Release: 1.1.0b1 | Release Date: not released

SQLAlchemy 1.1 Documentation

Relationship Loading Techniques

A big part of SQLAlchemy is providing a wide range of control over how related objects get loaded when querying. This behavior can be configured at mapper construction time using the lazy parameter to the relationship() function, as well as by using options with the Query object.

Using Loader Strategies: Lazy Loading, Eager Loading

By default, all inter-object relationships are lazy loading. The scalar or collection attribute associated with a relationship() contains a trigger which fires the first time the attribute is accessed. This trigger, in all but one case, issues a SQL call at the point of access in order to load the related object or objects:

sql>>> jack.addresses
[<Address(u'')>, <Address(u'')>]

The one case where SQL is not emitted is for a simple many-to-one relationship, when the related object can be identified by its primary key alone and that object is already present in the current Session.

This default behavior of “load upon attribute access” is known as “lazy” or “select” loading - the name “select” because a “SELECT” statement is typically emitted when the attribute is first accessed.

In the Object Relational Tutorial, we introduced the concept of Eager Loading. We used an option in conjunction with the Query object in order to indicate that a relationship should be loaded at the same time as the parent, within a single SQL query. This option, known as joinedload(), connects a JOIN (by default a LEFT OUTER join) to the statement and populates the scalar/collection from the same result set as that of the parent:

sql>>> jack = session.query(User).\
... options(joinedload('addresses')).\
... filter_by(name='jack').all() #doctest: +NORMALIZE_WHITESPACE

In addition to “joined eager loading”, a second option for eager loading exists, called “subquery eager loading”. This kind of eager loading emits an additional SQL statement for each collection requested, aggregated across all parent objects:

sql>>> jack = session.query(User).\
... options(subqueryload('addresses')).\
... filter_by(name='jack').all()

The default loader strategy for any relationship() is configured by the lazy keyword argument, which defaults to select - this indicates a “select” statement . Below we set it as joined so that the children relationship is eager loaded using a JOIN:

# load the 'children' collection using LEFT OUTER JOIN
class Parent(Base):
    __tablename__ = 'parent'

    id = Column(Integer, primary_key=True)
    children = relationship("Child", lazy='joined')

We can also set it to eagerly load using a second query for all collections, using subquery:

# load the 'children' collection using a second query which
# JOINS to a subquery of the original
class Parent(Base):
    __tablename__ = 'parent'

    id = Column(Integer, primary_key=True)
    children = relationship("Child", lazy='subquery')

When querying, all three choices of loader strategy are available on a per-query basis, using the joinedload(), subqueryload() and lazyload() query options:

# set children to load lazily

# set children to load eagerly with a join

# set children to load eagerly with a second statement

The Importance of Ordering

A query which makes use of subqueryload() in conjunction with a limiting modifier such as Query.first(), Query.limit(), or Query.offset() should always include Query.order_by() against unique column(s) such as the primary key, so that the additional queries emitted by subqueryload() include the same ordering as used by the parent query. Without it, there is a chance that the inner query could return the wrong rows:

# incorrect, no ORDER BY

# incorrect if is not unique

# correct

Loading Along Paths

To reference a relationship that is deeper than one level, method chaining may be used. The object returned by all loader options is an instance of the Load class, which provides a so-called “generative” interface:


Using method chaining, the loader style of each link in the path is explicitly stated. To navigate along a path without changing the existing loader style of a particular attribute, the defaultload() method/function may be used:


버전 0.9.0으로 변경: The previous approach of specifying dot-separated paths within loader options has been superseded by the less ambiguous approach of the Load object and related methods. With this system, the user specifies the style of loading for each link along the chain explicitly, rather than guessing between options like joinedload() vs. joinedload_all(). The orm.defaultload() is provided to allow path navigation without modification of existing loader options. The dot-separated path system as well as the _all() functions will remain available for backwards- compatibility indefinitely.

Default Loading Strategies

버전 0.7.5에 추가: Default loader strategies as a new feature.

