Constructing SQL Queries via Python Expressions {@name=sql}
===============================================

*Note:* This section describes how to use SQLAlchemy to construct SQL queries and receive result sets.  It does *not* cover the object relational mapping capabilities of SQLAlchemy; that is covered later on in [datamapping](rel:datamapping).  However, both areas of functionality work similarly in how selection criterion is constructed, so if you are interested just in ORM, you should probably skim through basic [sql_whereclause](rel:sql_whereclause) construction before moving on.

Once you have used the `sqlalchemy.schema` module to construct your tables and/or reflect them from the database, performing SQL queries using those table meta data objects is done via the `sqlalchemy.sql` package.  This package defines a large set of classes, each of which represents a particular kind of lexical construct within a SQL query; all are descendants of the common base class `sqlalchemy.sql.ClauseElement`.  A full query is represented via a structure of `ClauseElement`s.  A set of reasonably intuitive creation functions is provided by the `sqlalchemy.sql` package to create these structures; these functions are described in the rest of this section. 

Executing a `ClauseElement` structure can be performed in two general ways.  You can use an `Engine` or a `Connection` object's `execute()` method to which you pass the query structure; this is known as **explicit style**.  Or, if the `ClauseElement` structure is built upon Table metadata which is bound to an `Engine` directly, you can simply call `execute()` on the structure itself, known as **implicit style**.  In both cases, the execution returns a cursor-like object (more on that later).  The same clause structure can be executed repeatedly.  The `ClauseElement` is compiled into a string representation by an underlying `Compiler` object which is associated with the `Engine` via its `Dialect`.

The examples below all include a dump of the generated SQL corresponding to the query object, as well as a dump of the statement's bind parameters.  In all cases, bind parameters are shown as named parameters using the colon format (i.e. ':name').  When the statement is compiled into a database-specific version, the named-parameter statement and its bind values are converted to the proper paramstyle for that database automatically.

For this section, we will mostly use the implcit style of execution, meaning the `Table` objects are associated with an instance of `BoundMetaData`, and constructed `ClauseElement` objects support self-execution.  Assume the following configuration:

    {python}
    from sqlalchemy import *
    metadata = BoundMetaData('sqlite:///mydb.db', strategy='threadlocal', echo=True)
    
    # a table to store users
    users = Table('users', metadata,
        Column('user_id', Integer, primary_key = True),
        Column('user_name', String(40)),
        Column('password', String(80))
    )
    
    # a table that stores mailing addresses associated with a specific user
    addresses = Table('addresses', metadata,
        Column('address_id', Integer, primary_key = True),
        Column('user_id', Integer, ForeignKey("users.user_id")),
        Column('street', String(100)),
        Column('city', String(80)),
        Column('state', String(2)),
        Column('zip', String(10))
    )
    
    # a table that stores keywords
    keywords = Table('keywords', metadata,
        Column('keyword_id', Integer, primary_key = True),
        Column('name', VARCHAR(50))
    )
    
    # a table that associates keywords with users
    userkeywords = Table('userkeywords', metadata,
        Column('user_id', INT, ForeignKey("users")),
        Column('keyword_id', INT, ForeignKey("keywords"))
    )

### Simple Select {@name=select}

A select is done by constructing a `Select` object with the proper arguments, adding any extra arguments if desired, then calling its `execute()` method.

    {python title="Basic Select"}
    from sqlalchemy import *
    
    # use the select() function defined in the sql package
    s = select([users])
    
    # or, call the select() method off of a Table object
    s = users.select()
    
    # then, call execute on the Select object:
    {sql}result = s.execute() 
    SELECT users.user_id, users.user_name, users.password FROM users
    {}
    
    # the SQL text of any clause object can also be viewed via the str() call:
    >>> str(s)
    SELECT users.user_id, users.user_name, users.password FROM users

#### Explicit Execution {@name=explicit}

As mentioned above, `ClauseElement` structures can also be executed with a `Connection` object explicitly:

    {python}
    engine = create_engine('sqlite:///myfile.db')
    conn = engine.connect()
    
    s = users.select()
    {sql}result = conn.execute(s)
    SELECT users.user_id, users.user_name, users.password FROM users
    {}
    
    conn.close()

#### Binding ClauseElements to Engines {@name=binding}

For queries that don't contain any tables, `ClauseElement`s that represent a fully executeable statement support an `engine` keyword parameter which can bind the object to an `Engine`, thereby allowing implicit execution:

    {python}
    # select a literal
    {sql}select(["current_time"], engine=myengine).execute()
    SELECT current_time
    {}
    
    # select a function
    {sql}select([func.now()], engine=db).execute()
    SELECT now()
    {}

#### Getting Results {@name=resultproxy}

The object returned by `execute()` is a `sqlalchemy.engine.ResultProxy` object, which acts much like a DBAPI `cursor` object in the context of a result set, except that the rows returned can address their columns by ordinal position, column name, or even column object:

    {python title="Using the ResultProxy"}
    # select rows, get resulting ResultProxy object
    {sql}result = users.select().execute()  
    SELECT users.user_id, users.user_name, users.password FROM users
    {}
    
