SQLAlchemy 0.3 Documentation

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Version: 0.3.10 Last Updated: 07/20/07 17:20:04

This section details all the options available to Mappers, as well as advanced patterns.

To start, heres the tables we will work with again:

from sqlalchemy import *

metadata = MetaData()

# a table to store users
users_table = Table('users', metadata,
    Column('user_id', Integer, primary_key = True),
    Column('user_name', String(40)),
    Column('password', String(80))
)

# a table that stores mailing addresses associated with a specific user
addresses_table = Table('addresses', metadata,
    Column('address_id', Integer, primary_key = True),
    Column('user_id', Integer, ForeignKey("users.user_id")),
    Column('street', String(100)),
    Column('city', String(80)),
    Column('state', String(2)),
    Column('zip', String(10))
)

# a table that stores keywords
keywords_table = Table('keywords', metadata,
    Column('keyword_id', Integer, primary_key = True),
    Column('name', VARCHAR(50))
)

# a table that associates keywords with users
userkeywords_table = Table('userkeywords', metadata,
    Column('user_id', INT, ForeignKey("users")),
    Column('keyword_id', INT, ForeignKey("keywords"))
)

More On Mapper Properties

Overriding Column Names

When mappers are constructed, by default the column names in the Table metadata are used as the names of attributes on the mapped class. This can be customzed within the properties by stating the key/column combinations explicitly:

user_mapper = mapper(User, users_table, properties={
    'id' : users_table.c.user_id,
    'name' : users_table.c.user_name,
})

In the situation when column names overlap in a mapper against multiple tables, columns may be referenced together with a list:

# join users and addresses
usersaddresses = sql.join(users_table, addresses_table, users_table.c.user_id == addresses_table.c.user_id)
m = mapper(User, usersaddresses,   
    properties = {
        'id' : [users_table.c.user_id, addresses_table.c.user_id],
    }
    )
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Overriding Properties

A common request is the ability to create custom class properties that override the behavior of setting/getting an attribute. Currently, the easiest way to do this in SQLAlchemy is how it would be done in any Python program; define your attribute with a different name, such as "_attribute", and use a property to get/set its value. The mapper just needs to be told of the special name:

class MyClass(object):
    def _set_email(self, email):
       self._email = email
    def _get_email(self):
       return self._email
    email = property(_get_email, _set_email)

mapper(MyClass, mytable, properties = {
   # map the '_email' attribute to the "email" column
   # on the table
   '_email': mytable.c.email
})

It is also possible to route the the select_by and get_by functions on Query using the new property name, by establishing a synonym:

mapper(MyClass, mytable, properties = {
    # map the '_email' attribute to the "email" column
    # on the table
    '_email': mytable.c.email,

    # make a synonym 'email'
    'email' : synonym('_email')
})

# now you can select_by(email)
result = session.query(MyClass).select_by(email='john@smith.com')

Synonym can be established with the flag "proxy=True", to create a class-level proxy to the actual property. This has the effect of creating a fully functional synonym on class instances:

mapper(MyClass, mytable, properties = {
    '_email': mytable.c.email
    'email' : synonym('_email', proxy=True)
})

x = MyClass()
x.email = 'john@doe.com'

>>> x._email
'john@doe.com'
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Custom List Classes

Feature Status: Alpha API

A one-to-many or many-to-many relationship results in a list-holding element being attached to all instances of a class. The actual list is an "instrumented" list, which transparently maintains a relationship to a plain Python list. The implementation of the underlying plain list can be changed to be any object that implements a list-style append and __iter__ method. A common need is for a list-based relationship to actually be a dictionary. This can be achieved by subclassing dict to have list-like behavior.

In this example, a class MyClass is defined, which is associated with a parent object MyParent. The collection of MyClass objects on each MyParent object will be a dictionary, storing each MyClass instance keyed to its name attribute.

# a class to be stored in the list
class MyClass(object):
    def __init__(self, name):
        self.name = name

# create a dictionary that will act like a list, and store
# instances of MyClass
class MyDict(dict):
    def append(self, item):
        self[item.name] = item
    def __iter__(self):
        return self.values()

# parent class
class MyParent(object):
    pass

# mappers, constructed normally
mapper(MyClass, myclass_table)
mapper(MyParent, myparent_table, properties={
    'myclasses' : relation(MyClass, collection_class=MyDict)
})

# elements on 'myclasses' can be accessed via string keyname
myparent = MyParent()
myparent.myclasses.append(MyClass('this is myclass'))
myclass = myparent.myclasses['this is myclass']

Note: SQLAlchemy 0.4 has an overhauled and much improved implementation for custom list classes, with some slight API changes.

