Metadata-Version: 1.0
Name: SQLAlchemy
Version: 0.6.9
Summary: Database Abstraction Library
Home-page: http://www.sqlalchemy.org
Author: Mike Bayer
Author-email: mike_mp@zzzcomputing.com
License: MIT License
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 Work.
            * 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
              domain.
        
        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.
        
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Database :: Front-Ends
Classifier: Operating System :: OS Independent
