Release: 1.4.46 | Release Date: January 3, 2023

# SQLAlchemy 1.4 Documentation

# Mapping Table Columns

Introductory background on mapping to columns falls under the subject of `Table` configuration; the general form falls under one of three forms:

- Declarative Table - `Column` objects are associated with a `Table` as well as with an ORM mapping in one step by declaring them inline as class attributes.

- Declarative with Imperative Table (a.k.a. Hybrid Declarative) - `Column` objects are associated directly with their `Table` object, as detailed at Describing Databases with MetaData; the columns are then mapped by the Declarative process by associating the `Table` with the class to be mapped via the `__table__` attribute.

- Imperative Mapping - like “Imperative Table”, `Column` objects are associated directly with their `Table` object; the columns are then mapped by the Imperative process using `registry.map_imperatively()`.

In all of the above cases, the `mapper` constructor is ultimately invoked with a completed `Table` object passed as the selectable unit to be mapped. The behavior of `mapper` then is to assemble all the columns in the mapped `Table` into mapped object attributes, each of which are named according to the name of the column itself (specifically, the `key` attribute of `Column`). This behavior can be modified in several ways.

## Naming Columns Distinctly from Attribute Names

A mapping by default shares the same name for a `Column` as that of the mapped attribute - specifically it matches the `Column.key` attribute on `Column`, which by default is the same as the `Column.name`.

The name assigned to the Python attribute which maps to `Column` can be different from either `Column.name` or `Column.key` just by assigning it that way, as we illustrate here in a Declarative mapping:

    class User(Base):
        __tablename__ = "user"
        id = Column("user_id", Integer, primary_key=True)
        name = Column("user_name", String(50))

Where above `User.id` resolves to a column named `user_id` and `User.name` resolves to a column named `user_name`.

When mapping to an existing table, the `Column` object can be referenced directly:

    class User(Base):
        __table__ = user_table
        id = user_table.c.user_id
        name = user_table.c.user_name

The corresponding technique for an imperative mapping is to place the desired key in the `mapper.properties` dictionary with the desired key:

    mapper_registry.map_imperatively(
        User,
        user_table,
        properties={
            "id": user_table.c.user_id,
            "name": user_table.c.user_name,
        },
    )

## Automating Column Naming Schemes from Reflected Tables

In the previous section Naming Columns Distinctly from Attribute Names, we showed how a `Column` explicitly mapped to a class can have a different attribute name than the column. But what if we aren’t listing out `Column` objects explicitly, and instead are automating the production of `Table` objects using reflection (i.e. as described in Reflecting Database Objects)? In this case we can make use of the `DDLEvents.column_reflect()` event to intercept the production of `Column` objects and provide them with the `Column.key` of our choice. The event is most easily associated with the `MetaData` object that’s in use, such as below we use the one linked to the `declarative_base` instance:

    @event.listens_for(Base.metadata, "column_reflect")
    def column_reflect(inspector, table, column_info):
        # set column.key = "attr_<lower_case_name>"
        column_info["key"] = "attr_%s" % column_info["name"].lower()

With the above event, the reflection of `Column` objects will be intercepted with our event that adds a new “.key” element, such as in a mapping as below:

    class MyClass(Base):
        __table__ = Table("some_table", Base.metadata, autoload_with=some_engine)

The approach also works with both the `DeferredReflection` base class as well as with the Automap extension. For automap specifically, see the section Intercepting Column Definitions for background.

