Define an extension to the sqlalchemy.ext.declarative system which automatically generates mapped classes and relationships from a database schema, typically though not necessarily one which is reflected.
New in version 0.9.1: Added sqlalchemy.ext.automap.
Note
The sqlalchemy.ext.automap extension should be considered experimental as of 0.9.1. Featureset and API stability is not guaranteed at this time.
It is hoped that the AutomapBase system provides a quick and modernized solution to the problem that the very famous SQLSoup also tries to solve, that of generating a quick and rudimentary object model from an existing database on the fly. By addressing the issue strictly at the mapper configuration level, and integrating fully with existing Declarative class techniques, AutomapBase seeks to provide a well-integrated approach to the issue of expediently auto-generating ad-hoc mappings.
The simplest usage is to reflect an existing database into a new model. We create a new AutomapBase class in a similar manner as to how we create a declarative base class, using automap_base(). We then call AutomapBase.prepare() on the resulting base class, asking it to reflect the schema and produce mappings:
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine
Base = automap_base()
# engine, suppose it has two tables 'user' and 'address' set up
engine = create_engine("sqlite:///mydatabase.db")
# reflect the tables
Base.prepare(engine, reflect=True)
# mapped classes are now created with names by default
# matching that of the table name.
User = Base.classes.user
Address = Base.classes.address
session = Session(engine)
# rudimentary relationships are produced
session.add(Address(email_address="foo@bar.com", user=User(name="foo")))
session.commit()
# collection-based relationships are by default named
# "<classname>_collection"
print (u1.address_collection)Above, calling AutomapBase.prepare() while passing along the AutomapBase.prepare.reflect parameter indicates that the MetaData.reflect() method will be called on this declarative base classes’ MetaData collection; then, each viable Table within the MetaData will get a new mapped class generated automatically. The ForeignKeyConstraint objects which link the various tables together will be used to produce new, bidirectional relationship() objects between classes. The classes and relationships follow along a default naming scheme that we can customize. At this point, our basic mapping consisting of related User and Address classes is ready to use in the traditional way.
We can pass a pre-declared MetaData object to automap_base(). This object can be constructed in any way, including programmatically, from a serialized file, or from itself being reflected using MetaData.reflect(). Below we illustrate a combination of reflection and explicit table declaration:
from sqlalchemy import create_engine, MetaData, Table, Column, ForeignKey
engine = create_engine("sqlite:///mydatabase.db")
# produce our own MetaData object
metadata = MetaData()
# we can reflect it ourselves from a database, using options
# such as 'only' to limit what tables we look at...
metadata.reflect(engine, only=['user', 'address'])
# ... or just define our own Table objects with it (or combine both)
Table('user_order', metadata,
Column('id', Integer, primary_key=True),
Column('user_id', ForeignKey('user.id'))
)
# we can then produce a set of mappings from this MetaData.
Base = automap_base(metadata=metadata)
# calling prepare() just sets up mapped classes and relationships.
Base.prepare()
# mapped classes are ready
User, Address, Order = Base.classes.user, Base.classes.address, Base.classes.user_orderThe sqlalchemy.ext.automap extension allows classes to be defined explicitly, in a way similar to that of the DeferredReflection class. Classes that extend from AutomapBase act like regular declarative classes, but are not immediately mapped after their construction, and are instead mapped when we call AutomapBase.prepare(). The AutomapBase.prepare() method will make use of the classes we’ve established based on the table name we use. If our schema contains tables user and address, we can define one or both of the classes to be used:
from sqlalchemy.ext.automap import automap_base
from sqlalchemy import create_engine
# automap base
Base = automap_base()
# pre-declare User for the 'user' table
class User(Base):
__tablename__ = 'user'
# override schema elements like Columns
user_name = Column('name', String)
# override relationships too, if desired.
# we must use the same name that automap would use for the
# relationship, and also must refer to the class name that automap will
# generate for "address"
address_collection = relationship("address", collection_class=set)
# reflect
engine = create_engine("sqlite:///mydatabase.db")
Base.prepare(engine, reflect=True)
# we still have Address generated from the tablename "address",
# but User is the same as Base.classes.User now
Address = Base.classes.address
u1 = session.query(User).first()
print (u1.address_collection)
# the backref is still there:
a1 = session.query(Address).first()
print (a1.user)Above, one of the more intricate details is that we illustrated overriding one of the relationship() objects that automap would have created. To do this, we needed to make sure the names match up with what automap would normally generate, in that the relationship name would be User.address_collection and the name of the class referred to, from automap’s perspective, is called address, even though we are referring to it as Address within our usage of this class.