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Custom Join Conditions

When creating relations on a mapper, most examples so far have illustrated the mapper and relationship joining up based on the foreign keys of the tables they represent. in fact, this "automatic" inspection can be completely circumvented using the primaryjoin and secondaryjoin arguments to relation, as in this example which creates a User object which has a relationship to all of its Addresses which are in Boston:

class User(object):
    pass
class Address(object):
    pass

mapper(Address, addresses_table)
mapper(User, users_table, properties={
    'boston_addresses' : relation(Address, primaryjoin=
                and_(users_table.c.user_id==Address.c.user_id, 
                Addresses.c.city=='Boston'))
})

Many to many relationships can be customized by one or both of primaryjoin and secondaryjoin, shown below with just the default many-to-many relationship explicitly set:

class User(object):
    pass
class Keyword(object):
    pass
mapper(Keyword, keywords_table)
mapper(User, users_table, properties={
    'keywords':relation(Keyword, secondary=userkeywords_table,
        primaryjoin=users_table.c.user_id==userkeywords_table.c.user_id,
        secondaryjoin=userkeywords_table.c.keyword_id==keywords_table.c.keyword_id
        )
})
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Lazy/Eager Joins Multiple Times to One Table

The previous example leads in to the idea of joining against the same table multiple times. Below is a User object that has lists of its Boston and New York addresses:

mapper(User, users_table, properties={
    'boston_addresses' : relation(Address, primaryjoin=
                and_(users_table.c.user_id==Address.c.user_id, 
                Addresses.c.city=='Boston')),
    'newyork_addresses' : relation(Address, primaryjoin=
                and_(users_table.c.user_id==Address.c.user_id, 
                Addresses.c.city=='New York')),
})

Both lazy and eager loading support multiple joins equally well.

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Deferred Column Loading

This feature allows particular columns of a table to not be loaded by default, instead being loaded later on when first referenced. It is essentailly "column-level lazy loading". This feature is useful when one wants to avoid loading a large text or binary field into memory when its not needed. Individual columns can be lazy loaded by themselves or placed into groups that lazy-load together.

book_excerpts = Table('books', db, 
    Column('book_id', Integer, primary_key=True),
    Column('title', String(200), nullable=False),
    Column('summary', String(2000)),
    Column('excerpt', String),
    Column('photo', Binary)
)

class Book(object):
    pass

# define a mapper that will load each of 'excerpt' and 'photo' in 
# separate, individual-row SELECT statements when each attribute
# is first referenced on the individual object instance
mapper(Book, book_excerpts, properties = {
    'excerpt' : deferred(book_excerpts.c.excerpt),
    'photo' : deferred(book_excerpts.c.photo)
})

Deferred columns can be placed into groups so that they load together:

book_excerpts = Table('books', db, 
    Column('book_id', Integer, primary_key=True),
    Column('title', String(200), nullable=False),
    Column('summary', String(2000)),
    Column('excerpt', String),
    Column('photo1', Binary),
    Column('photo2', Binary),
    Column('photo3', Binary)
)

class Book(object):
    pass

# define a mapper with a 'photos' deferred group.  when one photo is referenced,
# all three photos will be loaded in one SELECT statement.  The 'excerpt' will 
# be loaded separately when it is first referenced.
mapper(Book, book_excerpts, properties = {
    'excerpt' : deferred(book_excerpts.c.excerpt),
    'photo1' : deferred(book_excerpts.c.photo1, group='photos'),
    'photo2' : deferred(book_excerpts.c.photo2, group='photos'),
    'photo3' : deferred(book_excerpts.c.photo3, group='photos')
})

You can defer or undefer columns at the Query level with the options method:

query = session.query(Book)
query.options(defer('summary')).all()
query.options(undefer('excerpt')).all()
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Working with Large Collections

SQLAlchemy relations are generally simplistic; the lazy loader loads in the full list of child objects when accessed, and the eager load builds a query that loads the full list of child objects. Additionally, when you are deleting a parent object, SQLAlchemy ensures that it has loaded the full list of child objects so that it can mark them as deleted as well (or to update their parent foreign key to NULL). It does not issue an en-masse "delete from table where parent_id=?" type of statement in such a scenario. This is because the child objects themselves may also have further dependencies, and additionally may also exist in the current session in which case SA needs to know their identity so that their state can be properly updated.

