SQLAlchemy 0.3 Documentation

This tutorial provides a relatively simple walking tour through the basic concepts of SQLAlchemy. You may wish to skip it and dive into the main manual which is more reference-oriented. The examples in this tutorial comprise a fully working interactive Python session, and are guaranteed to be functioning courtesy of doctest.

Installation

Installing SQLAlchemy

Installing SQLAlchemy from scratch is most easily achieved with setuptools. (setuptools installation). Just run this from the command-line:

# easy_install SQLAlchemy

This command will download the latest version of SQLAlchemy from the Python Cheese Shop and install it to your system.

Otherwise, you can install from the distribution using the setup.py script:

# python setup.py install

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Installing a Database API

SQLAlchemy is designed to operate with a DBAPI implementation built for a particular database, and includes support for the most popular databases. If you have one of the supported DBAPI implementations, you can proceed to the following section. Otherwise SQLite is an easy-to-use database to get started with, which works with plain files or in-memory databases.

SQLite is included with Python 2.5 and greater.

If you are working with Python 2.3 or 2.4, SQLite and the Python API for SQLite can be installed from the following packages:

pysqlite - Python interface for SQLite
SQLite library

Note that the SQLite library download is not required with Windows, as the Windows Pysqlite library already includes it linked in. Pysqlite and SQLite can also be installed on Linux or FreeBSD via pre-made packages or from sources.

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Getting Started

Checking the Version

Note: This tutorial is oriented towards version 0.3.10 of SQLAlchemy. It will *not* work with versions earlier than 0.3.9. Check the version of SQLAlchemy you have installed via:

>>> import sqlalchemy
>>> sqlalchemy.__version__ 
0.3.10

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Imports

To start connecting to databases and begin issuing queries, we want to import the base of SQLAlchemy's functionality, which is provided under the module name of sqlalchemy. For the purposes of this tutorial, we will import its full list of symbols into our own local namespace.

>>> from sqlalchemy import *

Note that importing using the * operator pulls all the names from sqlalchemy into the local module namespace, which in a real application can produce name conflicts. Therefore its recommended in practice to either import the individual symbols desired (i.e. from sqlalchemy import Table, Column) or to import under a distinct namespace (i.e. import sqlalchemy as sa).

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Connecting to the Database

After our imports, the next thing we need is a handle to the desired database, represented by an Engine object. This object handles the business of managing connections and dealing with the specifics of a particular database. Below, we will make a SQLite connection to a file-based database called "tutorial.db".

>>> db = create_engine('sqlite:///tutorial.db')

Technically, the above statement did not make an actual connection to the sqlite database just yet. As soon as we begine working with the engine, it will start creating connections. In the case of SQLite, the tutorial.db file will actually be created at the moment it is first used, if the file does not exist already.

For full information on creating database engines, including those for SQLite and others, see Database Engines.

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SQLAlchemy is Two Libraries in One

Now that the basics of installing SQLAlchemy and connecting to our database are established, we can start getting in to actually doing something. But first, a little bit of explanation is required.

A central concept of SQLAlchemy is that it actually contains two distinct areas of functionality, one of which builds upon the other. One is a SQL Construction Language and the other is an Object Relational Mapper ("ORM" for short). The SQL construction language allows you to construct objects called ClauseElements which represent SQL expressions. These ClauseElements can then be executed against any database, where they are compiled into strings that are appropriate for the target database, and return an object called a ResultProxy, which is essentially a result set object that acts very much like a deluxe version of the dbapi cursor object.

The Object Relational Mapper (ORM) is a set of tools completely distinct from the SQL Construction Language which serve the purpose of mapping Python object instances into database rows, providing a rich selection interface with which to retrieve instances from tables as well as a comprehensive solution to persisting changes on those instances back into the database. When working with the ORM, its underlying workings as well as its public API make extensive use of the SQL Construction Language, however the general theory of operation is slightly different. Instead of working with database rows directly, you work with your own user-defined classes and object instances. Additionally, the method of issuing queries to the database is different, as the ORM handles the job of generating most of the SQL required, and instead requires more information about what kind of class instances you'd like to load and where you'd like to put them.

