# SQLAlchemy 0.6 Documentation

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Release: 0.6.9 | Release Date: May 5, 2012

# Schema Definition Language

## Describing Databases with MetaData

The core of SQLAlchemy’s query and object mapping operations are supported by database metadata, which is comprised of Python objects that describe tables and other schema-level objects. These objects are at the core of three major types of operations - issuing CREATE and DROP statements (known as DDL), constructing SQL queries, and expressing information about structures that already exist within the database.

Database metadata can be expressed by explicitly naming the various components and their properties, using constructs such as `Table`, `Column`, `ForeignKey` and `Sequence`, all of which are imported from the `sqlalchemy.schema` package. It can also be generated by SQLAlchemy using a process called reflection, which means you start with a single object such as `Table`, assign it a name, and then instruct SQLAlchemy to load all the additional information related to that name from a particular engine source.

A key feature of SQLAlchemy’s database metadata constructs is that they are designed to be used in a declarative style which closely resembles that of real DDL. They are therefore most intuitive to those who have some background in creating real schema generation scripts.

A collection of metadata entities is stored in an object aptly named `MetaData`:

    from sqlalchemy import *

    metadata = MetaData()

`MetaData` is a container object that keeps together many different features of a database (or multiple databases) being described.

To represent a table, use the `Table` class. Its two primary arguments are the table name, then the `MetaData` object which it will be associated with. The remaining positional arguments are mostly `Column` objects describing each column:

    user = Table('user', metadata,
        Column('user_id', Integer, primary_key = True),
        Column('user_name', String(16), nullable = False),
        Column('email_address', String(60)),
        Column('password', String(20), nullable = False)
    )

Above, a table called `user` is described, which contains four columns. The primary key of the table consists of the `user_id` column. Multiple columns may be assigned the `primary_key=True` flag which denotes a multi-column primary key, known as a composite primary key.

Note also that each column describes its datatype using objects corresponding to genericized types, such as `Integer` and `String`. SQLAlchemy features dozens of types of varying levels of specificity as well as the ability to create custom types. Documentation on the type system can be found at types.

### Accessing Tables and Columns

The `MetaData` object contains all of the schema constructs we’ve associated with it. It supports a few methods of accessing these table objects, such as the `sorted_tables` accessor which returns a list of each `Table` object in order of foreign key dependency (that is, each table is preceded by all tables which it references):

    >>> for t in metadata.sorted_tables:
    ...    print t.name
    user
    user_preference
    invoice
    invoice_item

In most cases, individual `Table` objects have been explicitly declared, and these objects are typically accessed directly as module-level variables in an application. Once a `Table` has been defined, it has a full set of accessors which allow inspection of its properties. Given the following `Table` definition:

    employees = Table('employees', metadata,
        Column('employee_id', Integer, primary_key=True),
        Column('employee_name', String(60), nullable=False),
        Column('employee_dept', Integer, ForeignKey("departments.department_id"))
    )

Note the `ForeignKey` object used in this table - this construct defines a reference to a remote table, and is fully described in Defining Foreign Keys. Methods of accessing information about this table include:

    # access the column "EMPLOYEE_ID":
    employees.columns.employee_id

    # or just
    employees.c.employee_id

    # via string
    employees.c['employee_id']

    # iterate through all columns
    for c in employees.c:
        print c

    # get the table's primary key columns
    for primary_key in employees.primary_key:
        print primary_key

    # get the table's foreign key objects:
    for fkey in employees.foreign_keys:
        print fkey

    # access the table's MetaData:
    employees.metadata

    # access the table's bound Engine or Connection, if its MetaData is bound:
    employees.bind

    # access a column's name, type, nullable, primary key, foreign key
    employees.c.employee_id.name
    employees.c.employee_id.type
    employees.c.employee_id.nullable
    employees.c.employee_id.primary_key
    employees.c.employee_dept.foreign_keys

    # get the "key" of a column, which defaults to its name, but can
    # be any user-defined string:
    employees.c.employee_name.key

    # access a column's table:
    employees.c.employee_id.table is employees

    # get the table related by a foreign key
    list(employees.c.employee_dept.foreign_keys)[0].column.table

### Creating and Dropping Database Tables

Once you’ve defined some `Table` objects, assuming you’re working with a brand new database one thing you might want to do is issue CREATE statements for those tables and their related constructs (as an aside, it’s also quite possible that you don’t want to do this, if you already have some preferred methodology such as tools included with your database or an existing scripting system - if that’s the case, feel free to skip this section - SQLAlchemy has no requirement that it be used to create your tables).

The usual way to issue CREATE is to use `create_all()` on the `MetaData` object. This method will issue queries that first check for the existence of each individual table, and if not found will issue the CREATE statements:

>     engine = create_engine('sqlite:///:memory:')

>     metadata = MetaData()

>     user = Table('user', metadata,
>         Column('user_id', Integer, primary_key = True),
>         Column('user_name', String(16), nullable = False),
>         Column('email_address', String(60), key='email'),
>         Column('password', String(20), nullable = False)
>     )

>     user_prefs = Table('user_prefs', metadata,
>         Column('pref_id', Integer, primary_key=True),
>         Column('user_id', Integer, ForeignKey("user.user_id"), nullable=False),
>         Column('pref_name', String(40), nullable=False),
>         Column('pref_value', String(100))
>     )

>     sqlmetadata.create_all(engine)
>     PRAGMA table_info(user){}
>     CREATE TABLE user(
>             user_id INTEGER NOT NULL PRIMARY KEY,
>             user_name VARCHAR(16) NOT NULL,
>             email_address VARCHAR(60),
>             password VARCHAR(20) NOT NULL
>     )
>     PRAGMA table_info(user_prefs){}
>     CREATE TABLE user_prefs(
>             pref_id INTEGER NOT NULL PRIMARY KEY,
>             user_id INTEGER NOT NULL REFERENCES user(user_id),
>             pref_name VARCHAR(40) NOT NULL,
>             pref_value VARCHAR(100)
>     )

`create_all()` creates foreign key constraints between tables usually inline with the table definition itself, and for this reason it also generates the tables in order of their dependency. There are options to change this behavior such that `ALTER TABLE` is used instead.

Dropping all tables is similarly achieved using the `drop_all()` method. This method does the exact opposite of `create_all()` - the presence of each table is checked first, and tables are dropped in reverse order of dependency.

Creating and dropping individual tables can be done via the `create()` and `drop()` methods of `Table`. These methods by default issue the CREATE or DROP regardless of the table being present:

    engine = create_engine('sqlite:///:memory:')

    meta = MetaData()

    employees = Table('employees', meta,
        Column('employee_id', Integer, primary_key=True),
        Column('employee_name', String(60), nullable=False, key='name'),
        Column('employee_dept', Integer, ForeignKey("departments.department_id"))
    )
    sqlemployees.create(engine)
    CREATE TABLE employees(
    employee_id SERIAL NOT NULL PRIMARY KEY,
    employee_name VARCHAR(60) NOT NULL,
    employee_dept INTEGER REFERENCES departments(department_id)
    )

`drop()` method:

    sqlemployees.drop(engine)
    DROP TABLE employee

To enable the “check first for the table existing” logic, add the `checkfirst=True` argument to `create()` or `drop()`:

    employees.create(engine, checkfirst=True)
    employees.drop(engine, checkfirst=False)

### Binding MetaData to an Engine or Connection

Notice in the previous section the creator/dropper methods accept an argument for the database engine in use. When a schema construct is combined with an `Engine` object, or an individual `Connection` object, we call this the bind. In the above examples the bind is associated with the schema construct only for the duration of the operation. However, the option exists to persistently associate a bind with a set of schema constructs via the `MetaData` object’s `bind` attribute:

    engine = create_engine('sqlite://')

    # create MetaData
    meta = MetaData()

    # bind to an engine
    meta.bind = engine

We can now call methods like `create_all()` without needing to pass the `Engine`:

    meta.create_all()

The MetaData’s bind is used for anything that requires an active connection, such as loading the definition of a table from the database automatically (called reflection):

    # describe a table called 'users', query the database for its columns
    users_table = Table('users', meta, autoload=True)

As well as for executing SQL constructs that are derived from that MetaData’s table objects:

    # generate a SELECT statement and execute
    result = users_table.select().execute()

Binding the MetaData to the Engine is a completely optional feature. The above operations can be achieved without the persistent bind using parameters:

    # describe a table called 'users', query the database for its columns
    users_table = Table('users', meta, autoload=True, autoload_with=engine)

    # generate a SELECT statement and execute
    result = engine.execute(users_table.select())

Should you use bind ? It’s probably best to start without it, and wait for a specific need to arise. Bind is useful if:

- You aren’t using the ORM, are usually using “connectionless” execution, and find yourself constantly needing to specify the same `Engine` object throughout the entire application. Bind can be used here to provide “implicit” execution.
- Your application has multiple schemas that correspond to different engines. Using one `MetaData` for each schema, bound to each engine, provides a decent place to delineate between the schemas. The ORM will also integrate with this approach, where the `Session` will naturally use the engine that is bound to each table via its metadata (provided the `Session` itself has no `bind` configured.).

Alternatively, the `bind` attribute of `MetaData` is confusing if:

- Your application talks to multiple database engines at different times, which use the same set of `Table` objects. It’s usually confusing and unnecessary to begin to create “copies” of `Table` objects just so that different engines can be used for different operations. An example is an application that writes data to a “master” database while performing read-only operations from a “read slave”. A global `MetaData` object is not appropriate for per-request switching like this, although a `ThreadLocalMetaData` object is.
- You are using the ORM `Session` to handle which class/table is bound to which engine, or you are using the `Session` to manage switching between engines. Its a good idea to keep the “binding of tables to engines” in one place - either using `MetaData` only (the `Session` can of course be present, it just has no `bind` configured), or using `Session` only (the `bind` attribute of `MetaData` is left empty).