    # get one row
    row = result.fetchone()
    
    # get the 'user_id' column via integer index:
    user_id = row[0]
    
    # or column name
    user_name = row['user_name']
    
    # or column object
    password = row[users.c.password]
    
    # or column accessor
    password = row.password
    
    # ResultProxy object also supports fetchall()
    rows = result.fetchall()
    
    # or get the underlying DBAPI cursor object
    cursor = result.cursor
    
    # close the result.  If the statement was implicitly executed 
    # (i.e. without an explicit Connection), this will
    # return the underlying connection resources back to 
    # the connection pool.  de-referencing the result
    # will also have the same effect.  if an explicit Connection was 
    # used, then close() just closes the underlying cursor object.
    result.close()

#### Using Column Labels {@name=labels}

A common need when writing statements that reference multiple tables is to create labels for columns, thereby separating columns from different tables with the same name.  The Select construct supports automatic generation of column labels via the `use_labels=True` parameter:

    {python title="use_labels Flag"}
    {sql}c = select([users, addresses], 
    users.c.user_id==addresses.c.address_id, 
    use_labels=True).execute()  
    SELECT users.user_id AS users_user_id, users.user_name AS users_user_name, 
    users.password AS users_password, addresses.address_id AS addresses_address_id, 
    addresses.user_id AS addresses_user_id, addresses.street AS addresses_street, 
    addresses.city AS addresses_city, addresses.state AS addresses_state, 
    addresses.zip AS addresses_zip
    FROM users, addresses
    WHERE users.user_id = addresses.address_id
    {}

The table name part of the label is affected if you use a construct such as a table alias:

    {python title="use_labels with an Alias"}
    person = users.alias('person')
    {sql}c = select([person, addresses], 
        person.c.user_id==addresses.c.address_id, 
        use_labels=True).execute()  
    SELECT person.user_id AS person_user_id, person.user_name AS person_user_name, 
    person.password AS person_password, addresses.address_id AS addresses_address_id,
     addresses.user_id AS addresses_user_id, addresses.street AS addresses_street, 
     addresses.city AS addresses_city, addresses.state AS addresses_state, 
     addresses.zip AS addresses_zip FROM users AS person, addresses
    WHERE person.user_id = addresses.address_id

Labels are also generated in such a way as to never go beyond 30 characters.  Most databases support a limit on the length of symbols, such as Postgres, and particularly Oracle which has a rather short limit of 30:

    {python title="use_labels Generates Abbreviated Labels"}
    long_named_table = users.alias('this_is_the_person_table')
    {sql}c = select([person], use_labels=True).execute()  
    SELECT this_is_the_person_table.user_id AS this_is_the_person_table_b36c, 
    this_is_the_person_table.user_name AS this_is_the_person_table_f76a, 
    this_is_the_person_table.password AS this_is_the_person_table_1e7c
    FROM users AS this_is_the_person_table
    {}
    
You can also specify custom labels on a per-column basis using the `label()` function:

    {python title="label() Function on Column"}
    {sql}c = select([users.c.user_id.label('id'), 
               users.c.user_name.label('name')]).execute()  
    SELECT users.user_id AS id, users.user_name AS name
    FROM users
    {}

#### Table/Column Specification {@name=columns}

Calling `select` off a table automatically generates a column clause which includes all the table's columns, in the order they are specified in the source Table object.

But in addition to selecting all the columns off a single table, any set of columns can be specified, as well as full tables, and any combination of the two:

    {python title="Specify Columns to Select"}
    # individual columns
    {sql}c = select([users.c.user_id, users.c.user_name]).execute()  
    SELECT users.user_id, users.user_name FROM users
    {}
    
    # full tables
    {sql}c = select([users, addresses]).execute()  
    SELECT users.user_id, users.user_name, users.password, 
    addresses.address_id, addresses.user_id, 
    addresses.street, addresses.city, addresses.state, addresses.zip
    FROM users, addresses
    {}
    
    # combinations
    {sql}c = select([users, addresses.c.zip]).execute()  
    SELECT users.user_id, users.user_name, users.password, 
    addresses.zip FROM users, addresses
    {}

### WHERE Clause {@name=whereclause}
    
The WHERE condition is the named keyword argument `whereclause`, or the second positional argument to the `select()` constructor and the first positional argument to the `select()` method of `Table`.

WHERE conditions are constructed using column objects, literal values, and functions defined in the `sqlalchemy.sql` module.  Column objects override the standard Python operators to provide clause compositional objects, which compile down to SQL operations:

    {python title="Basic WHERE Clause"}
    {sql}c = users.select(users.c.user_id == 7).execute()  
    SELECT users.user_id, users.user_name, users.password, 
    FROM users WHERE users.user_id = :users_user_id
    {'users_user_id': 7}                

Notice that the literal value "7" was broken out of the query and placed into a bind parameter.  Databases such as Oracle must parse incoming SQL and create a "plan" when new queries are received, which is an expensive process.  By using bind parameters, the same query with various literal values can have its plan compiled only once, and used repeatedly with less overhead.
            