Each of joinedload(), subqueryload(), lazyload(), and noload() can be used to set the default style of relationship() loading for a particular query, affecting all relationship() -mapped attributes not otherwise specified in the Query. This feature is available by passing the string '*' as the argument to any of these options:


Above, the lazyload('*') option will supersede the lazy setting of all relationship() constructs in use for that query, except for those which use the 'dynamic' style of loading. If some relationships specify lazy='joined' or lazy='subquery', for example, using lazyload('*') will unilaterally cause all those relationships to use 'select' loading, e.g. emit a SELECT statement when each attribute is accessed.

The option does not supersede loader options stated in the query, such as eagerload(), subqueryload(), etc. The query below will still use joined loading for the widget relationship:


If multiple '*' options are passed, the last one overrides those previously passed.

Per-Entity Default Loading Strategies

버전 0.9.0에 추가: Per-entity default loader strategies.

A variant of the default loader strategy is the ability to set the strategy on a per-entity basis. For example, if querying for User and Address, we can instruct all relationships on Address only to use lazy loading by first applying the Load object, then specifying the * as a chained option:

session.query(User, Address).options(Load(Address).lazyload('*'))

Above, all relationships on Address will be set to a lazy load.

The Zen of Eager Loading

The philosophy behind loader strategies is that any set of loading schemes can be applied to a particular query, and the results don’t change - only the number of SQL statements required to fully load related objects and collections changes. A particular query might start out using all lazy loads. After using it in context, it might be revealed that particular attributes or collections are always accessed, and that it would be more efficient to change the loader strategy for these. The strategy can be changed with no other modifications to the query, the results will remain identical, but fewer SQL statements would be emitted. In theory (and pretty much in practice), nothing you can do to the Query would make it load a different set of primary or related objects based on a change in loader strategy.

How joinedload() in particular achieves this result of not impacting entity rows returned in any way is that it creates an anonymous alias of the joins it adds to your query, so that they can’t be referenced by other parts of the query. For example, the query below uses joinedload() to create a LEFT OUTER JOIN from users to addresses, however the ORDER BY added against Address.email_address is not valid - the Address entity is not named in the query:

>>> jack = session.query(User).\
... options(joinedload(User.addresses)).\
... filter('jack').\
... order_by(Address.email_address).all()
SELECT AS addresses_1_id, addresses_1.email_address AS addresses_1_email_address, addresses_1.user_id AS addresses_1_user_id, AS users_id, AS users_name, users.fullname AS users_fullname, users.password AS users_password FROM users LEFT OUTER JOIN addresses AS addresses_1 ON = addresses_1.user_id WHERE = ? ORDER BY addresses.email_address <-- this part is wrong ! ['jack']

Above, ORDER BY addresses.email_address is not valid since addresses is not in the FROM list. The correct way to load the User records and order by email address is to use Query.join():

>>> jack = session.query(User).\
... join(User.addresses).\
... filter('jack').\
... order_by(Address.email_address).all()
SELECT AS users_id, AS users_name, users.fullname AS users_fullname, users.password AS users_password FROM users JOIN addresses ON = addresses.user_id WHERE = ? ORDER BY addresses.email_address ['jack']

The statement above is of course not the same as the previous one, in that the columns from addresses are not included in the result at all. We can add joinedload() back in, so that there are two joins - one is that which we are ordering on, the other is used anonymously to load the contents of the User.addresses collection:

>>> jack = session.query(User).\
... join(User.addresses).\
... options(joinedload(User.addresses)).\
... filter('jack').\
... order_by(Address.email_address).all()
SELECT AS addresses_1_id, addresses_1.email_address AS addresses_1_email_address, addresses_1.user_id AS addresses_1_user_id, AS users_id, AS users_name, users.fullname AS users_fullname, users.password AS users_password FROM users JOIN addresses ON = addresses.user_id LEFT OUTER JOIN addresses AS addresses_1 ON = addresses_1.user_id WHERE = ? ORDER BY addresses.email_address ['jack']

