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:

  1. >>> messages = Table("messages", metadata_obj, autoload_with=engine)
  2. >>> [c.name for c in messages.columns]
  3. ['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:

  1. >>> shopping_cart_items = Table("shopping_cart_items", metadata_obj, autoload_with=engine)
  2. >>> "shopping_carts" in metadata_obj.tables
  3. 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:

  1. shopping_carts = Table("shopping_carts", metadata_obj)

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.:

  1. >>> mytable = Table(
  2. ...     "mytable",
  3. ...     metadata_obj,
  4. ...     Column(
  5. ...         "id", Integer, primary_key=True
  6. ...     ),  # override reflected 'id' to have primary key
  7. ...     Column("mydata", Unicode(50)),  # override reflected 'mydata' to be Unicode
  8. ...     # additional Column objects which require no change are reflected normally
  9. ...     autoload_with=some_engine,
  10. ... )

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:

  1. 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:

  1. my_view = Table(
  2.     "some_view",
  3.     metadata,
  4.     Column("view_id", Integer, primary_key=True),
  5.     Column("related_thing", Integer, ForeignKey("othertable.thing_id")),
  6.     autoload_with=engine,
  7. )

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:

  1. metadata_obj = MetaData()
  2. metadata_obj.reflect(bind=someengine)
  3. users_table = metadata_obj.tables["users"]
  4. addresses_table = metadata_obj.tables["addresses"]

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

  1. metadata_obj = MetaData()
  2. metadata_obj.reflect(bind=someengine)
  3. for table in reversed(metadata_obj.sorted_tables):
  4.     someengine.execute(table.delete())

Reflecting Tables from Other Schemas

The section Specifying the Schema Name introduces the concept of table schemas, which are namespaces within a database that contain tables and other objects, and which can be specified explicitly. The “schema” for a Table object, as well as for other objects like views, indexes and sequences, can be set up using the Table.schema parameter, and also as the default schema for a MetaData object using the MetaData.schema parameter.

The use of this schema parameter directly affects where the table reflection feature will look when it is asked to reflect objects. For example, given a MetaData object configured with a default schema name “project” via its MetaData.schema parameter:

  1. >>> metadata_obj = MetaData(schema="project")

The MetaData.reflect() will then utilize that configured .schema for reflection:

  1. >>> # uses `schema` configured in metadata_obj
  2. >>> metadata_obj.reflect(someengine)

The end result is that Table objects from the “project” schema will be reflected, and they will be populated as schema-qualified with that name:

  1. >>> metadata_obj.tables["project.messages"]
  2. Table('messages', MetaData(), Column('message_id', INTEGER(), table=<messages>), schema='project')

Similarly, an individual Table object that includes the Table.schema parameter will also be reflected from that database schema, overriding any default schema that may have been configured on the owning MetaData collection:

  1. >>> messages = Table("messages", metadata_obj, schema="project", autoload_with=someengine)
  2. >>> messages
  3. Table('messages', MetaData(), Column('message_id', INTEGER(), table=<messages>), schema='project')

Finally, the MetaData.reflect() method itself also allows a MetaData.reflect.schema parameter to be passed, so we could also load tables from the “project” schema for a default configured MetaData object:

  1. >>> metadata_obj = MetaData()
  2. >>> metadata_obj.reflect(someengine, schema="project")

We can call MetaData.reflect() any number of times with different MetaData.schema arguments (or none at all) to continue populating the MetaData object with more objects:

  1. >>> # add tables from the "customer" schema
  2. >>> metadata_obj.reflect(someengine, schema="customer")
  3. >>> # add tables from the default schema
  4. >>> metadata_obj.reflect(someengine)

Interaction of Schema-qualified Reflection with the Default Schema

Section Best Practices Summarized

In this section, we discuss SQLAlchemy’s reflection behavior regarding tables that are visible in the “default schema” of a database session, and how these interact with SQLAlchemy directives that include the schema explicitly. As a best practice, ensure the “default” schema for a database is just a single name, and not a list of names; for tables that are part of this “default” schema and can be named without schema qualification in DDL and SQL, leave corresponding Table.schema and similar schema parameters set to their default of None.

As described at Specifying a Default Schema Name with MetaData, databases that have the concept of schemas usually also include the concept of a “default” schema. The reason for this is naturally that when one refers to table objects without a schema as is common, a schema-capable database will still consider that table to be in a “schema” somewhere. Some databases such as PostgreSQL take this concept further into the notion of a schema search path where multiple schema names can be considered in a particular database session to be “implicit”; referring to a table name that it’s any of those schemas will not require that the schema name be present (while at the same time it’s also perfectly fine if the schema name is present).