### Specifying the Schema Name

Some databases support the concept of multiple schemas. A `Table` can reference this by specifying the `schema` keyword argument:

    financial_info = Table('financial_info', meta,
        Column('id', Integer, primary_key=True),
        Column('value', String(100), nullable=False),
        schema='remote_banks'
    )

Within the `MetaData` collection, this table will be identified by the combination of `financial_info` and `remote_banks`. If another table called `financial_info` is referenced without the `remote_banks` schema, it will refer to a different `Table`. `ForeignKey` objects can specify references to columns in this table using the form `remote_banks.financial_info.id`.

The `schema` argument should be used for any name qualifiers required, including Oracle’s “owner” attribute and similar. It also can accommodate a dotted name for longer schemes:

    schema="dbo.scott"

### Backend-Specific Options

`Table` supports database-specific options. For example, MySQL has different table backend types, including “MyISAM” and “InnoDB”. This can be expressed with `Table` using `mysql_engine`:

    addresses = Table('engine_email_addresses', meta,
        Column('address_id', Integer, primary_key = True),
        Column('remote_user_id', Integer, ForeignKey(users.c.user_id)),
        Column('email_address', String(20)),
        mysql_engine='InnoDB'
    )

Other backends may support table-level options as well - these would be described in the individual documentation sections for each dialect.

### Schema API Constructs

class `sqlalchemy.schema.``Column`(*args, **kwargs)

  Bases: `sqlalchemy.schema.SchemaItem`, `sqlalchemy.sql.expression.ColumnClause`

  Represents a column in a database table.

  `__init__`(*args, **kwargs)

    Construct a new `Column` object.

    - Parameters: | name – The name of this column as represented in the database. This argument may be the first positional argument, or specified via keyword. Names which contain no upper case characters will be treated as case insensitive names, and will not be quoted unless they are a reserved word. Names with any number of upper case characters will be quoted and sent exactly. Note that this behavior applies even for databases which standardize upper case names as case insensitive such as Oracle. The name field may be omitted at construction time and applied later, at any time before the Column is associated with a `Table`. This is to support convenient usage within the `declarative` extension. type_ – The column’s type, indicated using an instance which subclasses `AbstractType`. If no arguments are required for the type, the class of the type can be sent as well, e.g.:
          # use a type with arguments
          Column('data', String(50))

          # use no arguments
          Column('level', Integer)
    - The `type` argument may be the second positional argument or specified by keyword. There is partial support for automatic detection of the type based on that of a `ForeignKey` associated with this column, if the type is specified as `None`. However, this feature is not fully implemented and may not function in all cases. *args – Additional positional arguments include various `SchemaItem` derived constructs which will be applied as options to the column. These include instances of `Constraint`, `ForeignKey`, `ColumnDefault`, and `Sequence`. In some cases an equivalent keyword argument is available such as `server_default`, `default` and `unique`. autoincrement – This flag may be set to `False` to indicate an integer primary key column that should not be considered to be the “autoincrement” column, that is the integer primary key column which generates values implicitly upon INSERT and whose value is usually returned via the DBAPI cursor.lastrowid attribute. It defaults to `True` to satisfy the common use case of a table with a single integer primary key column. If the table has a composite primary key consisting of more than one integer column, set this flag to True only on the column that should be considered “autoincrement”. The setting only has an effect for columns which are: Integer derived (i.e. INT, SMALLINT, BIGINT) Part of the primary key Are not referenced by any foreign keys have no server side or client side defaults (with the exception of Postgresql SERIAL). The setting has these two effects on columns that meet the above criteria: DDL issued for the column will include database-specific keywords intended to signify this column as an “autoincrement” column, such as AUTO INCREMENT on MySQL, SERIAL on Postgresql, and IDENTITY on MS-SQL. It does not issue AUTOINCREMENT for SQLite since this is a special SQLite flag that is not required for autoincrementing behavior. See the SQLite dialect documentation for information on SQLite’s AUTOINCREMENT. The column will be considered to be available as cursor.lastrowid or equivalent, for those dialects which “post fetch” newly inserted identifiers after a row has been inserted (SQLite, MySQL, MS-SQL). It does not have any effect in this regard for databases that use sequences to generate primary key identifiers (i.e. Firebird, Postgresql, Oracle). default – A scalar, Python callable, or `ClauseElement` representing the default value for this column, which will be invoked upon insert if this column is otherwise not specified in the VALUES clause of the insert. This is a shortcut to using `ColumnDefault` as a positional argument. Contrast this argument to `server_default` which creates a default generator on the database side. doc – optional String that can be used by the ORM or similar to document attributes. This attribute does not render SQL comments (a future attribute ‘comment’ will achieve that). key – An optional string identifier which will identify this `Column` object on the `Table`. When a key is provided, this is the only identifier referencing the `Column` within the application, including ORM attribute mapping; the `name` field is used only when rendering SQL. index – When `True`, indicates that the column is indexed. This is a shortcut for using a `Index` construct on the table. To specify indexes with explicit names or indexes that contain multiple columns, use the `Index` construct instead. info – A dictionary which defaults to `{}`. A space to store application specific data. This must be a dictionary. nullable – If set to the default of `True`, indicates the column will be rendered as allowing NULL, else it’s rendered as NOT NULL. This parameter is only used when issuing CREATE TABLE statements. onupdate – A scalar, Python callable, or `ClauseElement` representing a default value to be applied to the column within UPDATE statements, which wil be invoked upon update if this column is not present in the SET clause of the update. This is a shortcut to using `ColumnDefault` as a positional argument with `for_update=True`. primary_key – If `True`, marks this column as a primary key column. Multiple columns can have this flag set to specify composite primary keys. As an alternative, the primary key of a `Table` can be specified via an explicit `PrimaryKeyConstraint` object. server_default – A `FetchedValue` instance, str, Unicode or `text()` construct representing the DDL DEFAULT value for the column. String types will be emitted as-is, surrounded by single quotes:
          Column('x', Text, server_default="val")

          x TEXT DEFAULT 'val'
      A `text()` expression will be rendered as-is, without quotes:
          Column('y', DateTime, server_default=text('NOW()'))0

          y DATETIME DEFAULT NOW()
    Strings and text() will be converted into a `DefaultClause` object upon initialization. Use `FetchedValue` to indicate that an already-existing column will generate a default value on the database side which will be available to SQLAlchemy for post-fetch after inserts. This construct does not specify any DDL and the implementation is left to the database, such as via a trigger. server_onupdate – A `FetchedValue` instance representing a database-side default generation function. This indicates to SQLAlchemy that a newly generated value will be available after updates. This construct does not specify any DDL and the implementation is left to the database, such as via a trigger. quote – Force quoting of this column’s name on or off, corresponding to `True` or `False`. When left at its default of `None`, the column identifier will be quoted according to whether the name is case sensitive (identifiers with at least one upper case character are treated as case sensitive), or if it’s a reserved word. This flag is only needed to force quoting of a reserved word which is not known by the SQLAlchemy dialect. unique – When `True`, indicates that this column contains a unique constraint, or if `index` is `True` as well, indicates that the `Index` should be created with the unique flag. To specify multiple columns in the constraint/index or to specify an explicit name, use the `UniqueConstraint` or `Index` constructs explicitly.

  `append_foreign_key`(fk)
  `copy`(**kw)

    Create a copy of this `Column`, unitialized.

    This is used in `Table.tometadata`.

  `get_children`(schema_visitor=False, **kwargs)
  `references`(column)

    Return True if this Column references the given column via foreign key.

class `sqlalchemy.schema.``MetaData`(bind=None, reflect=False)

  Bases: `sqlalchemy.schema.SchemaItem`

  A collection of Tables and their associated schema constructs.

  Holds a collection of Tables and an optional binding to an `Engine` or `Connection`. If bound, the `Table` objects in the collection and their columns may participate in implicit SQL execution.

  The Table objects themselves are stored in the metadata.tables dictionary.

  The `bind` property may be assigned to dynamically. A common pattern is to start unbound and then bind later when an engine is available:

      metadata = MetaData()
      # define tables
      Table('mytable', metadata, ...)
      # connect to an engine later, perhaps after loading a URL from a
      # configuration file
      metadata.bind = an_engine

  MetaData is a thread-safe object after tables have been explicitly defined or loaded via reflection.

  `__init__`(bind=None, reflect=False)

    Create a new MetaData object.

    Parameters: | bind – An Engine or Connection to bind to. May also be a string or URL instance, these are passed to create_engine() and this MetaData will be bound to the resulting engine. reflect – Optional, automatically load all tables from the bound database. Defaults to False. `bind` is required when this option is set. For finer control over loaded tables, use the `reflect` method of `MetaData`.

  `append_ddl_listener`(event, listener)

    Append a DDL event listener to this `MetaData`.

    The `listener` callable will be triggered when this `MetaData` is involved in DDL creates or drops, and will be invoked either before all Table-related actions or after.

    - Parameters: | event – One of `MetaData.ddl_events`; ‘before-create’, ‘after-create’, ‘before-drop’ or ‘after-drop’. listener – A callable, invoked with three positional arguments:
      event: | The event currently being handled
      target: | The `MetaData` object being operated upon
      bind: | The `Connection` bueing used for DDL execution.

    Listeners are added to the MetaData’s `ddl_listeners` attribute.

