# SQLAlchemy 0.9 Documentation

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Release: 0.9.3 | Release Date: February 19, 2014

# 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=True` flag. If the `MetaData` is not persistently bound, also add the `autoload_with` argument:

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

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

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

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

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

    shopping_carts = Table('shopping_carts', meta)

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

## Overriding Reflected Columns

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

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

## Reflecting Views

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

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

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

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

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

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

## Reflecting All Tables at Once

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

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

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

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

## Fine Grained Reflection with Inspector

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

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

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

  Performs database schema inspection.

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

  A `Inspector` object is usually created via the `inspect()` function:

      from sqlalchemy import inspect, create_engine
      engine = create_engine('...')
      insp = inspect(engine)

  The inspection method above is equivalent to using the `Inspector.from_engine()` method, i.e.:

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

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

  `__init__`(bind)

    Initialize a new `Inspector`.

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

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

  `default_schema_name`

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

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

  classmethod `from_engine`(bind)

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

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

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

    See the example at `Inspector`.

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

    Return information about columns in table_name.

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

    name
      the column’s name
    type
      `TypeEngine`
    nullable
      boolean
    default
      the column’s default value
    attrs
      dict containing optional column attributes

    Parameters: | table_name – string name of the table. For special quoting, use `quoted_name`. schema – string schema name; if omitted, uses the default schema of the database connection. For special quoting, use `quoted_name`.

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

    Return information about foreign_keys in table_name.

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

    constrained_columns
      a list of column names that make up the foreign key
    referred_schema
      the name of the referred schema
    referred_table
      the name of the referred table
    referred_columns
      a list of column names in the referred table that correspond to constrained_columns
    name
      optional name of the foreign key constraint.

    Parameters: | table_name – string name of the table. For special quoting, use `quoted_name`. schema – string schema name; if omitted, uses the default schema of the database connection. For special quoting, use `quoted_name`.

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

    Return information about indexes in table_name.

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

    name
      the index’s name
    column_names
      list of column names in order
    unique
      boolean

    Parameters: | table_name – string name of the table. For special quoting, use `quoted_name`. schema – string schema name; if omitted, uses the default schema of the database connection. For special quoting, use `quoted_name`.

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

    Return information about primary key constraint on table_name.

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

    constrained_columns
      a list of column names that make up the primary key
    name
      optional name of the primary key constraint.

    Parameters: | table_name – string name of the table. For special quoting, use `quoted_name`. schema – string schema name; if omitted, uses the default schema of the database connection. For special quoting, use `quoted_name`.

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

    Return information about primary keys in table_name.

    Deprecated since version 0.7: Call to deprecated method get_primary_keys. Use get_pk_constraint instead.

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

  `get_schema_names`()

    Return all schema names.

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

    Return all table names in referred to within a particular schema.

    The names are expected to be real tables only, not views. Views are instead returned using the `Inspector.get_view_names()` method.

    Parameters: | schema – Schema name. If `schema` is left at `None`, the database’s default schema is used, else the named schema is searched. If the database does not support named schemas, behavior is undefined if `schema` is not passed as `None`. For special quoting, use `quoted_name`. order_by – Optional, may be the string “foreign_key” to sort the result on foreign key dependencies. Changed in version 0.8: the “foreign_key” sorting sorts tables in order of dependee to dependent; that is, in creation order, rather than in drop order. This is to maintain consistency with similar features such as `MetaData.sorted_tables` and `util.sort_tables()`.

    See also

    `MetaData.sorted_tables`

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

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

    This currently includes some options that apply to MySQL tables.

    Parameters: | table_name – string name of the table. For special quoting, use `quoted_name`. schema – string schema name; if omitted, uses the default schema of the database connection. For special quoting, use `quoted_name`.

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

    Return information about unique constraints in table_name.

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

    name
      the unique constraint’s name
    column_names
      list of column names in order

    Parameters: | table_name – string name of the table. For special quoting, use `quoted_name`. schema – string schema name; if omitted, uses the default schema of the database connection. For special quoting, use `quoted_name`.

    New in version 0.8.4.

  `get_view_definition`(view_name, schema=None)

    Return definition for view_name.

    Parameters: | schema – Optional, retrieve names from a non-default schema. For special quoting, use `quoted_name`.

  `get_view_names`(schema=None)

    Return all view names in schema.

    Parameters: | schema – Optional, retrieve names from a non-default schema. For special quoting, use `quoted_name`.

  `reflecttable`(table, include_columns, exclude_columns=())

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

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

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

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

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

## Limitations of Reflection

It’s important to note that the reflection process recreates `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 `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 `Column.info` dictionary
- The value of the `.quote` setting for `Column` or `Table`
- The assocation of a particular `Sequence` with a given `Column`

The relational database also in many cases reports on table metadata in a different format than what was specified in SQLAlchemy. The `Table` objects returned from reflection cannot be always relied upon to produce the identical DDL as the original Python-defined `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.