Reflecting Database Objects — SQLAlchemy 2.0.0b1 documentation

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Sqlalchemy/docs/latest/core/reflection

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. In the most simple case you need only specify the table name, a MetaData object, and the autoload_with argument:

>>> messages = Table('messages', meta, 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_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_with=engine 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
... # additional Column objects which require no change are reflected normally
... autoload_with=some_engine)

See also

Working with Custom Types and Reflection - illustrates how the above column override technique applies to the use of custom datatypes with table reflection.


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_with=engine)

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_with=engine
)

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:

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

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

metadata_obj = MetaData()
metadata_obj.reflect(bind=someengine)
for table in reversed(metadata_obj.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 import inspect
engine = create_engine('...')
insp = inspect(engine)
print(insp.get_table_names())

Reflecting with Database-Agnostic Types

When the columns of a table are reflected, using either the :paramref:`_schema.Table.autoload_with` parameter of _schema.Table or the _reflection.Inspector.get_columns() method of _reflection.Inspector, the datatypes will be as specific as possible to the target database. This means that if an “integer” datatype is reflected from a MySQL database, the type will be represented by the sqlalchemy.dialects.mysql.INTEGER class, which includes MySQL-specific attributes such as “display_width”. Or on PostgreSQL, a PostgreSQL-specific datatype such as sqlalchemy.dialects.postgresql.INTERVAL or sqlalchemy.dialects.postgresql.ENUM may be returned.

There is a use case for reflection which is that a given _schema.Table is to be transferred to a different vendor database. To suit this use case, there is a technique by which these vendor-specific datatypes can be converted on the fly to be instance of SQLAlchemy backend-agnostic datatypes, for the examples above types such as _types.Integer, _types.Interval and _types.Enum. This may be achieved by intercepting the column reflection using the _events.DDLEvents.column_reflect() event in conjunction with the _types.TypeEngine.as_generic() method.

Given a table in MySQL (chosen because MySQL has a lot of vendor-specific datatypes and options):

CREATE TABLE IF NOT EXISTS my_table (
    id INTEGER PRIMARY KEY AUTO_INCREMENT,
    data1 VARCHAR(50) CHARACTER SET latin1,
    data2 MEDIUMINT(4),
    data3 TINYINT(2)
)

The above table includes MySQL-only integer types MEDIUMINT and TINYINT as well as a VARCHAR that includes the MySQL-only CHARACTER SET option. If we reflect this table normally, it produces a _schema.Table object that will contain those MySQL-specific datatypes and options:

>>> from sqlalchemy import MetaData, Table, create_engine
>>> mysql_engine = create_engine("mysql://scott:[email protected]/test")
>>> metadata_obj = MetaData()
>>> my_mysql_table = Table("my_table", metadata_obj, autoload_with=mysql_engine)

The above example reflects the above table schema into a new _schema.Table object. We can then, for demonstration purposes, print out the MySQL-specific “CREATE TABLE” statement using the _schema.CreateTable construct:

>>> from sqlalchemy.schema import CreateTable
>>> print(CreateTable(my_mysql_table).compile(mysql_engine))
{opensql}CREATE TABLE my_table (
id INTEGER(11) NOT NULL AUTO_INCREMENT,
data1 VARCHAR(50) CHARACTER SET latin1,
data2 MEDIUMINT(4),
data3 TINYINT(2),
PRIMARY KEY (id)
)ENGINE=InnoDB DEFAULT CHARSET=utf8mb4

Above, the MySQL-specific datatypes and options were maintained. If we wanted a _schema.Table that we could instead transfer cleanly to another database vendor, replacing the special datatypes sqlalchemy.dialects.mysql.MEDIUMINT and sqlalchemy.dialects.mysql.TINYINT with _types.Integer, we can choose instead to “genericize” the datatypes on this table, or otherwise change them in any way we’d like, by establishing a handler using the _events.DDLEvents.column_reflect() event. The custom handler will make use of the _types.TypeEngine.as_generic() method to convert the above MySQL-specific type objects into generic ones, by replacing the "type" entry within the column dictionary entry that is passed to the event handler. The format of this dictionary is described at _reflection.Inspector.get_columns():

>>> from sqlalchemy import event
>>> metadata_obj = MetaData()

>>> @event.listens_for(metadata_obj, "column_reflect")
>>> def genericize_datatypes(inspector, tablename, column_dict):
...     column_dict["type"] = column_dict["type"].as_generic()

>>> my_generic_table = Table("my_table", metadata_obj, autoload_with=mysql_engine)

We now get a new _schema.Table that is generic and uses _types.Integer for those datatypes. We can now emit a “CREATE TABLE” statement for example on a PostgreSQL database:

>>> pg_engine = create_engine("postgresql://scott:[email protected]/test", echo=True)
>>> my_generic_table.create(pg_engine)
{opensql}CREATE TABLE my_table (
    id SERIAL NOT NULL,
    data1 VARCHAR(50),
    data2 INTEGER,
    data3 INTEGER,
    PRIMARY KEY (id)
)

Noting above also that SQLAlchemy will usually make a decent guess for other behaviors, such as that the MySQL AUTO_INCREMENT directive is represented in PostgreSQL most closely using the SERIAL auto-incrementing datatype.

New in version 1.4: Added the _types.TypeEngine.as_generic() method and additionally improved the use of the _events.DDLEvents.column_reflect() event such that it may be applied to a _schema.MetaData object for convenience.


Limitations of Reflection

It’s important to note that the reflection process recreates _schema.Table metadata using only information which is represented in the relational database. This process by definition cannot restore aspects of a schema that aren’t actually stored in the database. State which is not available from reflection includes but is not limited to:

  • Client side defaults, either Python functions or SQL expressions defined using the default keyword of _schema.Column (note this is separate from server_default, which specifically is what’s available via reflection).
  • Column information, e.g. data that might have been placed into the _schema.Column.info dictionary
  • The value of the .quote setting for _schema.Column or _schema.Table
  • The association of a particular Sequence with a given _schema.Column

The relational database also in many cases reports on table metadata in a different format than what was specified in SQLAlchemy. The _schema.Table objects returned from reflection cannot be always relied upon to produce the identical DDL as the original Python-defined _schema.Table objects. Areas where this occurs includes server defaults, column-associated sequences and various idiosyncrasies regarding constraints and datatypes. Server side defaults may be returned with cast directives (typically PostgreSQL will include a ::<type> cast) or different quoting patterns than originally specified.

Another category of limitation includes schema structures for which reflection is only partially or not yet defined. Recent improvements to reflection allow things like views, indexes and foreign key options to be reflected. As of this writing, structures like CHECK constraints, table comments, and triggers are not reflected.