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:

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 Custom SQL Constructs and Compilation Extension for details.

Controlling DDL Sequences¶

The DDL construct introduced previously also has the ability to be invoked conditionally based on inspection of the database. This feature is available using the DDLElement.execute_if() method. For example, if we wanted to create a trigger but only on the PostgreSQL backend, we could invoke this as:

mytable = Table(
    'mytable', metadata,
    Column('id', Integer, primary_key=True),
    Column('data', String(50))
)

trigger = DDL(
    "CREATE TRIGGER dt_ins BEFORE INSERT ON mytable "
    "FOR EACH ROW BEGIN SET NEW.data='ins'; END"
)

event.listen(
    mytable,
    'after_create',
    trigger.execute_if(dialect='postgresql')
)

The DDLElement.execute_if.dialect keyword also accepts a tuple of string dialect names:

event.listen(
    mytable,
    "after_create",
    trigger.execute_if(dialect=('postgresql', 'mysql'))
)
event.listen(
    mytable,
    "before_drop",
    trigger.execute_if(dialect=('postgresql', 'mysql'))
)

The DDLElement.execute_if() method can also work against a callable function that will receive the database connection in use. In the example below, we use this to conditionally create a CHECK constraint, first looking within the PostgreSQL catalogs to see if it exists:

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",
    DDL(
        "ALTER TABLE users ADD CONSTRAINT "
        "cst_user_name_length CHECK (length(user_name) >= 8)"
    ).execute_if(callable_=should_create)
)
event.listen(
    users,
    "before_drop",
    DDL(
        "ALTER TABLE users DROP CONSTRAINT cst_user_name_length"
    ).execute_if(callable_=should_drop)
)

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 users

Using the built-in DDLElement Classes¶

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.). All of SQLAlchemy’s DDL oriented constructs are subclasses of the DDLElement base class; this is the base of all the objects corresponding to CREATE and DROP as well as ALTER, not only in SQLAlchemy but in Alembic Migrations as well. A full reference of available constructs is in DDL Expression Constructs API.

User-defined DDL constructs may also be created as subclasses of DDLElement itself. The documentation in Custom SQL Constructs and Compilation Extension has several examples of this.

The event-driven DDL system described in the previous section Controlling DDL Sequences is available with other DDLElement objects as well. However, when dealing with the built-in constructs such as CreateIndex, CreateSequence, etc, the event system is of limited use, as methods like Table.create() and MetaData.create_all() will invoke these constructs unconditionally. In a future SQLAlchemy release, the DDL event system including conditional execution will taken into account for built-in constructs that currently invoke in all cases.

We can illustrate an event-driven example with the AddConstraint and DropConstraint constructs, as the event-driven system will work for CHECK and UNIQUE constraints, using these as we did in our previous example of DDLElement.execute_if():

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)
)

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 users

While the above example is against the built-in AddConstraint and DropConstraint objects, the main usefulness of DDL events for now remains focused on the use of the DDL construct itself, as well as with user-defined subclasses of DDLElement that aren’t already part of the MetaData.create_all(), Table.create(), and corresponding “drop” processes.

DDL Expression Constructs API¶

sqlalchemy.schema.sort_tables(tables, skip_fn=None, extra_dependencies=None)¶

sort a collection of Table objects based on dependency.

This is a dependency-ordered sort which will emit Table objects such that they will follow their dependent Table objects. Tables are dependent on another based on the presence of ForeignKeyConstraint objects as well as explicit dependencies added by Table.add_is_dependent_on().

Warning

The sort_tables() function cannot by itself accommodate automatic resolution of dependency cycles between tables, which are usually caused by mutually dependent foreign key constraints. To resolve these cycles, either the ForeignKeyConstraint.use_alter parameter may be applied to those constraints, or use the sql.sort_tables_and_constraints() function which will break out foreign key constraints involved in cycles separately.

Parameters:

tables¶ – a sequence of Table objects. skip_fn¶ – optional callable which will be passed a ForeignKey object; if it returns True, this constraint will not be considered as a dependency. Note this is different from the same parameter in sort_tables_and_constraints(), which is instead passed the owning ForeignKeyConstraint object. extra_dependencies¶ – a sequence of 2-tuples of tables which will also be considered as dependent on each other.