Since most relational databases therefore have the concept of a particular table object which can be referred towards both in a schema-qualified way, as well as an “implicit” way where no schema is present, this presents a complexity for SQLAlchemy’s reflection feature. Reflecting a table in a schema-qualified manner will always populate its Table.schema attribute and additionally affect how this Table is organized into the MetaData.tables collection, that is, in a schema qualified manner. Conversely, reflecting the same table in a non-schema qualified manner will organize it into the MetaData.tables collection without being schema qualified. The end result is that there would be two separate Table objects in the single MetaData collection representing the same table in the actual database.

To illustrate the ramifications of this issue, consider tables from the “project” schema in the previous example, and suppose also that the “project” schema is the default schema of our database connection, or if using a database such as PostgreSQL suppose the “project” schema is set up in the PostgreSQL search_path. This would mean that the database accepts the following two SQL statements as equivalent:

  1. -- schema qualified
  2. SELECT message_id FROM project.messages
  4. -- non-schema qualified
  5. SELECT message_id FROM messages

This is not a problem as the table can be found in both ways. However in SQLAlchemy, it’s the identity of the Table object that determines its semantic role within a SQL statement. Based on the current decisions within SQLAlchemy, this means that if we reflect the same “messages” table in both a schema-qualified as well as a non-schema qualified manner, we get two Table objects that will not be treated as semantically equivalent:

  1. >>> # reflect in non-schema qualified fashion
  2. >>> messages_table_1 = Table("messages", metadata_obj, autoload_with=someengine)
  3. >>> # reflect in schema qualified fashion
  4. >>> messages_table_2 = Table(
  5. ...     "messages", metadata_obj, schema="project", autoload_with=someengine
  6. ... )
  7. >>> # two different objects
  8. >>> messages_table_1 is messages_table_2
  9. False
  10. >>> # stored in two different ways
  11. >>> metadata.tables["messages"] is messages_table_1
  12. True
  13. >>> metadata.tables["project.messages"] is messages_table_2
  14. True

The above issue becomes more complicated when the tables being reflected contain foreign key references to other tables. Suppose “messages” has a “project_id” column which refers to rows in another schema-local table “projects”, meaning there is a ForeignKeyConstraint object that is part of the definition of the “messages” table.

We can find ourselves in a situation where one MetaData collection may contain as many as four Table objects representing these two database tables, where one or two of the additional tables were generated by the reflection process; this is because when the reflection process encounters a foreign key constraint on a table being reflected, it branches out to reflect that referenced table as well. The decision making it uses to assign the schema to this referenced table is that SQLAlchemy will omit a default schema from the reflected ForeignKeyConstraint object if the owning Table also omits its schema name and also that these two objects are in the same schema, but will include it if it were not omitted.

The common scenario is when the reflection of a table in a schema qualified fashion then loads a related table that will also be performed in a schema qualified fashion:

  1. >>> # reflect "messages" in a schema qualified fashion
  2. >>> messages_table_1 = Table(
  3. ...     "messages", metadata_obj, schema="project", autoload_with=someengine
  4. ... )

The above messages_table_1 will refer to projects also in a schema qualified fashion. This “projects” table will be reflected automatically by the fact that “messages” refers to it:

  1. >>> messages_table_1.c.project_id
  2. Column('project_id', INTEGER(), ForeignKey('project.projects.project_id'), table=<messages>)

if some other part of the code reflects “projects” in a non-schema qualified fashion, there are now two projects tables that are not the same:

  1. >>> # reflect "projects" in a non-schema qualified fashion
  2. >>> projects_table_1 = Table("projects", metadata_obj, autoload_with=someengine)
  1. >>> # messages does not refer to projects_table_1 above
  2. >>> messages_table_1.c.project_id.references(projects_table_1.c.project_id)
  3. False
  1. >>> # it refers to this one
  2. >>> projects_table_2 = metadata_obj.tables["project.projects"]
  3. >>> messages_table_1.c.project_id.references(projects_table_2.c.project_id)
  4. True
  1. >>> # they're different, as one non-schema qualified and the other one is
  2. >>> projects_table_1 is projects_table_2
  3. False

The above confusion can cause problems within applications that use table reflection to load up application-level Table objects, as well as within migration scenarios, in particular such as when using Alembic Migrations to detect new tables and foreign key constraints.

The above behavior can be remedied by sticking to one simple practice:

  • Don’t include the Table.schema parameter for any Table that expects to be located in the default schema of the database.

For PostgreSQL and other databases that support a “search” path for schemas, add the following additional practice:

  • Keep the “search path” narrowed down to one schema only, which is the default schema.

See also

Remote-Schema Table Introspection and PostgreSQL search_path - additional details of this behavior as regards the PostgreSQL database.