So there are several techniques that can be used individually or combined together to address these issues, in the context of a large collection where you normally would not want to load the full list of relationships:

  • Use lazy=None to disable child object loading (i.e. noload)

    mapper(MyClass, table, properties=relation{
        'children':relation(MyOtherClass, lazy=None)
    })
  • To load child objects, just use a query. Of particular convenience is that Query is a generative object, so you can return it as is, allowing additional criterion to be added as needed:

    class Organization(object):
        def __init__(self, name):
            self.name = name
        member_query = property(lambda self: object_session(self).query(Member).with_parent(self))
    
    myorg = sess.query(Organization).get(5)
    
    # get all members
    members = myorg.member_query.list()
    
    # query a subset of members using LIMIT/OFFSET
    members = myorg.member_query[5:10]
  • Use passive_deletes=True to disable child object loading on a DELETE operation, in conjunction with "ON DELETE (CASCADE|SET NULL)" on your database to automatically cascade deletes to child objects. Note that "ON DELETE" is not supported on SQLite, and requires InnoDB tables when using MySQL:

    mytable = Table('mytable', meta,
        Column('id', Integer, primary_key=True),
        )
    
    myothertable = Table('myothertable', meta,
        Column('id', Integer, primary_key=True),
        Column('parent_id', Integer),
        ForeignKeyConstraint(['parent_id'],['mytable.id'], ondelete="CASCADE"),
        )
    
    mmapper(MyOtherClass, myothertable)
    
    mapper(MyClass, mytable, properties={
        'children':relation(MyOtherClass, passive_deletes=True)
    })
  • As an alternative to using "ON DELETE CASCADE", for very simple scenarios you can create a simple MapperExtension that will issue a DELETE for child objects before the parent object is deleted:

    class DeleteMemberExt(MapperExtension):
        def before_delete(self, mapper, connection, instance):
            connection.execute(member_table.delete(member_table.c.org_id==instance.org_id))
    
    mapper(Organization, org_table, extension=DeleteMemberExt(), properties = {
        'members' : relation(Member, lazy=None, passive_deletes=True, cascade="all, delete-orphan")
    })

Note that this approach is not nearly as efficient or general-purpose as "ON DELETE CASCADE", since the database itself can cascade the operation along any number of tables.

The latest distribution includes an example examples/collection/large_collection.py which illustrates most of these techniques.

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Relation Options

Options which can be sent to the relation() function. For arguments to mapper(), see Mapper Keyword Arguments.

  • association - Deprecated; as of version 0.3.0 the association keyword is synonomous with applying the "all, delete-orphan" cascade to a "one-to-many" relationship. SA can now automatically reconcile a "delete" and "insert" operation of two objects with the same "identity" in a flush() operation into a single "update" statement, which is the pattern that "association" used to indicate. See the updated example of association mappings in datamapping_association.
  • backref - indicates the name of a property to be placed on the related mapper's class that will handle this relationship in the other direction, including synchronizing the object attributes on both sides of the relation. Can also point to a backref() construct for more configurability. See Backreferences.
  • cascade - a string list of cascade rules which determines how persistence operations should be "cascaded" from parent to child. For a description of cascade rules, see Lifecycle Relations and Cascade rules.
  • collection_class - a class or function that returns a new list-holding object. will be used in place of a plain list for storing elements. See Custom List Classes.
  • foreign_keys - a list of columns which are to be used as "foreign key" columns. this parameter should be used in conjunction with explicit primaryjoin and secondaryjoin (if needed) arguments, and the columns within the foreign_keys list should be present within those join conditions. Normally, relation() will inspect the columns within the join conditions to determine which columns are the "foreign key" columns, based on information in the Table metadata. Use this argument when no ForeignKey's are present in the join condition, or to override the table-defined foreign keys.
  • foreignkey - deprecated. use the foreign_keys argument for foreign key specification, or remote_side for "directional" logic.
  • lazy=True - specifies how the related items should be loaded. a value of True indicates they should be loaded lazily when the property is first accessed. A value of False indicates they should be loaded by joining against the parent object query, so parent and child are loaded in one round trip (i.e. eagerly). A value of None indicates the related items are not loaded by the mapper in any case; the application will manually insert items into the list in some other way. In all cases, items added or removed to the parent object's collection (or scalar attribute) will cause the appropriate updates and deletes upon flush(), i.e. this option only affects load operations, not save operations.