See also

sort_tables_and_constraints()

MetaData.sorted_tables() - uses this function to sort

sqlalchemy.schema.sort_tables_and_constraints(tables, filter_fn=None, extra_dependencies=None)¶

sort a collection of Table / ForeignKeyConstraint objects.

This is a dependency-ordered sort which will emit tuples of (Table, [ForeignKeyConstraint, ...]) such that each Table follows its dependent Table objects. Remaining ForeignKeyConstraint objects that are separate due to dependency rules not satisfied by the sort are emitted afterwards as (None, [ForeignKeyConstraint ...]).

Tables are dependent on another based on the presence of ForeignKeyConstraint objects, explicit dependencies added by Table.add_is_dependent_on(), as well as dependencies stated here using the skip_fn and/or extra_dependencies parameters.

Parameters:

tables¶ – a sequence of Table objects. filter_fn¶ – optional callable which will be passed a ForeignKeyConstraint object, and returns a value based on whether this constraint should definitely be included or excluded as an inline constraint, or neither. If it returns False, the constraint will definitely be included as a dependency that cannot be subject to ALTER; if True, it will only be included as an ALTER result at the end. Returning None means the constraint is included in the table-based result unless it is detected as part of a dependency cycle. extra_dependencies¶ – a sequence of 2-tuples of tables which will also be considered as dependent on each other.

New in version 1.0.0.

See also

sort_tables()

class sqlalchemy.schema.DDLElement¶

Bases: sqlalchemy.sql.expression.Executable, sqlalchemy.schema._DDLCompiles

Base class for DDL expression constructs.

This class is the base for the general purpose DDL class, as well as the various create/drop clause constructs such as CreateTable, DropTable, AddConstraint, etc.

DDLElement integrates closely with SQLAlchemy events, introduced in Events. An instance of one is itself an event receiving callable:

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

See also

DDL

DDLEvents

Events

Controlling DDL Sequences

__call__(target, bind, **kw)¶

Execute the DDL as a ddl_listener.

against(target)¶

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

bind¶

Returns the Engine or Connection to which this Executable is bound, or None if none found.

This is a traversal which checks locally, then checks among the “from” clauses of associated objects until a bound engine or connection is found.

callable_ = None¶

dialect = None¶

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_name, target)¶

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

Deprecated since version 0.7: The DDLElement.execute_at() method is deprecated and will be removed in a future release. Please use the DDLEvents listener interface in conjunction with the DDLElement.execute_if() method.

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.

execute_if(dialect=None, callable_=None, state=None)¶

Return a callable that will execute this DDLElement conditionally.

Used to provide a wrapper for event listening:

event.listen(
            metadata,
            'before_create',
            DDL("my_ddl").execute_if(dialect='postgresql')
        )

Parameters:

dialect¶ – 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').execute_if(dialect='postgresql') If a tuple, specifies multiple dialect names: DDL('something').execute_if(dialect=('postgresql', 'mysql')) callable_¶ – A callable, which will be invoked with four positional arguments as well as optional keyword arguments: ddl: This DDL element. target: The Table or MetaData object which is the target of this event. May be None if the DDL is executed explicitly. bind: 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. state: Optional keyword argument - will be the state argument passed to this function. checkfirst: Keyword argument, will be True if the ‘checkfirst’ flag was set during the call to create(), create_all(), drop(), drop_all(). If the callable returns a true value, the DDL statement will be executed. state¶ – any value which will be passed to the callable_ as the state keyword argument.

See also

DDLEvents

Events

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 function as DDL event listeners, and can be subscribed to those events listed in DDLEvents, using either Table or MetaData objects as targets. Basic templating support allows a single DDL instance to handle repetitive tasks for multiple tables.

Examples:

from sqlalchemy import event, DDL

tbl = Table('users', metadata, Column('uid', Integer))
event.listen(tbl, 'before_create', DDL('DROP TRIGGER users_trigger'))

spow = DDL('ALTER TABLE %(table)s SET secretpowers TRUE')
event.listen(tbl, 'after_create', spow.execute_if(dialect='somedb'))

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

When operating on Table events, the following statement string substitutions 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 substitutions 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¶ – Deprecated since version 0.7: The DDL.on parameter is deprecated and will be removed in a future release. Please refer to DDLElement.execute_if(). 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.

See also

DDLEvents

Events

class sqlalchemy.schema._CreateDropBase(element, on=None, bind=None)¶

Bases: sqlalchemy.schema.DDLElement

Base class for DDL constructs that represent CREATE and DROP or equivalents.