    Note: MetaData listeners are invoked even when `Tables` are created in isolation. This may change in a future release. I.e.:

        # triggers all MetaData and Table listeners:
        metadata.create_all()

        # triggers MetaData listeners too:
        some.table.create()

  `bind`

    An Engine or Connection to which this MetaData is bound.

    This property may be assigned an `Engine` or `Connection`, or assigned a string or URL to automatically create a basic `Engine` for this bind with `create_engine()`.

  `clear`()

    Clear all Table objects from this MetaData.

  `create_all`(bind=None, tables=None, checkfirst=True)

    Create all tables stored in this metadata.

    Conditional by default, will not attempt to recreate tables already present in the target database.

    Parameters: | bind – A `Connectable` used to access the database; if None, uses the existing bind on this `MetaData`, if any. tables – Optional list of `Table` objects, which is a subset of the total tables in the `MetaData` (others are ignored). checkfirst – Defaults to True, don’t issue CREATEs for tables already present in the target database.

  `ddl_events` = ('before-create', 'after-create', 'before-drop', 'after-drop')
  `drop_all`(bind=None, tables=None, checkfirst=True)

    Drop all tables stored in this metadata.

    Conditional by default, will not attempt to drop tables not present in the target database.

    Parameters: | bind – A `Connectable` used to access the database; if None, uses the existing bind on this `MetaData`, if any. tables – Optional list of `Table` objects, which is a subset of the total tables in the `MetaData` (others are ignored). checkfirst – Defaults to True, only issue DROPs for tables confirmed to be present in the target database.

  `is_bound`()

    True if this MetaData is bound to an Engine or Connection.

  `reflect`(bind=None, schema=None, views=False, only=None)

    Load all available table definitions from the database.

    Automatically creates `Table` entries in this `MetaData` for any table available in the database but not yet present in the `MetaData`. May be called multiple times to pick up tables recently added to the database, however no special action is taken if a table in this `MetaData` no longer exists in the database.

    Parameters: | bind – A `Connectable` used to access the database; if None, uses the existing bind on this `MetaData`, if any. schema – Optional, query and reflect tables from an alterate schema. views – If True, also reflect views. only – Optional. Load only a sub-set of available named tables. May be specified as a sequence of names or a callable. If a sequence of names is provided, only those tables will be reflected. An error is raised if a table is requested but not available. Named tables already present in this `MetaData` are ignored. If a callable is provided, it will be used as a boolean predicate to filter the list of potential table names. The callable is called with a table name and this `MetaData` instance as positional arguments and should return a true value for any table to reflect.

  `remove`(table)

    Remove the given Table object from this MetaData.

  `sorted_tables`

    Returns a list of `Table` objects sorted in order of dependency.

class `sqlalchemy.schema.``Table`(*args, **kw)

  Bases: `sqlalchemy.schema.SchemaItem`, `sqlalchemy.sql.expression.TableClause`

  Represent a table in a database.

  e.g.:

      mytable = Table("mytable", metadata,
                      Column('mytable_id', Integer, primary_key=True),
                      Column('value', String(50))
                 )

  The Table object constructs a unique instance of itself based on its name within the given MetaData object. Constructor arguments are as follows:

  Parameters: | name – The name of this table as represented in the database. This property, along with the schema, indicates the singleton identity of this table in relation to its parent `MetaData`. Additional calls to `Table` with the same name, metadata, and schema name will return the same `Table` object. Names which contain no upper case characters will be treated as case insensitive names, and will not be quoted unless they are a reserved word. Names with any number of upper case characters will be quoted and sent exactly. Note that this behavior applies even for databases which standardize upper case names as case insensitive such as Oracle. metadata – a `MetaData` object which will contain this table. The metadata is used as a point of association of this table with other tables which are referenced via foreign key. It also may be used to associate this table with a particular `Connectable`. *args – Additional positional arguments are used primarily to add the list of `Column` objects contained within this table. Similar to the style of a CREATE TABLE statement, other `SchemaItem` constructs may be added here, including `PrimaryKeyConstraint`, and `ForeignKeyConstraint`. autoload – Defaults to False: the Columns for this table should be reflected from the database. Usually there will be no Column objects in the constructor if this property is set. autoload_with – If autoload==True, this is an optional Engine or Connection instance to be used for the table reflection. If `None`, the underlying MetaData’s bound connectable will be used. implicit_returning – True by default - indicates that RETURNING can be used by default to fetch newly inserted primary key values, for backends which support this. Note that create_engine() also provides an implicit_returning flag. include_columns – A list of strings indicating a subset of columns to be loaded via the `autoload` operation; table columns who aren’t present in this list will not be represented on the resulting `Table` object. Defaults to `None` which indicates all columns should be reflected. info – A dictionary which defaults to `{}`. A space to store application specific data. This must be a dictionary. mustexist – When `True`, indicates that this Table must already be present in the given `MetaData`` collection. prefixes – A list of strings to insert after CREATE in the CREATE TABLE statement. They will be separated by spaces. quote – Force quoting of this table’s name on or off, corresponding to `True` or `False`. When left at its default of `None`, the column identifier will be quoted according to whether the name is case sensitive (identifiers with at least one upper case character are treated as case sensitive), or if it’s a reserved word. This flag is only needed to force quoting of a reserved word which is not known by the SQLAlchemy dialect. quote_schema – same as ‘quote’ but applies to the schema identifier. schema – The schema name for this table, which is required if the table resides in a schema other than the default selected schema for the engine’s database connection. Defaults to `None`. useexisting – When `True`, indicates that if this Table is already present in the given `MetaData`, apply further arguments within the constructor to the existing `Table`. If this flag is not set, an error is raised when the parameters of an existing `Table` are overwritten.

  `__init__`(*args, **kw)

    Constructor for `Table`.

    This method is a no-op. See the top-level documentation for `Table` for constructor arguments.

  `add_is_dependent_on`(table)

    Add a ‘dependency’ for this Table.

    This is another Table object which must be created first before this one can, or dropped after this one.

    Usually, dependencies between tables are determined via ForeignKey objects. However, for other situations that create dependencies outside of foreign keys (rules, inheriting), this method can manually establish such a link.

  `append_column`(column)

    Append a `Column` to this `Table`.

    The “key” of the newly added `Column`, i.e. the value of its `.key` attribute, will then be available in the `.c` collection of this `Table`, and the column definition will be included in any CREATE TABLE, SELECT, UPDATE, etc. statements generated from this `Table` construct.

    Note that this does not change the definition of the table as it exists within any underlying database, assuming that table has already been created in the database. Relational databases support the addition of columns to existing tables using the SQL ALTER command, which would need to be emitted for an already-existing table that doesn’t contain the newly added column.

  `append_constraint`(constraint)

    Append a `Constraint` to this `Table`.

    This has the effect of the constraint being included in any future CREATE TABLE statement, assuming specific DDL creation events have not been associated with the given `Constraint` object.

    Note that this does not produce the constraint within the relational database automatically, for a table that already exists in the database. To add a constraint to an existing relational database table, the SQL ALTER command must be used. SQLAlchemy also provides the `AddConstraint` construct which can produce this SQL when invoked as an executable clause.

  `append_ddl_listener`(event, listener)

    Append a DDL event listener to this `Table`.

    The `listener` callable will be triggered when this `Table` is created or dropped, either directly before or after the DDL is issued to the database. The listener may modify the Table, but may not abort the event itself.

    - Parameters: | event – One of `Table.ddl_events`; e.g. ‘before-create’, ‘after-create’, ‘before-drop’ or ‘after-drop’. listener – A callable, invoked with three positional arguments:
      event: | The event currently being handled
      target: | The `Table` object being created or dropped
      bind: | The `Connection` bueing used for DDL execution.

    Listeners are added to the Table’s `ddl_listeners` attribute.

  `bind`

    Return the connectable associated with this Table.

  `create`(bind=None, checkfirst=False)

    Issue a `CREATE` statement for this table.

    See also `metadata.create_all()`.

  `ddl_events` = ('before-create', 'after-create', 'before-drop', 'after-drop')
  `drop`(bind=None, checkfirst=False)

    Issue a `DROP` statement for this table.

    See also `metadata.drop_all()`.

  `exists`(bind=None)

    Return True if this table exists.

  `get_children`(column_collections=True, schema_visitor=False, **kw)
  `key`
  `primary_key`
  `tometadata`(metadata, schema=<symbol 'retain_schema>)

    Return a copy of this `Table` associated with a different `MetaData`.

    E.g.:

        # create two metadata
        meta1 = MetaData('sqlite:///querytest.db')
        meta2 = MetaData()

        # load 'users' from the sqlite engine
        users_table = Table('users', meta1, autoload=True)

        # create the same Table object for the plain metadata
        users_table_2 = users_table.tometadata(meta2)

class `sqlalchemy.schema.``ThreadLocalMetaData`

  Bases: `sqlalchemy.schema.MetaData`

  A MetaData variant that presents a different `bind` in every thread.

  Makes the `bind` property of the MetaData a thread-local value, allowing this collection of tables to be bound to different `Engine` implementations or connections in each thread.

  The ThreadLocalMetaData starts off bound to None in each thread. Binds must be made explicitly by assigning to the `bind` property or using `connect()`. You can also re-bind dynamically multiple times per thread, just like a regular `MetaData`.

  `__init__`()

    Construct a ThreadLocalMetaData.

  `bind`

    The bound Engine or Connection for this thread.

    This property may be assigned an Engine or Connection, or assigned a string or URL to automatically create a basic Engine for this bind with `create_engine()`.

  `dispose`()

    Dispose all bound engines, in all thread contexts.

  `is_bound`()

    True if there is a bind for this thread.

