sqlalchemy/doc/build/core/schema.rst

.. _metadata_toplevel:

==========================
Schema Definition Language
==========================

.. module:: sqlalchemy.schema


.. _metadata_describing:

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
:class:`~sqlalchemy.schema.Table`, :class:`~sqlalchemy.schema.Column`,
:class:`~sqlalchemy.schema.ForeignKey` and
:class:`~sqlalchemy.schema.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 :class:`~sqlalchemy.schema.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
:class:`~sqlalchemy.schema.MetaData`::

    from sqlalchemy import *

    metadata = MetaData()

:class:`~sqlalchemy.schema.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 :class:`~sqlalchemy.schema.Table` class. Its two
primary arguments are the table name, then the
:class:`~sqlalchemy.schema.MetaData` object which it will be associated with.
The remaining positional arguments are mostly
:class:`~sqlalchemy.schema.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 :class:`~sqlalchemy.types.Integer` and
:class:`~sqlalchemy.types.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 :ref:`types`.

Accessing Tables and Columns
----------------------------

The :class:`~sqlalchemy.schema.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 :class:`~sqlalchemy.schema.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 :class:`~sqlalchemy.schema.Table` objects have been
explicitly declared, and these objects are typically accessed directly as
module-level variables in an application. Once a
:class:`~sqlalchemy.schema.Table` has been defined, it has a full set of
accessors which allow inspection of its properties. Given the following
:class:`~sqlalchemy.schema.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 :class:`~sqlalchemy.schema.ForeignKey` object used in this table -
this construct defines a reference to a remote table, and is fully described
in :ref:`metadata_foreignkeys`. 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 :class:`~sqlalchemy.schema.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
:func:`~sqlalchemy.schema.MetaData.create_all` on the
:class:`~sqlalchemy.schema.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:

    .. sourcecode:: python+sql

        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))
        )

        {sql}metadata.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)
        )

:func:`~sqlalchemy.schema.MetaData.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
:func:`~sqlalchemy.schema.MetaData.drop_all` method. This method does the
exact opposite of :func:`~sqlalchemy.schema.MetaData.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 :class:`~sqlalchemy.schema.Table`. These methods by
default issue the CREATE or DROP regardless of the table being present:

.. sourcecode:: python+sql

    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"))
    )
    {sql}employees.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:

.. sourcecode:: python+sql

    {sql}employees.drop(engine)
    DROP TABLE employees
    {}

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)

Altering Schemas through Migrations
-----------------------------------

While SQLAlchemy directly supports emitting CREATE and DROP statements for schema
constructs, the ability to alter those constructs, usually via the ALTER statement
as well as other database-specific constructs, is outside of the scope of SQLAlchemy
itself.  While it's easy enough to emit ALTER statements and similar by hand,
such as by passing a string to :meth:`.Connection.execute` or by using the
:class:`.DDL` construct, it's a common practice to automate the maintenance of
database schemas in relation to application code using schema migration tools.

There are two major migration tools available for SQLAlchemy:

* `Alembic <http://alembic.readthedocs.org>`_ - Written by the author of SQLAlchemy,
  Alembic features a highly customizable environment and a minimalistic usage pattern,
  supporting such features as transactional DDL, automatic generation of "candidate"
  migrations, an "offline" mode which generates SQL scripts, and support for branch
  resolution.
* `SQLAlchemy-Migrate <http://code.google.com/p/sqlalchemy-migrate/>`_ - The original
  migration tool for SQLAlchemy, SQLAlchemy-Migrate is widely used and continues
  under active development.   SQLAlchemy-Migrate includes features such as
  SQL script generation, ORM class generation, ORM model comparison, and extensive
  support for SQLite migrations.


Specifying the Schema Name
---------------------------

Some databases support the concept of multiple schemas. A
:class:`~sqlalchemy.schema.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 :class:`~sqlalchemy.schema.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
:class:`~sqlalchemy.schema.Table`. :class:`~sqlalchemy.schema.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
------------------------

:class:`~sqlalchemy.schema.Table` supports database-specific options. For
example, MySQL has different table backend types, including "MyISAM" and
"InnoDB". This can be expressed with :class:`~sqlalchemy.schema.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.

