The Python SQL Toolkit and Object Relational Mapper
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
Major SQLAlchemy features include:
An industrial strength ORM, built
from the core on the identity map, unit of work,
and data mapper patterns. These patterns
allow transparent persistence of objects
using a declarative configuration system.
can be constructed and manipulated naturally,
and changes are synchronized with the
current transaction automatically.
A relationally-oriented query system, exposing
the full range of SQL’s capabilities
explicitly, including joins, subqueries,
correlation, and most everything else,
in terms of the object model.
Writing queries with the ORM uses the same
techniques of relational composition you use
when writing SQL. While you can drop into
literal SQL at any time, it’s virtually never
A comprehensive and flexible system
of eager loading for related collections and objects.
Collections are cached within a session,
and can be loaded on individual access, all
at once using joins, or by query per collection
across the full result set.
A Core SQL construction system and DBAPI
interaction layer. The SQLAlchemy Core is
separate from the ORM and is a full database
abstraction layer in its own right, and includes
an extensible Python-based SQL expression
language, schema metadata, connection pooling,
type coercion, and custom types.
All primary and foreign key constraints are
assumed to be composite and natural. Surrogate
integer primary keys are of course still the
norm, but SQLAlchemy never assumes or hardcodes
to this model.
Database introspection and generation. Database
schemas can be “reflected” in one step into
Python structures representing database metadata;
those same structures can then generate
CREATE statements right back out - all within
the Core, independent of the ORM.
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 accommodate both of these
An ORM doesn’t need to hide the “R”. A relational
database provides rich, set-based functionality
that should be fully exposed. SQLAlchemy’s
ORM provides an open-ended set of patterns
that allow a developer to construct a custom
mediation layer between a domain model and
a relational schema, turning the so-called
“object relational impedance” issue into
a distant memory.
The developer, in all cases, makes all decisions
regarding the design, structure, and naming conventions
of both the object model as well as the relational
schema. SQLAlchemy only provides the means
to automate the execution of these decisions.
With SQLAlchemy, there’s no such thing as
“the ORM generated a bad query” - you
retain full control over the structure of
queries, including how joins are organized,
how subqueries and correlation is used, what
columns are requested. Everything SQLAlchemy
does is ultimately the result of a developer-
Don’t use an ORM if the problem doesn’t need one.
SQLAlchemy consists of a Core and separate ORM
component. The Core offers a full SQL expression
language that allows Pythonic construction
of SQL constructs that render directly to SQL
strings for a target database, returning
result sets that are essentially enhanced DBAPI
Transactions should be the norm. With SQLAlchemy’s
ORM, nothing goes to permanent storage until
commit() is called. SQLAlchemy encourages applications
to create a consistent means of delineating
the start and end of a series of operations.
Never render a literal value in a SQL statement.
Bound parameters are used to the greatest degree
possible, allowing query optimizers to cache
query plans effectively and making SQL injection
attacks a non-issue.