# SQLAlchemy 0.8 Documentation

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Release: 0.8.2 | Release Date: July 3, 2013

# Examples

The SQLAlchemy distribution includes a variety of code examples illustrating a select set of patterns, some typical and some not so typical. All are runnable and can be found in the `/examples` directory of the distribution. Each example contains a README in its `__init__.py` file, each of which are listed below.

Additional SQLAlchemy examples, some user contributed, are available on the wiki at http://www.sqlalchemy.org/trac/wiki/UsageRecipes.

## Adjacency List

Location: /examples/adjacency_list/

An example of a dictionary-of-dictionaries structure mapped using an adjacency list model.

E.g.:

    node = TreeNode('rootnode')
    node.append('node1')
    node.append('node3')
    session.add(node)
    session.commit()

    dump_tree(node)

## Associations

Location: /examples/association/

Examples illustrating the usage of the “association object” pattern, where an intermediary class mediates the relationship between two classes that are associated in a many-to-many pattern.

This directory includes the following examples:

- basic_association.py - illustrate a many-to-many relationship between an “Order” and a collection of “Item” objects, associating a purchase price with each via an association object called “OrderItem”
- proxied_association.py - same example as basic_association, adding in usage of `sqlalchemy.ext.associationproxy` to make explicit references to “OrderItem” optional.
- dict_of_sets_with_default.py - an advanced association proxy example which illustrates nesting of association proxies to produce multi-level Python collections, in this case a dictionary with string keys and sets of integers as values, which conceal the underlying mapped classes.

## Attribute Instrumentation

Location: /examples/custom_attributes/

Two examples illustrating modifications to SQLAlchemy’s attribute management system.

`listen_for_events.py` illustrates the usage of `AttributeExtension` to intercept attribute events. It additionally illustrates a way to automatically attach these listeners to all class attributes using a `InstrumentationManager`.

`custom_management.py` illustrates much deeper usage of `InstrumentationManager` as well as collection adaptation, to completely change the underlying method used to store state on an object. This example was developed to illustrate techniques which would be used by other third party object instrumentation systems to interact with SQLAlchemy’s event system and is only intended for very intricate framework integrations.

## Dogpile Caching

Location: /examples/dogpile_caching/

Illustrates how to embed dogpile.cache functionality within the `Query` object, allowing full cache control as well as the ability to pull “lazy loaded” attributes from long term cache as well.

Changed in version 0.8: The example was modernized to use dogpile.cache, replacing Beaker as the caching library in use.

In this demo, the following techniques are illustrated:

- Using custom subclasses of `Query`
- Basic technique of circumventing Query to pull from a custom cache source instead of the database.
- Rudimental caching with dogpile.cache, using “regions” which allow global control over a fixed set of configurations.
- Using custom `MapperOption` objects to configure options on a Query, including the ability to invoke the options deep within an object graph when lazy loads occur.

E.g.:

    # query for Person objects, specifying cache
    q = Session.query(Person).options(FromCache("default"))

    # specify that each Person's "addresses" collection comes from
    # cache too
    q = q.options(RelationshipCache(Person.addresses, "default"))

    # query
    print q.all()

To run, both SQLAlchemy and dogpile.cache must be installed or on the current PYTHONPATH. The demo will create a local directory for datafiles, insert initial data, and run. Running the demo a second time will utilize the cache files already present, and exactly one SQL statement against two tables will be emitted - the displayed result however will utilize dozens of lazyloads that all pull from cache.

The demo scripts themselves, in order of complexity, are run as follows:

    python examples/dogpile_caching/helloworld.py

    python examples/dogpile_caching/relationship_caching.py

    python examples/dogpile_caching/advanced.py

    python examples/dogpile_caching/local_session_caching.py

Listing of files:

> environment.py - Establish the Session, a dictionary of “regions”, a sample cache region against a .dbm file, data / cache file paths, and configurations, bootstrap fixture data if necessary.

> caching_query.py - Represent functions and classes which allow the usage of Dogpile caching with SQLAlchemy. Introduces a query option called FromCache.

> model.py - The datamodel, which represents Person that has multiple Address objects, each with PostalCode, City, Country

> fixture_data.py - creates demo PostalCode, Address, Person objects in the database.

> helloworld.py - the basic idea.

> relationship_caching.py - Illustrates how to add cache options on relationship endpoints, so that lazyloads load from cache.

> advanced.py - Further examples of how to use FromCache. Combines techniques from the first two scripts.

> local_session_caching.py - Grok everything so far ? This example creates a new dogpile.cache backend that will persist data in a dictionary which is local to the current session. remove() the session and the cache is gone.

## Directed Graphs

Location: /examples/graphs/

An example of persistence for a directed graph structure. The graph is stored as a collection of edges, each referencing both a “lower” and an “upper” node in a table of nodes. Basic persistence and querying for lower- and upper- neighbors are illustrated:

    n2 = Node(2)
    n5 = Node(5)
    n2.add_neighbor(n5)
    print n2.higher_neighbors()

## Dynamic Relations as Dictionaries

Location: /examples/dynamic_dict/

Illustrates how to place a dictionary-like facade on top of a “dynamic” relation, so that dictionary operations (assuming simple string keys) can operate upon a large collection without loading the full collection at once.

## Generic Associations

Location: /examples/generic_associations

Illustrates various methods of associating multiple types of parents with a particular child object.

The examples all use the declarative extension along with declarative mixins. Each one presents the identical use case at the end - two classes, `Customer` and `Supplier`, both subclassing the `HasAddresses` mixin, which ensures that the parent class is provided with an `addresses` collection which contains `Address` objects.

The configurations include:

- `table_per_related.py` - illustrates a distinct table per related collection.
- `table_per_association.py` - illustrates a shared collection table, using a table per association.
- `discriminator_on_association.py` - shared collection table and shared association table, including a discriminator column.

The `discriminator_on_association.py` script in particular is a modernized version of the “polymorphic associations” example present in older versions of SQLAlchemy, originally from the blog post at http://techspot.zzzeek.org/2007/05/29/polymorphic-associations-with-sqlalchemy/.

## Horizontal Sharding

Location: /examples/sharding

A basic example of using the SQLAlchemy Sharding API. Sharding refers to horizontally scaling data across multiple databases.

The basic components of a “sharded” mapping are:

- multiple databases, each assigned a ‘shard id’
- a function which can return a single shard id, given an instance to be saved; this is called “shard_chooser”
- a function which can return a list of shard ids which apply to a particular instance identifier; this is called “id_chooser”. If it returns all shard ids, all shards will be searched.
- a function which can return a list of shard ids to try, given a particular Query (“query_chooser”). If it returns all shard ids, all shards will be queried and the results joined together.

In this example, four sqlite databases will store information about weather data on a database-per-continent basis. We provide example shard_chooser, id_chooser and query_chooser functions. The query_chooser illustrates inspection of the SQL expression element in order to attempt to determine a single shard being requested.

The construction of generic sharding routines is an ambitious approach to the issue of organizing instances among multiple databases. For a more plain-spoken alternative, the “distinct entity” approach is a simple method of assigning objects to different tables (and potentially database nodes) in an explicit way - described on the wiki at EntityName.

## Inheritance Mappings

Location: /examples/inheritance/

Working examples of single-table, joined-table, and concrete-table inheritance as described in datamapping_inheritance.

## Large Collections

Location: /examples/large_collection/

Large collection example.

Illustrates the options to use with `relationship()` when the list of related objects is very large, including:

- “dynamic” relationships which query slices of data as accessed
- how to use ON DELETE CASCADE in conjunction with `passive_deletes=True` to greatly improve the performance of related collection deletion.

## Nested Sets

Location: /examples/nested_sets/

Illustrates a rudimentary way to implement the “nested sets” pattern for hierarchical data using the SQLAlchemy ORM.

## Polymorphic Associations

See Generic Associations for a modern version of polymorphic associations.

## PostGIS Integration

Location: /examples/postgis

A naive example illustrating techniques to help embed PostGIS functionality.

This example was originally developed in the hopes that it would be extrapolated into a comprehensive PostGIS integration layer. We are pleased to announce that this has come to fruition as GeoAlchemy.

The example illustrates:

- a DDL extension which allows CREATE/DROP to work in conjunction with AddGeometryColumn/DropGeometryColumn
- a Geometry type, as well as a few subtypes, which convert result row values to a GIS-aware object, and also integrates with the DDL extension.
- a GIS-aware object which stores a raw geometry value and provides a factory for functions such as AsText().
- an ORM comparator which can override standard column methods on mapped objects to produce GIS operators.
- an attribute event listener that intercepts strings and converts to GeomFromText().
- a standalone operator example.

The implementation is limited to only public, well known and simple to use extension points.

E.g.:

    print session.query(Road).filter(Road.road_geom.intersects(r1.road_geom)).all()

## Versioned Objects

Location: /examples/versioning

Illustrates an extension which creates version tables for entities and stores records for each change. The same idea as Elixir’s versioned extension, but more efficient (uses attribute API to get history) and handles class inheritance. The given extensions generate an anonymous “history” class which represents historical versions of the target object.

Usage is illustrated via a unit test module `test_versioning.py`, which can be run via nose:

    cd examples/versioning
    nosetests -v

A fragment of example usage, using declarative:

    from history_meta import Versioned, versioned_session

    Base = declarative_base()

    class SomeClass(Versioned, Base):
        __tablename__ = 'sometable'

        id = Column(Integer, primary_key=True)
        name = Column(String(50))

        def __eq__(self, other):
            assert type(other) is SomeClass and other.id == self.id

    Session = sessionmaker(bind=engine)
    versioned_session(Session)

    sess = Session()
    sc = SomeClass(name='sc1')
    sess.add(sc)
    sess.commit()

    sc.name = 'sc1modified'
    sess.commit()

    assert sc.version == 2

    SomeClassHistory = SomeClass.__history_mapper__.class_

    assert sess.query(SomeClassHistory).\
                filter(SomeClassHistory.version == 1).\
                all() \
                == [SomeClassHistory(version=1, name='sc1')]

The `Versioned` mixin is designed to work with declarative. To use the extension with classical mappers, the `_history_mapper` function can be applied:

    from history_meta import _history_mapper

    m = mapper(SomeClass, sometable)
    _history_mapper(m)

    SomeHistoryClass = SomeClass.__history_mapper__.class_

## Vertical Attribute Mapping

Location: /examples/vertical

Illustrates “vertical table” mappings.

A “vertical table” refers to a technique where individual attributes of an object are stored as distinct rows in a table. The “vertical table” technique is used to persist objects which can have a varied set of attributes, at the expense of simple query control and brevity. It is commonly found in content/document management systems in order to represent user-created structures flexibly.

Two variants on the approach are given. In the second, each row references a “datatype” which contains information about the type of information stored in the attribute, such as integer, string, or date.

Example:

    shrew = Animal(u'shrew')
    shrew[u'cuteness'] = 5
    shrew[u'weasel-like'] = False
    shrew[u'poisonous'] = True

    session.add(shrew)
    session.flush()

    q = (session.query(Animal).
         filter(Animal.facts.any(
           and_(AnimalFact.key == u'weasel-like',
                AnimalFact.value == True))))
    print 'weasel-like animals', q.all()

## XML Persistence

Location: /examples/elementtree/

Illustrates three strategies for persisting and querying XML documents as represented by ElementTree in a relational database. The techniques do not apply any mappings to the ElementTree objects directly, so are compatible with the native cElementTree as well as lxml, and can be adapted to suit any kind of DOM representation system. Querying along xpath-like strings is illustrated as well.

In order of complexity:

- `pickle.py` - Quick and dirty, serialize the whole DOM into a BLOB column. While the example is very brief, it has very limited functionality.
- `adjacency_list.py` - Each DOM node is stored in an individual table row, with attributes represented in a separate table. The nodes are associated in a hierarchy using an adjacency list structure. A query function is introduced which can search for nodes along any path with a given structure of attributes, basically a (very narrow) subset of xpath.
- `optimized_al.py` - Uses the same strategy as `adjacency_list.py`, but associates each DOM row with its owning document row, so that a full document of DOM nodes can be loaded using O(1) queries - the construction of the “hierarchy” is performed after the load in a non-recursive fashion and is much more efficient.

E.g.:

    # parse an XML file and persist in the database
    doc = ElementTree.parse("test.xml")
    session.add(Document(file, doc))
    session.commit()

    # locate documents with a certain path/attribute structure
    for document in find_document('/somefile/header/field2[@attr=foo]'):
        # dump the XML
        print document