See also

Mapping Declaratively with Reflected Tables

`DDLEvents.column_reflect()`

Intercepting Column Definitions - in the Automap documentation

## Using column_property for column level options

Options can be specified when mapping a `Column` using the `column_property()` function. This function explicitly creates the `ColumnProperty` used by the `mapper()` to keep track of the `Column`; normally, the `mapper()` creates this automatically. Using `column_property()`, we can pass additional arguments about how we’d like the `Column` to be mapped. Below, we pass an option `active_history`, which specifies that a change to this column’s value should result in the former value being loaded first:

    from sqlalchemy.orm import column_property

    class User(Base):
        __tablename__ = "user"

        id = Column(Integer, primary_key=True)
        name = column_property(Column(String(50)), active_history=True)

`column_property()` is also used to map a single attribute to multiple columns. This use case arises when mapping to a `join()` which has attributes which are equated to each other:

    class User(Base):
        __table__ = user.join(address)

        # assign "user.id", "address.user_id" to the
        # "id" attribute
        id = column_property(user_table.c.id, address_table.c.user_id)

For more examples featuring this usage, see Mapping a Class against Multiple Tables.

Another place where `column_property()` is needed is to specify SQL expressions as mapped attributes, such as below where we create an attribute `fullname` that is the string concatenation of the `firstname` and `lastname` columns:

    class User(Base):
        __tablename__ = "user"
        id = Column(Integer, primary_key=True)
        firstname = Column(String(50))
        lastname = Column(String(50))
        fullname = column_property(firstname + " " + lastname)

See examples of this usage at SQL Expressions as Mapped Attributes.

Object Name | Description
--- | ---
column_property(*columns, **kwargs) | Provide a column-level property for use with a mapping.

function sqlalchemy.orm.column_property(*columns, **kwargs)

  Provide a column-level property for use with a mapping.

  Column-based properties can normally be applied to the mapper’s `properties` dictionary using the `Column` element directly. Use this function when the given column is not directly present within the mapper’s selectable; examples include SQL expressions, functions, and scalar SELECT queries.

  The `column_property()` function returns an instance of `ColumnProperty`.

  Columns that aren’t present in the mapper’s selectable won’t be persisted by the mapper and are effectively “read-only” attributes.

  Parameters:

    - *cols – list of Column objects to be mapped.

    - active_history=False – When `True`, indicates that the “previous” value for a scalar attribute should be loaded when replaced, if not already loaded. Normally, history tracking logic for simple non-primary-key scalar values only needs to be aware of the “new” value in order to perform a flush. This flag is available for applications that make use of `get_history()` or `Session.is_modified()` which also need to know the “previous” value of the attribute.

    - comparator_factory – a class which extends `Comparator` which provides custom SQL clause generation for comparison operations.

    - group – a group name for this property when marked as deferred.

    - deferred – when True, the column property is “deferred”, meaning that it does not load immediately, and is instead loaded when the attribute is first accessed on an instance. See also `deferred()`.

    - doc – optional string that will be applied as the doc on the class-bound descriptor.

    - expire_on_flush=True – Disable expiry on flush. A column_property() which refers to a SQL expression (and not a single table-bound column) is considered to be a “read only” property; populating it has no effect on the state of data, and it can only return database state. For this reason a column_property()’s value is expired whenever the parent object is involved in a flush, that is, has any kind of “dirty” state within a flush. Setting this parameter to `False` will have the effect of leaving any existing value present after the flush proceeds. Note however that the `Session` with default expiration settings still expires all attributes after a `Session.commit()` call, however.

    - info – Optional data dictionary which will be populated into the `MapperProperty.info` attribute of this object.

    - raiseload –

      if True, indicates the column should raise an error when undeferred, rather than loading the value. This can be altered at query time by using the `deferred()` option with raiseload=False.

      New in version 1.4.

      See also

      Raiseload for Deferred Columns

  See also

  Using column_property for column level options - to map columns while including mapping options

  Using column_property - to map SQL expressions

## Mapping to an Explicit Set of Primary Key Columns

The `Mapper` construct in order to successfully map a table always requires that at least one column be identified as the “primary key” for that selectable. This is so that when an ORM object is loaded or persisted, it can be placed in the identity map with an appropriate identity key.

