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!
An acronym for “Atomicity, Consistency, Isolation, Durability”; a set of properties that guarantee that database transactions are processed reliably. (via Wikipedia)
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.
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)
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 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)
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.
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.
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_1and 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"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.
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)
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_idThe 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_idA 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
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)
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.
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
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
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.
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
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.
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
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:
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 usera 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')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_idThis 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.
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.