Key Features of SQLAlchemy

Some of the key features at a glance:

No ORM Required

SQLAlchemy consists of two distinct components, known as the Core and the ORM. The Core is itself a fully featured SQL abstraction toolkit, providing a smooth layer of abstraction over a wide variety of DBAPI implementations and behaviors, as well as a SQL Expression Language which allows expression of the SQL language via generative Python expressions. A schema representation system that can both emit DDL statements as well as introspect existing schemas, and a type system that allows any mapping of Python types to database types, rounds out the system. The Object Relational Mapper is then an optional package which builds upon the Core. Many applications are built strictly on the Core, using the SQL expression system to provide succinct and exact control over database interactions.

Mature, High Performing Architecture

Over seven years of constant development, profiling, and refactoring has led to a toolkit that is high performing and accurate, well covered in tests, and deployed in thousands of environments. With virtually every major component in its second or third full iteration, SQLAlchemy 0.6 is roughly twice the speed of older 0.4 versions from just a few years ago, and versions 0.7 and 0.8 continue to improve. It's raw execution speed is competitive with comparable tools, and advanced ORM features like its unit of work, in-memory collections, eager loading of collections via joins or secondary subselects, and other optimizations allow SQLAlchemy's ORM to emit fewer and more efficient queries than in any previous version.

DBA Approved

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 batched database writes using the Unit of Work pattern. All object-relational patterns are designed around the usage of proper referential integrity, and foreign keys are an integral part of its usage.


SQLAlchemy places the highest value on not getting in the way of database and application architecture. Unlike many tools, it never "generates" schemas (not to be confused with issuing user-defined DDL, in which it excels) or relies on naming conventions of any kind. SQLAlchemy supports the widest variety of database and architectural designs as is reasonably possible.

Unit Of Work

The Unit Of Work system, a central part of SQLAlchemy's Object Relational Mapper (ORM), organizes pending insert/update/delete operations into queues and flushes them all in one batch. To accomplish this it performs a topological "dependency sort" of all modified items in the queue so as to honor inter-row dependencies, and groups redundant statements together where they can sometimes be batched even further. This produces the maximum efficiency and transaction safety, and minimizes chances of deadlocks. Modeled after Fowler's "Unit of Work" pattern as well as Hibernate, Java's leading object-relational mapper.

Function-based query construction

Function-based query construction allows SQL clauses to be built via Python functions and expressions. The full range of what's possible includes boolean expressions, operators, functions, table aliases, selectable subqueries, insert/update/delete statements, correlated updates, selects, and EXISTS clauses, UNION clauses, inner and outer joins, bind parameters, and free mixing of literal text within expressions. Constructed expressions are compilable specific to any number of vendor database implementations (such as PostgreSQL or Oracle), as determined by the combination of a "dialect" and "compiler" provided by the implementation.

Modular and Extensible

Different parts of SQLAlchemy can be used independently of the rest. Elements like connection pooling, SQL statement compilation and transactional services can be used independently of each other, and can also be extended through various plugin points. An integrated event system allows custom code to be injected at over fifty points of interaction, including within core statement execution, schema generation and introspection, connection pool operation, object relational configuration, persistence operations, attribute mutation events, and transactional stages. New SQL expression elements and custom database types can be built and integrated seamlessly.

Separate mapping and class design

The ORM standardizes on a "Declarative" configurational system that allows construction of user-defined classes inline with the table metadata they map to, in the same way most other object-relational tools provide. However this system is totally optional - at its core, the ORM considers the user-defined class, the associated table metadata, and the mapping of the two to be entirely separate. Through the use of the mapper() function, any arbitrary Python class can be mapped to a database table or view. Mapped classes also retain serializability (pickling) for usage in various caching systems.

Eager-loading and caching of related objects and collections

The ORM caches collections and references between objects once loaded, so that no SQL need be emitted on each access. The eager loading feature allows entire graphs of objects linked by collections and references to be loaded with few or just one query, configurable down to the exact statement count on a per-mapping or per-query basis, with no changes to existing queries needed. The "N+1" problem, whereby an ORM needs to emit individual statements for all objects in a collection, is a thing of the past with SQLAlchemy.

Composite (multiple-column) primary keys

In SQLAlchemy, primary and foreign keys are represented as sets of columns; truly composite behavior is implemented from the ground up. The ORM has industrial strength support for meaningful (non-surrogate) primary keys, including mutability and compatibility with ON UPDATE CASCADE, as well as explicit support for other common composite PK patterns such as "association" objects (many-to-many relationships with extra meaning attached to each association).

Self-referential Object Mappings

Self-referential mappings are supported by the ORM. Adjacency list structures can be created, saved, and deleted with proper cascading, with no code overhead beyond that of non-self-referential structures. Loading of self-referential structures of any depth can be tuned to load collections recursively via a single statement with a series of joins (i.e. a joinedload), or via multiple statements where each loads the full set of records at a distinct level of depth (i.e. subqueryload). Persistence with tables that have mutually-dependent foreign key pairs (i.e. "many x"/"one particular x") are also supported natively using the "post update" feature.

Inheritance Mapping
Explicit support is available for single-table, concrete-table, and joined table inheritance. Polymorphic loading (that is, a query that returns objects of multiple descendant types) is supported for all three styles. The loading of each may be optimized such that only one round trip is used to fully load a polymorphic result set.
Raw SQL statement mapping

SQLA's object relational query facilities can accommodate raw SQL statements as well as plain result sets, and object instances can be generated from these results in the same manner as any other ORM operation. Any 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. Statements which represent multiple kinds of objects can be used as well, with results received as named-tuples, or with dependent objects routed into collections on parent objects.

Pre- and post-processing of data

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.

Supported Platforms

SQLAlchemy supports Python 2.5 through the latest 3.x versions. Other supported platforms include Jython and Pypy.

Supported Databases

SQLAlchemy includes dialects for SQLite, Postgresql, MySQL, Oracle, MS-SQL, Firebird, Sybase and others, most of which support multiple DBAPIs. Other dialects are published as external projects. The corresponding DB-API 2.0 implementation (or sometimes one of several available) is required to use each particular database. View Current DBAPI Support