To support this use case, all `FromClause` objects (where `FromClause` is the common base for objects such as `Table`, `Join`, `Subquery`, etc.) have an attribute `FromClause.primary_key` which returns a collection of those `Column` objects that indicate they are part of a “primary key”, which is derived from each `Column` object being a member of a `PrimaryKeyConstraint` collection that’s associated with the `Table` from which they ultimately derive.

In those cases where the selectable being mapped does not include columns that are explicitly part of the primary key constraint on their parent table, a user-defined set of primary key columns must be defined. The `mapper.primary_key` parameter is used for this purpose.

Given the following example of a Imperative Table mapping against an existing `Table` object, as would occur in a scenario such as when the `Table` were reflected from an existing database, where the table does not have any declared primary key, we may map such a table as in the following example:

    from sqlalchemy import Column
    from sqlalchemy import MetaData
    from sqlalchemy import String
    from sqlalchemy import Table
    from sqlalchemy import UniqueConstraint
    from sqlalchemy.orm import declarative_base

    metadata = MetaData()
    group_users = Table(
        "group_users",
        metadata,
        Column("user_id", String(40), nullable=False),
        Column("group_id", String(40), nullable=False),
        UniqueConstraint("user_id", "group_id"),
    )

    Base = declarative_base()

    class GroupUsers(Base):
        __table__ = group_users
        __mapper_args__ = {"primary_key": [group_users.c.user_id, group_users.c.group_id]}

Above, the `group_users` table is an association table of some kind with string columns `user_id` and `group_id`, but no primary key is set up; instead, there is only a `UniqueConstraint` establishing that the two columns represent a unique key. The `Mapper` does not automatically inspect unique constraints for primary keys; instead, we make use of the `mapper.primary_key` parameter, passing a collection of `[group_users.c.user_id, group_users.c.group_id]`, indicating that these two columns should be used in order to construct the identity key for instances of the `GroupUsers` class.

## Mapping a Subset of Table Columns

Sometimes, a `Table` object was made available using the reflection process described at Reflecting Database Objects to load the table’s structure from the database. For such a table that has lots of columns that don’t need to be referenced in the application, the `include_properties` or `exclude_properties` arguments can specify that only a subset of columns should be mapped. For example:

    class User(Base):
        __table__ = user_table
        __mapper_args__ = {"include_properties": ["user_id", "user_name"]}

…will map the `User` class to the `user_table` table, only including the `user_id` and `user_name` columns - the rest are not referenced. Similarly:

    class Address(Base):
        __table__ = address_table
        __mapper_args__ = {"exclude_properties": ["street", "city", "state", "zip"]}

…will map the `Address` class to the `address_table` table, including all columns present except `street`, `city`, `state`, and `zip`.

When this mapping is used, the columns that are not included will not be referenced in any SELECT statements emitted by `Query`, nor will there be any mapped attribute on the mapped class which represents the column; assigning an attribute of that name will have no effect beyond that of a normal Python attribute assignment.

In some cases, multiple columns may have the same name, such as when mapping to a join of two or more tables that share some column name. `include_properties` and `exclude_properties` can also accommodate `Column` objects to more accurately describe which columns should be included or excluded:

    class UserAddress(Base):
        __table__ = user_table.join(addresses_table)
        __mapper_args__ = {
            "exclude_properties": [address_table.c.id],
            "primary_key": [user_table.c.id],
        }

Note

insert and update defaults configured on individual `Column` objects, i.e. those described at Column INSERT/UPDATE Defaults including those configured by the `Column.default`, `Column.onupdate`, `Column.server_default` and `Column.server_onupdate` parameters, will continue to function normally even if those `Column` objects are not mapped. This is because in the case of `Column.default` and `Column.onupdate`, the `Column` object is still present on the underlying `Table`, thus allowing the default functions to take place when the ORM emits an INSERT or UPDATE, and in the case of `Column.server_default` and `Column.server_onupdate`, the relational database itself emits these defaults as a server side behavior.