The common theme of _CreateDropBase is a single element attribute which refers to the element to be created or dropped.

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

Bases: sqlalchemy.schema._CreateDropBase

Represent a CREATE TABLE statement.

__init__(element, on=None, bind=None, include_foreign_key_constraints=None)¶

Create a CreateTable construct.

Parameters:	element¶ – a Table that’s the subject of the CREATE on¶ – See the description for ‘on’ in DDL. bind¶ – See the description for ‘bind’ in DDL. include_foreign_key_constraints¶ – optional sequence of ForeignKeyConstraint objects that will be included inline within the CREATE construct; if omitted, all foreign key constraints that do not specify use_alter=True are included. New in version 1.0.0.

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

Bases: sqlalchemy.schema._CreateDropBase

Represent a DROP TABLE statement.

class sqlalchemy.schema.CreateColumn(element)¶

Bases: sqlalchemy.schema._DDLCompiles

Represent a Column as rendered in a CREATE TABLE statement, via the CreateTable construct.

This is provided to support custom column DDL within the generation of CREATE TABLE statements, by using the compiler extension documented in Custom SQL Constructs and Compilation Extension to extend CreateColumn.

Typical integration is to examine the incoming Column object, and to redirect compilation if a particular flag or condition is found:

from sqlalchemy import schema
from sqlalchemy.ext.compiler import compiles

@compiles(schema.CreateColumn)
def compile(element, compiler, **kw):
    column = element.element

    if "special" not in column.info:
        return compiler.visit_create_column(element, **kw)

    text = "%s SPECIAL DIRECTIVE %s" % (
            column.name,
            compiler.type_compiler.process(column.type)
        )
    default = compiler.get_column_default_string(column)
    if default is not None:
        text += " DEFAULT " + default

    if not column.nullable:
        text += " NOT NULL"

    if column.constraints:
        text += " ".join(
                    compiler.process(const)
                    for const in column.constraints)
    return text

The above construct can be applied to a Table as follows:

from sqlalchemy import Table, Metadata, Column, Integer, String
from sqlalchemy import schema

metadata = MetaData()

table = Table('mytable', MetaData(),
        Column('x', Integer, info={"special":True}, primary_key=True),
        Column('y', String(50)),
        Column('z', String(20), info={"special":True})
    )

metadata.create_all(conn)

Above, the directives we’ve added to the Column.info collection will be detected by our custom compilation scheme:

CREATE TABLE mytable (
        x SPECIAL DIRECTIVE INTEGER NOT NULL,
        y VARCHAR(50),
        z SPECIAL DIRECTIVE VARCHAR(20),
    PRIMARY KEY (x)
)

The CreateColumn construct can also be used to skip certain columns when producing a CREATE TABLE. This is accomplished by creating a compilation rule that conditionally returns None. This is essentially how to produce the same effect as using the system=True argument on Column, which marks a column as an implicitly-present “system” column.

For example, suppose we wish to produce a Table which skips rendering of the PostgreSQL xmin column against the PostgreSQL backend, but on other backends does render it, in anticipation of a triggered rule. A conditional compilation rule could skip this name only on PostgreSQL:

from sqlalchemy.schema import CreateColumn

@compiles(CreateColumn, "postgresql")
def skip_xmin(element, compiler, **kw):
    if element.element.name == 'xmin':
        return None
    else:
        return compiler.visit_create_column(element, **kw)


my_table = Table('mytable', metadata,
            Column('id', Integer, primary_key=True),
            Column('xmin', Integer)
        )

Above, a CreateTable construct will generate a CREATE TABLE which only includes the id column in the string; the xmin column will be omitted, but only against the PostgreSQL backend.

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.

class sqlalchemy.schema.CreateSchema(name, quote=None, **kw)¶

Bases: sqlalchemy.schema._CreateDropBase

Represent a CREATE SCHEMA statement.

The argument here is the string name of the schema.

__init__(name, quote=None, **kw)¶

Create a new CreateSchema construct.

class sqlalchemy.schema.DropSchema(name, quote=None, cascade=False, **kw)¶

Bases: sqlalchemy.schema._CreateDropBase

Represent a DROP SCHEMA statement.

The argument here is the string name of the schema.

__init__(name, quote=None, cascade=False, **kw)¶

Create a new DropSchema construct.