2, 2)]

>>> s = select([addresses.c.email_address, addresses.c.id]).distinct().\
...     order_by(addresses.c.email_address.desc(), addresses.c.id)
sql>>> conn.execute(s).fetchall() 
[(u'www@www.org', 3), (u'wendy@aol.com', 4), (u'jack@yahoo.com', 1), (u'jack@msn.com', 2)]

>>> s = select([addresses]).offset(1).limit(1)
sql>>> print conn.execute(s).fetchall() 
[(2, 1, u'jack@msn.com')]

Inserts and Updates

Finally, we’re back to INSERT for some more detail. The insert() construct provides a values() method which can be used to send any value or clause expression to the VALUES portion of the INSERT:

# insert from a function
users.insert().values(id=12, name=func.upper('jack'))

# insert from a concatenation expression
addresses.insert().values(email_address = name + '@' + host)

values() can be mixed with per-execution values:

    fullname='Jack Jones'

bindparam() constructs can be passed, however the names of the table’s columns are reserved for the “automatic” generation of bind names:

users.insert().values(id=bindparam('_id'), name=bindparam('_name'))

# insert many rows at once:
    users.insert().values(id=bindparam('_id'), name=bindparam('_name')),
        {'_id':1, '_name':'name1'},
        {'_id':2, '_name':'name2'},
        {'_id':3, '_name':'name3'},

Updates work a lot like INSERTS, except there is an additional WHERE clause that can be specified:

>>> # change 'jack' to 'ed'
sql>>> conn.execute(users.update().
...                    where(users.c.name=='jack').
...                    values(name='ed')
...                ) 
<sqlalchemy.engine.base.ResultProxy object at 0x...>

>>> # use bind parameters
>>> u = users.update().\
...             where(users.c.name==bindparam('oldname')).\
...             values(name=bindparam('newname'))
sql>>> conn.execute(u, oldname='jack', newname='ed') 
<sqlalchemy.engine.base.ResultProxy object at 0x...>

>>> # with binds, you can also update many rows at once
sql>>> conn.execute(u,
...     {'oldname':'jack', 'newname':'ed'},
...     {'oldname':'wendy', 'newname':'mary'},
...     {'oldname':'jim', 'newname':'jake'},
...     ) 
<sqlalchemy.engine.base.ResultProxy object at 0x...>

>>> # update a column to an expression.:
sql>>> conn.execute(users.update().
...                     values(fullname="Fullname: " + users.c.name)
...                 ) 
<sqlalchemy.engine.base.ResultProxy object at 0x...>

Correlated Updates

A correlated update lets you update a table using selection from another table, or the same table:

>>> s = select([addresses.c.email_address], addresses.c.user_id==users.c.id).limit(1)
sql>>> conn.execute(users.update().values(fullname=s)) 
<sqlalchemy.engine.base.ResultProxy object at 0x...>


Finally, a delete. Easy enough:

sql>>> conn.execute(addresses.delete()) 
<sqlalchemy.engine.base.ResultProxy object at 0x...>

sql>>> conn.execute(users.delete().where(users.c.name > 'm')) 
<sqlalchemy.engine.base.ResultProxy object at 0x...>

Further Reference

Expression Language Reference: SQL Statements and Expressions

Database Metadata Reference: Schema Definition Language

Engine Reference: Engine Configuration

Connection Reference: Working with Engines and Connections

Types Reference: Column and Data Types