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)
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)
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()
Limitations of Reflection
It's important to note that the reflection process recreates :class:`.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 :class:`.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 :attr:`.Column.info` dictionary
- The value of the .quote setting for :class:`.Column` or :class:`.Table`
- The assocation of a particular :class:`.Sequence` with a given :class:`.Column`
The relational database also in many cases reports on table metadata in a different format than what was specified in SQLAlchemy. The :class:`.Table` objects returned from reflection cannot be always relied upon to produce the identical DDL as the original Python-defined :class:`.Table` objects. Areas where this occurs includes server defaults, column-associated sequences and various idosyncrasies 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.