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”:

  1. from sqlalchemy import create_engine
  2. from sqlalchemy import inspect
  4. engine = create_engine("...")
  5. insp = inspect(engine)
  6. print(insp.get_table_names())
Object NameDescription

Inspector

Performs database schema inspection.

ReflectedCheckConstraint

Dictionary representing the reflected elements corresponding to CheckConstraint.

ReflectedColumn

Dictionary representing the reflected elements corresponding to a Column object.

ReflectedComputed

Represent the reflected elements of a computed column, corresponding to the Computed construct.

ReflectedForeignKeyConstraint

Dictionary representing the reflected elements corresponding to ForeignKeyConstraint.

ReflectedIdentity

represent the reflected IDENTITY structure of a column, corresponding to the Identity construct.

ReflectedIndex

Dictionary representing the reflected elements corresponding to Index.

ReflectedPrimaryKeyConstraint

Dictionary representing the reflected elements corresponding to PrimaryKeyConstraint.

ReflectedTableComment

Dictionary representing the reflected comment corresponding to the Table.comment attribute.

ReflectedUniqueConstraint

Dictionary representing the reflected elements corresponding to UniqueConstraint.

class sqlalchemy.engine.reflection.Inspector

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, which may be passed an Engine or a Connection:

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

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

Members

__init__(), bind, clear_cache(), default_schema_name, dialect, engine, from_engine(), get_check_constraints(), get_columns(), get_foreign_keys(), get_indexes(), get_materialized_view_names(), get_multi_check_constraints(), get_multi_columns(), get_multi_foreign_keys(), get_multi_indexes(), get_multi_pk_constraint(), get_multi_table_comment(), get_multi_table_options(), get_multi_unique_constraints(), get_pk_constraint(), get_schema_names(), get_sequence_names(), get_sorted_table_and_fkc_names(), get_table_comment(), get_table_names(), get_table_options(), get_temp_table_names(), get_temp_view_names(), get_unique_constraints(), get_view_definition(), get_view_names(), has_index(), has_schema(), has_sequence(), has_table(), info_cache, reflect_table(), sort_tables_on_foreign_key_dependency()

Class signature

class sqlalchemy.engine.reflection.Inspector (sqlalchemy.inspection.Inspectable)

  1. New in version 2.0.
  3. See also
  5. [Inspector.get\_view\_names()](#sqlalchemy.engine.reflection.Inspector.get_view_names "sqlalchemy.engine.reflection.Inspector.get_view_names")

  • method sqlalchemy.engine.reflection.Inspector.get_multi_check_constraints(schema: Optional[str] = None, filter_names: Optional[Sequence[str]] = None, kind: ObjectKind = ObjectKind.TABLE, scope: ObjectScope = ObjectScope.DEFAULT, **kw: Any) → Dict[TableKey, List[ReflectedCheckConstraint]]

    Return information about check constraints in all tables in the given schema.

    The tables can be filtered by passing the names to use to filter_names.

    For each table the value is a list of ReflectedCheckConstraint.

    • Parameters:

      • schema – string schema name; if omitted, uses the default schema of the database connection. For special quoting, use quoted_name.

      • filter_names – optionally return information only for the objects listed here.

      • kind – a ObjectKind that specifies the type of objects to reflect. Defaults to ObjectKind.TABLE.

      • scope – a ObjectScope that specifies if constraints of default, temporary or any tables should be reflected. Defaults to ObjectScope.DEFAULT.

      • **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

      Returns:

      a dictionary where the keys are two-tuple schema,table-name and the values are list of dictionaries, each representing the definition of a check constraints. The schema is None if no schema is provided.

    New in version 2.0.

    See also

    Inspector.get_check_constraints()

  • method sqlalchemy.engine.reflection.Inspector.get_multi_columns(schema: Optional[str] = None, filter_names: Optional[Sequence[str]] = None, kind: ObjectKind = ObjectKind.TABLE, scope: ObjectScope = ObjectScope.DEFAULT, **kw: Any) → Dict[TableKey, List[ReflectedColumn]]

    Return information about columns in all objects in the given schema.

    The objects can be filtered by passing the names to use to filter_names.

    For each table the value is a list of ReflectedColumn.

    • Parameters:

      • schema – string schema name; if omitted, uses the default schema of the database connection. For special quoting, use quoted_name.

      • filter_names – optionally return information only for the objects listed here.

      • kind – a ObjectKind that specifies the type of objects to reflect. Defaults to ObjectKind.TABLE.

      • scope – a ObjectScope that specifies if columns of default, temporary or any tables should be reflected. Defaults to ObjectScope.DEFAULT.

