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Mike Bayer
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and extensions into an external library, and also reorganizes most large documentation pages into many small areas to reduce scrolling and better present the context into a more fine-grained hierarchy.
44 lines
1.7 KiB
ReStructuredText
44 lines
1.7 KiB
ReStructuredText
.. _schema_toplevel:
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==========================
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Schema Definition Language
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==========================
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.. module:: sqlalchemy.schema
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This section references SQLAlchemy **schema metadata**, a comprehensive system of describing and inspecting
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database schemas.
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The core of SQLAlchemy's query and object mapping operations are supported by
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*database metadata*, which is comprised of Python objects that describe tables
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and other schema-level objects. These objects are at the core of three major
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types of operations - issuing CREATE and DROP statements (known as *DDL*),
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constructing SQL queries, and expressing information about structures that
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already exist within the database.
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Database metadata can be expressed by explicitly naming the various components
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and their properties, using constructs such as
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:class:`~sqlalchemy.schema.Table`, :class:`~sqlalchemy.schema.Column`,
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:class:`~sqlalchemy.schema.ForeignKey` and
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:class:`~sqlalchemy.schema.Sequence`, all of which are imported from the
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``sqlalchemy.schema`` package. It can also be generated by SQLAlchemy using a
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process called *reflection*, which means you start with a single object such
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as :class:`~sqlalchemy.schema.Table`, assign it a name, and then instruct
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SQLAlchemy to load all the additional information related to that name from a
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particular engine source.
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A key feature of SQLAlchemy's database metadata constructs is that they are
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designed to be used in a *declarative* style which closely resembles that of
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real DDL. They are therefore most intuitive to those who have some background
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in creating real schema generation scripts.
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.. toctree::
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:maxdepth: 2
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metadata
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reflection
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defaults
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constraints
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ddl
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