  • order_by - indicates the ordering that should be applied when loading these items. See the section Controlling Ordering for details.
  • passive_deletes=False - Indicates if lazy-loaders should not be executed during the flush() process, which normally occurs in order to locate all existing child items when a parent item is to be deleted. Setting this flag to True is appropriate when ON DELETE CASCADE rules have been set up on the actual tables so that the database may handle cascading deletes automatically. This strategy is useful particularly for handling the deletion of objects that have very large (and/or deep) child-object collections. See the example in Working with Large Collections.
  • post_update - this indicates that the relationship should be handled by a second UPDATE statement after an INSERT or before a DELETE. Currently, it also will issue an UPDATE after the instance was UPDATEd as well, although this technically should be improved. This flag is used to handle saving bi-directional dependencies between two individual rows (i.e. each row references the other), where it would otherwise be impossible to INSERT or DELETE both rows fully since one row exists before the other. Use this flag when a particular mapping arrangement will incur two rows that are dependent on each other, such as a table that has a one-to-many relationship to a set of child rows, and also has a column that references a single child row within that list (i.e. both tables contain a foreign key to each other). If a flush() operation returns an error that a "cyclical dependency" was detected, this is a cue that you might want to use post_update to "break" the cycle.
  • primaryjoin - a ClauseElement that will be used as the primary join of this child object against the parent object, or in a many-to-many relationship the join of the primary object to the association table. By default, this value is computed based on the foreign key relationships of the parent and child tables (or association table).
  • private=False - deprecated. setting private=True is the equivalent of setting cascade="all, delete-orphan", and indicates the lifecycle of child objects should be contained within that of the parent. See the example in Lifecycle Relations.
  • remote_side - used for self-referential relationships, indicates the column or list of columns that form the "remote side" of the relationship. See the examples in Self Referential Mappers.
  • secondary - for a many-to-many relationship, specifies the intermediary table. The secondary keyword argument should generally only be used for a table that is not otherwise expressed in any class mapping. In particular, using the Association Object Pattern is generally mutually exclusive against using the secondary keyword argument.
  • secondaryjoin - a ClauseElement that will be used as the join of an association table to the child object. By default, this value is computed based on the foreign key relationships of the association and child tables.
  • uselist=(True|False) - a boolean that indicates if this property should be loaded as a list or a scalar. In most cases, this value is determined automatically by relation(), based on the type and direction of the relationship - one to many forms a list, many to one forms a scalar, many to many is a list. If a scalar is desired where normally a list would be present, such as a bi-directional one-to-one relationship, set uselist to False.
  • viewonly=False - when set to True, the relation is used only for loading objects within the relationship, and has no effect on the unit-of-work flush process. Relations with viewonly can specify any kind of join conditions to provide additional views of related objects onto a parent object. Note that the functionality of a viewonly relationship has its limits - complicated join conditions may not compile into eager or lazy loaders properly. If this is the case, use an alternative method, such as those described in Working with Large Collections, Statement and Result-Set ORM Queries, or Mapping a Class against Arbitrary Selects.
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Controlling Ordering

By default, mappers will attempt to ORDER BY the "oid" column of a table, or the primary key column, when selecting rows. This can be modified in several ways.

The "order_by" parameter can be sent to a mapper, overriding the per-engine ordering if any. A value of None means that the mapper should not use any ordering. A non-None value, which can be a column, an asc or desc clause, or an array of either one, indicates the ORDER BY clause that should be added to all select queries:

# disable all ordering
mapper = mapper(User, users_table, order_by=None)

# order by a column
mapper = mapper(User, users_table, order_by=users_tableusers_table.c.user_id)

# order by multiple items
mapper = mapper(User, users_table, order_by=[users_table.c.user_id, desc(users_table.c.user_name)])

"order_by" can also be specified with queries, overriding all other per-engine/per-mapper orderings:

# order by a column
l = query.filter(users_table.c.user_name=='fred').order_by(users_table.c.user_id).all()

# order by multiple criterion
l = query.filter(users_table.c.user_name=='fred').order_by([users_table.c.user_id, desc(users_table.c.user_name)])

The "order_by" property can also be specified on a relation() which will control the ordering of the collection:

mapper(Address, addresses_table)

# order address objects by address id
mapper(User, users_table, properties = {
    'addresses' : relation(Address, order_by=addresses_table.c.address_id)
})

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Limiting Rows Combined with Eager Loads

As indicated in the docs on Query, you can limit rows using limit() and offset(). However, things get tricky when dealing with eager relationships, since a straight LIMIT of rows will interfere with the eagerly-loaded rows. So here is what SQLAlchemy will do when you use limit or offset with an eager relationship:

class User(object):
    pass
class Address(object):
    pass
    mapper(User, users_table, properties={
    'addresses' : relation(mapper(Address, addresses_table), lazy=False)
})
r = session.query(User).filter(User.c.user_name.like('F%')).limit(20).offset(10).all()
{opensql}SELECT users.user_id AS users_user_id, users.user_name AS users_user_name, 
users.password AS users_password, addresses.address_id AS addresses_address_id, 
addresses.user_id AS addresses_user_id, addresses.street AS addresses_street, 
addresses.city AS addresses_city, addresses.state AS addresses_state, 
addresses.zip AS addresses_zip 
FROM 
(SELECT users.user_id FROM users WHERE users.user_name LIKE %(users_user_name)s
ORDER BY users.oid LIMIT 20 OFFSET 10) AS rowcount, 
 users LEFT OUTER JOIN addresses ON users.user_id = addresses.user_id 
WHERE rowcount.user_id = users.user_id ORDER BY users.oid, addresses.oid
{'users_user_name': 'F%'}

The main WHERE clause as well as the limiting clauses are coerced into a subquery; this subquery represents the desired result of objects. A containing query, which handles the eager relationships, is joined against the subquery to produce the result. This is something to keep in mind as it's a complex query which may be problematic on databases with poor support for LIMIT, such as Oracle which does not support it natively.

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Mapping a Class with Table Inheritance

Inheritance in databases comes in three forms: single table inheritance, where several types of classes are stored in one table, concrete table inheritance, where each type of class is stored in its own table, and joined table inheritance, where the parent/child classes are stored in their own tables that are joined together in a select.

There is also the ability to load "polymorphically", which is that a single query loads objects of multiple types at once.

SQLAlchemy supports all three kinds of inheritance. Additionally, true "polymorphic" loading is supported in a straightfoward way for single table inheritance, and has some more manually-configured features that can make it happen for concrete and multiple table inheritance.

Working examples of polymorphic inheritance come with the distribution in the directory examples/polymorphic.

Here are the classes we will use to represent an inheritance relationship:

class Employee(object):
    def __init__(self, name):
        self.name = name
    def __repr__(self):
        return self.__class__.__name__ + " " + self.name

class Manager(Employee):
    def __init__(self, name, manager_data):
        self.name = name
        self.manager_data = manager_data
    def __repr__(self):
        return self.__class__.__name__ + " " + self.name + " " +  self.manager_data

class Engineer(Employee):
    def __init__(self, name, engineer_info):
        self.name = name
        self.engineer_info = engineer_info
    def __repr__(self):
        return self.__class__.__name__ + " " + self.name + " " +  self.engineer_info

Each class supports a common name attribute, while the Manager class has its own attribute manager_data and the Engineer class has its own attribute engineer_info.

Single Table Inheritance

This will support polymorphic loading via the Employee mapper.

employees_table = Table('employees', metadata, 
    Column('employee_id', Integer, primary_key=True),
    Column('name', String(50)),
    Column('manager_data', String(50)),
    Column('engineer_info', String(50)),
    Column('type', String(20))
)

employee_mapper = mapper(Employee, employees_table, polymorphic_on=employees_table.c.type)
manager_mapper = mapper(Manager, inherits=employee_mapper, polymorphic_identity='manager')
engineer_mapper = mapper(Engineer, inherits=employee_mapper, polymorphic_identity='engineer')
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Concrete Table Inheritance

Without polymorphic loading, you just define a separate mapper for each class.

Concrete Inheritance, Non-polymorphic
managers_table = Table('managers', metadata, 
    Column('employee_id', Integer, primary_key=True),
    Column('name', String(50)),
    Column('manager_data', String(50)),
)

engineers_table = Table('engineers', metadata, 
    Column('employee_id', Integer, primary_key=True),
    Column('name', String(50)),
    Column('engineer_info', String(50)),
)

manager_mapper = mapper(Manager, managers_table)
engineer_mapper = mapper(Engineer, engineers_table)

With polymorphic loading, the SQL query to do the actual polymorphic load must be constructed, usually as a UNION. There is a helper function to create these UNIONS called polymorphic_union.

Concrete Inheritance, Polymorphic
pjoin = polymorphic_union({
    'manager':managers_table,
    'engineer':engineers_table
}, 'type', 'pjoin')

employee_mapper = mapper(Employee, pjoin, polymorphic_on=pjoin.c.type)
manager_mapper = mapper(Manager, managers_table, inherits=employee_mapper, concrete=True, polymorphic_identity='manager')
engineer_mapper = mapper(Engineer, engineers_table, inherits=employee_mapper, concrete=True, polymorphic_identity='engineer')
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Joined Table Inheritance

Like concrete table inheritance, this can be done non-polymorphically, or with a little more complexity, polymorphically:

Multiple Table Inheritance, Non-polymorphic
employees = Table('employees', metadata, 
   Column('person_id', Integer, primary_key=True),
   Column('name', String(50)),
   Column('type', String(30)))

engineers = Table('engineers', metadata, 
   Column('person_id', Integer, ForeignKey('employees.person_id'), primary_key=True),
   Column('engineer_info', String(50)),
  )

managers = Table('managers', metadata, 
   Column('person_id', Integer, ForeignKey('employees.person_id'), primary_key=True),
   Column('manager_data', String(50)),
   )

person_mapper = mapper(Employee, employees)
mapper(Engineer, engineers, inherits=person_mapper)
mapper(Manager, managers, inherits=person_mapper)