      • **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

      Returns:

      a dictionary where the keys are two-tuple schema,table-name and the values are list of dictionaries, each representing the definition of a database column. The schema is None if no schema is provided.

    New in version 2.0.

    See also

    Inspector.get_columns()

  • method sqlalchemy.engine.reflection.Inspector.get_multi_foreign_keys(schema: Optional[str] = None, filter_names: Optional[Sequence[str]] = None, kind: ObjectKind = ObjectKind.TABLE, scope: ObjectScope = ObjectScope.DEFAULT, **kw: Any) → Dict[TableKey, List[ReflectedForeignKeyConstraint]]

    Return information about foreign_keys in all tables in the given schema.

    The tables can be filtered by passing the names to use to filter_names.

    For each table the value is a list of ReflectedForeignKeyConstraint.

    • Parameters:

      • schema – string schema name; if omitted, uses the default schema of the database connection. For special quoting, use quoted_name.

      • filter_names – optionally return information only for the objects listed here.

      • kind – a ObjectKind that specifies the type of objects to reflect. Defaults to ObjectKind.TABLE.

      • scope – a ObjectScope that specifies if foreign keys of default, temporary or any tables should be reflected. Defaults to ObjectScope.DEFAULT.

      • **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

      Returns:

      a dictionary where the keys are two-tuple schema,table-name and the values are list of dictionaries, each representing a foreign key definition. The schema is None if no schema is provided.

    New in version 2.0.

    See also

    Inspector.get_foreign_keys()

  • method sqlalchemy.engine.reflection.Inspector.get_multi_indexes(schema: Optional[str] = None, filter_names: Optional[Sequence[str]] = None, kind: ObjectKind = ObjectKind.TABLE, scope: ObjectScope = ObjectScope.DEFAULT, **kw: Any) → Dict[TableKey, List[ReflectedIndex]]

    Return information about indexes in in all objects in the given schema.

    The objects can be filtered by passing the names to use to filter_names.

    For each table the value is a list of ReflectedIndex.

    • Parameters:

      • schema – string schema name; if omitted, uses the default schema of the database connection. For special quoting, use quoted_name.

      • filter_names – optionally return information only for the objects listed here.

      • kind – a ObjectKind that specifies the type of objects to reflect. Defaults to ObjectKind.TABLE.

      • scope – a ObjectScope that specifies if indexes of default, temporary or any tables should be reflected. Defaults to ObjectScope.DEFAULT.

      • **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

      Returns:

      a dictionary where the keys are two-tuple schema,table-name and the values are list of dictionaries, each representing the definition of an index. The schema is None if no schema is provided.

    New in version 2.0.

    See also

    Inspector.get_indexes()

  • method sqlalchemy.engine.reflection.Inspector.get_multi_pk_constraint(schema: Optional[str] = None, filter_names: Optional[Sequence[str]] = None, kind: ObjectKind = ObjectKind.TABLE, scope: ObjectScope = ObjectScope.DEFAULT, **kw: Any) → Dict[TableKey, ReflectedPrimaryKeyConstraint]

    Return information about primary key constraints in all tables in the given schema.

    The tables can be filtered by passing the names to use to filter_names.

    For each table the value is a ReflectedPrimaryKeyConstraint.

    • Parameters:

      • schema – string schema name; if omitted, uses the default schema of the database connection. For special quoting, use quoted_name.

      • filter_names – optionally return information only for the objects listed here.

      • kind – a ObjectKind that specifies the type of objects to reflect. Defaults to ObjectKind.TABLE.

      • scope – a ObjectScope that specifies if primary keys of default, temporary or any tables should be reflected. Defaults to ObjectScope.DEFAULT.

      • **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

      Returns:

      a dictionary where the keys are two-tuple schema,table-name and the values are dictionaries, each representing the definition of a primary key constraint. The schema is None if no schema is provided.

    New in version 2.0.

    See also

    Inspector.get_pk_constraint()

  • method sqlalchemy.engine.reflection.Inspector.get_multi_table_comment(schema: Optional[str] = None, filter_names: Optional[Sequence[str]] = None, kind: ObjectKind = ObjectKind.TABLE, scope: ObjectScope = ObjectScope.DEFAULT, **kw: Any) → Dict[TableKey, ReflectedTableComment]

    Return information about the table comment in all objects in the given schema.

    The objects can be filtered by passing the names to use to filter_names.

    For each table the value is a ReflectedTableComment.

    Raises NotImplementedError for a dialect that does not support comments.

    • Parameters:

      • schema – string schema name; if omitted, uses the default schema of the database connection. For special quoting, use quoted_name.

      • filter_names – optionally return information only for the objects listed here.

