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")) )
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], } )
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'
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.
back to section topWhen 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 ) })
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.
back to section topThis 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()
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.
Options which can be sent to the relation()
function. For arguments to mapper()
, see Mapper Keyword Arguments.
backref()
construct for more configurability. See Backreferences.
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.
foreign_keys
argument for foreign key specification, or remote_side
for "directional" logic.
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.
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.
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.
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.
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.
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) })
back to section top
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.
back to section topInheritance 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
.
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')
Without polymorphic loading, you just define a separate mapper for each class.
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
.
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')
Like concrete table inheritance, this can be done non-polymorphically, or with a little more complexity, polymorphically:
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:
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 )
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.
back to section topSimilar 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.
back to section topThe 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.
non_primary=True
, and represents a load-only mapper. Objects that are loaded with a secondary mapper will have their save operation processed by the primary mapper. It is also invalid to add new relation()
s to a non-primary mapper. To use this mapper with the Session, specify it to the query
method:
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()
entity_name
parameter. Instances loaded with this mapper will be totally managed by this new mapper and have no connection to the original one. Most methods on Session
include an optional entity_name
parameter in order to specify this condition.
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()
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.
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]
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())
Keyword arguments which can be used with the mapper()
function. For arguments to relation()
, see Relation Options.
relation()
which has the same name as a column in the mapped table. The table column will no longer be mapped.
query.load()
, session.refresh()
, session.expunge()
, or session.clear()
.
MapperExtension
objects are used to attach logic to before_insert()
, before_update()
, etc., and the user-defined logic requires that the full persistence of each instance must be completed before moving onto the next (such as logic which queries the tables for the most recent ID). Note that this flag has a significant impact on the efficiency of a large save operation.
column_prefix='_'
is equivalent to defining all column-based properties as _columnname=table.c.columnname
. See Overriding Column Names for information on overriding column-based attribute names.
inherits
to be set.
session.query(somemapper)
. Note that it is usually invalid to define additional relationships on a non_primary mapper as they will conflict with those of the primary. See Multiple Mappers for One Class.
polymorphic_identity
value to be set for all mappers in the inheritance hierarchy.
polymorphic_on
, corresponding to the "class identity" of this mapper. See Mapping a Class with Table Inheritance.
local_table
of the mapper combined against any inherited tables. When this argument is specified, the primary keys of the mapped table if any are disregarded in place of the columns given. This can be used to provide primary key identity to a table that has no PKs defined at the schema level, or to modify what defines "identity" for a particular table.
Selectable
which will take the place of the Mapper
's main table argument when
performing queries.
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])