sqlalchemy.ext.automap is tasked with producing mapped classes and relationship names based on a schema, which means it has decision points in how these names are determined. These three decision points are provided using functions which can be passed to the AutomapBase.prepare() method, and are known as classname_for_table(), name_for_scalar_relationship(), and name_for_collection_relationship(). Any or all of these functions are provided as in the example below, where we use a “camel case” scheme for class names and a “pluralizer” for collection names using the Inflect package:
import re
import inflect
def camelize_classname(base, tablename, table):
"Produce a 'camelized' class name, e.g. "
"'words_and_underscores' -> 'WordsAndUnderscores'"
return str(tablename[0].upper() + \
re.sub(r'_(\w)', lambda m: m.group(1).upper(), tablename[1:]))
_pluralizer = inflect.engine()
def pluralize_collection(base, local_cls, referred_cls, constraint):
"Produce an 'uncamelized', 'pluralized' class name, e.g. "
"'SomeTerm' -> 'some_terms'"
referred_name = referred_cls.__name__
uncamelized = referred_name[0].lower() + \
re.sub(r'\W',
lambda m: "_%s" % m.group(0).lower(),
referred_name[1:])
pluralized = _pluralizer.plural(uncamelized)
return pluralized
from sqlalchemy.ext.automap import automap_base
Base = automap_base()
engine = create_engine("sqlite:///mydatabase.db")
Base.prepare(engine, reflect=True,
classname_for_table=camelize_classname,
name_for_collection_relationship=pluralize_collection
)From the above mapping, we would now have classes User and Address, where the collection from User to Address is called User.addresses:
User, Address = Base.classes.User, Base.classes.Address
u1 = User(addresses=[Address(email="foo@bar.com")])The vast majority of what automap accomplishes is the generation of relationship() structures based on foreign keys. The mechanism by which this works for many-to-one and one-to-many relationships is as follows:
The AutomapBase.prepare.generate_relationship hook can be used to add parameters to relationships. For most cases, we can make use of the existing automap.generate_relationship() function to return the object, after augmenting the given keyword dictionary with our own arguments.
Below is an illustration of how to send relationship.cascade and relationship.passive_deletes options along to all one-to-many relationships:
from sqlalchemy.ext.automap import generate_relationship
def _gen_relationship(base, direction, return_fn,
attrname, local_cls, referred_cls, **kw):
if direction is interfaces.ONETOMANY:
kw['cascade'] = 'all, delete-orphan'
kw['passive_deletes'] = True
# make use of the built-in function to actually return
# the result.
return generate_relationship(base, direction, return_fn,
attrname, local_cls, referred_cls, **kw)
from sqlalchemy.ext.automap import automap_base
from sqlalchemy import create_engine
# automap base
Base = automap_base()
engine = create_engine("sqlite:///mydatabase.db")
Base.prepare(engine, reflect=True,
generate_relationship=_gen_relationship)sqlalchemy.ext.automap will generate many-to-many relationships, e.g. those which contain a secondary argument. The process for producing these is as follows:
sqlalchemy.ext.automap will not generate any relationships between two classes that are in an inheritance relationship. That is, with two classes given as follows:
class Employee(Base):
__tablename__ = 'employee'
id = Column(Integer, primary_key=True)
type = Column(String(50))
__mapper_args__ = {
'polymorphic_identity':'employee', 'polymorphic_on': type
}
class Engineer(Employee):
__tablename__ = 'engineer'
id = Column(Integer, ForeignKey('employee.id'), primary_key=True)
__mapper_args__ = {
'polymorphic_identity':'engineer',
}The foreign key from Engineer to Employee is used not for a relationship, but to establish joined inheritance between the two classes.
Note that this means automap will not generate any relationships for foreign keys that link from a subclass to a superclass. If a mapping has actual relationships from subclass to superclass as well, those need to be explicit. Below, as we have two separate foreign keys from Engineer to Employee, we need to set up both the relationship we want as well as the inherit_condition, as these are not things SQLAlchemy can guess:
class Employee(Base):
__tablename__ = 'employee'
id = Column(Integer, primary_key=True)
type = Column(String(50))
__mapper_args__ = {
'polymorphic_identity':'employee', 'polymorphic_on':type
}
class Engineer(Employee):
__tablename__ = 'engineer'
id = Column(Integer, ForeignKey('employee.id'), primary_key=True)
favorite_employee_id = Column(Integer, ForeignKey('employee.id'))
favorite_employee = relationship(Employee,
foreign_keys=favorite_employee_id)
__mapper_args__ = {
'polymorphic_identity':'engineer',
'inherit_condition': id == Employee.id
}In the case of naming conflicts during mapping, override any of classname_for_table(), name_for_scalar_relationship(), and name_for_collection_relationship() as needed. For example, if automap is attempting to name a many-to-one relationship the same as an existing column, an alternate convention can be conditionally selected. Given a schema:
CREATE TABLE table_a (
id INTEGER PRIMARY KEY
);
CREATE TABLE table_b (
id INTEGER PRIMARY KEY,
table_a INTEGER,
FOREIGN KEY(table_a) REFERENCES table_a(id)
);The above schema will first automap the table_a table as a class named table_a; it will then automap a relationship onto the class for table_b with the same name as this related class, e.g. table_a. This relationship name conflicts with the mapping column table_b.table_a, and will emit an error on mapping.