Polymorphically, joined-table inheritance is easier than concrete, as a simple outer join can usually work:

Joined Table Inheritance, Polymorphic
person_join = people.outerjoin(engineers).outerjoin(managers)

person_mapper = mapper(Person, people, select_table=person_join,polymorphic_on=people.c.type, polymorphic_identity='person')
mapper(Engineer, engineers, inherits=person_mapper, polymorphic_identity='engineer')
mapper(Manager, managers, inherits=person_mapper, polymorphic_identity='manager')

In SQLAlchemy 0.4, the above mapper setup can load polymorphically without the join as well, by issuing distinct queries for each subclasses' table.

The join condition in a joined table inheritance structure can be specified explicitly, using inherit_condition:

AddressUser.mapper = mapper(
        AddressUser,
        addresses_table, inherits=User.mapper, 
        inherit_condition=users_table.c.user_id==addresses_table.c.user_id
    )
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Mapping a Class against Multiple Tables

Mappers can be constructed against arbitrary relational units (called Selectables) as well as plain Tables. For example, The join keyword from the SQL package creates a neat selectable unit comprised of multiple tables, complete with its own composite primary key, which can be passed in to a mapper as the table.

# a class
class AddressUser(object):
    pass

# define a Join
j = join(users_table, addresses_table)

# map to it - the identity of an AddressUser object will be 
# based on (user_id, address_id) since those are the primary keys involved
m = mapper(AddressUser, j, properties={
    'user_id':[users_table.c.user_id, addresses_table.c.user_id]
})

A second example:

# many-to-many join on an association table
j = join(users_table, userkeywords, 
        users_table.c.user_id==userkeywords.c.user_id).join(keywords, 
           userkeywords.c.keyword_id==keywords.c.keyword_id)

# a class 
class KeywordUser(object):
    pass

# map to it - the identity of a KeywordUser object will be
# (user_id, keyword_id) since those are the primary keys involved
m = mapper(KeywordUser, j, properties={
    'user_id':[users_table.c.user_id, userkeywords.c.user_id],
    'keyword_id':[userkeywords.c.keyword_id, keywords.c.keyword_id]
})

In both examples above, "composite" columns were added as properties to the mappers; these are aggregations of multiple columns into one mapper property, which instructs the mapper to keep both of those columns set at the same value.

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Mapping a Class against Arbitrary Selects

Similar to mapping against a join, a plain select() object can be used with a mapper as well. Below, an example select which contains two aggregate functions and a group_by is mapped to a class:

s = select([customers, 
            func.count(orders).label('order_count'), 
            func.max(orders.price).label('highest_order')],
            customers.c.customer_id==orders.c.customer_id,
            group_by=[c for c in customers.c]
            ).alias('somealias')
class Customer(object):
    pass

m = mapper(Customer, s)

Above, the "customers" table is joined against the "orders" table to produce a full row for each customer row, the total count of related rows in the "orders" table, and the highest price in the "orders" table, grouped against the full set of columns in the "customers" table. That query is then mapped against the Customer class. New instances of Customer will contain attributes for each column in the "customers" table as well as an "order_count" and "highest_order" attribute. Updates to the Customer object will only be reflected in the "customers" table and not the "orders" table. This is because the primary keys of the "orders" table are not represented in this mapper and therefore the table is not affected by save or delete operations.

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Multiple Mappers for One Class

The first mapper created for a certain class is known as that class's "primary mapper." Other mappers can be created as well, these come in two varieties.

example:

# primary mapper
mapper(User, users_table)

# make a secondary mapper to load User against a join
othermapper = mapper(User, users_table.join(someothertable), non_primary=True)

# select
result = session.query(othermapper).select()

example:

# primary mapper
mapper(User, users_table)

# make an entity name mapper that stores User objects in another table
mapper(User, alternate_users_table, entity_name='alt')

# make two User objects
user1 = User()
user2 = User()

# save one in in the "users" table
session.save(user1)

# save the other in the "alternate_users_table"
session.save(user2, entity_name='alt')

session.flush()

# select from the alternate mapper
session.query(User, entity_name='alt').select()
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Self Referential Mappers

A self-referential mapper is a mapper that is designed to operate with an adjacency list table. This is a table that contains one or more foreign keys back to itself, and is usually used to create hierarchical tree structures. SQLAlchemy's default model of saving items based on table dependencies is not sufficient in this case, as an adjacency list table introduces dependencies between individual rows. Fortunately, SQLAlchemy will automatically detect a self-referential mapper and do the extra lifting to make it work.

