SQLAlchemy (source code) is a well-regarded database toolkit and object-relational mapper (ORM) implementation written in Python. SQLAlchemy provides a generalized interface for creating and executing database-agnostic code without needing to write SQL statements.
SQLAlchemy isn't just an ORM- it also provides SQLAlchemy Core for performing database work that is abstracted from the implementation differences between PostgreSQL, SQLite, etc. In some ways, the ORM is a bonus to Core that automates commonly-required create, read, update and delete operations.
SQLAlchemy can be used with or without the ORM features. Any given project can choose to just use SQLAlchemy Core or both Core and the ORM. The following diagram shows a few example configurations with various application software stacks and backend databases. Any of these configurations can be a valid option depending on what type of application you are coding.
A benefit many developers enjoy with SQLAlchemy is that it allows them to write Python code in their project to map from the database schema to the applications' Python objects. No SQL is required to create, maintain and query the database. The mapping allows SQLAlchemy to handle the underlying database so developers can work with their Python objects instead of writing bridge code to get data in and out of relational tables.
Below is an example of a SQLAlchemy model definition from the open source compare-python-web-frameworks project that uses SQLAlchemy with Flask and Flask-SQLAlchemy.
SQLAlchemy handles the table creation that otherwise we would have had to write a create table statement like this one to do the work:
By using SQLAlchemy in our Python code, all records can be obtained with a
line like contacts = Contact.query.all() instead of a plain SQL such as
SELECT * FROM contacts. That may not look like much of a difference in
syntax but writing the queries in Python is often faster and easier for
many Python developers once multiple tables and specific filtering on fields
for queries have to be written. In addition, SQLAlchemy abstracts away
idiosyncratic differences between database implementations in
SQLite, MySQL and
PostgreSQL.
Take a look at the SQLAlchemy extensions, plug-ins and related libraries page for a curated list of useful code libraries to use with SQLAlchemy.
There is no reason why you cannot use the SQLAlchemy library in any application that requires a database backend. However, if you are building a web app with Flask, Bottle or another web framework then take a look at the following extensions. They provide some glue code along with helper functions that can reduce the boilerplate code needed to connect your application's code with the SQLAlchemy library.
SQLAlchemy is typically used with Flask as the database ORM via the Flask-SQLAlchemy extension.
The bottle-sqlalchemy extension for Bottle provides a bridge between the standard SQLAlchemy library and Bottle. However, from my experience using the library it does not have quite as many helper functions as Flask-SQLAlchemy.
Pyramid uses the alchemy scaffold to make it easy to add SQLAlchemy to a Pyramid web app.
While Django does not yet support easy swapping of the default Django backend ORM with SQLAlchemy (like it does for template engines), there are hacks for using SQLAlchemy within Django projects.
Morepath has easy-to-use support for SQLAlchemy via its more.transaction module. There is a morepath-sqlalchemy demo that serves as a working example.
Merging Django ORM with SQLAlchemy for Easier Data Analysis has details on why, how and when you may want to use SQLAlchemy to augment the Django ORM.
Building a Simple Birthday App with Flask-SQLAlchemy combines SQLAlchemy with Flask to create a birthday reminder application.
The best way to get comfortable with SQLAlchemy is to dig in and write a database-driven application. The following resources can be helpful if you are having trouble getting started or are starting to run into some edge cases.
There is an entire chapter in the Architecture of Open Source Applications book on SQLAlchemy. The content is detailed and well worth reading to understand what is executing under the covers.
The SQLAlchemy cheatsheet has many examples for querying, generating database metadata and many other common (and not so common) operations when working with Core and the ORM.
10 reasons to love SQLAlchemy is a bit of a non-critical lovefest for the code library. However, the post makes some good points about the quality of SQLAlchemy's documentation and what a pleasure it can be to use it in a Python project.
SQLAlchemy and Django explains how one development team uses the Django ORM for most of their standard queries but relies on SQLAlchemy for really advanced queries.
This SQLAlchemy tutorial provides a slew of code examples that cover the basics for working with SQLAlchemy.
Implementing User Comments with SQLAlchemy gives a wonderful walkthrough of how to build your own online commenting system in Python using SQLAlchemy.
Master SQLAlchemy Relationships in a Performance Friendly Way dives into code that shows how to improve performance when setting and accessing relationship-based data in your models.
SQLAlchemy and data access in Python is a podcast interview with the creator of SQLAlchemy that covers the project's history and how it has evolved over the past decade.
Most Flask developers use SQLAlchemy as an ORM to relational databases. If you're unfamiliar with SQLAlchemy questions will often come up such as what's the difference between flush and commit? that are important to understand as you build out your app.
SQLAlchemy in batches shows the code that a popular iOS application runs in background batch scripts which uses SQLAlchemy to generate playlists. They provide some context and advice for using SQLAlchemy in batch scripts.
Getting PostgreSQL transactions under control with SQLAlchemy provides a quick introduction to the tool Chryso that they are working on to provide better transaction management in SQLAlchemy connections.
SQLAlchemy is one of many Python object-relational mapper (ORM) implementations. Several open source projects and articles are listed here to make it a bit easier to understand the differences between these implementations.
Introduction to SQLAlchemy ORM for Django Developers is written by a developer who typically used the Django ORM at work and then had a chance to try SQLAlchemy for one project. He covers differences in how each one handles transactions, models and queries.
SQLAlchemy vs Other ORMs provides a detailed comparison of SQLAlchemy against alternatives.
If you're interested in the differences between SQLAlchemy and the Django ORM I recommend reading SQLAlchemy and You by Armin Ronacher.
This GitHub project named PythonORMSleepy implements the same Flask application with several different ORMs: SQLAlchemy, Peewee, MongoEngine, stdnet and PonyORM. Looking through the code is helpful for understanding the varying approaches each library takes to accomplish a similar objective.
Quora has several answers to the question of which is better and why: Django ORM or SQLALchemy based on various developers' experiences.
Many open source projects rely on SQLAlchemy. A great way to learn how to properly work with this tool is to read the code that shows how those projects use SQLAlchemy. This section alphabetically lists these code examples by class and function in the SQLAlchemy code base.