What we see above is that our usage of Query.join() is to supply JOIN clauses we’d like to use in subsequent query criterion, whereas our usage of joinedload() only concerns itself with the loading of the User.addresses collection, for each User in the result. In this case, the two joins most probably appear redundant - which they are. If we wanted to use just one JOIN for collection loading as well as ordering, we use the contains_eager() option, described in Routing Explicit Joins/Statements into Eagerly Loaded Collections below. But to see why joinedload() does what it does, consider if we were filtering on a particular Address:

>>> jack = session.query(User).\
... join(User.addresses).\
... options(joinedload(User.addresses)).\
... filter('jack').\
... filter(Address.email_address=='').\
... all()
SELECT AS addresses_1_id, addresses_1.email_address AS addresses_1_email_address, addresses_1.user_id AS addresses_1_user_id, AS users_id, AS users_name, users.fullname AS users_fullname, users.password AS users_password FROM users JOIN addresses ON = addresses.user_id LEFT OUTER JOIN addresses AS addresses_1 ON = addresses_1.user_id WHERE = ? AND addresses.email_address = ? ['jack', '']

Above, we can see that the two JOINs have very different roles. One will match exactly one row, that of the join of User and Address where Address.email_address==''. The other LEFT OUTER JOIN will match all Address rows related to User, and is only used to populate the User.addresses collection, for those User objects that are returned.

By changing the usage of joinedload() to another style of loading, we can change how the collection is loaded completely independently of SQL used to retrieve the actual User rows we want. Below we change joinedload() into subqueryload():

>>> jack = session.query(User).\
... join(User.addresses).\
... options(subqueryload(User.addresses)).\
... filter('jack').\
... filter(Address.email_address=='').\
... all()
SELECT AS users_id, AS users_name, users.fullname AS users_fullname, users.password AS users_password FROM users JOIN addresses ON = addresses.user_id WHERE = ? AND addresses.email_address = ? ['jack', ''] # ... subqueryload() emits a SELECT in order # to load all address records ...

When using joined eager loading, if the query contains a modifier that impacts the rows returned externally to the joins, such as when using DISTINCT, LIMIT, OFFSET or equivalent, the completed statement is first wrapped inside a subquery, and the joins used specifically for joined eager loading are applied to the subquery. SQLAlchemy’s joined eager loading goes the extra mile, and then ten miles further, to absolutely ensure that it does not affect the end result of the query, only the way collections and related objects are loaded, no matter what the format of the query is.

What Kind of Loading to Use ?

Which type of loading to use typically comes down to optimizing the tradeoff between number of SQL executions, complexity of SQL emitted, and amount of data fetched. Lets take two examples, a relationship() which references a collection, and a relationship() that references a scalar many-to-one reference.

  • One to Many Collection
  • When using the default lazy loading, if you load 100 objects, and then access a collection on each of them, a total of 101 SQL statements will be emitted, although each statement will typically be a simple SELECT without any joins.
  • When using joined loading, the load of 100 objects and their collections will emit only one SQL statement. However, the total number of rows fetched will be equal to the sum of the size of all the collections, plus one extra row for each parent object that has an empty collection. Each row will also contain the full set of columns represented by the parents, repeated for each collection item - SQLAlchemy does not re-fetch these columns other than those of the primary key, however most DBAPIs (with some exceptions) will transmit the full data of each parent over the wire to the client connection in any case. Therefore joined eager loading only makes sense when the size of the collections are relatively small. The LEFT OUTER JOIN can also be performance intensive compared to an INNER join.
  • When using subquery loading, the load of 100 objects will emit two SQL statements. The second statement will fetch a total number of rows equal to the sum of the size of all collections. An INNER JOIN is used, and a minimum of parent columns are requested, only the primary keys. So a subquery load makes sense when the collections are larger.
  • When multiple levels of depth are used with joined or subquery loading, loading collections-within- collections will multiply the total number of rows fetched in a cartesian fashion. Both forms of eager loading always join from the original parent class.
  • Many to One Reference
  • When using the default lazy loading, a load of 100 objects will like in the case of the collection emit as many as 101 SQL statements. However - there is a significant exception to this, in that if the many-to-one reference is a simple foreign key reference to the target’s primary key, each reference will be checked first in the current identity map using Query.get(). So here, if the collection of objects references a relatively small set of target objects, or the full set of possible target objects have already been loaded into the session and are strongly referenced, using the default of lazy=’select’ is by far the most efficient way to go.
  • When using joined loading, the load of 100 objects will emit only one SQL statement. The join will be a LEFT OUTER JOIN, and the total number of rows will be equal to 100 in all cases. If you know that each parent definitely has a child (i.e. the foreign key reference is NOT NULL), the joined load can be configured with innerjoin set to True, which is usually specified within the relationship(). For a load of objects where there are many possible target references which may have not been loaded already, joined loading with an INNER JOIN is extremely efficient.
  • Subquery loading will issue a second load for all the child objects, so for a load of 100 objects there would be two SQL statements emitted. There’s probably not much advantage here over joined loading, however, except perhaps that subquery loading can use an INNER JOIN in all cases whereas joined loading requires that the foreign key is NOT NULL.

