SQL Expression Language Tutorial — SQLAlchemy 0.7.1 Documentation

# SQLAlchemy 0.7.1 Documentation

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Version: 0.7.1 Last Updated: 06/05/2011 13:13:59

# SQL Expression Language Tutorial

## Introduction

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 0.7 of SQLAlchemy:

    >>> import sqlalchemy
    >>> sqlalchemy.__version__
    0.7.0

## 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 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)
    PRAGMA table_info("users")
    ()
    PRAGMA table_info("addresses")
    ()
    CREATE TABLE users (
        id INTEGER NOT NULL,
        name VARCHAR,
        fullname VARCHAR,
        PRIMARY KEY (id)
    )
    ()
    COMMIT
    CREATE TABLE addresses (
        id INTEGER NOT NULL,
        user_id INTEGER,
        email_address VARCHAR NOT NULL,
        PRIMARY KEY (id),
        FOREIGN KEY(user_id) REFERENCES users (id)
    )
    ()
    COMMIT

Note

Users familiar with the syntax of CREATE TABLE may notice that the VARCHAR columns were generated without a length; on SQLite and Postgresql, this is a valid datatype, but on others, it’s not allowed. So if running this tutorial on one of those databases, and you wish to use SQLAlchemy to issue CREATE TABLE, a “length” may be provided to the `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'}

## 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.inserted_primary_key
    [1]

The value of `1` was automatically generated by SQLite, but only because we did not specify the `id` column in our `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")
    INSERT INTO users (name, fullname) VALUES (?, ?)
    ('fred', 'Fred Flintstone')
    COMMIT

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")
    INSERT INTO users (name, fullname) VALUES (?, ?)
    ('mary', 'Mary Contrary')
    COMMIT

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)
    SELECT users.id, users.name, users.fullname
    FROM users
    ()>>> 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]
    SELECT users.id, users.name, users.fullname
    FROM users
    ()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)
    SELECT users.name, users.fullname
    FROM users
    ()>>> 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
    SELECT users.id, users.name, users.fullname, addresses.id, addresses.user_id, addresses.email_address
    FROM users, addresses
    ()(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
    SELECT users.id, users.name, users.fullname, addresses.id, addresses.user_id, addresses.email_address
    FROM users, addresses
    WHERE users.id = addresses.user_id
    ()(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`.

## 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()
    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',)]

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()
    SELECT users.fullname || ', ' || addresses.email_address AS title
    FROM users, addresses
    WHERE users.id = addresses.user_id AND users.name BETWEEN 'm' AND 'z' AND (addresses.email_address LIKE ? OR addresses.email_address LIKE ?)
    ('%@aol.com', '%@msn.com')[(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 relationship, 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()
    SELECT users.id, users.name, users.fullname
    FROM users, addresses AS a1, addresses AS a2
    WHERE users.id = a1.user_id AND users.id = a2.user_id AND a1.email_address = ? AND a2.email_address = ?
    ('jack@msn.com', 'jack@yahoo.com')[(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()
    SELECT users.id, users.name, users.fullname
    FROM users, addresses AS addresses_1, addresses AS addresses_2
    WHERE users.id = addresses_1.user_id AND users.id = addresses_2.user_id AND addresses_1.email_address = ? AND addresses_2.email_address = ?
    ('jack@msn.com', 'jack@yahoo.com')[(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()
    SELECT users.name
    FROM users, (SELECT users.id AS id, users.name AS name, users.fullname AS fullname
    FROM users, addresses AS addresses_1, addresses AS addresses_2
    WHERE users.id = addresses_1.user_id AND users.id = addresses_2.user_id AND addresses_1.email_address = ? AND addresses_2.email_address = ?) AS anon_1
    WHERE users.id = anon_1.id
    ('jack@msn.com', 'jack@yahoo.com')[(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()
    SELECT users.fullname
    FROM users JOIN addresses ON addresses.email_address LIKE users.name || ?
    ('%',)[(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 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]
    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 = ? AND (EXISTS (SELECT addresses_1.id
    FROM addresses AS addresses_1
    WHERE addresses_1.user_id = users.id AND addresses_1.email_address LIKE ?)) ORDER BY users.fullname DESC
    ('jack', '%@msn.com')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()
    SELECT users.id, users.name, users.fullname
    FROM users
    WHERE users.name = ?
    ('wendy',)[(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()
    SELECT users.id, users.name, users.fullname
    FROM users
    WHERE users.name LIKE ? || '%'
    ('wendy',)[(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()
    SELECT users.id, users.name, users.fullname, addresses.id, addresses.user_id, addresses.email_address
    FROM users LEFT OUTER JOIN addresses ON users.id = addresses.user_id
    WHERE users.name LIKE ? || '%' OR addresses.email_address LIKE ? || '@%'
    ('jack', 'jack')[(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`.

### Window Functions

Any `FunctionElement`, including functions generated by `func`, can be turned into a “window function”, that is an OVER clause, using the `over()` method:

    >>> s = select([users.c.id, func.row_number().over(order_by=users.c.name)])
    >>> print s
    SELECT users.id, row_number() OVER (ORDER BY users.name) AS anon_1
    FROM users

### 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()
    SELECT addresses.id, addresses.user_id, addresses.email_address
    FROM addresses
    WHERE addresses.email_address = ? UNION SELECT addresses.id, addresses.user_id, addresses.email_address
    FROM addresses
    WHERE addresses.email_address LIKE ? ORDER BY addresses.email_address
    ('foo@bar.com', '%@yahoo.com')[(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()
    SELECT addresses.id, addresses.user_id, addresses.email_address
    FROM addresses
    WHERE addresses.email_address LIKE ? EXCEPT SELECT addresses.id, addresses.user_id, addresses.email_address
    FROM addresses
    WHERE addresses.email_address LIKE ?
    ('%@%.com', '%@msn.com')[(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()
    SELECT anon_1.id, anon_1.user_id, anon_1.email_address
    FROM (SELECT addresses.id AS id, addresses.user_id AS user_id,
    addresses.email_address AS email_address FROM addresses
    WHERE addresses.email_address LIKE ? UNION SELECT addresses.id AS id,
    addresses.user_id AS user_id, addresses.email_address AS email_address
    FROM addresses WHERE addresses.email_address LIKE ?) AS anon_1 EXCEPT
    SELECT addresses.id, addresses.user_id, addresses.email_address
    FROM addresses
    WHERE addresses.email_address LIKE ?
    ('%@yahoo.com', '%@msn.com', '%@msn.com')[(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()
    SELECT users.name, (SELECT count(addresses.id) AS count_1
    FROM addresses
    WHERE users.id = addresses.user_id) AS anon_1
    FROM users
    ()[(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()
    SELECT users.name, (SELECT count(addresses.id) AS count_1
    FROM addresses
    WHERE users.id = addresses.user_id) AS address_count
    FROM users
    ()[(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()
    SELECT addresses.user_id, count(addresses.id) AS count_1
    FROM addresses GROUP BY addresses.user_id
    HAVING count(addresses.id) > ?
    (1,)[(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()
    SELECT DISTINCT addresses.email_address, addresses.id
    FROM addresses ORDER BY addresses.email_address DESC, addresses.id
    ()[(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()
    SELECT addresses.id, addresses.user_id, addresses.email_address
    FROM addresses
    LIMIT 1 OFFSET 1
    ()[(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:

    conn.execute(
        users.insert().values(name=func.upper('jack')),
        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=bindaparam('_name'))

    # insert many rows at once:
    conn.execute(
        users.insert().values(id=bindparam('_id'), name=bindaparam('_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')
    ...                )
    UPDATE users SET name=? WHERE users.name = ?
    ('ed', 'jack')
    COMMIT<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')
    UPDATE users SET name=? WHERE users.name = ?
    ('ed', 'jack')
    COMMIT<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'},
    ...     )
    UPDATE users SET name=? WHERE users.name = ?
    [('ed', 'jack'), ('mary', 'wendy'), ('jake', 'jim')]
    COMMIT<sqlalchemy.engine.base.ResultProxy object at 0x...>

    >>> # update a column to an expression.:
    sql>>> conn.execute(users.update().
    ...                     values(fullname="Fullname: " + users.c.name)
    ...                 )
    UPDATE users SET fullname=(? || users.name)
    ('Fullname: ',)
    COMMIT<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))
    UPDATE users SET fullname=(SELECT addresses.email_address
    FROM addresses
    WHERE addresses.user_id = users.id
    LIMIT 1 OFFSET 0)
    ()
    COMMIT<sqlalchemy.engine.base.ResultProxy object at 0x...>

## Deletes

Finally, a delete. Easy enough:

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

    sql>>> conn.execute(users.delete().where(users.c.name > 'm'))
    DELETE FROM users WHERE users.name > ?
    ('m',)
    COMMIT<sqlalchemy.engine.base.ResultProxy object at 0x...>

## Further Reference

Expression Language Reference: SQL Statements and Expressions API

Database Metadata Reference: Schema Definition Language

Engine Reference: Engine Configuration

Connection Reference: Working with Engines and Connections

Types Reference: Column and Data Types