More where clauses:

    {python}
    # another comparison operator
    {sql}c = select([users], users.c.user_id>7).execute() 
    SELECT users.user_id, users.user_name, users.password, 
    FROM users WHERE users.user_id > :users_user_id
    {'users_user_id': 7}
    
    # OR keyword
    {sql}c = users.select(or_(users.c.user_name=='jack', users.c.user_name=='ed')).execute()  
    SELECT users.user_id, users.user_name, users.password 
    FROM users WHERE users.user_name = :users_user_name 
    OR users.user_name = :users_user_name_1
    {'users_user_name_1': 'ed', 'users_user_name': 'jack'}
    
    # AND keyword
    {sql}c = users.select(and_(users.c.user_name=='jack', users.c.password=='dog')).execute()  
    SELECT users.user_id, users.user_name, users.password
    FROM users WHERE users.user_name = :users_user_name 
    AND users.password = :users_password
    {'users_user_name': 'jack', 'users_password': 'dog'}
    
    # NOT keyword
    {sql}c = users.select(not_(
            or_(users.c.user_name=='jack', users.c.password=='dog')
        )).execute()  
    SELECT users.user_id, users.user_name, users.password 
    FROM users 
    WHERE NOT (users.user_name = :users_user_name 
        OR users.password = :users_password)
    {'users_user_name': 'jack', 'users_password': 'dog'}
                
    # IN clause
    {sql}c = users.select(users.c.user_name.in_('jack', 'ed', 'fred')).execute()  
    SELECT users.user_id, users.user_name, users.password
    FROM users WHERE users.user_name 
    IN (:users_user_name, :users_user_name_1, :users_user_name_2)
    {'users_user_name': 'jack', 'users_user_name_1': 'ed', 
    'users_user_name_2': 'fred'}

                
    # join users and addresses together
    {sql}c = select([users, addresses], users.c.user_id==addresses.c.address_id).execute()  
    SELECT users.user_id, users.user_name, users.password,  
    addresses.address_id, addresses.user_id, addresses.street, addresses.city, 
    addresses.state, addresses.zip
    FROM users, addresses
    WHERE users.user_id = addresses.address_id
    {}

                
    # join users and addresses together, but dont specify "addresses" in the 
    # selection criterion.  The WHERE criterion adds it to the FROM list 
    # automatically.
    {sql}c = select([users], and_(
                    users.c.user_id==addresses.c.user_id,
                    users.c.user_name=='fred'
                )).execute()
    SELECT users.user_id, users.user_name, users.password
    FROM users, addresses WHERE users.user_id = addresses.user_id 
    AND users.user_name = :users_user_name
    {'users_user_name': 'fred'}                
            

Select statements can also generate a WHERE clause based on the parameters you give it.  If a given parameter, which matches the name of a column or its "label" (the combined tablename + "_" + column name), and does not already correspond to a bind parameter in the select object, it will be added as a comparison against that column.  This is a shortcut to creating a full WHERE clause:

    {python}
    # specify a match for the "user_name" column
    {sql}c = users.select().execute(user_name='ed')
    SELECT users.user_id, users.user_name, users.password
    FROM users WHERE users.user_name = :users_user_name
    {'users_user_name': 'ed'}

    # specify a full where clause for the "user_name" column, as well as a
    # comparison for the "user_id" column
    {sql}c = users.select(users.c.user_name=='ed').execute(user_id=10)
    SELECT users.user_id, users.user_name, users.password
    FROM users WHERE users.user_name = :users_user_name AND users.user_id = :users_user_id
    {'users_user_name': 'ed', 'users_user_id': 10}

#### Operators {@name=operators}

Supported column operators so far are all the numerical comparison operators, i.e. '==', '>', '>=', etc., as well as `like()`, `startswith()`, `endswith()`, `between()`, and `in()`.  Boolean operators include `not_()`, `and_()` and `or_()`, which also can be used inline via '~', '&', and '|'.  Math operators are '+', '-', '*', '/'.  Any custom operator can be specified via the `op()` function shown below.
 
    {python}
    # "like" operator
    users.select(users.c.user_name.like('%ter'))
    
    # equality operator
    users.select(users.c.user_name == 'jane')
    
    # in opertator
    users.select(users.c.user_id.in_(1,2,3))
    
    # and_, endswith, equality operators
    users.select(and_(addresses.c.street.endswith('green street'),
                    addresses.c.zip=='11234'))
    
    # & operator subsituting for 'and_'
    users.select(addresses.c.street.endswith('green street') & (addresses.c.zip=='11234'))
    
    # + concatenation operator
    select([users.c.user_name + '_name'])
    
    # NOT operator
    users.select(~(addresses.c.street == 'Green Street'))
    
    # any custom operator
    select([users.c.user_name.op('||')('_category')])

    # "null" comparison via == (converts to IS)
    {sql}users.select(users.c.user_name==None).execute()
    SELECT users.user_id, users.user_name, users.password 
    FROM users 
    WHERE users.user_name IS NULL

    # or via explicit null() construct
    {sql}users.select(users.c.user_name==null()).execute()
    SELECT users.user_id, users.user_name, users.password 
    FROM users 
    WHERE users.user_name IS NULL
    
#### Functions {@name=functions}

Functions can be specified using the `func` keyword:

    {python}
    {sql}select([func.count(users.c.user_id)]).execute()
    SELECT count(users.user_id) FROM users
    
    {sql}users.select(func.substr(users.c.user_name, 1) == 'J').execute()
    SELECT users.user_id, users.user_name, users.password FROM users 
    WHERE substr(users.user_name, :substr) = :substr_1
    {'substr_1': 'J', 'substr': 1}

Functions also are callable as standalone values:

    {python}
    # call the "now()" function
    time = func.now(engine=myengine).scalar()

    # call myfunc(1,2,3)
    myvalue = func.myfunc(1, 2, 3, engine=db).execute()

    # or call them off the engine
    db.func.now().scalar()

#### Literals {@name=literals}

You can drop in a literal value anywhere there isnt a column to attach to via the `literal` keyword:

    {python}
    {sql}select([literal('foo') + literal('bar'), users.c.user_name]).execute()
    SELECT :literal + :literal_1, users.user_name 
    FROM users
    {'literal_1': 'bar', 'literal': 'foo'}

    # literals have all the same comparison functions as columns
    {sql}select([literal('foo') == literal('bar')], engine=myengine).scalar()
    SELECT :literal = :literal_1
    {'literal_1': 'bar', 'literal': 'foo'}

Literals also take an optional `type` parameter to give literals a type.  This can sometimes be significant, for example when using the "+" operator with SQLite, the String type is detected and the operator is converted to "||":

    {python}
    {sql}select([literal('foo', type=String) + 'bar'], engine=e).execute()
    SELECT ? || ?
    ['foo', 'bar']

#### Order By {@name=orderby}

The ORDER BY clause of a select statement can be specified as individual columns to order by within an array specified via the `order_by` parameter, and optional usage of the asc() and desc() functions:

    {python}
    # straight order by
    {sql}c = users.select(order_by=[users.c.user_name]).execute() 
    SELECT users.user_id, users.user_name, users.password
    FROM users ORDER BY users.user_name                

    # descending/ascending order by on multiple columns
    {sql}c = users.select(
        users.c.user_name>'J', 
        order_by=[desc(users.c.user_id), asc(users.c.user_name)]).execute() 
    SELECT users.user_id, users.user_name, users.password
    FROM users WHERE users.user_name > :users_user_name 
    ORDER BY users.user_id DESC, users.user_name ASC
    {'users_user_name':'J'}

#### DISTINCT, LIMIT and OFFSET {@name=options}

These are specified as keyword arguments:

    {python}
    {sql}c = select([users.c.user_name], distinct=True).execute()
    SELECT DISTINCT users.user_name FROM users

    {sql}c = users.select(limit=10, offset=20).execute()
    SELECT users.user_id, users.user_name, users.password FROM users LIMIT 10 OFFSET 20
        
The Oracle driver does not support LIMIT and OFFSET directly, but instead wraps the generated query into a subquery and uses the "rownum" variable to control the rows selected (this is somewhat experimental).

### Inner and Outer Joins {@name=join}

As some of the examples indicated above, a regular inner join can be implicitly stated, just like in a SQL expression, by just specifying the tables to be joined as well as their join conditions:

    {python}
    {sql}addresses.select(addresses.c.user_id==users.c.user_id).execute() 
    SELECT addresses.address_id, addresses.user_id, addresses.street, 
    addresses.city, addresses.state, addresses.zip FROM addresses, users
    WHERE addresses.user_id = users.user_id
    {}                   

There is also an explicit join constructor, which can be embedded into a select query via the `from_obj` parameter of the select statement:

    {python}
    {sql}addresses.select(from_obj=[
        addresses.join(users, addresses.c.user_id==users.c.user_id)
    ]).execute() 
    SELECT addresses.address_id, addresses.user_id, addresses.street, addresses.city, 
    addresses.state, addresses.zip 
    FROM addresses JOIN users ON addresses.user_id = users.user_id
    {}                

The join constructor can also be used by itself:

    {python}
    {sql}join(users, addresses, users.c.user_id==addresses.c.user_id).select().execute()
    SELECT users.user_id, users.user_name, users.password, 
    addresses.address_id, addresses.user_id, addresses.street, addresses.city, 
    addresses.state, addresses.zip 
    FROM addresses JOIN users ON addresses.user_id = users.user_id
    {}                

The join criterion in a join() call is optional.  If not specified, the condition will be derived from the foreign key relationships of the two tables.  If no criterion can be constructed, an exception will be raised.

    {python}
    {sql}join(users, addresses).select().execute()
    SELECT users.user_id, users.user_name, users.password, 
    addresses.address_id, addresses.user_id, addresses.street, addresses.city, 
    addresses.state, addresses.zip 
    FROM addresses JOIN users ON addresses.user_id = users.user_id
    {}                

Notice that this is the first example where the FROM criterion of the select statement is explicitly specified.  In most cases, the FROM criterion is automatically determined from the columns requested as well as the WHERE clause.  The `from_obj` keyword argument indicates a list of explicit FROM clauses to be used in the statement.

A join can be created on its own using the `join` or `outerjoin` functions, or can be created off of an existing Table or other selectable unit via the `join` or `outerjoin` methods:
        
    {python}
    {sql}outerjoin(users, addresses, 
               users.c.user_id==addresses.c.address_id).select().execute()
    SELECT users.user_id, users.user_name, users.password, addresses.address_id, 
    addresses.user_id, addresses.street, addresses.city, addresses.state, addresses.zip
    FROM users LEFT OUTER JOIN addresses ON users.user_id = addresses.address_id
    {}                

    {sql}users.select(keywords.c.name=='running', from_obj=[
            users.join(
                userkeywords, userkeywords.c.user_id==users.c.user_id).join(
                    keywords, keywords.c.keyword_id==userkeywords.c.keyword_id)
            ]).execute()   
    SELECT users.user_id, users.user_name, users.password FROM users 
    JOIN userkeywords ON userkeywords.user_id = users.user_id 
    JOIN keywords ON keywords.keyword_id = userkeywords.keyword_id
    WHERE keywords.name = :keywords_name
    {'keywords_name': 'running'}                

### Table Aliases {@name=alias}

Aliases are used primarily when you want to use the same table more than once as a FROM expression in a statement:
    
    {python}
    address_b = addresses.alias('addressb')
    {sql}# select users who have an address on Green street as well as Orange street
    users.select(and_(
        users.c.user_id==addresses.c.user_id,
        addresses.c.street.like('%Green%'),
        users.c.user_id==address_b.c.user_id,
        address_b.c.street.like('%Orange%')
    )).execute()
    SELECT users.user_id, users.user_name, users.password
    FROM users, addresses, addresses AS addressb
    WHERE users.user_id = addresses.user_id 
    AND addresses.street LIKE :addresses_street 
    AND users.user_id = addressb.user_id 
    AND addressb.street LIKE :addressb_street
    {'addressb_street': '%Orange%', 'addresses_street': '%Green%'}

### Subqueries {@name=subqueries}

SQLAlchemy allows the creation of select statements from not just Table objects, but from a whole class of objects that implement the `Selectable` interface.  This includes Tables, Aliases, Joins and Selects.  Therefore, if you have a Select, you can select from the Select:
    
    {python}
    >>> s = users.select()
    >>> str(s)
    SELECT users.user_id, users.user_name, users.password FROM users
    
    {python}
    >>> s = s.select()
    >>> str(s)
    SELECT user_id, user_name, password
    FROM (SELECT users.user_id, users.user_name, users.password FROM users)                

Any Select, Join, or Alias object supports the same column accessors as a Table:

    {python}
    >>> s = users.select()
    >>> [c.key for c in s.columns]
    ['user_id', 'user_name', 'password']            

When you use `use_labels=True` in a Select object, the label version of the column names become the keys of the accessible columns.  In effect you can create your own "view objects":
        
    {python}
    s = select([users, addresses], users.c.user_id==addresses.c.user_id, use_labels=True)
    {sql}select([
        s.c.users_user_name, s.c.addresses_street, s.c.addresses_zip
    ], s.c.addresses_city=='San Francisco').execute()
    SELECT users_user_name, addresses_street, addresses_zip
    FROM (SELECT users.user_id AS users_user_id, users.user_name AS users_user_name,
    users.password AS users_password, addresses.address_id AS addresses_address_id,
    addresses.user_id AS addresses_user_id, addresses.street AS addresses_street, 
    addresses.city AS addresses_city, addresses.state AS addresses_state, 
    addresses.zip AS addresses_zip
    FROM users, addresses
    WHERE users.user_id = addresses.user_id)
    WHERE addresses_city = :addresses_city
    {'addresses_city': 'San Francisco'}

To specify a SELECT statement as one of the selectable units in a FROM clause, it usually should be given an alias.

    {python}
    {sql}s = users.select().alias('u')
    select([addresses, s]).execute()
    SELECT addresses.address_id, addresses.user_id, addresses.street, addresses.city, 
    addresses.state, addresses.zip, u.user_id, u.user_name, u.password 
    FROM addresses, 
    (SELECT users.user_id, users.user_name, users.password FROM users) AS u
    {}

Select objects can be used in a WHERE condition, in operators such as IN:

    {python}
    # select user ids for all users whos name starts with a "p"
    s = select([users.c.user_id], users.c.user_name.like('p%'))
    
    # now select all addresses for those users
    {sql}addresses.select(addresses.c.user_id.in_(s)).execute()
    SELECT addresses.address_id, addresses.user_id, addresses.street, 
    addresses.city, addresses.state, addresses.zip
    FROM addresses WHERE addresses.address_id IN 
    (SELECT users.user_id FROM users WHERE users.user_name LIKE :users_user_name)
    {'users_user_name': 'p%'}

The sql package supports embedding select statements into other select statements as the criterion in a WHERE condition, or as one of the "selectable" objects in the FROM list of the query.  It does not at the moment directly support embedding a SELECT statement as one of the column criterion for a statement, although this can be achieved via direct text insertion, described later.
        
#### Scalar Column Subqueries {@name=scalar}

Subqueries can be used in the column clause of a select statement by specifying the `scalar=True` flag:

    {python}
    {sql}select([table2.c.col1, table2.c.col2, 
                    select([table1.c.col1], table1.c.col2==7, scalar=True)])
    SELECT table2.col1, table2.col2, 
    (SELECT table1.col1 AS col1 FROM table1 WHERE col2=:table1_col2) 
    FROM table2
    {'table1_col2': 7}

### Correlated Subqueries {@name=correlated}

When a select object is embedded inside of another select object, and both objects reference the same table, SQLAlchemy makes the assumption that the table should be correlated from the child query to the parent query.  To disable this behavior, specify the flag `correlate=False` to the Select statement.

    {python}
    # make an alias of a regular select.   
    s = select([addresses.c.street], addresses.c.user_id==users.c.user_id).alias('s')
    >>> str(s)
    SELECT addresses.street FROM addresses, users 
    WHERE addresses.user_id = users.user_id

    # now embed that select into another one.  the "users" table is removed from
    # the embedded query's FROM list and is instead correlated to the parent query
    s2 = select([users, s.c.street])
    >>> str(s2)
    SELECT users.user_id, users.user_name, users.password, s.street
    FROM users, (SELECT addresses.street FROM addresses
    WHERE addresses.user_id = users.user_id) s

#### EXISTS Clauses {@name=exists}

An EXISTS clause can function as a higher-scaling version of an IN clause, and is usually used in a correlated fashion:

    {python}
    # find all users who have an address on Green street:
    {sql}users.select(
        exists(
            [addresses.c.address_id], 
            and_(
                addresses.c.user_id==users.c.user_id, 
                addresses.c.street.like('%Green%')
            )
        )
    )
    SELECT users.user_id, users.user_name, users.password 
    FROM users WHERE EXISTS (SELECT addresses.address_id 
    FROM addresses WHERE addresses.user_id = users.user_id 
    AND addresses.street LIKE :addresses_street)
    {'addresses_street': '%Green%'}

### Unions {@name=unions}

Unions come in two flavors, UNION and UNION ALL, which are available via module level functions or methods off a Selectable:

    {python}
    {sql}union(
        addresses.select(addresses.c.street=='123 Green Street'),
        addresses.select(addresses.c.street=='44 Park Ave.'),
        addresses.select(addresses.c.street=='3 Mill Road'),
        order_by=[addresses.c.street]
    ).execute()
    SELECT addresses.address_id, addresses.user_id, addresses.street, 
    addresses.city, addresses.state, addresses.zip 
    FROM addresses WHERE addresses.street = :addresses_street 
    UNION 
    SELECT addresses.address_id, addresses.user_id, addresses.street, 
    addresses.city, addresses.state, addresses.zip 
    FROM addresses WHERE addresses.street = :addresses_street_1 
    UNION 
    SELECT addresses.address_id, addresses.user_id, addresses.street, 
    addresses.city, addresses.state, addresses.zip 
    FROM addresses WHERE addresses.street = :addresses_street_2 
    ORDER BY addresses.street
    {'addresses_street_1': '44 Park Ave.', 
    'addresses_street': '123 Green Street', 
    'addresses_street_2': '3 Mill Road'}
    
    {sql}users.select(
        users.c.user_id==7
      ).union_all(
          users.select(
              users.c.user_id==9
          ), 
          order_by=[users.c.user_id]   # order_by is an argument to union_all()
      ).execute() 
    SELECT users.user_id, users.user_name, users.password 
    FROM users WHERE users.user_id = :users_user_id 
    UNION ALL 
    SELECT users.user_id, users.user_name, users.password 
    FROM users WHERE users.user_id = :users_user_id_1 
    ORDER BY users.user_id
    {'users_user_id_1': 9, 'users_user_id': 7}

### Custom Bind Parameters {@name=bindparams}

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 bind parameters, shown here in the "named" format, will be converted to the appropriate named or positional style according to the database implementation being used.

    {python title="Custom Bind Params"}
    s = users.select(users.c.user_name==bindparam('username'))
    
    # execute implicitly
    {sql}s.execute(username='fred')
    SELECT users.user_id, users.user_name, users.password 
    FROM users WHERE users.user_name = :username
    {'username': 'fred'}
    
    # execute explicitly
    conn = engine.connect()
    {sql}conn.execute(s, username='fred')
    SELECT users.user_id, users.user_name, users.password 
    FROM users WHERE users.user_name = :username
    {'username': 'fred'}
    

`executemany()` is also available by supplying multiple dictionary arguments instead of keyword arguments to the `execute()` method of `ClauseElement` or `Connection`.  Examples can be found later in the sections on INSERT/UPDATE/DELETE.

#### Precompiling a Query {@name=precompiling}

By throwing the `compile()` method onto the end of any query object, the query can be "compiled" by the SQLEngine into a `sqlalchemy.sql.Compiled` object just once, and the resulting compiled object reused, which eliminates repeated internal compilation of the SQL string:

    {python}s = users.select(users.c.user_name==bindparam('username')).compile()
    s.execute(username='fred')
    s.execute(username='jane')
    s.execute(username='mary')

### Literal Text Blocks {@name=textual}

The sql package tries to allow free textual placement in as many ways as possible.  In the examples below, note that the from_obj parameter is used only when no other information exists within the select object with which to determine table metadata.  Also note that in a query where there isnt even table metadata used, the SQLEngine to be used for the query has to be explicitly specified:

    {python}
    # strings as column clauses
    {sql}select(["user_id", "user_name"], from_obj=[users]).execute()
    SELECT user_id, user_name FROM users
    {}
    
    # strings for full column lists
    {sql}select(
            ["user_id, user_name, password, addresses.*"], 
            from_obj=[users.alias('u'), addresses]).execute()
    SELECT u.user_id, u.user_name, u.password, addresses.* 
    FROM users AS u, addresses
    {}
    
    # functions, etc.
    {sql}select([users.c.user_id, "process_string(user_name)"]).execute()
    SELECT users.user_id, process_string(user_name) FROM users
    {}
    
    # where clauses
    {sql}users.select(and_(users.c.user_id==7, "process_string(user_name)=27")).execute()
    SELECT users.user_id, users.user_name, users.password FROM users 
    WHERE users.user_id = :users_user_id AND process_string(user_name)=27
    {'users_user_id': 7}
    
    # subqueries
    {sql}users.select(
        "exists (select 1 from addresses where addresses.user_id=users.user_id)").execute()
    SELECT users.user_id, users.user_name, users.password FROM users 
    WHERE exists (select 1 from addresses where addresses.user_id=users.user_id)
    {}
    
    # custom FROM objects
    {sql}select(
            ["*"], 
            from_obj=["(select user_id, user_name from users)"], 
            engine=db).execute()
    SELECT * FROM (select user_id, user_name from users)
    {}
    
    # a full query
    {sql}text("select user_name from users", engine=db).execute()
    SELECT user_name FROM users
    {}
    

#### Using Bind Parameters in Text Blocks {@name=textual_binds}

Use the format `':paramname'` to define bind parameters inside of a text block.  They will be converted to the appropriate format upon compilation:

    {python}t = engine.text("select foo from mytable where lala=:hoho")
    r = t.execute(hoho=7)

Bind parameters can also be explicit, which allows typing information to be added.  Just specify them as a list with keys that match those inside the textual statement:

    {python}t = engine.text("select foo from mytable where lala=:hoho", 
                    bindparams=[bindparam('hoho', type=types.String)])
        r = t.execute(hoho="im hoho")

Result-row type processing can be added via the `typemap` argument, which is a dictionary of return columns mapped to types:

    {python}# specify DateTime type for the 'foo' column in the result set
    # sqlite, for example, uses result-row post-processing to construct dates
    t = engine.text("select foo from mytable where lala=:hoho", 
            bindparams=[bindparam('hoho', type=types.String)],
            typemap={'foo':types.DateTime}
            )
    r = t.execute(hoho="im hoho")
    
    # 'foo' is a datetime
    year = r.fetchone()['foo'].year

### Building Select Objects {@name=building}

One of the primary motivations for a programmatic SQL library is to allow the piecemeal construction of a SQL statement based on program variables.  All the above examples typically show Select objects being created all at once.  The Select object also includes "builder" methods to allow building up an object.  The below example is a "user search" function, where users can be selected based on primary key, user name, street address, keywords, or any combination:

    {python}
    def find_users(id=None, name=None, street=None, keywords=None):
        statement = users.select()
        if id is not None:
            statement.append_whereclause(users.c.user_id==id)
        if name is not None:
            statement.append_whereclause(users.c.user_name==name)
        if street is not None:
            # append_whereclause joins "WHERE" conditions together with AND
            statement.append_whereclause(users.c.user_id==addresses.c.user_id)
            statement.append_whereclause(addresses.c.street==street)
        if keywords is not None:
            statement.append_from(
                    users.join(userkeywords, users.c.user_id==userkeywords.c.user_id).join(
                            keywords, userkeywords.c.keyword_id==keywords.c.keyword_id))
            statement.append_whereclause(keywords.c.name.in_(keywords))
            # to avoid multiple repeats, set query to be DISTINCT:
            statement.distinct=True
        return statement.execute()
    
    {sql}find_users(id=7)
    SELECT users.user_id, users.user_name, users.password 
    FROM users 
    WHERE users.user_id = :users_user_id
    {'users_user_id': 7}

    {sql}find_users(street='123 Green Street')
    SELECT users.user_id, users.user_name, users.password 
    FROM users, addresses 
    WHERE users.user_id = addresses.user_id AND addresses.street = :addresses_street
    {'addresses_street': '123 Green Street'}

    {sql}find_users(name='Jack', keywords=['jack','foo'])
    SELECT DISTINCT users.user_id, users.user_name, users.password 
    FROM users JOIN userkeywords ON users.user_id = userkeywords.user_id 
    JOIN keywords ON userkeywords.keyword_id = keywords.keyword_id 
    WHERE users.user_name = :users_user_name AND keywords.name IN ('jack', 'foo')
    {'users_user_name': 'Jack'}

### Inserts {@name=insert}

An INSERT involves just one table.  The Insert object is used via the insert() function, and the specified columns determine what columns show up in the generated SQL.  If primary key columns are left out of the criterion, the SQL generator will try to populate them as specified by the particular database engine and sequences, i.e. relying upon an auto-incremented column or explicitly calling a sequence beforehand.  Insert statements, as well as updates and deletes, can also execute multiple parameters in one pass via specifying an array of dictionaries as parameters.

The values to be populated for an INSERT or an UPDATE can be specified to the insert()/update() functions as the `values` named argument, or the query will be compiled based on the values of the parameters sent to the execute() method.

    {python title="Using insert()"}
    # basic insert
    {sql}users.insert().execute(user_id=1, user_name='jack', password='asdfdaf')
    INSERT INTO users (user_id, user_name, password) 
    VALUES (:user_id, :user_name, :password)
    {'user_name': 'jack', 'password': 'asdfdaf', 'user_id': 1}
    
    # insert just user_name, NULL for others
    # will auto-populate primary key columns if they are configured
    # to do so
    {sql}users.insert().execute(user_name='ed')
    INSERT INTO users (user_name) VALUES (:user_name)
    {'user_name': 'ed'}
    
    # INSERT with a list:
    {sql}users.insert(values=(3, 'jane', 'sdfadfas')).execute()
    INSERT INTO users (user_id, user_name, password) 
    VALUES (:user_id, :user_name, :password)
    {'user_id': 3, 'password': 'sdfadfas', 'user_name': 'jane'}
    
    # INSERT with user-defined bind parameters
    i = users.insert(
        values={'user_name':bindparam('name'), 'password':bindparam('pw')}
        )
    {sql}i.execute(name='mary', pw='adas5fs')
    INSERT INTO users (user_name, password) VALUES (:name, :pw)
    {'name': 'mary', 'pw': 'adas5fs'}
    
    # INSERT many - if no explicit 'values' parameter is sent,
    # the first parameter list in the list determines
    # the generated SQL of the insert (i.e. what columns are present)
    # executemany() is used at the DBAPI level
    {sql}users.insert().execute(
        {'user_id':7, 'user_name':'jack', 'password':'asdfasdf'},
        {'user_id':8, 'user_name':'ed', 'password':'asdffcadf'},
        {'user_id':9, 'user_name':'fred', 'password':'asttf'},
    )
    INSERT INTO users (user_id, user_name, password) 
    VALUES (:user_id, :user_name, :password)
    [{'user_name': 'jack', 'password': 'asdfasdf', 'user_id': 7}, 
    {'user_name': 'ed', 'password': 'asdffcadf', 'user_id': 8}, 
    {'user_name': 'fred', 'password': 'asttf', 'user_id': 9}]

### Updates {@name=update}

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

    {python title="Using update()"}
    # change 'jack' to 'ed'
    {sql}users.update(users.c.user_name=='jack').execute(user_name='ed')
                UPDATE users SET user_name=:user_name WHERE users.user_name = :users_user_name
    {'users_user_name': 'jack', 'user_name': 'ed'}
    
    # use bind parameters
    u = users.update(users.c.user_name==bindparam('name'), 
                    values={'user_name':bindparam('newname')})
    {sql}u.execute(name='jack', newname='ed')
    UPDATE users SET user_name=:newname WHERE users.user_name = :name
    {'newname': 'ed', 'name': 'jack'}
    
    # update a column to another column
    {sql}users.update(values={users.c.password:users.c.user_name}).execute()
    UPDATE users SET password=users.user_name
    {}
    
    # multi-update
    {sql}users.update(users.c.user_id==bindparam('id')).execute(
            {'id':7, 'user_name':'jack', 'password':'fh5jks'},
            {'id':8, 'user_name':'ed', 'password':'fsr234ks'},
            {'id':9, 'user_name':'mary', 'password':'7h5jse'},
        )
    UPDATE users SET user_name=:user_name, password=:password WHERE users.user_id = :id
    [{'password': 'fh5jks', 'user_name': 'jack', 'id': 7}, 
    {'password': 'fsr234ks', 'user_name': 'ed', 'id': 8}, 
    {'password': '7h5jse', 'user_name': 'mary', 'id': 9}]

#### Correlated Updates {@name=correlated}

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

    {python}s = select([addresses.c.city], addresses.c.user_id==users.c.user_id)
    {sql}users.update(
        and_(users.c.user_id>10, users.c.user_id<20), 
        values={users.c.user_name:s}
    ).execute() 
    UPDATE users SET user_name=(SELECT addresses.city 
    FROM addresses 
    WHERE addresses.user_id = users.user_id) 
    WHERE users.user_id > :users_user_id AND users.user_id < :users_user_id_1
    {'users_user_id_1': 20, 'users_user_id': 10}

### Deletes {@name=delete}

A delete is formulated like an update, except theres no values:

    {python}users.delete(users.c.user_id==7).execute()
    users.delete(users.c.user_name.like(bindparam('name'))).execute(
            {'name':'%Jack%'},
            {'name':'%Ed%'},
            {'name':'%Jane%'},
        )
    users.delete(exists())