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Diffstat (limited to 'lib/python2.7/site-packages/SQLAlchemy-0.7.0-py2.7-linux-x86_64.egg/EGG-INFO/PKG-INFO')
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diff --git a/lib/python2.7/site-packages/SQLAlchemy-0.7.0-py2.7-linux-x86_64.egg/EGG-INFO/PKG-INFO b/lib/python2.7/site-packages/SQLAlchemy-0.7.0-py2.7-linux-x86_64.egg/EGG-INFO/PKG-INFO deleted file mode 100644 index 005a0d30..00000000 --- a/lib/python2.7/site-packages/SQLAlchemy-0.7.0-py2.7-linux-x86_64.egg/EGG-INFO/PKG-INFO +++ /dev/null @@ -1,137 +0,0 @@ -Metadata-Version: 1.1 -Name: SQLAlchemy -Version: 0.7.0 -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 |