      • kind – a ObjectKind that specifies the type of objects to reflect. Defaults to ObjectKind.TABLE.

      • scope – a ObjectScope that specifies if comments of default, temporary or any tables should be reflected. Defaults to ObjectScope.DEFAULT.

      • **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

      Returns:

      a dictionary where the keys are two-tuple schema,table-name and the values are dictionaries, representing the table comments. The schema is None if no schema is provided.

    New in version 2.0.

    See also

    Inspector.get_table_comment()

  • method sqlalchemy.engine.reflection.Inspector.get_multi_table_options(schema: Optional[str] = None, filter_names: Optional[Sequence[str]] = None, kind: ObjectKind = ObjectKind.TABLE, scope: ObjectScope = ObjectScope.DEFAULT, **kw: Any) → Dict[TableKey, Dict[str, Any]]

    Return a dictionary of options specified when the tables in the given schema were created.

    The tables can be filtered by passing the names to use to filter_names.

    This currently includes some options that apply to MySQL and Oracle tables.

    • Parameters:

      • schema – string schema name; if omitted, uses the default schema of the database connection. For special quoting, use quoted_name.

      • filter_names – optionally return information only for the objects listed here.

      • kind – a ObjectKind that specifies the type of objects to reflect. Defaults to ObjectKind.TABLE.

      • scope – a ObjectScope that specifies if options of default, temporary or any tables should be reflected. Defaults to ObjectScope.DEFAULT.

      • **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

      Returns:

      a dictionary where the keys are two-tuple schema,table-name and the values are dictionaries with the table options. The returned keys in each dict depend on the dialect in use. Each one is prefixed with the dialect name. The schema is None if no schema is provided.

    New in version 2.0.

    See also

    Inspector.get_table_options()

  • method sqlalchemy.engine.reflection.Inspector.get_multi_unique_constraints(schema: Optional[str] = None, filter_names: Optional[Sequence[str]] = None, kind: ObjectKind = ObjectKind.TABLE, scope: ObjectScope = ObjectScope.DEFAULT, **kw: Any) → Dict[TableKey, List[ReflectedUniqueConstraint]]

    Return information about unique constraints in all tables in the given schema.

    The tables can be filtered by passing the names to use to filter_names.

    For each table the value is a list of ReflectedUniqueConstraint.

    • Parameters:

      • schema – string schema name; if omitted, uses the default schema of the database connection. For special quoting, use quoted_name.

      • filter_names – optionally return information only for the objects listed here.

      • kind – a ObjectKind that specifies the type of objects to reflect. Defaults to ObjectKind.TABLE.

      • scope – a ObjectScope that specifies if constraints of default, temporary or any tables should be reflected. Defaults to ObjectScope.DEFAULT.

      • **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

      Returns:

      a dictionary where the keys are two-tuple schema,table-name and the values are list of dictionaries, each representing the definition of an unique constraint. The schema is None if no schema is provided.

    New in version 2.0.

    See also

    Inspector.get_unique_constraints()

  • method sqlalchemy.engine.reflection.Inspector.get_pk_constraint(table_name: str, schema: Optional[str] = None, **kw: Any) → ReflectedPrimaryKeyConstraint

    Return information about primary key constraint in table_name.

    Given a string table_name, and an optional string schema, return primary key information as a ReflectedPrimaryKeyConstraint.

    • 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.

      • **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

      Returns:

      a dictionary representing the definition of a primary key constraint.

    See also

    Inspector.get_multi_pk_constraint()

  • method sqlalchemy.engine.reflection.Inspector.get_schema_names(**kw: Any) → List[str]

    Return all schema names.

    • Parameters:

      **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

  • method sqlalchemy.engine.reflection.Inspector.get_sequence_names(schema: Optional[str] = None, **kw: Any) → List[str]

    Return all sequence names in schema.

    • Parameters:

      • schema – Optional, retrieve names from a non-default schema. For special quoting, use quoted_name.

      • **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

  • method sqlalchemy.engine.reflection.Inspector.get_sorted_table_and_fkc_names(schema: Optional[str] = None, **kw: Any) → List[Tuple[Optional[str], List[Tuple[str, Optional[str]]]]]

    Return dependency-sorted table and foreign key constraint names in referred to within a particular schema.

    This will yield 2-tuples of (tablename, [(tname, fkname), (tname, fkname), ...]) consisting of table names in CREATE order grouped with the foreign key constraint names that are not detected as belonging to a cycle. The final element will be (None, [(tname, fkname), (tname, fkname), ..]) which will consist of remaining foreign key constraint names that would require a separate CREATE step after-the-fact, based on dependencies between tables.

    New in version 1.0.-.

    • Parameters:

      • schema – schema name to query, if not the default schema.

      • **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

  1. See also
  3. [Inspector.get\_table\_names()](#sqlalchemy.engine.reflection.Inspector.get_table_names "sqlalchemy.engine.reflection.Inspector.get_table_names")
  5. [sort\_tables\_and\_constraints()]($ea3c09bf5d063318.md#sqlalchemy.schema.sort_tables_and_constraints "sqlalchemy.schema.sort_tables_and_constraints") - similar method which works with an already-given [MetaData]($e81afa1a43dcc92a.md#sqlalchemy.schema.MetaData "sqlalchemy.schema.MetaData").

  • method sqlalchemy.engine.reflection.Inspector.get_table_comment(table_name: str, schema: Optional[str] = None, **kw: Any) → ReflectedTableComment

    Return information about the table comment for table_name.

    Given a string table_name and an optional string schema, return table comment information as a ReflectedTableComment.

    Raises NotImplementedError for a dialect that does not support comments.

    • 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.

      • **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

      Returns:

      a dictionary, with the table comment.

    New in version 1.2.

    See also

    Inspector.get_multi_table_comment()

  • method sqlalchemy.engine.reflection.Inspector.get_table_names(schema: Optional[str] = None, **kw: Any) → List[str]

    Return all table names 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() and/or Inspector.get_materialized_view_names() methods.

    • 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.

      • **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

  1. See also
  3. [Inspector.get\_sorted\_table\_and\_fkc\_names()](#sqlalchemy.engine.reflection.Inspector.get_sorted_table_and_fkc_names "sqlalchemy.engine.reflection.Inspector.get_sorted_table_and_fkc_names")
  5. [MetaData.sorted\_tables]($e81afa1a43dcc92a.md#sqlalchemy.schema.MetaData.sorted_tables "sqlalchemy.schema.MetaData.sorted_tables")

  • method sqlalchemy.engine.reflection.Inspector.get_table_options(table_name: str, schema: Optional[str] = None, **kw: Any) → Dict[str, Any]

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

    This currently includes some options that apply to MySQL and Oracle 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.

      • **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

      Returns:

      a dict with the table options. The returned keys depend on the dialect in use. Each one is prefixed with the dialect name.

    See also

    Inspector.get_multi_table_options()

  • method sqlalchemy.engine.reflection.Inspector.get_temp_table_names(**kw: Any) → List[str]

    Return a list of temporary table names for the current bind.

    This method is unsupported by most dialects; currently only Oracle, PostgreSQL and SQLite implements it.

    • Parameters:

      **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

    New in version 1.0.0.

  • method sqlalchemy.engine.reflection.Inspector.get_temp_view_names(**kw: Any) → List[str]

    Return a list of temporary view names for the current bind.

    This method is unsupported by most dialects; currently only PostgreSQL and SQLite implements it.

    • Parameters:

      **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

    New in version 1.0.0.

  • method sqlalchemy.engine.reflection.Inspector.get_unique_constraints(table_name: str, schema: Optional[str] = None, **kw: Any) → List[ReflectedUniqueConstraint]

    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 ReflectedUniqueConstraint.

    • 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.

      • **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

      Returns:

      a list of dictionaries, each representing the definition of an unique constraint.

    See also

    Inspector.get_multi_unique_constraints()

  • method sqlalchemy.engine.reflection.Inspector.get_view_definition(view_name: str, schema: Optional[str] = None, **kw: Any) → str

    Return definition for the plain or materialized view called view_name.

    • Parameters:

      • view_name – Name of the view.

      • schema – Optional, retrieve names from a non-default schema. For special quoting, use quoted_name.

      • **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

  • method sqlalchemy.engine.reflection.Inspector.get_view_names(schema: Optional[str] = None, **kw: Any) → List[str]

    Return all non-materialized view names in schema.

    • Parameters:

      • schema – Optional, retrieve names from a non-default schema. For special quoting, use quoted_name.

      • **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

  1. Changed in version 2.0: For those dialects that previously included the names of materialized views in this list (currently PostgreSQL), this method no longer returns the names of materialized views. the [Inspector.get\_materialized\_view\_names()](#sqlalchemy.engine.reflection.Inspector.get_materialized_view_names "sqlalchemy.engine.reflection.Inspector.get_materialized_view_names") method should be used instead.
  3. See also
  5. [Inspector.get\_materialized\_view\_names()](#sqlalchemy.engine.reflection.Inspector.get_materialized_view_names "sqlalchemy.engine.reflection.Inspector.get_materialized_view_names")

  • method sqlalchemy.engine.reflection.Inspector.has_index(table_name: str, index_name: str, schema: Optional[str] = None, **kw: Any) → bool

    Check the existence of a particular index name in the database.

    • Parameters:

      • table_name – the name of the table the index belongs to

      • index_name – the name of the index to check

      • schema – schema name to query, if not the default schema.

      • **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

  1. New in version 2.0.

  • method sqlalchemy.engine.reflection.Inspector.has_schema(schema_name: str, **kw: Any) → bool

    Return True if the backend has a schema with the given name.

    • Parameters:

      • schema_name – name of the schema to check

      • **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

  1. New in version 2.0.

  • method sqlalchemy.engine.reflection.Inspector.has_sequence(sequence_name: str, schema: Optional[str] = None, **kw: Any) → bool

    Return True if the backend has a sequence with the given name.

    • Parameters:

      • sequence_name – name of the sequence to check

      • schema – schema name to query, if not the default schema.

      • **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

  1. New in version 1.4.

  • method sqlalchemy.engine.reflection.Inspector.has_table(table_name: str, schema: Optional[str] = None, **kw: Any) → bool

    Return True if the backend has a table, view, or temporary table of the given name.

    • Parameters:

      • table_name – name of the table to check

      • schema – schema name to query, if not the default schema.

      • **kw – Additional keyword argument to pass to the dialect specific implementation. See the documentation of the dialect in use for more information.

  1. New in version 1.4: - the [Inspector.has\_table()](#sqlalchemy.engine.reflection.Inspector.has_table "sqlalchemy.engine.reflection.Inspector.has_table") method replaces the `Engine.has_table()` method.
  3. Changed in version 2.0::: [Inspector.has\_table()](#sqlalchemy.engine.reflection.Inspector.has_table "sqlalchemy.engine.reflection.Inspector.has_table") now formally supports checking for additional table-like objects:
  5. -   any type of views (plain or materialized)
  7. -   temporary tables of any kind
  9. Previously, these two checks were not formally specified and different dialects would vary in their behavior. The dialect testing suite now includes tests for all of these object types and should be supported by all SQLAlchemy-included dialects. Support among third party dialects may be lagging, however.

  • attribute sqlalchemy.engine.reflection.Inspector.info_cache: Dict[Any, Any]

  • method sqlalchemy.engine.reflection.Inspector.reflect_table(table: Table, include_columns: Optional[Collection[str]], exclude_columns: Collection[str] = (), resolve_fks: bool = True, _extend_on: Optional[Set[Table]] = None, _reflect_info: Optional[_ReflectionInfo] = None) → None

    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:

    1. from sqlalchemy import create_engine, MetaData, Table
    2. from sqlalchemy import inspect
    4. engine = create_engine('...')
    5. meta = MetaData()
    6. user_table = Table('user', meta)
    7. insp = inspect(engine)
    8. insp.reflect_table(user_table, None)

    Changed in version 1.4: Renamed from reflecttable to reflect_table

    • 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.

  • method sqlalchemy.engine.reflection.Inspector.sort_tables_on_foreign_key_dependency(consider_schemas: Collection[Optional[str]] = (None,), **kw: Any) → List[Tuple[Optional[Tuple[Optional[str], str]], List[Tuple[Tuple[Optional[str], str], Optional[str]]]]]

    Return dependency-sorted table and foreign key constraint names referred to within multiple schemas.

    This method may be compared to Inspector.get_sorted_table_and_fkc_names(), which works on one schema at a time; here, the method is a generalization that will consider multiple schemas at once including that it will resolve for cross-schema foreign keys.

    New in version 2.0.

class sqlalchemy.engine.interfaces.ReflectedColumn

Dictionary representing the reflected elements corresponding to a Column object.

The ReflectedColumn structure is returned by the get_columns method.

Members

autoincrement, comment, computed, default, dialect_options, identity, name, nullable, type

Class signature

class sqlalchemy.engine.interfaces.ReflectedColumn (builtins.dict)

class sqlalchemy.engine.interfaces.ReflectedComputed

Represent the reflected elements of a computed column, corresponding to the Computed construct.

The ReflectedComputed structure is part of the ReflectedColumn structure, which is returned by the Inspector.get_columns() method.

Members

persisted, sqltext

Class signature

class sqlalchemy.engine.interfaces.ReflectedComputed (builtins.dict)

class sqlalchemy.engine.interfaces.ReflectedCheckConstraint

Dictionary representing the reflected elements corresponding to CheckConstraint.

The ReflectedCheckConstraint structure is returned by the Inspector.get_check_constraints() method.

Members

dialect_options, sqltext

Class signature

class sqlalchemy.engine.interfaces.ReflectedCheckConstraint (builtins.dict)

class sqlalchemy.engine.interfaces.ReflectedForeignKeyConstraint

Dictionary representing the reflected elements corresponding to ForeignKeyConstraint.