# define a self-referential table
trees = Table('treenodes', engine,
    Column('node_id', Integer, primary_key=True),
    Column('parent_node_id', Integer, ForeignKey('treenodes.node_id'), nullable=True),
    Column('node_name', String(50), nullable=False),
    )

# treenode class
class TreeNode(object):
    pass

# mapper defines "children" property, pointing back to TreeNode class,
# with the mapper unspecified.  it will point back to the primary 
# mapper on the TreeNode class.
TreeNode.mapper = mapper(TreeNode, trees, properties={
        'children' : relation(
                        TreeNode, 
                        cascade="all"
                     ),
        }
    )

This kind of mapper goes through a lot of extra effort when saving and deleting items, to determine the correct dependency graph of nodes within the tree.

A self-referential mapper where there is more than one relationship on the table requires that all join conditions be explicitly spelled out. Below is a self-referring table that contains a "parent_node_id" column to reference parent/child relationships, and a "root_node_id" column which points child nodes back to the ultimate root node:

# define a self-referential table with several relations
trees = Table('treenodes', engine,
    Column('node_id', Integer, primary_key=True),
    Column('parent_node_id', Integer, ForeignKey('treenodes.node_id'), nullable=True),
    Column('root_node_id', Integer, ForeignKey('treenodes.node_id'), nullable=True),
    Column('node_name', String(50), nullable=False),
    )

# treenode class
class TreeNode(object):
    pass

# define the "children" property as well as the "root" property
mapper(TreeNode, trees, properties={
        'children' : relation(
                        TreeNode, 
                        primaryjoin=trees.c.parent_node_id==trees.c.node_id
                        cascade="all",
                        backref=backref("parent", remote_side=[trees.c.node_id])
                     ),
        'root' : relation(
                TreeNode,
                primaryjoin=trees.c.root_node_id=trees.c.node_id, 
                remote_side=[trees.c.node_id],
                uselist=False
            )
        }
    )

The "root" property on a TreeNode is a many-to-one relationship. By default, a self-referential mapper declares relationships as one-to-many, so the extra parameter remote_side, pointing to a column or list of columns on the remote side of a relationship, is needed to indicate a "many-to-one" self-referring relationship (note the previous keyword argument foreignkey is deprecated). Both TreeNode examples above are available in functional form in the examples/adjacencytree directory of the distribution.

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Statement and Result-Set ORM Queries

Take any textual statement, constructed statement or result set and feed it into a Query to produce objects. Below, we define two class/mapper combinations, issue a SELECT statement, and send the result object to the method instances() method on Query:

class User(object):
    pass

class Address(object):
    pass

mapper(User, users_table)

mapper(Address, addresses_table)

# select users and addresses in one query
# use_labels is so that the user_id column in both tables are distinguished
s = select([users_table, addresses_table], users_table.c.user_id==addresses_table.c.user_id, use_labels=True)

# execute it, and process the results, asking for both User and Address objects
r = session.query(User, Address).instances(s.execute())

# result rows come back as tuples
for entry in r:
    user = r[0]
    address = r[1]

Alternatively, the from_statement() method may be used with either a textual string or SQL construct:

s = select([users_table, addresses_table], users_table.c.user_id==addresses_table.c.user_id, use_labels=True)

r = session.query(User, Address).from_statement(s).all()

for entry in r:
    user = r[0]
    address = r[1]

Combining Eager Loads with Statement/Result Set Queries

When full statement/result loads are used with Query, SQLAlchemy does not affect the SQL query itself, and therefore has no way of tacking on its own LEFT [OUTER] JOIN conditions that are normally used to eager load relationships. If the query being constructed is created in such a way that it returns rows not just from a parent table (or tables) but also returns rows from child tables, the result-set mapping can be notified as to which additional properties are contained within the result set. This is done using the contains_eager() query option, which specifies the name of the relationship to be eagerly loaded.

# mapping is the users->addresses mapping
mapper(User, users_table, properties={
    'addresses':relation(Address, addresses_table)
})

# define a query on USERS with an outer join to ADDRESSES
statement = users_table.outerjoin(addresses_table).select(use_labels=True)

# construct a Query object which expects the "addresses" results 
query = session.query(User).options(contains_eager('addresses'))

# get results normally
r = query.instances(statement.execute())

If the "eager" portion of the statement is "alisaed", the alias keyword argument to contains_eager() may be used to indicate it. This is a string alias name or reference to an actual Alias object:

# use an alias of the addresses table
adalias = addresses_table.alias('adalias')

# define a query on USERS with an outer join to adalias
statement = users_table.outerjoin(adalias).select(use_labels=True)