## Reflecting Database Objects

A `Table` object can be instructed to load information about itself from the corresponding database schema object already existing within the database. This process is called reflection. Most simply you need only specify the table name, a `MetaData` object, and the `autoload=True` flag. If the `MetaData` is not persistently bound, also add the `autoload_with` argument:

    >>> messages = Table('messages', meta, autoload=True, autoload_with=engine)
    >>> [c.name for c in messages.columns]
    ['message_id', 'message_name', 'date']

The above operation will use the given engine to query the database for information about the `messages` table, and will then generate `Column`, `ForeignKey`, and other objects corresponding to this information as though the `Table` object were hand-constructed in Python.

When tables are reflected, if a given table references another one via foreign key, a second `Table` object is created within the `MetaData` object representing the connection. Below, assume the table `shopping_cart_items` references a table named `shopping_carts`. Reflecting the `shopping_cart_items` table has the effect such that the `shopping_carts` table will also be loaded:

    >>> shopping_cart_items = Table('shopping_cart_items', meta, autoload=True, autoload_with=engine)
    >>> 'shopping_carts' in meta.tables:
    True

The `MetaData` has an interesting “singleton-like” behavior such that if you requested both tables individually, `MetaData` will ensure that exactly one `Table` object is created for each distinct table name. The `Table` constructor actually returns to you the already-existing `Table` object if one already exists with the given name. Such as below, we can access the already generated `shopping_carts` table just by naming it:

    shopping_carts = Table('shopping_carts', meta)

Of course, it’s a good idea to use `autoload=True` with the above table regardless. This is so that the table’s attributes will be loaded if they have not been already. The autoload operation only occurs for the table if it hasn’t already been loaded; once loaded, new calls to `Table` with the same name will not re-issue any reflection queries.

### Overriding Reflected Columns

Individual columns can be overridden with explicit values when reflecting tables; this is handy for specifying custom datatypes, constraints such as primary keys that may not be configured within the database, etc.:

    >>> mytable = Table('mytable', meta,
    ... Column('id', Integer, primary_key=True),   # override reflected 'id' to have primary key
    ... Column('mydata', Unicode(50)),    # override reflected 'mydata' to be Unicode
    ... autoload=True)

### Reflecting Views

The reflection system can also reflect views. Basic usage is the same as that of a table:

    my_view = Table("some_view", metadata, autoload=True)

Above, `my_view` is a `Table` object with `Column` objects representing the names and types of each column within the view “some_view”.

Usually, it’s desired to have at least a primary key constraint when reflecting a view, if not foreign keys as well. View reflection doesn’t extrapolate these constraints.

Use the “override” technique for this, specifying explicitly those columns which are part of the primary key or have foreign key constraints:

    my_view = Table("some_view", metadata,
                    Column("view_id", Integer, primary_key=True),
                    Column("related_thing", Integer, ForeignKey("othertable.thing_id")),
                    autoload=True
    )

### Reflecting All Tables at Once

The `MetaData` object can also get a listing of tables and reflect the full set. This is achieved by using the `reflect()` method. After calling it, all located tables are present within the `MetaData` object’s dictionary of tables:

    meta = MetaData()
    meta.reflect(bind=someengine)
    users_table = meta.tables['users']
    addresses_table = meta.tables['addresses']

`metadata.reflect()` also provides a handy way to clear or delete all the rows in a database:

    meta = MetaData()
    meta.reflect(bind=someengine)
    for table in reversed(meta.sorted_tables):
        someengine.execute(table.delete())

### Fine Grained Reflection with Inspector

A low level interface which provides a backend-agnostic system of loading lists of schema, table, column, and constraint descriptions from a given database is also available. This is known as the “Inspector”:

    from sqlalchemy import create_engine
    from sqlalchemy.engine import reflection
    engine = create_engine('...')
    insp = reflection.Inspector.from_engine(engine)
    print insp.get_table_names()

class `sqlalchemy.engine.reflection.``Inspector`(bind)

  Bases: `object`

  Performs database schema inspection.

  The Inspector acts as a proxy to the reflection methods of the `Dialect`, providing a consistent interface as well as caching support for previously fetched metadata.

  The preferred method to construct an `Inspector` is via the `Inspector.from_engine()` method. I.e.:

      engine = create_engine('...')
      insp = Inspector.from_engine(engine)

  Where above, the `Dialect` may opt to return an `Inspector` subclass that provides additional methods specific to the dialect’s target database.

  `__init__`(bind)

    Initialize a new `Inspector`.

    Parameters: | bind – a `Connectable`, which is typically an instance of `Engine` or `Connection`.

    For a dialect-specific instance of `Inspector`, see `Inspector.from_engine()`

  `default_schema_name`

    Return the default schema name presented by the dialect for the current engine’s database user.

    E.g. this is typically `public` for Postgresql and `dbo` for SQL Server.

  classmethod `from_engine`(bind)

    Construct a new dialect-specific Inspector object from the given engine or connection.

    Parameters: | bind – a `Connectable`, which is typically an instance of `Engine` or `Connection`.

    This method differs from direct a direct constructor call of `Inspector` in that the `Dialect` is given a chance to provide a dialect-specific `Inspector` instance, which may provide additional methods.

    See the example at `Inspector`.

  `get_columns`(table_name, schema=None, **kw)

    Return information about columns in table_name.

    Given a string table_name and an optional string schema, return column information as a list of dicts with these keys:

    name
      the column’s name
    type
      `TypeEngine`
    nullable
      boolean
    default
      the column’s default value
    attrs
      dict containing optional column attributes
  `get_foreign_keys`(table_name, schema=None, **kw)

    Return information about foreign_keys in table_name.

    Given a string table_name, and an optional string schema, return foreign key information as a list of dicts with these keys:

    constrained_columns
      a list of column names that make up the foreign key
    referred_schema
      the name of the referred schema
    referred_table
      the name of the referred table
    referred_columns
      a list of column names in the referred table that correspond to constrained_columns
    name
      optional name of the foreign key constraint.
    **kw
      other options passed to the dialect’s get_foreign_keys() method.
  `get_indexes`(table_name, schema=None, **kw)

    Return information about indexes in table_name.

    Given a string table_name and an optional string schema, return index information as a list of dicts with these keys:

    name
      the index’s name
    column_names
      list of column names in order
    unique
      boolean
    **kw
      other options passed to the dialect’s get_indexes() method.
  `get_pk_constraint`(table_name, schema=None, **kw)

    Return information about primary key constraint on table_name.

    Given a string table_name, and an optional string schema, return primary key information as a dictionary with these keys:

    constrained_columns
      a list of column names that make up the primary key
    name
      optional name of the primary key constraint.
  `get_primary_keys`(table_name, schema=None, **kw)

    Return information about primary keys in table_name.

    Given a string table_name, and an optional string schema, return primary key information as a list of column names.

  `get_schema_names`()

    Return all schema names.

  `get_table_names`(schema=None, order_by=None)

    Return all table names in schema.

    Parameters: | schema – Optional, retrieve names from a non-default schema. order_by – Optional, may be the string “foreign_key” to sort the result on foreign key dependencies.

    This should probably not return view names or maybe it should return them with an indicator t or v.

  `get_table_options`(table_name, schema=None, **kw)

    Return a dictionary of options specified when the table of the given name was created.

    This currently includes some options that apply to MySQL tables.

  `get_view_definition`(view_name, schema=None)

    Return definition for view_name.

    Parameters: | schema – Optional, retrieve names from a non-default schema.

  `get_view_names`(schema=None)

    Return all view names in schema.

    Parameters: | schema – Optional, retrieve names from a non-default schema.

  `reflecttable`(table, include_columns)

    Given a Table object, load its internal constructs based on introspection.

    This is the underlying method used by most dialects to produce table reflection. Direct usage is like:

        from sqlalchemy import create_engine, MetaData, Table
        from sqlalchemy.engine import reflection

        engine = create_engine('...')
        meta = MetaData()
        user_table = Table('user', meta)
        insp = Inspector.from_engine(engine)
        insp.reflecttable(user_table, None)

    Parameters: | table – a `Table` instance. include_columns – a list of string column names to include in the reflection process. If `None`, all columns are reflected.

## Column Insert/Update Defaults

SQLAlchemy provides a very rich featureset regarding column level events which take place during INSERT and UPDATE statements. Options include:

- Scalar values used as defaults during INSERT and UPDATE operations
- Python functions which execute upon INSERT and UPDATE operations
- SQL expressions which are embedded in INSERT statements (or in some cases execute beforehand)
- SQL expressions which are embedded in UPDATE statements
- Server side default values used during INSERT
- Markers for server-side triggers used during UPDATE

The general rule for all insert/update defaults is that they only take effect if no value for a particular column is passed as an `execute()` parameter; otherwise, the given value is used.

### Scalar Defaults

The simplest kind of default is a scalar value used as the default value of a column:

    Table("mytable", meta,
        Column("somecolumn", Integer, default=12)
    )

Above, the value “12” will be bound as the column value during an INSERT if no other value is supplied.

A scalar value may also be associated with an UPDATE statement, though this is not very common (as UPDATE statements are usually looking for dynamic defaults):

    Table("mytable", meta,
        Column("somecolumn", Integer, onupdate=25)
    )

### Python-Executed Functions

The `default` and `onupdate` keyword arguments also accept Python functions. These functions are invoked at the time of insert or update if no other value for that column is supplied, and the value returned is used for the column’s value. Below illustrates a crude “sequence” that assigns an incrementing counter to a primary key column:

    # a function which counts upwards
    i = 0
    def mydefault():
        global i
        i += 1
        return i

    t = Table("mytable", meta,
        Column('id', Integer, primary_key=True, default=mydefault),
    )

It should be noted that for real “incrementing sequence” behavior, the built-in capabilities of the database should normally be used, which may include sequence objects or other autoincrementing capabilities. For primary key columns, SQLAlchemy will in most cases use these capabilities automatically. See the API documentation for `Column` including the `autoincrement` flag, as well as the section on `Sequence` later in this chapter for background on standard primary key generation techniques.

