# SQLAlchemy 0.8 Documentation

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Release: 0.8.1 | Release Date: April 27, 2013

# Glossary

Note

The Glossary is a brand new addition to the documentation. While sparse at the moment we hope to fill it up with plenty of new terms soon!

annotations

  Annotations are a concept used internally by SQLAlchemy in order to store additional information along with `ClauseElement` objects. A Python dictionary is associated with a copy of the object, which contains key/value pairs significant to various internal systems, mostly within the ORM:

      some_column = Column('some_column', Integer)
      some_column_annotated = some_column._annotate({"entity": User})

  The annotation system differs from the public dictionary `Column.info` in that the above annotation operation creates a copy of the new `Column`, rather than considering all annotation values to be part of a single unit. The ORM creates copies of expression objects in order to apply annotations that are specific to their context, such as to differentiate columns that should render themselves as relative to a joined-inheritance entity versus those which should render relative to their immediate parent table alone, as well as to differentiate columns within the “join condition” of a relationship where the column in some cases needs to be expressed in terms of one particular table alias or another, based on its position within the join expression.

correlates
correlated subquery
correlated subqueries

  A subquery is correlated if it depends on data in the enclosing `SELECT`.

  Below, a subquery selects the aggregate value `MIN(a.id)` from the `email_address` table, such that it will be invoked for each value of `user_account.id`, correlating the value of this column against the `email_address.user_account_id` column:

      SELECT user_account.name, email_address.email
       FROM user_account
       JOIN email_address ON user_account.id=email_address.user_account_id
       WHERE email_address.id = (
          SELECT MIN(a.id) FROM email_address AS a
          WHERE a.user_account_id=user_account.id
       )

  The above subquery refers to the `user_account` table, which is not itself in the `FROM` clause of this nested query. Instead, the `user_account` table is recieved from the enclosing query, where each row selected from `user_account` results in a distinct execution of the subquery.

  A correlated subquery is nearly always present in the WHERE clause or columns clause of the enclosing `SELECT` statement, and never in the FROM clause; this is because the correlation can only proceed once the original source rows from the enclosing statement’s FROM clause are available.

DBAPI

  DBAPI is shorthand for the phrase “Python Database API Specification”. This is a widely used specification within Python to define common usage patterns for all database connection packages. The DBAPI is a “low level” API which is typically the lowest level system used in a Python application to talk to a database. SQLAlchemy’s dialect system is constructed around the operation of the DBAPI, providing individual dialect classes which service a specific DBAPI on top of a specific database engine; for example, the `create_engine()` URL `postgresql+psycopg2://@localhost/test` refers to the `psycopg2` DBAPI/dialect combination, whereas the URL `mysql+mysqldb://@localhost/test` refers to the `MySQL for Python` DBAPI DBAPI/dialect combination.

  See also

  PEP 249 - Python Database API Specification v2.0

descriptor
descriptors

  In Python, a descriptor is an object attribute with “binding behavior”, one whose attribute access has been overridden by methods in the descriptor protocol. Those methods are __get__(), __set__(), and __delete__(). If any of those methods are defined for an object, it is said to be a descriptor.

  In SQLAlchemy, descriptors are used heavily in order to provide attribute behavior on mapped classes. When a class is mapped as such:

      class MyClass(Base):
          __tablename__ = 'foo'

          id = Column(Integer, primary_key=True)
          data = Column(String)

  The `MyClass` class will be mapped when its definition is complete, at which point the `id` and `data` attributes, starting out as `Column` objects, will be replaced by the instrumentation system with instances of `InstrumentedAttribute`, which are descriptors that provide the above mentioned `__get__()`, `__set__()` and `__delete__()` methods. The `InstrumentedAttribute` will generate a SQL expression when used at the class level:

      >>> print MyClass.data == 5
      data = :data_1

  and at the instance level, keeps track of changes to values, and also lazy loads unloaded attributes from the database:

      >>> m1 = MyClass()
      >>> m1.id = 5
      >>> m1.data = "some data"

      >>> from sqlalchemy import inspect
      >>> inspect(m1).attrs.data.history.added
      "some data"

discriminator

  A result-set column which is used during polymorphic loading to determine what kind of mapped class should be applied to a particular incoming result row. In SQLAlchemy, the classes are always part of a hierarchy mapping using inheritance mapping.

  See also

  Mapping Class Inheritance Hierarchies

generative
  A term that SQLAlchemy uses to refer what’s normally known as method chaining; see that term for details.
instrumentation
instrumented
  Instrumentation refers to the process of augmenting the functionality and attribute set of a particular class. Ideally, the behavior of the class should remain close to a regular class, except that additional behviors and features are made available. The SQLAlchemy mapping process, among other things, adds database-enabled descriptors to a mapped class which each represent a particular database column or relationship to a related class.
lazy load
lazy loads

  In object relational mapping, a “lazy load” refers to an attribute that does not contain its database-side value for some period of time, typically when the object is first loaded. Instead, the attribute receives a memoization that causes it to go out to the database and load its data when it’s first used. Using this pattern, the complexity and time spent within object fetches can sometimes be reduced, in that attributes for related tables don’t need to be addressed immediately.

  See also

  Lazy Load (on Martin Fowler)

  N plus one problem

  Relationship Loading Techniques

mapping
mapped
  We say a class is “mapped” when it has been passed through the `orm.mapper()` function. This process associates the class with a database table or other selectable construct, so that instances of it can be persisted using a `Session` as well as loaded using a `Query`.
method chaining

  An object-oriented technique whereby the state of an object is constructed by calling methods on the object. The object features any number of methods, each of which return a new object (or in some cases the same object) with additional state added to the object.

  The two SQLAlchemy objects that make the most use of method chaining are the `Select` object and the `Query` object. For example, a `Select` object can be assigned two expressions to its WHERE clause as well as an ORDER BY clause by calling upon the `where()` and `order_by()` methods:

      stmt = select([user.c.name]).\
                  where(user.c.id > 5).\
                  where(user.c.name.like('e%').\
                  order_by(user.c.name)

  Each method call above returns a copy of the original `Select` object with additional qualifiers added.

  See also

  generative

N plus one problem

  The N plus one problem is a common side effect of the lazy load pattern, whereby an application wishes to iterate through a related attribute or collection on each member of a result set of objects, where that attribute or collection is set to be loaded via the lazy load pattern. The net result is that a SELECT statement is emitted to load the initial result set of parent objects; then, as the application iterates through each member, an additional SELECT statement is emitted for each member in order to load the related attribute or collection for that member. The end result is that for a result set of N parent objects, there will be N + 1 SELECT statements emitted.

  The N plus one problem is alleviated using eager loading.

  See also

  Relationship Loading Techniques

polymorphic
polymorphically

  Refers to a function that handles several types at once. In SQLAlchemy, the term is usually applied to the concept of an ORM mapped class whereby a query operation will return different subclasses based on information in the result set, typically by checking the value of a particular column in the result known as the discriminator.

  Polymorphic loading in SQLAlchemy implies that a one or a combination of three different schemes are used to map a hierarchy of classes; “joined”, “single”, and “concrete”. The section Mapping Class Inheritance Hierarchies describes inheritance mapping fully.

release
releases
released

  In the context of SQLAlchemy, the term “released” refers to the process of ending the usage of a particular database connection. SQLAlchemy features the usage of connection pools, which allows configurability as to the lifespan of database connections. When using a pooled connection, the process of “closing” it, i.e. invoking a statement like `connection.close()`, may have the effect of the connection being returned to an existing pool, or it may have the effect of actually shutting down the underlying TCP/IP connection referred to by that connection - which one takes place depends on configuration as well as the current state of the pool. So we used the term released instead, to mean “do whatever it is you do with connections when we’re done using them”.

  The term will sometimes be used in the phrase, “release transactional resources”, to indicate more explicitly that what we are actually “releasing” is any transactional state which as accumulated upon the connection. In most situations, the proces of selecting from tables, emitting updates, etc. acquires isolated state upon that connection as well as potential row or table locks. This state is all local to a particular transaction on the connection, and is released when we emit a rollback. An important feature of the connection pool is that when we return a connection to the pool, the `connection.rollback()` method of the DBAPI is called as well, so that as the connection is set up to be used again, it’s in a “clean” state with no references held to the previous series of operations.

  See also

  Connection Pooling

unit of work

  This pattern is where the system transparently keeps track of changes to objects and periodically flushes all those pending changes out to the database. SQLAlchemy’s Session implements this pattern fully in a manner similar to that of Hibernate.

  See also

  Unit of Work by Martin Fowler

  Using the Session