# construct a Query object which expects the "addresses" results 
query = session.query(User).options(contains_eager('addresses', alias=adalias))

# get results normally
sqlr = query.from_statement(query).all()

In the case that the main table itself is also aliased, the contains_alias() option can be used:

# define an aliased UNION called 'ulist'
statement = users.select(users.c.user_id==7).union(users.select(users.c.user_id>7)).alias('ulist')

# add on an eager load of "addresses"
statement = statement.outerjoin(addresses).select(use_labels=True)

# create query, indicating "ulist" is an alias for the main table, "addresses" property should
# be eager loaded
query = create_session().query(User).options(contains_alias('ulist'), contains_eager('addresses'))

# results
r = query.instances(statement.execute())
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Mapper Keyword Arguments

Keyword arguments which can be used with the mapper() function. For arguments to relation(), see Relation Options.

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Extending Mapper

Mappers can have functionality augmented or replaced at many points in its execution via the usage of the MapperExtension class. This class is just a series of "hooks" where various functionality takes place. An application can make its own MapperExtension objects, overriding only the methods it needs. Methods that are not overridden return the special value sqlalchemy.orm.mapper.EXT_PASS, which indicates the operation should proceed as normally.

class MapperExtension(object):
    """base implementation for an object that provides overriding behavior to various
    Mapper functions.  For each method in MapperExtension, a result of EXT_PASS indicates
    the functionality is not overridden."""
    def get_session(self):
        """called to retrieve a contextual Session instance with which to
        register a new object. Note: this is not called if a session is 
        provided with the __init__ params (i.e. _sa_session)"""
        return EXT_PASS
    def select_by(self, query, *args, **kwargs):
        """overrides the select_by method of the Query object"""
        return EXT_PASS
    def select(self, query, *args, **kwargs):
        """overrides the select method of the Query object"""
        return EXT_PASS
    def create_instance(self, mapper, selectcontext, row, class_):
        """called when a new object instance is about to be created from a row.  
        the method can choose to create the instance itself, or it can return 
        None to indicate normal object creation should take place.

        mapper - the mapper doing the operation

        selectcontext - SelectionContext corresponding to the instances() call

        row - the result row from the database

        class_ - the class we are mapping.
        """
        return EXT_PASS
    def append_result(self, mapper, selectcontext, row, instance, identitykey, result, isnew):
        """called when an object instance is being appended to a result list.

        If this method returns EXT_PASS, it is assumed that the mapper should do the appending, else
        if this method returns any other value or None, it is assumed that the append was handled by this method.

        mapper - the mapper doing the operation

        selectcontext - SelectionContext corresponding to the instances() call

        row - the result row from the database

        instance - the object instance to be appended to the result

        identitykey - the identity key of the instance

        result - list to which results are being appended

        isnew - indicates if this is the first time we have seen this object instance in the current result
        set.  if you are selecting from a join, such as an eager load, you might see the same object instance
        many times in the same result set.
        """
        return EXT_PASS
    def populate_instance(self, mapper, selectcontext, row, instance, identitykey, isnew):
        """called right before the mapper, after creating an instance from a row, passes the row
        to its MapperProperty objects which are responsible for populating the object's attributes.
        If this method returns EXT_PASS, it is assumed that the mapper should do the appending, else
        if this method returns any other value or None, it is assumed that the append was handled by this method.

        Essentially, this method is used to have a different mapper populate the object:

            def populate_instance(self, mapper, selectcontext, instance, row, identitykey, isnew):
                othermapper.populate_instance(selectcontext, instance, row, identitykey, isnew, frommapper=mapper)
                return True
        """
        return EXT_PASS
    def before_insert(self, mapper, connection, instance):
        """called before an object instance is INSERTed into its table.

        this is a good place to set up primary key values and such that arent handled otherwise."""
        return EXT_PASS
    def before_update(self, mapper, connection, instance):
        """called before an object instance is UPDATED"""
        return EXT_PASS
    def after_update(self, mapper, connection, instance):
        """called after an object instance is UPDATED"""
        return EXT_PASS
    def after_insert(self, mapper, connection, instance):
        """called after an object instance has been INSERTed"""
        return EXT_PASS
    def before_delete(self, mapper, connection, instance):
        """called before an object instance is DELETEed"""
        return EXT_PASS
    def after_delete(self, mapper, connection, instance):
        """called after an object instance is DELETEed"""
        return EXT_PASS

To use MapperExtension, make your own subclass of it and just send it off to a mapper:

m = mapper(User, users_table, extension=MyExtension())

Multiple extensions will be chained together and processed in order; they are specified as a list:

m = mapper(User, users_table, extension=[ext1, ext2, ext3])
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