Release: 1.1.0b1 | Release Date: not released

SQLAlchemy 1.1 Documentation

Mapping Table Columns

The default behavior of mapper() 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

The name assigned to the Python attribute which maps to Column can be different from either 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 resolves to a column named user_id and 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

Or in a classical mapping, placed in the properties dictionary with the desired key:

mapper(User, user_table, properties={
   'id': user_table.c.user_id,
   'name': user_table.c.user_name,

In the next section we’ll examine the usage of .key more closely.

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 (e.g. 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:

@event.listens_for(Table, "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=True, autoload_with=some_engine)

If we want to qualify our event to only react for the specific MetaData object above, we can check for it in our event:

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

Naming All Columns with a Prefix

A quick approach to prefix column names, typically when mapping to an existing Table object, is to use column_prefix:

class User(Base):
    __table__ = user_table
    __mapper_args__ = {'column_prefix':'_'}

The above will place attribute names such as _user_id, _user_name, _password etc. on the mapped User class.

This approach is uncommon in modern usage. For dealing with reflected tables, a more flexible approach is to use that described in Automating Column Naming Schemes from Reflected Tables.

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 "", "address.user_id" to the
    # "id" attribute
    id = column_property(, 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.

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

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

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.

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

매개 변수:
  • *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 attributes.get_history() or Session.is_modified() which also need to know the “previous” value of the attribute.

    버전 0.6.6에 추가.

  • comparator_factory – a class which extends ColumnProperty.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.

    버전 0.7.3에 추가.

  • info

    Optional data dictionary which will be populated into the attribute of this object.

    버전 0.8에 추가.

  • extension – an AttributeExtension instance, or list of extensions, which will be prepended to the list of attribute listeners for the resulting descriptor placed on the class. Deprecated. Please see AttributeEvents.

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' :[],
        'primary_key' : []


insert and update defaults configured on individual Column objects, i.e. those described at Column Insert/Update Defaults including those configured by the default, update, server_default and server_onupdate arguments, will continue to function normally even if those Column objects are not mapped. This is because in the case of default and update, 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 server_default and server_onupdate, the relational database itself maintains these functions.