Switch branches/tags
Nothing to show
Find file Copy path
Fetching contributors…
Cannot retrieve contributors at this time
229 lines (166 sloc) 8.07 KB

Storing data

Storing data

Most Flask applications are going to deal with storing data at some point. There are many different ways to store data. Finding the best one depends entirely on the data you are going to store. If you are storing relational data (e.g. a user has posts, posts have a user, etc.) a relational database is probably going to be the way to go (big suprise). Other types of data might be more suited to NoSQL data stores, such as MongoDB.

I'm not going to tell you how to choose a database engine for your application. There are people who will tell you that NoSQL is the only way to go and those who will say the same about relational databases. All I will say on that subject is that if you are unsure, a relational database (MySQL, PostgreSQL, etc.) will almost certainly work for whatever you're doing.

Plus, when you use a relational database you get to use SQLAlchemy and SQLAlchemy is fun.


SQLAlchemy is an ORM (Object Relational Mapper). It's basically an abstraction layer that sits on top of the raw SQL queries being executed on our database. It provides a consistent API to a long list of database engines. The most popular include MySQL, PostgreSQL and SQLite. This makes it easy to move data between our models and our database and it makes it really easy to do other things like switch database engines and migrate our schemas.

There is a great Flask extension that makes using SQLAlchemy in Flask even easier. It's called Flask-SQLAlchemy. Flask-SQLAlchemy configures a lot of sane defaults for SQLAlchemy. It also handles some session management so we don't have to deal with janitorial stuff in our application code.

Let's dive into some code. We're going to define some models then configure some SQLAlchemy. The models are going to go in myapp/, but first we are going to define our database in myapp/

# ourapp/

from flask import Flask
from flask_sqlalchemy import SQLAlchemy

app = Flask(__name__, instance_relative_config=True)


db = SQLAlchemy(app)

First we initialize and configure our Flask app and then we use it to initialize our SQLAlchemy database handler. We're going to use an instance folder for our database configuration so we should use the instance_relative_config option when initializing the app and then call app.config.from_pyfile to load it. Then we can define our models.

# ourapp/

from . import db

class Engine(db.Model):

    # Columns

    id = db.Column(db.Integer, primary_key=True, autoincrement=True)

    title = db.Column(db.String(128))

    thrust = db.Column(db.Integer, default=0)

Column, Integer, String, Model and other SQLAlchemy classes are all available via the db object constructed from Flask-SQLAlchemy. We have defined a model to store the current state of our spacecraft's engines. Each engine has an id, a title and a thrust level.

We still need to add some database information to our configuration. We're using an instance folder to keep confidential configuration variables out of version control, so we are going to put it in instance/

# instance/

SQLALCHEMY_DATABASE_URI = "postgresql://user:password@localhost/spaceshipDB"


Your database URI will be different depending on the engine you use and where it's hosted. See the SQLAlchemy documentation for this syntax.

Initializing the database

Now that the database is configured and we have defined a model, we can initialize the database. This step basically involves creating the database schema from the model definitions.

Normally that process might be a pain in the ... neck. Lucky for us, SQLAlchemy has a really cool command that will do all of this for us.

Let's open up a Python terminal in our repository root.

$ pwd
$ workon myapp
(myapp)$ python
Python 2.7.5 (default, Aug 25 2013, 00:04:04)
[GCC 4.2.1 Compatible Apple LLVM 5.0 (clang-500.0.68)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from myapp import db
>>> db.create_all()

Now, thanks to SQLAlchemy, our tables have been created in the database specified in our configuration.

Alembic migrations

The schema of a database is not set in stone. For example, we may want to add a last_fired column to the engine table. If we don't have any data, we can just update the model and run db.create_all() again. However, if we have six months of engine data logged in that table, we probably don't want to start over from scratch. That's where database migrations come in.

Alembic is a database migration tool created specifically for use with SQLAlchemy. It lets us keep a versioned history of our database schema so that we can later upgrade to a new schema and even downgrade back to an older one.

Alembic has an extensive tutorial to get you started, so I'll just give you a quick overview and point out a couple of things to watch out for.

We'll create our alembic "migration environment" via the alembic init command. Once we run this in our repository root we'll have a new directory with the very creative name alembic. Our repository will end up looking something like the example in this listing, adapted from the Alembic tutorial.


The alembic/ directory has the scripts that migrate our data between versions. There is also an alembic.ini file that contains configuration information.


Add alembic.ini to .gitignore! You are going to have your database credentials in this file, so you do not want it to end up in version control.

You do want to keep alembic/ in version control though. It does not contain sensitive information (that can't already be derived from your source code) and keeping it in version control will mean having multiple copies should something happen to the files on your computer.

When it comes time to make a schema change, there are a couple of steps. First we run alembic revision to generate a migration script. Then we'll open up the newly generated Python file in myapp/alembic/versions/ and fill in the upgrade and downgrade functions using the tools provided by Alembic's op object.

Once we have our migration script ready, we can run alembic upgrade head to migrade our data to the latest version.


For the details on configuring Alembic, creating your migration scripts and running your migrations, see the Alembic tutorial.


Don't forget to put a plan in place to back up your data. The details of that plan are outside the scope of this book, but you should always have your database backed up in a secure and robust way.


The NoSQL scene is less established with Flask, but as long as the database engine of your choice has a Python library, you should be able to use it. There are even several extensions in the Flask extension registry to help integrate NoSQL engines with Flask.


  • Use SQLAlchemy to work with relational databases.
  • Use Flask-SQLAlchemy to work with SQLAlchemy.
  • Alembic helps you migrate your data between schema changes.
  • You can use NoSQL databases with Flask, but the methods and tools vary between engines.
  • Back up your data!