To illustrate onupdate, we assign the Python `datetime` function `now` to the `onupdate` attribute:

    import datetime

    t = Table("mytable", meta,
        Column('id', Integer, primary_key=True),

        # define 'last_updated' to be populated with datetime.now()
        Column('last_updated', DateTime, onupdate=datetime.datetime.now),
    )

When an update statement executes and no value is passed for `last_updated`, the `datetime.datetime.now()` Python function is executed and its return value used as the value for `last_updated`. Notice that we provide `now` as the function itself without calling it (i.e. there are no parenthesis following) - SQLAlchemy will execute the function at the time the statement executes.

#### Context-Sensitive Default Functions

The Python functions used by `default` and `onupdate` may also make use of the current statement’s context in order to determine a value. The context of a statement is an internal SQLAlchemy object which contains all information about the statement being executed, including its source expression, the parameters associated with it and the cursor. The typical use case for this context with regards to default generation is to have access to the other values being inserted or updated on the row. To access the context, provide a function that accepts a single `context` argument:

    def mydefault(context):
        return context.current_parameters['counter'] + 12

    t = Table('mytable', meta,
        Column('counter', Integer),
        Column('counter_plus_twelve', Integer, default=mydefault, onupdate=mydefault)
    )

Above we illustrate a default function which will execute for all INSERT and UPDATE statements where a value for `counter_plus_twelve` was otherwise not provided, and the value will be that of whatever value is present in the execution for the `counter` column, plus the number 12.

While the context object passed to the default function has many attributes, the `current_parameters` member is a special member provided only during the execution of a default function for the purposes of deriving defaults from its existing values. For a single statement that is executing many sets of bind parameters, the user-defined function is called for each set of parameters, and `current_parameters` will be provided with each individual parameter set for each execution.

### SQL Expressions

The “default” and “onupdate” keywords may also be passed SQL expressions, including select statements or direct function calls:

    t = Table("mytable", meta,
        Column('id', Integer, primary_key=True),

        # define 'create_date' to default to now()
        Column('create_date', DateTime, default=func.now()),

        # define 'key' to pull its default from the 'keyvalues' table
        Column('key', String(20), default=keyvalues.select(keyvalues.c.type='type1', limit=1)),

        # define 'last_modified' to use the current_timestamp SQL function on update
        Column('last_modified', DateTime, onupdate=func.utc_timestamp())
        )

Above, the `create_date` column will be populated with the result of the `now()` SQL function (which, depending on backend, compiles into `NOW()` or `CURRENT_TIMESTAMP` in most cases) during an INSERT statement, and the `key` column with the result of a SELECT subquery from another table. The `last_modified` column will be populated with the value of `UTC_TIMESTAMP()`, a function specific to MySQL, when an UPDATE statement is emitted for this table.

Note that when using `func` functions, unlike when using Python datetime functions we do call the function, i.e. with parenthesis “()” - this is because what we want in this case is the return value of the function, which is the SQL expression construct that will be rendered into the INSERT or UPDATE statement.

The above SQL functions are usually executed “inline” with the INSERT or UPDATE statement being executed, meaning, a single statement is executed which embeds the given expressions or subqueries within the VALUES or SET clause of the statement. Although in some cases, the function is “pre-executed” in a SELECT statement of its own beforehand. This happens when all of the following is true:

- the column is a primary key column
- the database dialect does not support a usable `cursor.lastrowid` accessor (or equivalent); this currently includes PostgreSQL, Oracle, and Firebird, as well as some MySQL dialects.
- the dialect does not support the “RETURNING” clause or similar, or the `implicit_returning` flag is set to `False` for the dialect. Dialects which support RETURNING currently include Postgresql, Oracle, Firebird, and MS-SQL.
- the statement is a single execution, i.e. only supplies one set of parameters and doesn’t use “executemany” behavior
- the `inline=True` flag is not set on the `Insert()` or `Update()` construct, and the statement has not defined an explicit returning() clause.

Whether or not the default generation clause “pre-executes” is not something that normally needs to be considered, unless it is being addressed for performance reasons.

When the statement is executed with a single set of parameters (that is, it is not an “executemany” style execution), the returned `ResultProxy` will contain a collection accessible via `result.postfetch_cols()` which contains a list of all `Column` objects which had an inline-executed default. Similarly, all parameters which were bound to the statement, including all Python and SQL expressions which were pre-executed, are present in the `last_inserted_params()` or `last_updated_params()` collections on `ResultProxy`. The `inserted_primary_key` collection contains a list of primary key values for the row inserted (a list so that single-column and composite-column primary keys are represented in the same format).

### Server Side Defaults

A variant on the SQL expression default is the `server_default`, which gets placed in the CREATE TABLE statement during a `create()` operation:

    t = Table('test', meta,
        Column('abc', String(20), server_default='abc'),
        Column('created_at', DateTime, server_default=text("sysdate"))
    )

A create call for the above table will produce:

    CREATE TABLE test (
        abc varchar(20) default 'abc',
        created_at datetime default sysdate
    )

The behavior of `server_default` is similar to that of a regular SQL default; if it’s placed on a primary key column for a database which doesn’t have a way to “postfetch” the ID, and the statement is not “inlined”, the SQL expression is pre-executed; otherwise, SQLAlchemy lets the default fire off on the database side normally.

### Triggered Columns

Columns with values set by a database trigger or other external process may be called out with a marker:

    t = Table('test', meta,
        Column('abc', String(20), server_default=FetchedValue()),
        Column('def', String(20), server_onupdate=FetchedValue())
    )

These markers do not emit a “default” clause when the table is created, however they do set the same internal flags as a static `server_default` clause, providing hints to higher-level tools that a “post-fetch” of these rows should be performed after an insert or update.

### Defining Sequences

SQLAlchemy represents database sequences using the `Sequence` object, which is considered to be a special case of “column default”. It only has an effect on databases which have explicit support for sequences, which currently includes Postgresql, Oracle, and Firebird. The `Sequence` object is otherwise ignored.

The `Sequence` may be placed on any column as a “default” generator to be used during INSERT operations, and can also be configured to fire off during UPDATE operations if desired. It is most commonly used in conjunction with a single integer primary key column:

    table = Table("cartitems", meta,
        Column("cart_id", Integer, Sequence('cart_id_seq'), primary_key=True),
        Column("description", String(40)),
        Column("createdate", DateTime())
    )

Where above, the table “cartitems” is associated with a sequence named “cart_id_seq”. When INSERT statements take place for “cartitems”, and no value is passed for the “cart_id” column, the “cart_id_seq” sequence will be used to generate a value.

When the `Sequence` is associated with a table, CREATE and DROP statements issued for that table will also issue CREATE/DROP for the sequence object as well, thus “bundling” the sequence object with its parent table.

The `Sequence` object also implements special functionality to accommodate Postgresql’s SERIAL datatype. The SERIAL type in PG automatically generates a sequence that is used implicitly during inserts. This means that if a `Table` object defines a `Sequence` on its primary key column so that it works with Oracle and Firebird, the `Sequence` would get in the way of the “implicit” sequence that PG would normally use. For this use case, add the flag `optional=True` to the `Sequence` object - this indicates that the `Sequence` should only be used if the database provides no other option for generating primary key identifiers.

The `Sequence` object also has the ability to be executed standalone like a SQL expression, which has the effect of calling its “next value” function:

    seq = Sequence('some_sequence')
    nextid = connection.execute(seq)

### Default Geneation API Constructs

class `sqlalchemy.schema.``ColumnDefault`(arg, **kwargs)

  Bases: `sqlalchemy.schema.DefaultGenerator`

  A plain default value on a column.

  This could correspond to a constant, a callable function, or a SQL clause.

  `ColumnDefault` is generated automatically whenever the `default`, `onupdate` arguments of `Column` are used. A `ColumnDefault` can be passed positionally as well.

  For example, the following:

      Column('foo', Integer, default=50)

  Is equivalent to:

      Column('foo', Integer, ColumnDefault(50))

class `sqlalchemy.schema.``DefaultClause`(arg, for_update=False)

  Bases: `sqlalchemy.schema.FetchedValue`

  A DDL-specified DEFAULT column value.

  `DefaultClause` is a `FetchedValue` that also generates a “DEFAULT” clause when “CREATE TABLE” is emitted.

  `DefaultClause` is generated automatically whenever the `server_default`, `server_onupdate` arguments of `Column` are used. A `DefaultClause` can be passed positionally as well.

  For example, the following:

      Column('foo', Integer, server_default="50")

  Is equivalent to:

      Column('foo', Integer, DefaultClause("50"))

class `sqlalchemy.schema.``DefaultGenerator`(for_update=False)

  Bases: `sqlalchemy.schema.SchemaItem`

  Base class for column default values.

class `sqlalchemy.schema.``FetchedValue`(for_update=False)

  Bases: `object`

  A marker for a transparent database-side default.

  Use `FetchedValue` when the database is configured to provide some automatic default for a column.

  E.g.:

      Column('foo', Integer, FetchedValue())

  Would indicate that some trigger or default generator will create a new value for the `foo` column during an INSERT.

class `sqlalchemy.schema.``PassiveDefault`(*arg, **kw)

  Bases: `sqlalchemy.schema.DefaultClause`

  A DDL-specified DEFAULT column value.