Routing Explicit Joins/Statements into Eagerly Loaded Collections

The behavior of joinedload() is such that joins are created automatically, using anonymous aliases as targets, the results of which are routed into collections and scalar references on loaded objects. It is often the case that a query already includes the necessary joins which represent a particular collection or scalar reference, and the joins added by the joinedload feature are redundant - yet you’d still like the collections/references to be populated.

For this SQLAlchemy supplies the contains_eager() option. This option is used in the same manner as the joinedload() option except it is assumed that the Query will specify the appropriate joins explicitly. Below, we specify a join between User and Address and addtionally establish this as the basis for eager loading of User.addresses:

class User(Base):
    __tablename__ = 'user'
    id = Column(Integer, primary_key=True)
    addresses = relationship("Address")

class Address(Base):
    __tablename__ = 'address'

    # ...

q = session.query(User).join(User.addresses).\

If the “eager” portion of the statement is “aliased”, the alias keyword argument to contains_eager() may be used to indicate it. This is sent as a reference to an aliased() or Alias construct:

# use an alias of the Address entity
adalias = aliased(Address)

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

# get results normally
sqlr = query.all()

The path given as the argument to contains_eager() needs to be a full path from the starting entity. For example if we were loading Users->orders->Order->items->Item, the string version would look like:


Or using the class-bound descriptor:


Using contains_eager() to load a custom-filtered collection result

When we use contains_eager(), we are constructing ourselves the SQL that will be used to populate collections. From this, it naturally follows that we can opt to modify what values the collection is intended to store, by writing our SQL to load a subset of elements for collections or scalar attributes.

As an example, we can load a User object and eagerly load only particular addresses into its .addresses collection just by filtering:

q = session.query(User).join(User.addresses).\

The above query will load only User objects which contain at least Address object that contains the substring 'ed' in its email field; the User.addresses collection will contain only these Address entries, and not any other Address entries that are in fact associated with the collection.


Keep in mind that when we load only a subset of objects into a collection, that collection no longer represents what’s actually in the database. If we attempted to add entries to this collection, we might find ourselves conflicting with entries that are already in the database but not locally loaded.

In addition, the collection will fully reload normally once the object or attribute is expired. This expiration occurs whenever the Session.commit(), Session.rollback() methods are used assuming default session settings, or the Session.expire_all() or Session.expire() methods are used.

For these reasons, prefer returning separate fields in a tuple rather than artificially altering a collection, when an object plus a custom set of related objects is desired:

q = session.query(User, Address).join(User.addresses).\

Advanced Usage with Arbitrary Statements

The alias argument can be more creatively used, in that it can be made to represent any set of arbitrary names to match up into a statement. Below it is linked to a select() which links a set of column objects to a string SQL statement:

# label the columns of the addresses table
eager_columns = select([

# select from a raw SQL statement which uses those label names for the
# addresses table.  contains_eager() matches them up.
query = session.query(User).\
    from_statement("select users.*, addresses.address_id as a1, "
            "addresses.email_address as a2, addresses.user_id as a3 "
            "from users left outer join addresses on users.user_id=addresses.user_id").\
    options(contains_eager(User.addresses, alias=eager_columns))

Creating Custom Load Rules


This is an advanced technique! Great care and testing should be applied.