We can resolve this conflict by using an underscore as follows:
def name_for_scalar_relationship(base, local_cls, referred_cls, constraint):
name = referred_cls.__name__.lower()
local_table = local_cls.__table__
if name in local_table.columns:
newname = name + "_"
warnings.warn(
"Already detected name %s present. using %s" %
(name, newname))
return newname
return name
Base.prepare(engine, reflect=True,
name_for_scalar_relationship=name_for_scalar_relationship)Alternatively, we can change the name on the column side. The columns that are mapped can be modified using the technique described at Naming Columns Distinctly from Attribute Names, by assigning the column explicitly to a new name:
Base = automap_base()
class TableB(Base):
__tablename__ = 'table_b'
_table_a = Column('table_a', ForeignKey('table_a.id'))
Base.prepare(engine, reflect=True)As noted previously, automap has no dependency on reflection, and can make use of any collection of Table objects within a MetaData collection. From this, it follows that automap can also be used generate missing relationships given an otherwise complete model that fully defines table metadata:
from sqlalchemy.ext.automap import automap_base
from sqlalchemy import Column, Integer, String, ForeignKey
Base = automap_base()
class User(Base):
__tablename__ = 'user'
id = Column(Integer, primary_key=True)
name = Column(String)
class Address(Base):
__tablename__ = 'address'
id = Column(Integer, primary_key=True)
email = Column(String)
user_id = Column(ForeignKey('user.id'))
# produce relationships
Base.prepare()
# mapping is complete, with "address_collection" and
# "user" relationships
a1 = Address(email='u1')
a2 = Address(email='u2')
u1 = User(address_collection=[a1, a2])
assert a1.user is u1Above, given mostly complete User and Address mappings, the ForeignKey which we defined on Address.user_id allowed a bidirectional relationship pair Address.user and User.address_collection to be generated on the mapped classes.
Note that when subclassing AutomapBase, the AutomapBase.prepare() method is required; if not called, the classes we’ve declared are in an un-mapped state.
Produce a declarative automap base.
This function produces a new base class that is a product of the AutomapBase class as well a declarative base produced by declarative.declarative_base().
All parameters other than declarative_base are keyword arguments that are passed directly to the declarative.declarative_base() function.
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Base class for an “automap” schema.
The AutomapBase class can be compared to the “declarative base” class that is produced by the declarative.declarative_base() function. In practice, the AutomapBase class is always used as a mixin along with an actual declarative base.
A new subclassable AutomapBase is typically instantated using the automap_base() function.
See also
An instance of util.Properties containing classes.
This object behaves much like the .c collection on a table. Classes are present under the name they were given, e.g.:
Base = automap_base()
Base.prepare(engine=some_engine, reflect=True)
User, Address = Base.classes.User, Base.classes.AddressExtract mapped classes and relationships from the MetaData and perform mappings.
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Return the class name that should be used, given the name of a table.
The default implementation is:
return str(tablename)Alternate implementations can be specified using the AutomapBase.prepare.classname_for_table parameter.
| Parameters: | |
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| Returns: | a string class name. Note In Python 2, the string used for the class name must be a non-Unicode object, e.g. a str() object. The .name attribute of Table is typically a Python unicode subclass, so the str() function should be applied to this name, after accounting for any non-ASCII characters. |
Return the attribute name that should be used to refer from one class to another, for a scalar object reference.
The default implementation is:
return referred_cls.__name__.lower()Alternate implementations can be specified using the AutomapBase.prepare.name_for_scalar_relationship parameter.
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Return the attribute name that should be used to refer from one class to another, for a collection reference.
The default implementation is:
return referred_cls.__name__.lower() + "_collection"Alternate implementations can be specified using the AutomapBase.prepare.name_for_collection_relationship parameter.
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Generate a relationship() or backref() on behalf of two mapped classes.
An alternate implementation of this function can be specified using the AutomapBase.prepare.generate_relationship parameter.
The default implementation of this function is as follows:
if return_fn is backref:
return return_fn(attrname, **kw)
elif return_fn is relationship:
return return_fn(referred_cls, **kw)
else:
raise TypeError("Unknown relationship function: %s" % return_fn)| Parameters: |
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| Attrname : | the attribute name to which this relationship is being assigned. If the value of generate_relationship.return_fn is the backref() function, then this name is the name that is being assigned to the backref. |
| Returns: | a relationship() or backref() construct, as dictated by the generate_relationship.return_fn parameter. |