The SQLAlchemy SQL Toolkit and Object Relational Mapper is a comprehensive set of tools for working with databases and Python. It has several distinct areas of functionality which can be used individually or combined together. Its major components are illustrated below, with component dependencies organized into layers:
Above, the two most significant front-facing portions of SQLAlchemy are the Object Relational Mapper (ORM) and the Core.
Core contains the breadth of SQLAlchemy's SQL and database integration and description services, the most prominent part of this being the SQL Expression Language.
The SQL Expression Language is a toolkit all its own, independent of the ORM package, which provides a system of constructing SQL expressions represented by composable objects, which can then be "executed" against a target database within the scope of a specific transaction, returning a result set. Inserts, updates and deletes (i.e. :term:`DML`) are achieved by passing SQL expression objects representing these statements along with dictionaries that represent parameters to be used with each statement.
The ORM builds upon Core to provide a means of working with a domain object model mapped to a database schema. When using the ORM, SQL statements are constructed in mostly the same way as when using Core, however the task of DML, which here refers to the persistence of business objects in a database, is automated using a pattern called :term:`unit of work`, which translates changes in state against mutable objects into INSERT, UPDATE and DELETE constructs which are then invoked in terms of those objects. SELECT statements are also augmented by ORM-specific automations and object-centric querying capabilities.
Whereas working with Core and the SQL Expression language presents a schema-centric view of the database, along with a programming paradigm that is oriented around immutability, the ORM builds on top of this a domain-centric view of the database with a programming paradigm that is more explcitly object-oriented and reliant upon mutability. Since a relational database is itself a mutable service, the difference is that Core/SQL Expression language is command oriented whereas the ORM is state oriented.
The documentation is separated into four sections:
- :ref:`unified_tutorial` - this all-new tutorial for the 1.4/2.0 series of SQLAlchemy introduces the entire library holistically, starting from a description of Core and working more and more towards ORM-specific concepts. New users, as well as users coming from :term:`1.x style`, who wish to work in :term:`2.0 style` should start here.
- :ref:`orm_toplevel` - In this section, reference documentation for the ORM is presented; this section also includes the now-legacy :ref:`ormtutorial_toplevel`.
- :ref:`core_toplevel` - Here, reference documentation for everything else within Core is presented; section also includes the legacy :ref:`sqlexpression_toplevel`. SQLAlchemy engine, connection, and pooling services are also described here.
- :ref:`dialect_toplevel` - Provides reference documentation for all :term:`dialect` implementations, including :term:`DBAPI` specifics.
Working code examples, mostly regarding the ORM, are included in the SQLAlchemy distribution. A description of all the included example applications is at :ref:`examples_toplevel`.
There is also a wide variety of examples involving both core SQLAlchemy constructs as well as the ORM on the wiki. See Theatrum Chemicum.
SQLAlchemy supports the following platforms:
- cPython 3.7 and higher
- Python-3 compatible versions of PyPy
.. versionchanged:: 2.0 SQLAlchemy now targets Python 3.7 and above.
SQLAlchemy's asyncio support depends upon the
greenlet project. This dependency
will be installed by default on common machine platforms, however is not
supported on every architecture and also may not install by default on
less common architectures. See the section :ref:`asyncio_install` for
additional details on ensuring asyncio support is present.
SQLAlchemy installation is via standard Python methodologies that are
based on setuptools, either
by referring to setup.py directly or by using
pip or other setuptools-compatible
approaches.
Warning
This section does not apply until SQLAlchemy 2.0 is actually released, which as of January 26, 2022 the library is not released yet. In the interim, these instructions will not function and will install the latest 1.4 release.
When pip is available, the distribution can be
downloaded from PyPI and installed in one step:
pip install SQLAlchemy
This command will download the latest released version of SQLAlchemy from the Python Cheese Shop and install it to your system. For most common platforms, a Python Wheel file will be downloaded which provides native Cython / C extensions prebuilt.
In order to install the latest prerelease version, such as 2.0.0b1,
pip requires that the --pre flag be used:
pip install --pre SQLAlchemy
Where above, if the most recent version is a prerelease, it will be installed instead of the latest released version.
When not installing from pip, the source distribution may be installed
using the setup.py script:
python setup.py install
The source install is platform agnostic and will install on any platform
regardless of whether or not Cython / C build tools are installed. As the next
section :ref:`c_extensions` details, setup.py will attempt to build using
Cython / C if possible but will fall back to a pure Python installation
otherwise.
SQLAlchemy includes Cython extensions which provide an extra speed boost within various areas, with a current emphasis on the speed of Core result sets.
.. versionchanged:: 2.0 The SQLAlchemy C extensions have been rewritten using Cython.
setup.py will automatically build the extensions if an appropriate platform
is detected, assuming the Cython package is installed. A complete manual
build looks like:
# cd into SQLAlchemy source distribution cd path/to/sqlalchemy # install cython pip install cython # optionally build Cython extensions ahead of install python setup.py build_ext # run the install python setup.py install
Source builds may also be performed using PEP 517 techniques, such as using build:
# cd into SQLAlchemy source distribution cd path/to/sqlalchemy # install build pip install build # build source / wheel dists python -m build
If the build of the Cython extensions fails due to Cython not being installed, a missing compiler or other issue, the setup process will output a warning message and re-run the build without the Cython extensions upon completion, reporting final status.
To run the build/install without even attempting to compile the Cython
extensions, the DISABLE_SQLALCHEMY_CEXT environment variable may be
specified. The use case for this is either for special testing circumstances,
or in the rare case of compatibility/build issues not overcome by the usual
"rebuild" mechanism:
export DISABLE_SQLALCHEMY_CEXT=1; python setup.py install
SQLAlchemy is designed to operate with a :term:`DBAPI` implementation built for a particular database, and includes support for the most popular databases. The individual database sections in :doc:`/dialects/index` enumerate the available DBAPIs for each database, including external links.
This documentation covers SQLAlchemy version 2.0. If you're working on a system that already has SQLAlchemy installed, check the version from your Python prompt like this:
>>> import sqlalchemy
>>> sqlalchemy.__version__ # doctest: +SKIP
2.0.0
With SQLAlchemy installed, new and old users alike can :ref:`Proceed to the SQLAlchemy Tutorial <unified_tutorial>`.
Notes on the new API released in SQLAlchemy 2.0 is available here at :doc:`changelog/migration_20`.