The ORM has various edge cases where the value of an attribute is locally available, however the ORM itself doesn’t have awareness of this. There are also cases when a user-defined system of loading attributes is desirable. To support the use case of user-defined loading systems, a key function attributes.set_committed_value() is provided. This function is basically equivalent to Python’s own setattr() function, except that when applied to a target object, SQLAlchemy’s “attribute history” system which is used to determine flush-time changes is bypassed; the attribute is assigned in the same way as if the ORM loaded it that way from the database.

The use of attributes.set_committed_value() can be combined with another key event known as InstanceEvents.load() to produce attribute-population behaviors when an object is loaded. One such example is the bi-directional “one-to-one” case, where loading the “many-to-one” side of a one-to-one should also imply the value of the “one-to-many” side. The SQLAlchemy ORM does not consider backrefs when loading related objects, and it views a “one-to-one” as just another “one-to-many”, that just happens to be one row.

Given the following mapping:

from sqlalchemy import Integer, ForeignKey, Column
from sqlalchemy.orm import relationship, backref
from sqlalchemy.ext.declarative import declarative_base

Base = declarative_base()

class A(Base):
    __tablename__ = 'a'
    id = Column(Integer, primary_key=True)
    b_id = Column(ForeignKey(''))
    b = relationship("B", backref=backref("a", uselist=False), lazy='joined')

class B(Base):
    __tablename__ = 'b'
    id = Column(Integer, primary_key=True)

If we query for an A row, and then ask it for a.b.a, we will get an extra SELECT:

>>> a1.b.a
SELECT AS a_id, a.b_id AS a_b_id
WHERE ? = a.b_id

This SELECT is redundant becasue b.a is the same value as a1. We can create an on-load rule to populate this for us:

from sqlalchemy import event
from sqlalchemy.orm import attributes

@event.listens_for(A, "load")
def load_b(target, context):
    if 'b' in target.__dict__:
        attributes.set_committed_value(target.b, 'a', target)

Now when we query for A, we will get A.b from the joined eager load, and A.b.a from our event:

sqla1 = s.query(A).first()
assert a1.b.a is a1

Relationship Loader API


Return a MapperOption that will indicate to the Query that the main table has been aliased.

This is a seldom-used option to suit the very rare case that contains_eager() is being used in conjunction with a user-defined SELECT statement that aliases the parent table. E.g.:

# define an aliased UNION called 'ulist'
ulist =\

# add on an eager load of "addresses"
statement = ulist.outerjoin(addresses).\

# create query, indicating "ulist" will be an
# alias for the main table, "addresses"
# property should be eager loaded
query = session.query(User).options(

# then get results via the statement
results = query.from_statement(statement).all()
매개 변수:alias – is the string name of an alias, or a Alias object representing the alias.
sqlalchemy.orm.contains_eager(*keys, **kw)

Indicate that the given attribute should be eagerly loaded from columns stated manually in the query.

This function is part of the Load interface and supports both method-chained and standalone operation.

The option is used in conjunction with an explicit join that loads the desired rows, i.e.:


The above query would join from the Order entity to its related User entity, and the returned Order objects would have the Order.user attribute pre-populated.

contains_eager() also accepts an alias argument, which is the string name of an alias, an alias() construct, or an aliased() construct. Use this when the eagerly-loaded rows are to come from an aliased table:

user_alias = aliased(User)
        join((user_alias, Order.user)).\
        options(contains_eager(Order.user, alias=user_alias))

Indicate an attribute should load using its default loader style.