Where SA is somewhat unique, more powerful, and slightly more complicated is that the two areas of functionality can be mixed together in many ways. A key strategy to working with SA effectively is to have a solid awareness of these two distinct toolsets, and which concepts of SA belong to each - even some publications have confused the SQL Construction Language with the ORM. The key difference between the two is that when you're working with cursor-like result sets its the SQL Construction Language, and when working with collections of your own class instances its the Object Relational Mapper.

This tutorial will first focus on the basic configuration that is common to using both the SQL Construction Language as well as the ORM, which is to declare information about your database called table metadata. This will be followed by some constructed SQL examples, and then into usage of the ORM utilizing the same data we established in the SQL construction examples.

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Working with Database Objects

Defining Metadata, Binding to Engines

Configuring SQLAlchemy for your database consists of creating objects called Tables, each of which represent an actual table in the database. A collection of Table objects resides in a MetaData object which is essentially a table collection. We will create a MetaData and connect it to our Engine (connecting a schema object to an Engine is called binding):

>>> metadata = MetaData()
>>> metadata.bind = db

An equivalent operation is to create the MetaData object directly with the Engine:

>>> metadata = MetaData(db)

Now, when we tell "metadata" about the tables in our database, we can issue CREATE statements for those tables, as well as execute SQL statements derived from them, without needing to open or close any connections; that will be all done automatically.

Note that SQLALchemy allows us to use explicit connection objects for everything, if we wanted to, and there are reasons why you might want to do this. But for the purposes of this tutorial, using bind removes the need for us to deal with explicit connections.

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Creating a Table

With metadata as our established home for tables, lets make a Table for it:

>>> users_table = Table('users', metadata,
...     Column('user_id', Integer, primary_key=True),
...     Column('user_name', String(40)),
...     Column('password', String(15))
... )

As you might have guessed, we have just defined a table named users which has three columns: user_id (which is a primary key column), user_name and password. Currently it is just an object that doesn't necessarily correspond to an existing table in our database. To actually create the table, we use the create() method. To make it interesting, we will have SQLAlchemy echo the SQL statements it sends to the database, by setting the echo flag on the Engine associated with our MetaData:

>>> metadata.bind.echo = True
>>> users_table.create() 
CREATE TABLE users (
    user_id INTEGER NOT NULL,
    user_name VARCHAR(40),
    password VARCHAR(15),
    PRIMARY KEY (user_id)
)
...

Alternatively, the users table might already exist (such as, if you're running examples from this tutorial for the second time), in which case you can just skip the create() method call. You can even skip defining the individual columns in the users table and ask SQLAlchemy to load its definition from the database:

>>> users_table = Table('users', metadata, autoload=True)
>>> list(users_table.columns)[0].name
'user_id'

Loading a table's columns from the database is called reflection. Documentation on table metadata, including reflection, is available in Database Meta Data.

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Inserting Rows

Inserting is achieved via the insert() method, which defines a clause object (known as a ClauseElement) representing an INSERT statement:

>>> i = users_table.insert()
>>> i 
<sqlalchemy.sql._Insert object at 0x...>
>>> # the string form of the Insert object is a generic SQL representation
>>> print i
INSERT INTO users (user_id, user_name, password) VALUES (?, ?, ?)

Since we created this insert statement object from the users table which is bound to our Engine, the statement itself is also bound to the Engine, and supports executing itself. The execute() method of the clause object will compile the object into a string according to the underlying dialect of the Engine to which the statement is bound, and will then execute the resulting statement.

>>> # insert a single row
>>> i.execute(user_name='Mary', password='secure') 
INSERT INTO users (user_name, password) VALUES (?, ?)