Column, Table, MetaData API
---------------------------

.. autoclass:: Column
    :members:
    :inherited-members:
    :undoc-members:
    :show-inheritance:

.. autoclass:: MetaData
    :members:
    :undoc-members:
    :show-inheritance:

.. autoclass:: SchemaItem
    :show-inheritance:
    :members:

.. autoclass:: Table
    :members:
    :inherited-members:
    :undoc-members:
    :show-inheritance:

.. autoclass:: ThreadLocalMetaData
    :members:
    :undoc-members:
    :show-inheritance:


.. _metadata_reflection:

Reflecting Database Objects
===========================

A :class:`~sqlalchemy.schema.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*. In the
most simple case you need only specify the table name, a :class:`~sqlalchemy.schema.MetaData`
object, and the ``autoload=True`` flag. If the
:class:`~sqlalchemy.schema.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
:class:`~sqlalchemy.schema.Column`, :class:`~sqlalchemy.schema.ForeignKey`,
and other objects corresponding to this information as though the
:class:`~sqlalchemy.schema.Table` object were hand-constructed in Python.

When tables are reflected, if a given table references another one via foreign
key, a second :class:`~sqlalchemy.schema.Table` object is created within the
:class:`~sqlalchemy.schema.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 :class:`~sqlalchemy.schema.MetaData` has an interesting "singleton-like"
behavior such that if you requested both tables individually,
:class:`~sqlalchemy.schema.MetaData` will ensure that exactly one
:class:`~sqlalchemy.schema.Table` object is created for each distinct table
name. The :class:`~sqlalchemy.schema.Table` constructor actually returns to
you the already-existing :class:`~sqlalchemy.schema.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
:class:`~sqlalchemy.schema.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 :class:`~sqlalchemy.schema.Table` object with
:class:`~sqlalchemy.schema.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 :class:`~sqlalchemy.schema.MetaData` object can also get a listing of
tables and reflect the full set. This is achieved by using the
:func:`~sqlalchemy.schema.MetaData.reflect` method. After calling it, all
located tables are present within the :class:`~sqlalchemy.schema.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()

.. autoclass:: sqlalchemy.engine.reflection.Inspector
    :members:
    :undoc-members:
    :show-inheritance:


.. _metadata_defaults:

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
:class:`~sqlalchemy.schema.Column` including the ``autoincrement`` flag, as
well as the section on :class:`~sqlalchemy.schema.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
  :class:`~sqlalchemy.sql.expression.Insert()` or
  :class:`~sqlalchemy.sql.expression.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
:class:`~sqlalchemy.engine.ResultProxy` will contain a collection
accessible via ``result.postfetch_cols()`` which contains a list of all
:class:`~sqlalchemy.schema.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
:class:`~sqlalchemy.engine.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:

.. sourcecode:: python+sql

    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:

Triggered Columns
------------------

Columns with values set by a database trigger or other external process may be
called out using :class:`.FetchedValue` as a marker::

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

.. versionchanged:: 0.8.0b2,0.7.10
    The ``for_update`` argument on :class:`.FetchedValue` is set automatically
    when specified as the ``server_onupdate`` argument.  If using an older version,
    specify the onupdate above as ``server_onupdate=FetchedValue(for_update=True)``.

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.

.. note::

    It's generally not appropriate to use :class:`.FetchedValue` in
    conjunction with a primary key column, particularly when using the
    ORM or any other scenario where the :attr:`.ResultProxy.inserted_primary_key`
    attribute is required.  This is becaue the "post-fetch" operation requires
    that the primary key value already be available, so that the
    row can be selected on its primary key.

    For a server-generated primary key value, all databases provide special
    accessors or other techniques in order to acquire the "last inserted
    primary key" column of a table.  These mechanisms aren't affected by the presence
    of :class:`.FetchedValue`.  For special situations where triggers are
    used to generate primary key values, and the database in use does not
    support the ``RETURNING`` clause, it may be necessary to forego the usage
    of the trigger and instead apply the SQL expression or function as a
    "pre execute" expression::

        t = Table('test', meta,
                Column('abc', MyType, default=func.generate_new_value(), primary_key=True)
        )

    Where above, when :meth:`.Table.insert` is used,
    the ``func.generate_new_value()`` expression will be pre-executed
    in the context of a scalar ``SELECT`` statement, and the new value will
    be applied to the subsequent ``INSERT``, while at the same time being
    made available to the :attr:`.ResultProxy.inserted_primary_key`
    attribute.