This method is used to link to other loader options, such as to set the orm.defer() option on a class that is linked to a relationship of the parent class being loaded, orm.defaultload() can be used to navigate this path without changing the loading style of the relationship:

sqlalchemy.orm.eagerload(*args, **kwargs)

A synonym for joinedload().

sqlalchemy.orm.eagerload_all(*args, **kwargs)

A synonym for joinedload_all()


Indicate that the given attribute should be loaded using an immediate load with a per-attribute SELECT statement.

This function is part of the Load interface and supports both method-chained and standalone operation.

sqlalchemy.orm.joinedload(*keys, **kw)

Indicate that the given attribute should be loaded using joined eager loading.

This function is part of the Load interface and supports both method-chained and standalone operation.


# joined-load the "orders" collection on "User"

# joined-load Order.items and then Item.keywords

# lazily load Order.items, but when Items are loaded,
# joined-load the keywords collection
매개 변수:innerjoin

if True, indicates that the joined eager load should use an inner join instead of the default of left outer join:

query(Order).options(joinedload(Order.user, innerjoin=True))

In order to chain multiple eager joins together where some may be OUTER and others INNER, right-nested joins are used to link them:

    joinedload(, innerjoin=False).
        joinedload(B.cs, innerjoin=True)

The above query, linking via “outer” join and B.cs via “inner” join would render the joins as “a LEFT OUTER JOIN (b JOIN c)”. When using SQLite, this form of JOIN is translated to use full subqueries as this syntax is otherwise not directly supported.

The innerjoin flag can also be stated with the term "unnested". This will prevent joins from being right-nested, and will instead link an “innerjoin” eagerload to an “outerjoin” eagerload by bypassing the “inner” join. Using this form as follows:

    joinedload(, innerjoin=False).
        joinedload(B.cs, innerjoin="unnested")

Joins will be rendered as “a LEFT OUTER JOIN b LEFT OUTER JOIN c”, so that all of “a” is matched rather than being incorrectly limited by a “b” that does not contain a “c”.


The “unnested” flag does not affect the JOIN rendered from a many-to-many association table, e.g. a table configured as relationship.secondary, to the target table; for correctness of results, these joins are always INNER and are therefore right-nested if linked to an OUTER join.

버전 0.9.4에 추가: Added support for “nesting” of eager “inner” joins. See Right-nested inner joins available in joined eager loads.

버전 1.0.0으로 변경: innerjoin=True now implies innerjoin="nested", whereas in 0.9 it implied innerjoin="unnested". In order to achieve the pre-1.0 “unnested” inner join behavior, use the value innerjoin="unnested". See Right inner join nesting now the default for joinedload with innerjoin=True.


The joins produced by orm.joinedload() are anonymously aliased. The criteria by which the join proceeds cannot be modified, nor can the Query refer to these joins in any way, including ordering.

To produce a specific SQL JOIN which is explicitly available, use Query.join(). To combine explicit JOINs with eager loading of collections, use orm.contains_eager(); see Routing Explicit Joins/Statements into Eagerly Loaded Collections.

sqlalchemy.orm.joinedload_all(*keys, **kw)

Produce a standalone “all” option for orm.joinedload().

버전 0.9.0 폐지: The “_all()” style is replaced by method chaining, e.g.:


Indicate that the given attribute should be loaded using “lazy” loading.

This function is part of the Load interface and supports both method-chained and standalone operation.

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Indicate that the given relationship attribute should remain unloaded.

This function is part of the Load interface and supports both method-chained and standalone operation.

orm.noload() applies to relationship() attributes; for column-based attributes, see orm.defer().


Indicate that the given attribute should be loaded using subquery eager loading.

This function is part of the Load interface and supports both method-chained and standalone operation.


# subquery-load the "orders" collection on "User"

# subquery-load Order.items and then Item.keywords

# lazily load Order.items, but when Items are loaded,
# subquery-load the keywords collection

Produce a standalone “all” option for orm.subqueryload().

버전 0.9.0 폐지: The “_all()” style is replaced by method chaining, e.g.: