# SQLAlchemy 0.7 Documentation

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Release: 0.7.3 | Release Date: October 16, 2011

# 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
    SELECT addresses.id AS addresses_id, addresses.email_address AS addresses_email_address,
    addresses.user_id AS addresses_user_id
    FROM addresses
    WHERE ? = addresses.user_id
    [5][<Address(u'jack@google.com')>, <Address(u'j25@yahoo.com')>]

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()
    SELECT addresses_1.id AS addresses_1_id, addresses_1.email_address AS addresses_1_email_address,
    addresses_1.user_id AS addresses_1_user_id, users.id AS users_id, users.name AS users_name,
    users.fullname AS users_fullname, users.password AS users_password
    FROM users LEFT OUTER JOIN addresses AS addresses_1 ON users.id = addresses_1.user_id
    WHERE users.name = ?
    ['jack']

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()
    SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname,
    users.password AS users_password
    FROM users
    WHERE users.name = ?
    ('jack',)
    SELECT addresses.id AS addresses_id, addresses.email_address AS addresses_email_address,
    addresses.user_id AS addresses_user_id, anon_1.users_id AS anon_1_users_id
    FROM (SELECT users.id AS users_id
    FROM users
    WHERE users.name = ?) AS anon_1 JOIN addresses ON anon_1.users_id = addresses.user_id
    ORDER BY anon_1.users_id, addresses.id
    ('jack',)

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 loading, using a join:

    # load the 'children' collection using LEFT OUTER JOIN
    mapper(Parent, parent_table, properties={
        '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' attribute using a join to a subquery
    mapper(Parent, parent_table, properties={
        '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
    session.query(Parent).options(lazyload('children')).all()

    # set children to load eagerly with a join
    session.query(Parent).options(joinedload('children')).all()

    # set children to load eagerly with a second statement
    session.query(Parent).options(subqueryload('children')).all()

To reference a relationship that is deeper than one level, separate the names by periods:

    session.query(Parent).options(joinedload('foo.bar.bat')).all()

When using dot-separated names with `joinedload()` or `subqueryload()`, the option applies only to the actual attribute named, and not its ancestors. For example, suppose a mapping from `A` to `B` to `C`, where the relationships, named `atob` and `btoc`, are both lazy-loading. A statement like the following:

    session.query(A).options(joinedload('atob.btoc')).all()

will load only `A` objects to start. When the `atob` attribute on each `A` is accessed, the returned `B` objects will eagerly load their `C` objects.

Therefore, to modify the eager load to load both `atob` as well as `btoc`, place joinedloads for both:

    session.query(A).options(joinedload('atob'), joinedload('atob.btoc')).all()

or more simply just use `joinedload_all()` or `subqueryload_all()`:

    session.query(A).options(joinedload_all('atob.btoc')).all()

There are two other loader strategies available, dynamic loading and no loading; these are described in Working with Large Collections.

## 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(User.name=='jack').\
    ... order_by(Address.email_address).all()
    SELECT addresses_1.id AS addresses_1_id, addresses_1.email_address AS addresses_1_email_address,
    addresses_1.user_id AS addresses_1_user_id, users.id AS users_id, users.name AS users_name,
    users.fullname AS users_fullname, users.password AS users_password
    FROM users LEFT OUTER JOIN addresses AS addresses_1 ON users.id = addresses_1.user_id
    WHERE users.name = ? 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(User.name=='jack').\
    ... order_by(Address.email_address).all()

    SELECT users.id AS users_id, users.name AS users_name,
    users.fullname AS users_fullname, users.password AS users_password
    FROM users JOIN addresses ON users.id = addresses.user_id
    WHERE users.name = ? 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(User.name=='jack').\
    ... order_by(Address.email_address).all()
    SELECT addresses_1.id AS addresses_1_id, addresses_1.email_address AS addresses_1_email_address,
    addresses_1.user_id AS addresses_1_user_id, users.id AS users_id, users.name AS users_name,
    users.fullname AS users_fullname, users.password AS users_password
    FROM users JOIN addresses ON users.id = addresses.user_id
    LEFT OUTER JOIN addresses AS addresses_1 ON users.id = addresses_1.user_id
    WHERE users.name = ? 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(User.name=='jack').\
    ... filter(Address.email_address=='someaddress@foo.com').\
    ... all()
    SELECT addresses_1.id AS addresses_1_id, addresses_1.email_address AS addresses_1_email_address,
    addresses_1.user_id AS addresses_1_user_id, users.id AS users_id, users.name AS users_name,
    users.fullname AS users_fullname, users.password AS users_password
    FROM users JOIN addresses ON users.id = addresses.user_id
    LEFT OUTER JOIN addresses AS addresses_1 ON users.id = addresses_1.user_id
    WHERE users.name = ? AND addresses.email_address = ?
    ['jack', 'someaddress@foo.com']

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=='someaddress@foo.com'`. 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(User.name=='jack').\
    ... filter(Address.email_address=='someaddress@foo.com').\
    ... all()
    SELECT users.id AS users_id, users.name AS users_name,
    users.fullname AS users_fullname, users.password AS users_password
    FROM users JOIN addresses ON users.id = addresses.user_id
    WHERE users.name = ? AND addresses.email_address = ?
    ['jack', 'someaddress@foo.com']

    # ... 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=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 it’s used with a `from_statement` load:

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

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

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

    # get results normally
    r = query.from_statement(statement)

It works just as well with an inline `Query.join()` or `Query.outerjoin()`:

    session.query(User).outerjoin(User.addresses).options(contains_eager(User.addresses)).all()

If the “eager” portion of the statement is “aliased”, 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` (or other selectable) object:

    # 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()
    SELECT users.user_id AS users_user_id, users.user_name AS users_user_name, adalias.address_id AS adalias_address_id,
    adalias.user_id AS adalias_user_id, adalias.email_address AS adalias_email_address, (...other columns...)
    FROM users LEFT OUTER JOIN email_addresses AS email_addresses_1 ON users.user_id = email_addresses_1.user_id

The `alias` argument is used only as a source of columns to match up to the result set. You can use it even to match up the result to arbitrary label names in a string SQL statement, by passing a selectable() which links those labels to the mapped `Table`:

    # label the columns of the addresses table
    eager_columns = select([
                        addresses.c.address_id.label('a1'),
                        addresses.c.email_address.label('a2'),
                        addresses.c.user_id.label('a3')])

    # 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))

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:

    query(User).options(contains_eager('orders', 'items'))

Or using the class-bound descriptor:

    query(User).options(contains_eager(User.orders, Order.items))

A variant on `contains_eager()` is the `contains_alias()` option, which is used in the rare case that the parent object is loaded from an alias within a user-defined SELECT statement:

    # 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().apply_labels()

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

    # results
    r = query.from_statement(statement)

## Relation Loader API

`sqlalchemy.orm.``contains_alias`(alias)

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

  alias is the string name or `Alias` object representing the alias.

`sqlalchemy.orm.``contains_eager`(*keys, **kwargs)

  Return a `MapperOption` that will indicate to the query that the given attribute should be eagerly loaded from columns currently in the query.

  Used with `options()`.

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

      sess.query(Order).\
              join(Order.user).\
              options(contains_eager(Order.user))

  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)
      sess.query(Order).\
              join((user_alias, Order.user)).\
              options(contains_eager(Order.user, alias=user_alias))

  See also `eagerload()` for the “automatic” version of this functionality.

  For additional examples of `contains_eager()` see Routing Explicit Joins/Statements into Eagerly Loaded Collections.

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

  A synonym for `joinedload()`.

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

  A synonym for `joinedload_all()`

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

  Return a `MapperOption` that will convert the property of the given name or series of mapped attributes into an joined eager load.

  Note

  This function is known as `eagerload()` in all versions of SQLAlchemy prior to version 0.6beta3, including the 0.5 and 0.4 series. `eagerload()` will remain available for the foreseeable future in order to enable cross-compatibility.

  Used with `options()`.

  examples:

      # joined-load the "orders" colleciton on "User"
      query(User).options(joinedload(User.orders))

      # joined-load the "keywords" collection on each "Item",
      # but not the "items" collection on "Order" - those
      # remain lazily loaded.
      query(Order).options(joinedload(Order.items, Item.keywords))

      # to joined-load across both, use joinedload_all()
      query(Order).options(joinedload_all(Order.items, Item.keywords))

  `joinedload()` also accepts a keyword argument innerjoin=True which indicates using an inner join instead of an outer:

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

  Note

  The join created by `joinedload()` is anonymously aliased such that it does not affect the query results. An `Query.order_by()` or `Query.filter()` call cannot reference these aliased tables - so-called “user space” joins are constructed using `Query.join()`. The rationale for this is that `joinedload()` is only applied in order to affect how related objects or collections are loaded as an optimizing detail - it can be added or removed with no impact on actual results. See the section The Zen of Eager Loading for a detailed description of how this is used, including how to use a single explicit JOIN for filtering/ordering and eager loading simultaneously.

  See also: `subqueryload()`, `lazyload()`

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

  Return a `MapperOption` that will convert all properties along the given dot-separated path or series of mapped attributes into an joined eager load.

  Note

  This function is known as `eagerload_all()` in all versions of SQLAlchemy prior to version 0.6beta3, including the 0.5 and 0.4 series. `eagerload_all()` will remain available for the foreseeable future in order to enable cross-compatibility.

  Used with `options()`.

  For example:

      query.options(joinedload_all('orders.items.keywords'))...

  will set all of ‘orders’, ‘orders.items’, and ‘orders.items.keywords’ to load in one joined eager load.

  Individual descriptors are accepted as arguments as well:

      query.options(joinedload_all(User.orders, Order.items, Item.keywords))

  The keyword arguments accept a flag innerjoin=True|False which will override the value of the innerjoin flag specified on the relationship().

  See also: `subqueryload_all()`, `lazyload()`

`sqlalchemy.orm.``lazyload`(*keys)

  Return a `MapperOption` that will convert the property of the given name or series of mapped attributes into a lazy load.

  Used with `options()`.

  See also: `eagerload()`, `subqueryload()`, `immediateload()`

`sqlalchemy.orm.``subqueryload`(*keys)

  Return a `MapperOption` that will convert the property of the given name or series of mapped attributes into an subquery eager load.

  Used with `options()`.

  examples:

      # subquery-load the "orders" colleciton on "User"
      query(User).options(subqueryload(User.orders))

      # subquery-load the "keywords" collection on each "Item",
      # but not the "items" collection on "Order" - those
      # remain lazily loaded.
      query(Order).options(subqueryload(Order.items, Item.keywords))

      # to subquery-load across both, use subqueryload_all()
      query(Order).options(subqueryload_all(Order.items, Item.keywords))

  See also: `joinedload()`, `lazyload()`

`sqlalchemy.orm.``subqueryload_all`(*keys)

  Return a `MapperOption` that will convert all properties along the given dot-separated path or series of mapped attributes into a subquery eager load.

  Used with `options()`.

  For example:

      query.options(subqueryload_all('orders.items.keywords'))...

  will set all of ‘orders’, ‘orders.items’, and ‘orders.items.keywords’ to load in one subquery eager load.

  Individual descriptors are accepted as arguments as well:

      query.options(subqueryload_all(User.orders, Order.items,
      Item.keywords))

  See also: `joinedload_all()`, `lazyload()`, `immediateload()`