The ReflectedForeignKeyConstraint structure is returned by the Inspector.get_foreign_keys() method.

Members

constrained_columns, options, referred_columns, referred_schema, referred_table

Class signature

class sqlalchemy.engine.interfaces.ReflectedForeignKeyConstraint (builtins.dict)

class sqlalchemy.engine.interfaces.ReflectedIdentity

represent the reflected IDENTITY structure of a column, corresponding to the Identity construct.

The ReflectedIdentity structure is part of the ReflectedColumn structure, which is returned by the Inspector.get_columns() method.

Members

always, cache, cycle, increment, maxvalue, minvalue, nomaxvalue, nominvalue, on_null, order, start

Class signature

class sqlalchemy.engine.interfaces.ReflectedIdentity (builtins.dict)

class sqlalchemy.engine.interfaces.ReflectedIndex

Dictionary representing the reflected elements corresponding to Index.

The ReflectedIndex structure is returned by the Inspector.get_indexes() method.

Members

column_names, column_sorting, dialect_options, duplicates_constraint, expressions, include_columns, name, unique

Class signature

class sqlalchemy.engine.interfaces.ReflectedIndex (builtins.dict)

class sqlalchemy.engine.interfaces.ReflectedPrimaryKeyConstraint

Dictionary representing the reflected elements corresponding to PrimaryKeyConstraint.

The ReflectedPrimaryKeyConstraint structure is returned by the Inspector.get_pk_constraint() method.

Members

constrained_columns, dialect_options

Class signature

class sqlalchemy.engine.interfaces.ReflectedPrimaryKeyConstraint (builtins.dict)

class sqlalchemy.engine.interfaces.ReflectedUniqueConstraint

Dictionary representing the reflected elements corresponding to UniqueConstraint.

The ReflectedUniqueConstraint structure is returned by the Inspector.get_unique_constraints() method.

Members

column_names, dialect_options, duplicates_index

Class signature

class sqlalchemy.engine.interfaces.ReflectedUniqueConstraint (builtins.dict)

class sqlalchemy.engine.interfaces.ReflectedTableComment

Dictionary representing the reflected comment corresponding to the Table.comment attribute.

The ReflectedTableComment structure is returned by the Inspector.get_table_comment() method.

Members

text

Class signature

class sqlalchemy.engine.interfaces.ReflectedTableComment (builtins.dict)

Reflecting with Database-Agnostic Types

When the columns of a table are reflected, using either the Table.autoload_with parameter of Table or the Inspector.get_columns() method of 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 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 Integer, Interval and Enum. This may be achieved by intercepting the column reflection using the DDLEvents.column_reflect() event in conjunction with the TypeEngine.as_generic() method.

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

  1. CREATE TABLE IF NOT EXISTS my_table (
  2.     id INTEGER PRIMARY KEY AUTO_INCREMENT,
  3.     data1 VARCHAR(50) CHARACTER SET latin1,
  4.     data2 MEDIUMINT(4),
  5.     data3 TINYINT(2)
  6. )

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 Table object that will contain those MySQL-specific datatypes and options:

  1. >>> from sqlalchemy import MetaData, Table, create_engine
  2. >>> mysql_engine = create_engine("mysql+mysqldb://scott:tiger@localhost/test")
  3. >>> metadata_obj = MetaData()
  4. >>> my_mysql_table = Table("my_table", metadata_obj, autoload_with=mysql_engine)

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

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

Above, the MySQL-specific datatypes and options were maintained. If we wanted a 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 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 DDLEvents.column_reflect() event. The custom handler will make use of the 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 Inspector.get_columns():

  1. >>> from sqlalchemy import event
  2. >>> metadata_obj = MetaData()
  4. >>> @event.listens_for(metadata_obj, "column_reflect")
  5. ... def genericize_datatypes(inspector, tablename, column_dict):
  6. ...     column_dict["type"] = column_dict["type"].as_generic()
  8. >>> my_generic_table = Table("my_table", metadata_obj, autoload_with=mysql_engine)

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

  1. >>> pg_engine = create_engine("postgresql+psycopg2://scott:tiger@localhost/test", echo=True)
  2. >>> my_generic_table.create(pg_engine)
  3. CREATE TABLE my_table (
  4.     id SERIAL NOT NULL,
  5.     data1 VARCHAR(50),
  6.     data2 INTEGER,
  7.     data3 INTEGER,
  8.     PRIMARY KEY (id)
  9. )

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 TypeEngine.as_generic() method and additionally improved the use of the DDLEvents.column_reflect() event such that it may be applied to a MetaData object for convenience.

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 association 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 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.