  Deprecated since version 0.6: `PassiveDefault` is deprecated. Use `DefaultClause`.

class `sqlalchemy.schema.``Sequence`(name, start=None, increment=None, schema=None, optional=False, quote=None, metadata=None, for_update=False)

  Bases: `sqlalchemy.schema.DefaultGenerator`

  Represents a named database sequence.

## Defining Constraints and Indexes

### Defining Foreign Keys

A foreign key in SQL is a table-level construct that constrains one or more columns in that table to only allow values that are present in a different set of columns, typically but not always located on a different table. We call the columns which are constrained the foreign key columns and the columns which they are constrained towards the referenced columns. The referenced columns almost always define the primary key for their owning table, though there are exceptions to this. The foreign key is the “joint” that connects together pairs of rows which have a relationship with each other, and SQLAlchemy assigns very deep importance to this concept in virtually every area of its operation.

In SQLAlchemy as well as in DDL, foreign key constraints can be defined as additional attributes within the table clause, or for single-column foreign keys they may optionally be specified within the definition of a single column. The single column foreign key is more common, and at the column level is specified by constructing a `ForeignKey` object as an argument to a `Column` object:

    user_preference = Table('user_preference', metadata,
        Column('pref_id', Integer, primary_key=True),
        Column('user_id', Integer, ForeignKey("user.user_id"), nullable=False),
        Column('pref_name', String(40), nullable=False),
        Column('pref_value', String(100))
    )

Above, we define a new table `user_preference` for which each row must contain a value in the `user_id` column that also exists in the `user` table’s `user_id` column.

The argument to `ForeignKey` is most commonly a string of the form <tablename>.<columnname>, or for a table in a remote schema or “owner” of the form <schemaname>.<tablename>.<columnname>. It may also be an actual `Column` object, which as we’ll see later is accessed from an existing `Table` object via its `c` collection:

    ForeignKey(user.c.user_id)

The advantage to using a string is that the in-python linkage between `user` and `user_preference` is resolved only when first needed, so that table objects can be easily spread across multiple modules and defined in any order.

Foreign keys may also be defined at the table level, using the `ForeignKeyConstraint` object. This object can describe a single- or multi-column foreign key. A multi-column foreign key is known as a composite foreign key, and almost always references a table that has a composite primary key. Below we define a table `invoice` which has a composite primary key:

    invoice = Table('invoice', metadata,
        Column('invoice_id', Integer, primary_key=True),
        Column('ref_num', Integer, primary_key=True),
        Column('description', String(60), nullable=False)
    )

And then a table `invoice_item` with a composite foreign key referencing `invoice`:

    invoice_item = Table('invoice_item', metadata,
        Column('item_id', Integer, primary_key=True),
        Column('item_name', String(60), nullable=False),
        Column('invoice_id', Integer, nullable=False),
        Column('ref_num', Integer, nullable=False),
        ForeignKeyConstraint(['invoice_id', 'ref_num'], ['invoice.invoice_id', 'invoice.ref_num'])
    )

It’s important to note that the `ForeignKeyConstraint` is the only way to define a composite foreign key. While we could also have placed individual `ForeignKey` objects on both the `invoice_item.invoice_id` and `invoice_item.ref_num` columns, SQLAlchemy would not be aware that these two values should be paired together - it would be two individual foreign key constraints instead of a single composite foreign key referencing two columns.

#### Creating/Dropping Foreign Key Constraints via ALTER

In all the above examples, the `ForeignKey` object causes the “REFERENCES” keyword to be added inline to a column definition within a “CREATE TABLE” statement when `create_all()` is issued, and `ForeignKeyConstraint` invokes the “CONSTRAINT” keyword inline with “CREATE TABLE”. There are some cases where this is undesireable, particularly when two tables reference each other mutually, each with a foreign key referencing the other. In such a situation at least one of the foreign key constraints must be generated after both tables have been built. To support such a scheme, `ForeignKey` and `ForeignKeyConstraint` offer the flag `use_alter=True`. When using this flag, the constraint will be generated using a definition similar to “ALTER TABLE <tablename> ADD CONSTRAINT <name> ...”. Since a name is required, the `name` attribute must also be specified. For example:

    node = Table('node', meta,
        Column('node_id', Integer, primary_key=True),
        Column('primary_element', Integer,
            ForeignKey('element.element_id', use_alter=True, name='fk_node_element_id')
        )
    )

    element = Table('element', meta,
        Column('element_id', Integer, primary_key=True),
        Column('parent_node_id', Integer),
        ForeignKeyConstraint(
            ['parent_node_id'],
            ['node.node_id'],
            use_alter=True,
            name='fk_element_parent_node_id'
        )
    )

#### ON UPDATE and ON DELETE

Most databases support cascading of foreign key values, that is the when a parent row is updated the new value is placed in child rows, or when the parent row is deleted all corresponding child rows are set to null or deleted. In data definition language these are specified using phrases like “ON UPDATE CASCADE”, “ON DELETE CASCADE”, and “ON DELETE SET NULL”, corresponding to foreign key constraints. The phrase after “ON UPDATE” or “ON DELETE” may also other allow other phrases that are specific to the database in use. The `ForeignKey` and `ForeignKeyConstraint` objects support the generation of this clause via the `onupdate` and `ondelete` keyword arguments. The value is any string which will be output after the appropriate “ON UPDATE” or “ON DELETE” phrase:

    child = Table('child', meta,
        Column('id', Integer,
                ForeignKey('parent.id', onupdate="CASCADE", ondelete="CASCADE"),
                primary_key=True
        )
    )

    composite = Table('composite', meta,
        Column('id', Integer, primary_key=True),
        Column('rev_id', Integer),
        Column('note_id', Integer),
        ForeignKeyConstraint(
                    ['rev_id', 'note_id'],
                    ['revisions.id', 'revisions.note_id'],
                    onupdate="CASCADE", ondelete="SET NULL"
        )
    )

Note that these clauses are not supported on SQLite, and require `InnoDB` tables when used with MySQL. They may also not be supported on other databases.

#### Foreign Key API Constructs

class `sqlalchemy.schema.``ForeignKey`(column, _constraint=None, use_alter=False, name=None, onupdate=None, ondelete=None, deferrable=None, initially=None, link_to_name=False)

  Bases: `sqlalchemy.schema.SchemaItem`

  Defines a dependency between two columns.

  `ForeignKey` is specified as an argument to a `Column` object, e.g.:

      t = Table("remote_table", metadata,
          Column("remote_id", ForeignKey("main_table.id"))
      )

  Note that `ForeignKey` is only a marker object that defines a dependency between two columns. The actual constraint is in all cases represented by the `ForeignKeyConstraint` object. This object will be generated automatically when a `ForeignKey` is associated with a `Column` which in turn is associated with a `Table`. Conversely, when `ForeignKeyConstraint` is applied to a `Table`, `ForeignKey` markers are automatically generated to be present on each associated `Column`, which are also associated with the constraint object.

  Note that you cannot define a “composite” foreign key constraint, that is a constraint between a grouping of multiple parent/child columns, using `ForeignKey` objects. To define this grouping, the `ForeignKeyConstraint` object must be used, and applied to the `Table`. The associated `ForeignKey` objects are created automatically.

  The `ForeignKey` objects associated with an individual `Column` object are available in the foreign_keys collection of that column.

  Further examples of foreign key configuration are in Defining Foreign Keys.

  `__init__`(column, _constraint=None, use_alter=False, name=None, onupdate=None, ondelete=None, deferrable=None, initially=None, link_to_name=False)

    Construct a column-level FOREIGN KEY.

    The `ForeignKey` object when constructed generates a `ForeignKeyConstraint` which is associated with the parent `Table` object’s collection of constraints.

    Parameters: | column – A single target column for the key relationship. A `Column` object or a column name as a string: `tablename.columnkey` or `schema.tablename.columnkey`. `columnkey` is the `key` which has been assigned to the column (defaults to the column name itself), unless `link_to_name` is `True` in which case the rendered name of the column is used. name – Optional string. An in-database name for the key if constraint is not provided. onupdate – Optional string. If set, emit ON UPDATE <value> when issuing DDL for this constraint. Typical values include CASCADE, DELETE and RESTRICT. ondelete – Optional string. If set, emit ON DELETE <value> when issuing DDL for this constraint. Typical values include CASCADE, DELETE and RESTRICT. deferrable – Optional bool. If set, emit DEFERRABLE or NOT DEFERRABLE when issuing DDL for this constraint. initially – Optional string. If set, emit INITIALLY <value> when issuing DDL for this constraint. link_to_name – if True, the string name given in `column` is the rendered name of the referenced column, not its locally assigned `key`. use_alter – passed to the underlying `ForeignKeyConstraint` to indicate the constraint should be generated/dropped externally from the CREATE TABLE/ DROP TABLE statement. See that classes’ constructor for details.

  `column`

    Return the target `Column` referenced by this `ForeignKey`.

    If this `ForeignKey` was created using a string-based target column specification, this attribute will on first access initiate a resolution process to locate the referenced remote `Column`. The resolution process traverses to the parent `Column`, `Table`, and `MetaData` to proceed - if any of these aren’t yet present, an error is raised.

  `copy`(schema=None)

    Produce a copy of this `ForeignKey` object.

    The new `ForeignKey` will not be bound to any `Column`.

    This method is usually used by the internal copy procedures of `Column`, `Table`, and `MetaData`.

    Parameters: | schema – The returned `ForeignKey` will reference the original table and column name, qualified by the given string schema name.

  `get_referent`(table)

    Return the `Column` in the given `Table` referenced by this `ForeignKey`.

    Returns None if this `ForeignKey` does not reference the given `Table`.

  `references`(table)

    Return True if the given `Table` is referenced by this `ForeignKey`.

