SQLAlchemy 0.5 Documentation

Release: 0.5.8 | Release Date: January 16, 2010 | Download PDF
SQLAlchemy 0.5 Documentation » SQL Expression Language Tutorial

SQL Expression Language Tutorial

SQL Expression Language Tutorial

This tutorial will cover SQLAlchemy SQL Expressions, which are Python constructs that represent SQL statements. The 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 0.5 of SQLAlchemy:

>>> import sqlalchemy
>>> sqlalchemy.__version__ 
0.5.0

Connecting

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 Database Meta Data.

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, this is a valid datatype, but on most databases it’s not allowed. So if running this tutorial on a database such as PostgreSQL or MySQL, and you wish to use SQLAlchemy to generate the tables, a “length” may be provided to the String type as below:

Column('name', String(50))

The length field on String, as well as similar fields available on Integer, Numeric, etc. are not referenced by SQLAlchemy other than when creating tables.

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'}

Executing

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.last_inserted_ids()
[1]

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 databases’ Dialect knows the specific steps needed to determine the correct value (or values; note that last_inserted_ids() 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 opens and closes a connection for you:

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.

Selecting

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 resources referenced by the ResultProxy will be closed 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 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.

Operators

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:

somecolumn.op('&')(0xff)

is a bitwise AND of the value in somecolumn.

Conjunctions

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 corresponds to a “renamed” version of a table or arbitrary relation, which occurs anytime you say “SELECT .. FROM sometable AS someothername”. The AS creates a new name for the table. Aliases are super important in SQL as they allow you to reference the same table more than once. Scenarios where you need to do this include when you self-join a table to itself, or more commonly when you need to join from a parent table to a child table multiple times. For example, we know that our user jack has two email addresses. How can we locate jack based on the combination of those two addresses? We need to join twice to it. Let’s construct two distinct aliases for the addresses table and join:

>>> a1 = addresses.alias('a1')
>>> a2 = addresses.alias('a2')
>>> 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')]

Easy enough. One thing that we’re going for with the SQL Expression Language is the melding of programmatic behavior with SQL generation. Coming up with names like a1 and a2 is messy; we really didn’t need to use those names anywhere, it’s just the database that needed them. Plus, we might write some code that uses alias objects that came from several different places, and it’s difficult to ensure that they all have unique names. So instead, we just let SQLAlchemy make the names for us, using “anonymous” aliases:

>>> 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')]

One super-huge advantage of anonymous aliases is that not only did we not have to guess up a random name, but we can also be guaranteed that the above SQL string is deterministically generated to be the same every time. This is important for databases such as Oracle which cache compiled “query plans” for their statements, and need to see the same SQL string in order to make use of it.

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()
[(u'jack',)]

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.databases.oracle import 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 and Transformations

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')]

So we started small, added one little thing at a time, and at the end we have a huge statement..which actually works. Now let’s do one more thing; the searching function wants to add another email_address criterion on, however it doesn’t want to construct an alias of the addresses table; suppose many parts of the application are written to deal specifically with the addresses table, and to change all those functions to support receiving an arbitrary alias of the address would be cumbersome. We can actually convert the addresses table within the existing statement to be an alias of itself, using replace_selectable():

>>> 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

One more thing though, with automatic labeling applied as well as anonymous aliasing, how do we retrieve the columns from the rows for this thing ? The label for the email_addresses column is now the generated name addresses_1_email_address; and in another statement might be something different ! This is where accessing by result columns by Column object becomes very useful:

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

The above example, by its end, got significantly more intense than the typical end-user constructed SQL will usually be. However when writing higher-level tools such as ORMs, they become more significant. SQLAlchemy’s ORM relies very heavily on techniques like this.

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')]

Functions

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

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

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 []
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')],
...     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')]

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')]

Updates

Finally, we’re back to UPDATE. 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...>

>>> # 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...>

Deletes

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

API docs: sqlalchemy.sql.expression

Table Metadata Reference: Database Meta Data

Engine/Connection/Execution Reference: Database Engines

SQL Types: Column and Data Types