# SQLAlchemy 0.9 Documentation

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Release: 0.9.3 | Release Date: February 19, 2014

# Glossary

ACID
ACID model

  An acronym for “Atomicity, Consistency, Isolation, Durability”; a set of properties that guarantee that database transactions are processed reliably. (via Wikipedia)

  See also

  atomicity

  consistency

  isolation

  durability

  http://en.wikipedia.org/wiki/ACID_Model

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.

association relationship

  A two-tiered relationship which links two tables together using an association table in the middle. The association relationship differs from a many to many relationship in that the many-to-many table is mapped by a full class, rather than invisibly handled by the `sqlalchemy.orm.relationship()` construct as in the case with many-to-many, so that additional attributes are explicitly available.

  For example, if we wanted to associate employees with projects, also storing the specific role for that employee with the project, the relational schema might look like:

      CREATE TABLE employee (
          id INTEGER PRIMARY KEY,
          name VARCHAR(30)
      )

      CREATE TABLE project (
          id INTEGER PRIMARY KEY,
          name VARCHAR(30)
      )

      CREATE TABLE employee_project (
          employee_id INTEGER PRIMARY KEY,
          project_id INTEGER PRIMARY KEY,
          role_name VARCHAR(30),
          FOREIGN KEY employee_id REFERENCES employee(id),
          FOREIGN KEY project_id REFERENCES project(id)
      )

  A SQLAlchemy declarative mapping for the above might look like:

      class Employee(Base):
          __tablename__ = 'employee'

          id = Column(Integer, primary_key)
          name = Column(String(30))

      class Project(Base):
          __tablename__ = 'project'

          id = Column(Integer, primary_key)
          name = Column(String(30))

      class EmployeeProject(Base):
          __tablename__ = 'employee_project'

          employee_id = Column(Integer, ForeignKey('employee.id'), primary_key=True)
          project_id = Column(Integer, ForeignKey('project.id'), primary_key=True)
          role_name = Column(String(30))

          project = relationship("Project", backref="project_employees")
          employee = relationship("Employee", backref="employee_projects")

  Employees can be added to a project given a role name:

      proj = Project(name="Client A")

      emp1 = Employee(name="emp1")
      emp2 = Employee(name="emp2")

      proj.project_employees.extend([
          EmployeeProject(employee=emp1, role="tech lead"),
          EmployeeProject(employee=emp2, role="account executive")
      ])

  See also

  many to many

atomicity

  Atomicity is one of the components of the ACID model, and requires that each transaction is “all or nothing”: if one part of the transaction fails, the entire transaction fails, and the database state is left unchanged. An atomic system must guarantee atomicity in each and every situation, including power failures, errors, and crashes. (via Wikipedia)

  See also

  ACID

  http://en.wikipedia.org/wiki/Atomicity_(database_systems)

backref
bidirectional relationship

  An extension to the relationship system whereby two distinct `relationship()` objects can be mutually associated with each other, such that they coordinate in memory as changes occur to either side. The most common way these two relationships are constructed is by using the `relationship()` function explicitly for one side and specifying the `backref` keyword to it so that the other `relationship()` is created automatically. We can illustrate this against the example we’ve used in one to many as follows:

      class Department(Base):
          __tablename__ = 'department'
          id = Column(Integer, primary_key=True)
          name = Column(String(30))
          employees = relationship("Employee", backref="department")

      class Employee(Base):
          __tablename__ = 'employee'
          id = Column(Integer, primary_key=True)
          name = Column(String(30))
          dep_id = Column(Integer, ForeignKey('department.id'))

  A backref can be applied to any relationship, including one to many, many to one, and many to many.

  See also

  relationship

  one to many

  many to one

  many to many

candidate key

  A relational algebra term referring to an attribute or set of attributes that form a uniquely identifying key for a row. A row may have more than one candidate key, each of which is suitable for use as the primary key of that row. The primary key of a table is always a candidate key.

  See also

  primary key

  http://en.wikipedia.org/wiki/Candidate_key

check constraint

  A check constraint is a condition that defines valid data when adding or updating an entry in a table of a relational database. A check constraint is applied to each row in the table.

  (via Wikipedia)

  A check constraint can be added to a table in standard SQL using DDL like the following:

      ALTER TABLE distributors ADD CONSTRAINT zipchk CHECK (char_length(zipcode) = 5);

  See also

  http://en.wikipedia.org/wiki/Check_constraint

columns clause

  The portion of the `SELECT` statement which enumerates the SQL expressions to be returned in the result set. The expressions follow the `SELECT` keyword directly and are a comma-separated list of individual expressions.

  E.g.:

      SELECT user_account.name, user_account.email
      FROM user_account WHERE user_account.name = 'fred'

  Above, the list of columns `user_acount.name`, `user_account.email` is the columns clause of the `SELECT`.

consistency

  Consistency is one of the compoments of the ACID model, and ensures that any transaction will bring the database from one valid state to another. Any data written to the database must be valid according to all defined rules, including but not limited to constraints, cascades, triggers, and any combination thereof. (via Wikipedia)

  See also

  ACID

  http://en.wikipedia.org/wiki/Consistency_(database_systems)

constraint
constraints
constrained
  Rules established within a relational database that ensure the validity and consistency of data. Common forms of constraint include primary key constraint, foreign key constraint, and check constraint.
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 in most cases present in the WHERE clause or columns clause of the immediately enclosing `SELECT` statement, as well as in the ORDER BY or HAVING clause.

  In less common cases, a correlated subquery may be present in the FROM clause of an enclosing `SELECT`; in these cases the correlation is typically due to the enclosing `SELECT` itself being enclosed in the WHERE, ORDER BY, columns or HAVING clause of another `SELECT`, such as:

      SELECT parent.id FROM parent
      WHERE EXISTS (
          SELECT * FROM (
              SELECT child.id AS id, child.parent_id AS parent_id, child.pos AS pos
              FROM child
              WHERE child.parent_id = parent.id ORDER BY child.pos
          LIMIT 3)
      WHERE id = 7)

  Correlation from one `SELECT` directly to one which encloses the correlated query via its `FROM` clause is not possible, because the correlation can only proceed once the original source rows from the enclosing statement’s FROM clause are available.

crud
  An acronym meaning “Create, Update, Delete”. The term in SQL refers to the set of operations that create, modify and delete data from the database, also known as DML, and typically refers to the `INSERT`, `UPDATE`, and `DELETE` statements.
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"

detached

  This describes one of the four major object states which an object can have within a session; a detached object is an object that has a database identity (i.e. a primary key) but is not associated with any session. An object that was previously persistent and was removed from its session either because it was expunged, or the owning session was closed, moves into the detached state. The detached state is generally used when objects are being moved between sessions or when being moved to/from an external object cache.

  See also

  Quickie Intro to Object States

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

domain model

  A domain model in problem solving and software engineering is a conceptual model of all the topics related to a specific problem. It describes the various entities, their attributes, roles, and relationships, plus the constraints that govern the problem domain.

  (via Wikipedia)

  See also

  Domain Model (wikipedia)

durability

  Durability is a property of the ACID model which means that once a transaction has been committed, it will remain so, even in the event of power loss, crashes, or errors. In a relational database, for instance, once a group of SQL statements execute, the results need to be stored permanently (even if the database crashes immediately thereafter). (via Wikipedia)

  See also

  ACID

  http://en.wikipedia.org/wiki/Durability_(database_systems)

foreign key constraint

  A referential constraint between two tables. A foreign key is a field or set of fields in a relational table that matches a candidate key of another table. The foreign key can be used to cross-reference tables. (via Wikipedia)

  A foreign key constraint can be added to a table in standard SQL using DDL like the following:

      ALTER TABLE employee ADD CONSTRAINT dep_id_fk
      FOREIGN KEY (employee) REFERENCES department (dep_id)

  See also

  http://en.wikipedia.org/wiki/Foreign_key_constraint

FROM clause

  The portion of the `SELECT` statement which incicates the initial source of rows.

  A simple `SELECT` will feature one or more table names in its FROM clause. Multiple sources are separated by a comma:

      SELECT user.name, address.email_address
      FROM user, address
      WHERE user.id=address.user_id

  The FROM clause is also where explicit joins are specified. We can rewrite the above `SELECT` using a single `FROM` element which consists of a `JOIN` of the two tables:

      SELECT user.name, address.email_address
      FROM user JOIN address ON user.id=address.user_id

generative
  A term that SQLAlchemy uses to refer what’s normally known as method chaining; see that term for details.
identity map

  A mapping between Python objects and their database identities. The identity map is a collection that’s associated with an ORM session object, and maintains a single instance of every database object keyed to its identity. The advantage to this pattern is that all operations which occur for a particular database identity are transparently coordinated onto a single object instance. When using an identity map in conjunction with an isolated transaction, having a reference to an object that’s known to have a particular primary key can be considered from a practical standpoint to be a proxy to the actual database row.

  See also

  Martin Fowler - Identity Map - http://martinfowler.com/eaaCatalog/identityMap.html

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.
isolation
isolated

  The isolation property of the ACID model ensures that the concurrent execution of transactions results in a system state that would be obtained if transactions were executed serially, i.e. one after the other. Each transaction must execute in total isolation i.e. if T1 and T2 execute concurrently then each should remain independent of the other. (via Wikipedia)

  See also

  ACID

  http://en.wikipedia.org/wiki/Isolation_(database_systems)

lazy load
lazy loads
lazy loading

  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

many to many

  A style of `sqlalchemy.orm.relationship()` which links two tables together via an intermediary table in the middle. Using this configuration, any number of rows on the left side may refer to any number of rows on the right, and vice versa.

  A schema where employees can be associated with projects:

      CREATE TABLE employee (
          id INTEGER PRIMARY KEY,
          name VARCHAR(30)
      )

      CREATE TABLE project (
          id INTEGER PRIMARY KEY,
          name VARCHAR(30)
      )

      CREATE TABLE employee_project (
          employee_id INTEGER PRIMARY KEY,
          project_id INTEGER PRIMARY KEY,
          FOREIGN KEY employee_id REFERENCES employee(id),
          FOREIGN KEY project_id REFERENCES project(id)
      )

  Above, the `employee_project` table is the many-to-many table, which naturally forms a composite primary key consisting of the primary key from each related table.

  In SQLAlchemy, the `sqlalchemy.orm.relationship()` function can represent this style of relationship in a mostly transparent fashion, where the many-to-many table is specified using plain table metadata:

      class Employee(Base):
          __tablename__ = 'employee'

          id = Column(Integer, primary_key)
          name = Column(String(30))

          projects = relationship(
              "Project",
              secondary=Table('employee_project', Base.metadata,
                          Column("employee_id", Integer, ForeignKey('employee.id'),
                                      primary_key=True),
                          Column("project_id", Integer, ForeignKey('project.id'),
                                      primary_key=True)
                      ),
              backref="employees"
              )

      class Project(Base):
          __tablename__ = 'project'

          id = Column(Integer, primary_key)
          name = Column(String(30))

  Above, the `Employee.projects` and back-referencing `Project.employees` collections are defined:

      proj = Project(name="Client A")

      emp1 = Employee(name="emp1")
      emp2 = Employee(name="emp2")

      proj.employees.extend([emp1, emp2])

  See also

  association relationship

  relationship

  one to many

  many to one

many to one

  A style of `relationship()` which links a foreign key in the parent mapper’s table to the primary key of a related table. Each parent object can then refer to exactly zero or one related object.

  The related objects in turn will have an implicit or explicit one to many relationship to any number of parent objects that refer to them.

  An example many to one schema (which, note, is identical to the one to many schema):

      CREATE TABLE department (
          id INTEGER PRIMARY KEY,
          name VARCHAR(30)
      )

      CREATE TABLE employee (
          id INTEGER PRIMARY KEY,
          name VARCHAR(30),
          dep_id INTEGER REFERENCES department(id)
      )

  The relationship from `employee` to `department` is many to one, since many employee records can be associated with a single department. A SQLAlchemy mapping might look like:

      class Department(Base):
          __tablename__ = 'department'
          id = Column(Integer, primary_key=True)
          name = Column(String(30))

      class Employee(Base):
          __tablename__ = 'employee'
          id = Column(Integer, primary_key=True)
          name = Column(String(30))
          dep_id = Column(Integer, ForeignKey('department.id'))
          department = relationship("Department")

  See also

  relationship

  one to many

  backref

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

one to many

  A style of `relationship()` which links the primary key of the parent mapper’s table to the foreign key of a related table. Each unique parent object can then refer to zero or more unique related objects.

  The related objects in turn will have an implicit or explicit many to one relationship to their parent object.

  An example one to many schema (which, note, is identical to the many to one schema):

      CREATE TABLE department (
          id INTEGER PRIMARY KEY,
          name VARCHAR(30)
      )

      CREATE TABLE employee (
          id INTEGER PRIMARY KEY,
          name VARCHAR(30),
          dep_id INTEGER REFERENCES department(id)
      )

  The relationship from `department` to `employee` is one to many, since many employee records can be associated with a single department. A SQLAlchemy mapping might look like:

      class Department(Base):
          __tablename__ = 'department'
          id = Column(Integer, primary_key=True)
          name = Column(String(30))
          employees = relationship("Employee")

      class Employee(Base):
          __tablename__ = 'employee'
          id = Column(Integer, primary_key=True)
          name = Column(String(30))
          dep_id = Column(Integer, ForeignKey('department.id'))

  See also

  relationship

  many to one

  backref

pending

  This describes one of the four major object states which an object can have within a session; a pending object is a new object that doesn’t have any database identity, but has been recently associated with a session. When the session emits a flush and the row is inserted, the object moves to the persistent state.

  See also

  Quickie Intro to Object States

persistent

  This describes one of the four major object states which an object can have within a session; a persistent object is an object that has a database identity (i.e. a primary key) and is currently associated with a session. Any object that was previously pending and has now been inserted is in the persistent state, as is any object that’s been loaded by the session from the database. When a persistent object is removed from a session, it is known as detached.

  See also

  Quickie Intro to Object States

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.

primary key
primary key constraint

  A constraint that uniquely defines the characteristics of each row. The primary key has to consist of characteristics that cannot be duplicated by any other row. The primary key may consist of a single attribute or multiple attributes in combination. (via Wikipedia)

  The primary key of a table is typically, though not always, defined within the `CREATE TABLE` DDL:

      CREATE TABLE employee (
           emp_id INTEGER,
           emp_name VARCHAR(30),
           dep_id INTEGER,
           PRIMARY KEY (emp_id)
      )

  See also

  http://en.wikipedia.org/wiki/Primary_Key

relationship
relationships

  A connecting unit between two mapped classes, corresponding to some relationship between the two tables in the database.

  The relationship is defined using the SQLAlchemy function `relationship()`. Once created, SQLAlchemy inspects the arguments and underlying mappings involved in order to classify the relationship as one of three types: one to many, many to one, or many to many. With this classification, the relationship construct handles the task of persisting the appropriate linkages in the database in response to in-memory object associations, as well as the job of loading object references and collections into memory based on the current linkages in the database.

  See also

  Relationship Configuration

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

RETURNING

  This is a non-SQL standard clause provided in various forms by certain backends, which provides the service of returning a result set upon execution of an INSERT, UPDATE or DELETE statement. Any set of columns from the matched rows can be returned, as though they were produced from a SELECT statement.

  The RETURNING clause provides both a dramatic performance boost to common update/select scenarios, including retrieval of inline- or default- generated primary key values and defaults at the moment they were created, as well as a way to get at server-generated default values in an atomic way.

  An example of RETURNING, idiomatic to Postgresql, looks like:

      INSERT INTO user_account (name) VALUES ('new name') RETURNING id, timestamp

  Above, the INSERT statement will provide upon execution a result set which includes the values of the columns `user_account.id` and `user_account.timestamp`, which above should have been generated as default values as they are not included otherwise (but note any series of columns or SQL expressions can be placed into RETURNING, not just default-value columns).

  The backends that currently support RETURNING or a similar construct are Postgresql, SQL Server, Oracle, and Firebird. The Postgresql and Firebird implementations are generally full featured, whereas the implementations of SQL Server and Oracle have caveats. On SQL Server, the clause is known as “OUTPUT INSERTED” for INSERT and UPDATE statements and “OUTPUT DELETED” for DELETE statements; the key caveat is that triggers are not supported in conjunction with this keyword. On Oracle, it is known as “RETURNING...INTO”, and requires that the value be placed into an OUT paramter, meaning not only is the syntax awkward, but it can also only be used for one row at a time.

  SQLAlchemy’s `UpdateBase.returning()` system provides a layer of abstraction on top of the RETURNING systems of these backends to provide a consistent interface for returning columns. The ORM also includes many optimizations that make use of RETURNING when available.

Session

  The container or scope for ORM database operations. Sessions load instances from the database, track changes to mapped instances and persist changes in a single unit of work when flushed.

  See also

  Using the Session

subquery

  Refers to a `SELECT` statement that is embedded within an enclosing `SELECT`.

  A subquery comes in two general flavors, one known as a “scalar select” which specifically must return exactly one row and one column, and the other form which acts as a “derived table” and serves as a source of rows for the FROM clause of another select. A scalar select is eligble to be placed in the WHERE clause, columns clause, ORDER BY clause or HAVING clause of the enclosing select, whereas the derived table form is eligible to be placed in the FROM clause of the enclosing `SELECT`.

  Examples:

  1. a scalar subquery placed in the columns clause of an enclosing `SELECT`. The subquery in this example is a correlated subquery because part of the rows which it selects from are given via the enclosing statement.

         SELECT id, (SELECT name FROM address WHERE address.user_id=user.id)
         FROM user

  2. a scalar subquery placed in the WHERE clause of an enclosing `SELECT`. This subquery in this example is not correlated as it selects a fixed result.

         SELECT id, name FROM user
         WHERE status=(SELECT status_id FROM status_code WHERE code='C')

  3. a derived table subquery placed in the FROM clause of an enclosing `SELECT`. Such a subquery is almost always given an alias name.

         SELECT user.id, user.name, ad_subq.email_address
         FROM
             user JOIN
             (select user_id, email_address FROM address WHERE address_type='Q') AS ad_subq
             ON user.id = ad_subq.user_id

transient

  This describes one of the four major object states which an object can have within a session; a transient object is a new object that doesn’t have any database identity and has not been associated with a session yet. When the object is added to the session, it moves to the pending state.

  See also

  Quickie Intro to Object States

unique constraint
unique key index

  A unique key index can uniquely identify each row of data values in a database table. A unique key index comprises a single column or a set of columns in a single database table. No two distinct rows or data records in a database table can have the same data value (or combination of data values) in those unique key index columns if NULL values are not used. Depending on its design, a database table may have many unique key indexes but at most one primary key index.

  (via Wikipedia)

  See also

  http://en.wikipedia.org/wiki/Unique_key#Defining_unique_keys

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

WHERE clause

  The portion of the `SELECT` statement which indicates criteria by which rows should be filtered. It is a single SQL expression which follows the keyword `WHERE`.

      SELECT user_account.name, user_account.email
      FROM user_account
      WHERE user_account.name = 'fred' AND user_account.status = 'E'

  Above, the phrase `WHERE user_account.name = 'fred' AND user_account.status = 'E'` comprises the WHERE clause of the `SELECT`.