  `target_fullname`

    Return a string based ‘column specification’ for this `ForeignKey`.

    This is usually the equivalent of the string-based “tablename.colname” argument first passed to the object’s constructor.

class `sqlalchemy.schema.``ForeignKeyConstraint`(columns, refcolumns, name=None, onupdate=None, ondelete=None, deferrable=None, initially=None, use_alter=False, link_to_name=False, table=None)

  Bases: `sqlalchemy.schema.Constraint`

  A table-level FOREIGN KEY constraint.

  Defines a single column or composite FOREIGN KEY ... REFERENCES constraint. For a no-frills, single column foreign key, adding a `ForeignKey` to the definition of a `Column` is a shorthand equivalent for an unnamed, single column `ForeignKeyConstraint`.

  Examples of foreign key configuration are in Defining Foreign Keys.

  `__init__`(columns, refcolumns, name=None, onupdate=None, ondelete=None, deferrable=None, initially=None, use_alter=False, link_to_name=False, table=None)

    Construct a composite-capable FOREIGN KEY.

    Parameters: | columns – A sequence of local column names. The named columns must be defined and present in the parent Table. The names should match the `key` given to each column (defaults to the name) unless `link_to_name` is True. refcolumns – A sequence of foreign column names or Column objects. The columns must all be located within the same Table. name – Optional, the in-database name of the key. onupdate – Optional string. If set, emit ON UPDATE <value> when issuing DDL for this constraint. Typical values include CASCADE, DELETE and RESTRICT. ondelete – Optional string. If set, emit ON DELETE <value> when issuing DDL for this constraint. Typical values include CASCADE, DELETE and RESTRICT. deferrable – Optional bool. If set, emit DEFERRABLE or NOT DEFERRABLE when issuing DDL for this constraint. initially – Optional string. If set, emit INITIALLY <value> when issuing DDL for this constraint. link_to_name – if True, the string name given in `column` is the rendered name of the referenced column, not its locally assigned `key`. use_alter – If True, do not emit the DDL for this constraint as part of the CREATE TABLE definition. Instead, generate it via an ALTER TABLE statement issued after the full collection of tables have been created, and drop it via an ALTER TABLE statement before the full collection of tables are dropped. This is shorthand for the usage of `AddConstraint` and `DropConstraint` applied as “after-create” and “before-drop” events on the MetaData object. This is normally used to generate/drop constraints on objects that are mutually dependent on each other.

### UNIQUE Constraint

Unique constraints can be created anonymously on a single column using the `unique` keyword on `Column`. Explicitly named unique constraints and/or those with multiple columns are created via the `UniqueConstraint` table-level construct.

    meta = MetaData()
    mytable = Table('mytable', meta,

        # per-column anonymous unique constraint
        Column('col1', Integer, unique=True),

        Column('col2', Integer),
        Column('col3', Integer),

        # explicit/composite unique constraint.  'name' is optional.
        UniqueConstraint('col2', 'col3', name='uix_1')
        )

class `sqlalchemy.schema.``UniqueConstraint`(*columns, **kw)

  Bases: `sqlalchemy.schema.ColumnCollectionConstraint`

  A table-level UNIQUE constraint.

  Defines a single column or composite UNIQUE constraint. For a no-frills, single column constraint, adding `unique=True` to the `Column` definition is a shorthand equivalent for an unnamed, single column UniqueConstraint.

### CHECK Constraint

Check constraints can be named or unnamed and can be created at the Column or Table level, using the `CheckConstraint` construct. The text of the check constraint is passed directly through to the database, so there is limited “database independent” behavior. Column level check constraints generally should only refer to the column to which they are placed, while table level constraints can refer to any columns in the table.

Note that some databases do not actively support check constraints such as MySQL.

    meta = MetaData()
    mytable = Table('mytable', meta,

        # per-column CHECK constraint
        Column('col1', Integer, CheckConstraint('col1>5')),

        Column('col2', Integer),
        Column('col3', Integer),

        # table level CHECK constraint.  'name' is optional.
        CheckConstraint('col2 > col3 + 5', name='check1')
        )

    sqlmytable.create(engine)
    CREATE TABLE mytable (
        col1 INTEGER  CHECK (col1>5),
        col2 INTEGER,
        col3 INTEGER,
        CONSTRAINT check1  CHECK (col2 > col3 + 5)
    )

class `sqlalchemy.schema.``CheckConstraint`(sqltext, name=None, deferrable=None, initially=None, table=None, _create_rule=None)

  Bases: `sqlalchemy.schema.Constraint`

  A table- or column-level CHECK constraint.

  Can be included in the definition of a Table or Column.

### Other Constraint Classes

class `sqlalchemy.schema.``Constraint`(name=None, deferrable=None, initially=None, _create_rule=None)

  Bases: `sqlalchemy.schema.SchemaItem`

  A table-level SQL constraint.

class `sqlalchemy.schema.``ColumnCollectionConstraint`(*columns, **kw)

  Bases: `sqlalchemy.schema.Constraint`

  A constraint that proxies a ColumnCollection.

class `sqlalchemy.schema.``PrimaryKeyConstraint`(*columns, **kw)

  Bases: `sqlalchemy.schema.ColumnCollectionConstraint`

  A table-level PRIMARY KEY constraint.

  Defines a single column or composite PRIMARY KEY constraint. For a no-frills primary key, adding `primary_key=True` to one or more `Column` definitions is a shorthand equivalent for an unnamed single- or multiple-column PrimaryKeyConstraint.

### Indexes

Indexes can be created anonymously (using an auto-generated name `ix_<column label>`) for a single column using the inline `index` keyword on `Column`, which also modifies the usage of `unique` to apply the uniqueness to the index itself, instead of adding a separate UNIQUE constraint. For indexes with specific names or which encompass more than one column, use the `Index` construct, which requires a name.

Note that the `Index` construct is created externally to the table which it corresponds, using `Column` objects and not strings.

Below we illustrate a `Table` with several `Index` objects associated. The DDL for “CREATE INDEX” is issued right after the create statements for the table:

    meta = MetaData()
    mytable = Table('mytable', meta,
        # an indexed column, with index "ix_mytable_col1"
        Column('col1', Integer, index=True),

        # a uniquely indexed column with index "ix_mytable_col2"
        Column('col2', Integer, index=True, unique=True),

        Column('col3', Integer),
        Column('col4', Integer),

        Column('col5', Integer),
        Column('col6', Integer),
        )

    # place an index on col3, col4
    Index('idx_col34', mytable.c.col3, mytable.c.col4)

    # place a unique index on col5, col6
    Index('myindex', mytable.c.col5, mytable.c.col6, unique=True)

    sqlmytable.create(engine)
    CREATE TABLE mytable (
        col1 INTEGER,
        col2 INTEGER,
        col3 INTEGER,
        col4 INTEGER,
        col5 INTEGER,
        col6 INTEGER
    )
    CREATE INDEX ix_mytable_col1 ON mytable (col1)
    CREATE UNIQUE INDEX ix_mytable_col2 ON mytable (col2)
    CREATE UNIQUE INDEX myindex ON mytable (col5, col6)
    CREATE INDEX idx_col34 ON mytable (col3, col4)

The `Index` object also supports its own `create()` method:

    i = Index('someindex', mytable.c.col5)
    sqli.create(engine)
    CREATE INDEX someindex ON mytable (col5)

class `sqlalchemy.schema.``Index`(name, *columns, **kwargs)

  Bases: `sqlalchemy.schema.SchemaItem`

  A table-level INDEX.

  Defines a composite (one or more column) INDEX. For a no-frills, single column index, adding `index=True` to the `Column` definition is a shorthand equivalent for an unnamed, single column Index.

## Customizing DDL

In the preceding sections we’ve discussed a variety of schema constructs including `Table`, `ForeignKeyConstraint`, `CheckConstraint`, and `Sequence`. Throughout, we’ve relied upon the `create()` and `create_all()` methods of `Table` and `MetaData` in order to issue data definition language (DDL) for all constructs. When issued, a pre-determined order of operations is invoked, and DDL to create each table is created unconditionally including all constraints and other objects associated with it. For more complex scenarios where database-specific DDL is required, SQLAlchemy offers two techniques which can be used to add any DDL based on any condition, either accompanying the standard generation of tables or by itself.

### Controlling DDL Sequences

The `sqlalchemy.schema` package contains SQL expression constructs that provide DDL expressions. For example, to produce a `CREATE TABLE` statement:

    from sqlalchemy.schema import CreateTable
    sqlengine.execute(CreateTable(mytable))
    CREATE TABLE mytable (
        col1 INTEGER,
        col2 INTEGER,
        col3 INTEGER,
        col4 INTEGER,
        col5 INTEGER,
        col6 INTEGER
    )

Above, the `CreateTable` construct works like any other expression construct (such as `select()`, `table.insert()`, etc.). A full reference of available constructs is in DDL API.

The DDL constructs all extend a common base class which provides the capability to be associated with an individual `Table` or `MetaData` object, to be invoked upon create/drop events. Consider the example of a table which contains a CHECK constraint:

    users = Table('users', metadata,
                   Column('user_id', Integer, primary_key=True),
                   Column('user_name', String(40), nullable=False),
                   CheckConstraint('length(user_name) >= 8',name="cst_user_name_length")
                   )

    sqlusers.create(engine)
    CREATE TABLE users (
        user_id SERIAL NOT NULL,
        user_name VARCHAR(40) NOT NULL,
        PRIMARY KEY (user_id),
        CONSTRAINT cst_user_name_length  CHECK (length(user_name) >= 8)
    )

The above table contains a column “user_name” which is subject to a CHECK constraint that validates that the length of the string is at least eight characters. When a `create()` is issued for this table, DDL for the `CheckConstraint` will also be issued inline within the table definition.

The `CheckConstraint` construct can also be constructed externally and associated with the `Table` afterwards:

    constraint = CheckConstraint('length(user_name) >= 8',name="cst_user_name_length")
    users.append_constraint(constraint)

So far, the effect is the same. However, if we create DDL elements corresponding to the creation and removal of this constraint, and associate them with the `Table` as events, these new events will take over the job of issuing DDL for the constraint. Additionally, the constraint will be added via ALTER:

    AddConstraint(constraint).execute_at("after-create", users)
    DropConstraint(constraint).execute_at("before-drop", users)

    sqlusers.create(engine)
    CREATE TABLE users (
        user_id SERIAL NOT NULL,
        user_name VARCHAR(40) NOT NULL,
        PRIMARY KEY (user_id)
    )

    ALTER TABLE users ADD CONSTRAINT cst_user_name_length  CHECK (length(user_name) >= 8)
    sqlusers.drop(engine)
    ALTER TABLE users DROP CONSTRAINT cst_user_name_length
    DROP TABLE user

The real usefulness of the above becomes clearer once we illustrate the `on` attribute of a DDL event. The `on` parameter is part of the constructor, and may be a string name of a database dialect name, a tuple containing dialect names, or a Python callable. This will limit the execution of the item to just those dialects, or when the return value of the callable is `True`. So if our `CheckConstraint` was only supported by Postgresql and not other databases, we could limit it to just that dialect:

    AddConstraint(constraint, on='postgresql').execute_at("after-create", users)
    DropConstraint(constraint, on='postgresql').execute_at("before-drop", users)

Or to any set of dialects:

    AddConstraint(constraint, on=('postgresql', 'mysql')).execute_at("after-create", users)
    DropConstraint(constraint, on=('postgresql', 'mysql')).execute_at("before-drop", users)

When using a callable, the callable is passed the ddl element, event name, the `Table` or `MetaData` object whose “create” or “drop” event is in progress, and the `Connection` object being used for the operation, as well as additional information as keyword arguments. The callable can perform checks, such as whether or not a given item already exists. Below we define `should_create()` and `should_drop()` callables that check for the presence of our named constraint:

    def should_create(ddl, event, target, connection, **kw):
        row = connection.execute("select conname from pg_constraint where conname='%s'" % ddl.element.name).scalar()
        return not bool(row)

    def should_drop(ddl, event, target, connection, **kw):
        return not should_create(ddl, event, target, connection, **kw)

    AddConstraint(constraint, on=should_create).execute_at("after-create", users)
    DropConstraint(constraint, on=should_drop).execute_at("before-drop", users)

    sqlusers.create(engine)
    CREATE TABLE users (
        user_id SERIAL NOT NULL,
        user_name VARCHAR(40) NOT NULL,
        PRIMARY KEY (user_id)
    )

    select conname from pg_constraint where conname='cst_user_name_length'
    ALTER TABLE users ADD CONSTRAINT cst_user_name_length  CHECK (length(user_name) >= 8)
    sqlusers.drop(engine)
    select conname from pg_constraint where conname='cst_user_name_length'
    ALTER TABLE users DROP CONSTRAINT cst_user_name_length
    DROP TABLE user

### Custom DDL

Custom DDL phrases are most easily achieved using the `DDL` construct. This construct works like all the other DDL elements except it accepts a string which is the text to be emitted:

    DDL("ALTER TABLE users ADD CONSTRAINT "
        "cst_user_name_length "
        " CHECK (length(user_name) >= 8)").execute_at("after-create", metadata)

A more comprehensive method of creating libraries of DDL constructs is to use custom compilation - see Custom SQL Constructs and Compilation Extension for details.

### DDL API

class `sqlalchemy.schema.``DDLElement`

  Bases: `sqlalchemy.sql.expression.Executable`, `sqlalchemy.sql.expression.ClauseElement`

  Base class for DDL expression constructs.

  `against`(target)

    Return a copy of this DDL against a specific schema item.

  `bind`
  `execute`(bind=None, target=None)

    Execute this DDL immediately.

    Executes the DDL statement in isolation using the supplied `Connectable` or `Connectable` assigned to the `.bind` property, if not supplied. If the DDL has a conditional `on` criteria, it will be invoked with None as the event.

    Parameters: | bind – Optional, an `Engine` or `Connection`. If not supplied, a valid `Connectable` must be present in the `.bind` property. target – Optional, defaults to None. The target SchemaItem for the execute call. Will be passed to the `on` callable if any, and may also provide string expansion data for the statement. See `execute_at` for more information.

  `execute_at`(event, target)

    Link execution of this DDL to the DDL lifecycle of a SchemaItem.

    Links this `DDLElement` to a `Table` or `MetaData` instance, executing it when that schema item is created or dropped. The DDL statement will be executed using the same Connection and transactional context as the Table create/drop itself. The `.bind` property of this statement is ignored.

    Parameters: | event – One of the events defined in the schema item’s `.ddl_events`; e.g. ‘before-create’, ‘after-create’, ‘before-drop’ or ‘after-drop’ target – The Table or MetaData instance for which this DDLElement will be associated with.

    A DDLElement instance can be linked to any number of schema items.

    `execute_at` builds on the `append_ddl_listener` interface of `MetaData` and `Table` objects.

    Caveat: Creating or dropping a Table in isolation will also trigger any DDL set to `execute_at` that Table’s MetaData. This may change in a future release.

  `on` = None
  `target` = None

class `sqlalchemy.schema.``DDL`(statement, on=None, context=None, bind=None)

  Bases: `sqlalchemy.schema.DDLElement`

  A literal DDL statement.

  Specifies literal SQL DDL to be executed by the database. DDL objects can be attached to `Tables` or `MetaData` instances, conditionally executing SQL as part of the DDL lifecycle of those schema items. Basic templating support allows a single DDL instance to handle repetitive tasks for multiple tables.

  Examples:

      tbl = Table('users', metadata, Column('uid', Integer)) # ...
      DDL('DROP TRIGGER users_trigger').execute_at('before-create', tbl)

      spow = DDL('ALTER TABLE %(table)s SET secretpowers TRUE', on='somedb')
      spow.execute_at('after-create', tbl)

      drop_spow = DDL('ALTER TABLE users SET secretpowers FALSE')
      connection.execute(drop_spow)

  When operating on Table events, the following `statement` string substitions are available:

      %(table)s  - the Table name, with any required quoting applied
      %(schema)s - the schema name, with any required quoting applied
      %(fullname)s - the Table name including schema, quoted if needed

  The DDL’s `context`, if any, will be combined with the standard substutions noted above. Keys present in the context will override the standard substitutions.

  `__init__`(statement, on=None, context=None, bind=None)

    Create a DDL statement.

    - Parameters: | statement – A string or unicode string to be executed. Statements will be processed with Python’s string formatting operator. See the `context` argument and the `execute_at` method. A literal ‘%’ in a statement must be escaped as ‘%%’. SQL bind parameters are not available in DDL statements. on – Optional filtering criteria. May be a string, tuple or a callable predicate. If a string, it will be compared to the name of the executing database dialect:
          DDL('something', on='postgresql')
      If a tuple, specifies multiple dialect names:
          DDL('something', on=('postgresql', 'mysql'))
      > If a callable, it will be invoked with four positional arguments as well as optional keyword arguments:
      > ddl: | This DDL element.
      > event: | The name of the event that has triggered this DDL, such as ‘after-create’ Will be None if the DDL is executed explicitly.
      > target: | The `Table` or `MetaData` object which is the target of this event. May be None if the DDL is executed explicitly.
      > connection: | The `Connection` being used for DDL execution
      > tables: | Optional keyword argument - a list of Table objects which are to be created/ dropped within a MetaData.create_all() or drop_all() method call.
    If the callable returns a true value, the DDL statement will be executed. context – Optional dictionary, defaults to None. These values will be available for use in string substitutions on the DDL statement. bind – Optional. A `Connectable`, used by default when `execute()` is invoked without a bind argument.

class `sqlalchemy.schema.``CreateTable`(element, on=None, bind=None)

  Bases: `sqlalchemy.schema._CreateDropBase`

  Represent a CREATE TABLE statement.

class `sqlalchemy.schema.``DropTable`(element, on=None, bind=None)

  Bases: `sqlalchemy.schema._CreateDropBase`

  Represent a DROP TABLE statement.

class `sqlalchemy.schema.``CreateSequence`(element, on=None, bind=None)

  Bases: `sqlalchemy.schema._CreateDropBase`

  Represent a CREATE SEQUENCE statement.

class `sqlalchemy.schema.``DropSequence`(element, on=None, bind=None)

  Bases: `sqlalchemy.schema._CreateDropBase`

  Represent a DROP SEQUENCE statement.

class `sqlalchemy.schema.``CreateIndex`(element, on=None, bind=None)

  Bases: `sqlalchemy.schema._CreateDropBase`

  Represent a CREATE INDEX statement.

class `sqlalchemy.schema.``DropIndex`(element, on=None, bind=None)

  Bases: `sqlalchemy.schema._CreateDropBase`

  Represent a DROP INDEX statement.

class `sqlalchemy.schema.``AddConstraint`(element, *args, **kw)

  Bases: `sqlalchemy.schema._CreateDropBase`

  Represent an ALTER TABLE ADD CONSTRAINT statement.

class `sqlalchemy.schema.``DropConstraint`(element, cascade=False, **kw)

  Bases: `sqlalchemy.schema._CreateDropBase`

  Represent an ALTER TABLE DROP CONSTRAINT statement.