Defining Sequences
-------------------

SQLAlchemy represents database sequences using the
:class:`~sqlalchemy.schema.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 :class:`~sqlalchemy.schema.Sequence` object is
otherwise ignored.

The :class:`~sqlalchemy.schema.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 :class:`~sqlalchemy.schema.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 :class:`~sqlalchemy.schema.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 :class:`~sqlalchemy.schema.Table` object defines a
:class:`~sqlalchemy.schema.Sequence` on its primary key column so that it
works with Oracle and Firebird, the :class:`~sqlalchemy.schema.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
:class:`~sqlalchemy.schema.Sequence` object - this indicates that the
:class:`~sqlalchemy.schema.Sequence` should only be used if the database
provides no other option for generating primary key identifiers.

The :class:`~sqlalchemy.schema.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 Objects API
-------------------

.. autoclass:: ColumnDefault
    :show-inheritance:

.. autoclass:: DefaultClause
    :show-inheritance:

.. autoclass:: DefaultGenerator
    :show-inheritance:

.. autoclass:: FetchedValue
    :show-inheritance:

.. autoclass:: PassiveDefault
    :show-inheritance:

.. autoclass:: Sequence
    :show-inheritance:
    :members:

Defining Constraints and Indexes
=================================

.. _metadata_foreignkeys:
.. _metadata_constraints:

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 :class:`~sqlalchemy.schema.ForeignKey` object
as an argument to a :class:`~sqlalchemy.schema.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 :class:`~sqlalchemy.schema.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 :class:`~sqlalchemy.schema.Column` object, which as we'll
see later is accessed from an existing :class:`~sqlalchemy.schema.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
:class:`~sqlalchemy.schema.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
:class:`~sqlalchemy.schema.ForeignKeyConstraint` is the only way to define a
composite foreign key. While we could also have placed individual
:class:`~sqlalchemy.schema.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.

.. _use_alter:

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

In all the above examples, the :class:`~sqlalchemy.schema.ForeignKey` object
causes the "REFERENCES" keyword to be added inline to a column definition
within a "CREATE TABLE" statement when
:func:`~sqlalchemy.schema.MetaData.create_all` is issued, and
:class:`~sqlalchemy.schema.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, :class:`~sqlalchemy.schema.ForeignKey` and
:class:`~sqlalchemy.schema.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
:class:`~sqlalchemy.schema.ForeignKey` and
:class:`~sqlalchemy.schema.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.


UNIQUE Constraint
-----------------

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

.. sourcecode:: python+sql

    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')
        )

CHECK Constraint
----------------

Check constraints can be named or unnamed and can be created at the Column or
Table level, using the :class:`~sqlalchemy.schema.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.

.. sourcecode:: python+sql

    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')
        )

    {sql}mytable.create(engine)
    CREATE TABLE mytable (
        col1 INTEGER  CHECK (col1>5),
        col2 INTEGER,
        col3 INTEGER,
        CONSTRAINT check1  CHECK (col2 > col3 + 5)
    ){stop}

Setting up Constraints when using the Declarative ORM Extension
----------------------------------------------------------------

The :class:`.Table` is the SQLAlchemy Core construct that allows one to define
table metadata, which among other things can be used by the SQLAlchemy ORM
as a target to map a class.  The :ref:`Declarative <declarative_toplevel>`
extension allows the :class:`.Table` object to be created automatically, given
the contents of the table primarily as a mapping of :class:`.Column` objects.

To apply table-level constraint objects such as :class:`.ForeignKeyConstraint`
to a table defined using Declarative, use the ``__table_args__`` attribute,
described at :ref:`declarative_table_args`.

Constraints API
---------------
.. autoclass:: Constraint
    :show-inheritance:

.. autoclass:: CheckConstraint
    :show-inheritance:

.. autoclass:: ColumnCollectionConstraint
    :show-inheritance:

.. autoclass:: ForeignKey
    :members:
    :show-inheritance:

.. autoclass:: ForeignKeyConstraint
    :members:
    :show-inheritance:

.. autoclass:: PrimaryKeyConstraint
    :show-inheritance:

.. autoclass:: UniqueConstraint
    :show-inheritance:

.. _schema_indexes:

Indexes
-------

Indexes can be created anonymously (using an auto-generated name ``ix_<column
label>``) for a single column using the inline ``index`` keyword on
:class:`~sqlalchemy.schema.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 :class:`~sqlalchemy.schema.Index` construct,
which requires a name.

Below we illustrate a :class:`~sqlalchemy.schema.Table` with several
:class:`~sqlalchemy.schema.Index` objects associated. The DDL for "CREATE
INDEX" is issued right after the create statements for the table:

.. sourcecode:: python+sql

    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)

    {sql}mytable.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){stop}

Note in the example above, the :class:`.Index` construct is created
externally to the table which it corresponds, using :class:`.Column`
objects directly.  :class:`.Index` also supports
"inline" definition inside the :class:`.Table`, using string names to
identify columns::

    meta = MetaData()
    mytable = Table('mytable', meta,
        Column('col1', Integer),

        Column('col2', Integer),

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

        # place an index on col1, col2
        Index('idx_col12', 'col1', 'col2'),

        # place a unique index on col3, col4
        Index('idx_col34', 'col3', 'col4', unique=True)
    )

.. versionadded:: 0.7
    Support of "inline" definition inside the :class:`.Table`
    for :class:`.Index`\ .

The :class:`~sqlalchemy.schema.Index` object also supports its own ``create()`` method:

.. sourcecode:: python+sql

    i = Index('someindex', mytable.c.col5)
    {sql}i.create(engine)
    CREATE INDEX someindex ON mytable (col5){stop}

.. _schema_indexes_functional:

Functional Indexes
~~~~~~~~~~~~~~~~~~~

:class:`.Index` supports SQL and function expressions, as supported by the
target backend.  To create an index against a column using a descending
value, the :meth:`.ColumnElement.desc` modifier may be used::

    from sqlalchemy import Index

    Index('someindex', mytable.c.somecol.desc())

Or with a backend that supports functional indexes such as Postgresql,
a "case insensitive" index can be created using the ``lower()`` function::

    from sqlalchemy import func, Index

    Index('someindex', func.lower(mytable.c.somecol))

.. versionadded:: 0.8 :class:`.Index` supports SQL expressions and functions
   as well as plain columns.

Index API
---------

.. autoclass:: Index
    :show-inheritance:
    :members:

.. _metadata_ddl:

Customizing DDL
===============

In the preceding sections we've discussed a variety of schema constructs
including :class:`~sqlalchemy.schema.Table`,
:class:`~sqlalchemy.schema.ForeignKeyConstraint`,
:class:`~sqlalchemy.schema.CheckConstraint`, and
:class:`~sqlalchemy.schema.Sequence`. Throughout, we've relied upon the
``create()`` and :func:`~sqlalchemy.schema.MetaData.create_all` methods of
:class:`~sqlalchemy.schema.Table` and :class:`~sqlalchemy.schema.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.

.. _schema_ddl_sequences:

Controlling DDL Sequences
-------------------------

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

.. sourcecode:: python+sql

    from sqlalchemy.schema import CreateTable
    {sql}engine.execute(CreateTable(mytable))
    CREATE TABLE mytable (
        col1 INTEGER,
        col2 INTEGER,
        col3 INTEGER,
        col4 INTEGER,
        col5 INTEGER,
        col6 INTEGER
    ){stop}

Above, the :class:`~sqlalchemy.schema.CreateTable` construct works like any
other expression construct (such as ``select()``, ``table.insert()``, etc.). A
full reference of available constructs is in :ref:`schema_api_ddl`.

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

.. sourcecode:: python+sql

    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")
                   )

    {sql}users.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)
    ){stop}

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
:class:`~sqlalchemy.schema.CheckConstraint` will also be issued inline within
the table definition.

The :class:`~sqlalchemy.schema.CheckConstraint` construct can also be
constructed externally and associated with the
:class:`~sqlalchemy.schema.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 :class:`.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:

.. sourcecode:: python+sql

    from sqlalchemy import event

    event.listen(
        users,
        "after_create",
        AddConstraint(constraint)
    )
    event.listen(
        users,
        "before_drop",
        DropConstraint(constraint)
    )

    {sql}users.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){stop}

    {sql}users.drop(engine)
    ALTER TABLE users DROP CONSTRAINT cst_user_name_length
    DROP TABLE users{stop}

The real usefulness of the above becomes clearer once we illustrate the
:meth:`.DDLElement.execute_if` method.  This method returns a modified form of
the DDL callable which will filter on criteria before responding to a
received event.   It accepts a parameter ``dialect``, which is the string
name of a dialect or a tuple of such, which will limit the execution of the
item to just those dialects.  It also accepts a ``callable_`` parameter which
may reference a Python callable which will be invoked upon event reception,
returning ``True`` or ``False`` indicating if the event should proceed.

If our :class:`~sqlalchemy.schema.CheckConstraint` was only supported by
Postgresql and not other databases, we could limit its usage to just that dialect::

    event.listen(
        users,
        'after_create',
        AddConstraint(constraint).execute_if(dialect='postgresql')
    )
    event.listen(
        users,
        'before_drop',
        DropConstraint(constraint).execute_if(dialect='postgresql')
    )

Or to any set of dialects::

    event.listen(
        users,
        "after_create",
        AddConstraint(constraint).execute_if(dialect=('postgresql', 'mysql'))
    )
    event.listen(
        users,
        "before_drop",
        DropConstraint(constraint).execute_if(dialect=('postgresql', 'mysql'))
    )

When using a callable, the callable is passed the ddl element, the
:class:`.Table` or :class:`.MetaData`
object whose "create" or "drop" event is in progress, and the
:class:`.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:

.. sourcecode:: python+sql

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

    event.listen(
        users,
        "after_create",
        AddConstraint(constraint).execute_if(callable_=should_create)
    )
    event.listen(
        users,
        "before_drop",
        DropConstraint(constraint).execute_if(callable_=should_drop)
    )

    {sql}users.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){stop}

    {sql}users.drop(engine)
    select conname from pg_constraint where conname='cst_user_name_length'
    ALTER TABLE users DROP CONSTRAINT cst_user_name_length
    DROP TABLE users{stop}

Custom DDL
----------

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

.. sourcecode:: python+sql

    event.listen(
        metadata,
        "after_create",
        DDL("ALTER TABLE users ADD CONSTRAINT "
            "cst_user_name_length "
            " CHECK (length(user_name) >= 8)")
    )

A more comprehensive method of creating libraries of DDL constructs is to use
custom compilation - see :ref:`sqlalchemy.ext.compiler_toplevel` for
details.

.. _schema_api_ddl:

DDL Expression Constructs API
-----------------------------

.. autoclass:: DDLElement
    :members:
    :undoc-members:
    :show-inheritance:

.. autoclass:: DDL
    :members:
    :undoc-members:
    :show-inheritance:

.. autoclass:: CreateTable
    :members:
    :undoc-members:
    :show-inheritance:

.. autoclass:: DropTable
    :members:
    :undoc-members:
    :show-inheritance:

.. autoclass:: CreateColumn
    :members:
    :undoc-members:
    :show-inheritance:

.. autoclass:: CreateSequence
    :members:
    :undoc-members:
    :show-inheritance:

.. autoclass:: DropSequence
    :members:
    :undoc-members:
    :show-inheritance:

.. autoclass:: CreateIndex
    :members:
    :undoc-members:
    :show-inheritance:

.. autoclass:: DropIndex
    :members:
    :undoc-members:
    :show-inheritance:

.. autoclass:: AddConstraint
    :members:
    :undoc-members:
    :show-inheritance:

.. autoclass:: DropConstraint
    :members:
    :undoc-members:
    :show-inheritance:

.. autoclass:: CreateSchema
    :members:
    :undoc-members:
    :show-inheritance:

.. autoclass:: DropSchema
    :members:
    :undoc-members:
    :show-inheritance: