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Database Abstraction Library

Project description

SQLAlchemy is:

* The Python SQL toolkit and Object Relational Mapper that gives application
developers the full power and flexibility of SQL. SQLAlchemy provides a full
suite of well known enterprise-level persistence patterns, designed for
efficient and high-performing database access, adapted into a simple and
Pythonic domain language.
* extremely easy to use for all the basic tasks, such as: accessing pooled
connections, constructing SQL from Python expressions, finding object instances,
and commiting object modifications back to the database.
* powerful enough for complicated tasks, such as: eager load a graph of
objects and their dependencies via joins; map recursive adjacency structures
automatically; map objects to not just tables but to any arbitrary join or
select statement; combine multiple tables together to load whole sets of
otherwise unrelated objects from a single result set; commit entire graphs of
object changes in one step.
* built to conform to what DBAs demand, including the ability to swap out
generated SQL with hand-optimized statements, full usage of bind parameters for
all literal values, fully transactionalized and consistent updates using Unit of
* modular. Different parts of SQLAlchemy can be used independently of the
rest, including the connection pool, SQL construction, and ORM. SQLAlchemy is
constructed in an open style that allows plenty of customization, with an
architecture that supports custom datatypes, custom SQL extensions, and ORM
plugins which can augment or extend mapping functionality.

SQLAlchemy's Philosophy:

* SQL databases behave less and less like object collections the more size
and performance start to matter; object collections behave less and less like
tables and rows the more abstraction starts to matter. SQLAlchemy aims to
accomodate both of these principles.
* Your classes aren't tables, and your objects aren't rows. Databases aren't
just collections of tables; they're relational algebra engines. You don't have
to select from just tables, you can select from joins, subqueries, and unions.
Database and domain concepts should be visibly decoupled from the beginning,
allowing both sides to develop to their full potential.
* For example, table metadata (objects that describe tables) are declared
distinctly from the classes theyre designed to store. That way database
relationship concepts don't interfere with your object design concepts, and
vice-versa; the transition from table-mapping to selectable-mapping is seamless;
a class can be mapped against the database in more than one way. SQLAlchemy
provides a powerful mapping layer that can work as automatically or as manually
as you choose, determining relationships based on foreign keys or letting you
define the join conditions explicitly, to bridge the gap between database and

SQLAlchemy's Advantages:

* The Unit Of Work system organizes pending CRUD operations into queues and
commits them all in one batch. It then performs a topological "dependency sort"
of all items to be committed and deleted and groups redundant statements
together. This produces the maxiumum efficiency and transaction safety, and
minimizes chances of deadlocks. Modeled after Fowler's "Unit of Work" pattern as
well as Java Hibernate.
* Function-based query construction allows boolean expressions, operators,
functions, table aliases, selectable subqueries, create/update/insert/delete
queries, correlated updates, correlated EXISTS clauses, UNION clauses, inner and
outer joins, bind parameters, free mixing of literal text within expressions, as
little or as much as desired. Query-compilation is vendor-specific; the same
query object can be compiled into any number of resulting SQL strings depending
on its compilation algorithm.
* Database mapping and class design are totally separate. Persisted objects
have no subclassing requirement (other than 'object') and are POPO's : plain old
Python objects. They retain serializability (pickling) for usage in various
caching systems and session objects. SQLAlchemy "decorates" classes with
non-intrusive property accessors to automatically log object creates and
modifications with the UnitOfWork engine, to lazyload related data, as well as
to track attribute change histories.
* Custom list classes can be used with eagerly or lazily loaded child object
lists, allowing rich relationships to be created on the fly as SQLAlchemy
appends child objects to an object attribute.
* Composite (multiple-column) primary keys are supported, as are
"association" objects that represent the middle of a "many-to-many" relationship.
* Self-referential tables and mappers are supported. Adjacency list
structures can be created, saved, and deleted with proper cascading, with no
extra programming.
* Data mapping can be used in a row-based manner. Any bizarre
hyper-optimized query that you or your DBA can cook up, you can run in
SQLAlchemy, and as long as it returns the expected columns within a rowset, you
can get your objects from it. For a rowset that contains more than one kind of
object per row, multiple mappers can be chained together to return multiple
object instance lists from a single database round trip.
* The type system allows pre- and post- processing of data, both at the bind
parameter and the result set level. User-defined types can be freely mixed with
built-in types. Generic types as well as SQL-specific types are available.

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