Flask-FileAlchemy

YAML-formatted plain-text file based models for Flask backed by Flask-SQLAlchemy

Description

Flask-FileAlchemy lets you enter your data in YAML formatted plain text files and loads them according to the SQLAlchemy models you've defined using Flask-SQLAlchemy This data is then put into whatever data store you're using (in-memory SQLite works best) and is then ready for your app to query however it pleases.

This lets you retain the comfort of dynamic sites without compromising on the simplicity of static sites.

Programming language: Python

License: MIT License

Tags: YAML     Database     RESTful API     Flask     Static Site Generator     Utilities     Internet     SQL    

Latest version: v0.6.1

README

Flask-FileAlchemy

Flask-FileAlchemy is a Flask extension that lets you use Markdown or YAML formatted plain-text files as the main data store for your apps.

Installation

$ pip install flask-filealchemy

Background

The constraints on which data-store to use for applications that only have to run locally are quite relaxed as compared to the ones that have to serve production traffic. For such applications, it's normally OK to sacrifice on performance for ease of use.

One very strong use case here is generating static sites. While you can use Frozen-Flask to "freeze" an entire Flask application to a set of HTML files, your application still needs to read data from somewhere. This means you'll need to set up a data store, which (locally) tends to be file based SQLite. While that does the job extremely well, this also means executing SQL statements to input data.

Depending on how many data models you have and what types they contain, this can quickly get out of hand (imagine having to write an INSERT statement for a blog post).

In addition, you can't version control your data. Well, technically you can, but the diffs won't make any sense to a human.

Flask-FileAlchemy lets you use an alternative data store - plain text files.

Plain text files have the advantage of being much easier to handle for a human. Plus, you can version control them so your application data and code are both checked in together and share history.

Flask-FileAlchemy lets you enter your data in Markdown or YAML formatted plain text files and loads them according to the SQLAlchemy models you've defined using Flask-SQLAlchemy This data is then put into whatever data store you're using (in-memory SQLite works best) and is then ready for your app to query however it pleases.

This lets you retain the comfort of dynamic sites without compromising on the simplicity of static sites.

Usage

Define data models

Define your data models using the standard (Flask-)SQLAlchemy API. As an example, a BlogPost model can defined as follows.

app = Flask(__name__)

# configure Flask-SQLAlchemy
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///:memory:'

db = SQLAlchemy(app)

class BlogPost(db.Model):
   __tablename__ = 'blog_posts'

   slug = Column(String(255), primary_key=True)
   title = Column(String(255), nullable=False)
   content = Column(Text, nullable=False)

Add some data

Next, create a data/ directory somewhere on your disk (to keep things simple, it's recommended to have this directory in the application root). For each model you've defined, create a directory under this data/ directory with the same name as the __tablename__ attribute.

We currently support three different ways to define data.

1. Multiple YAML files

The first way is to have multiple YAML files inside the data/<__tablename__>/ directory, each file corresponding to one record.

In case of the "blog" example, we can define a new BlogPost record by creating the file data/blog_posts/first-post-ever.yml with the following content.

slug: first-post-ever
title: First post ever!
content: |
  This blog post talks about how it's the first post ever!

Adding more such files in the same directory would result in more records.

2. Single YAML file

For "smaller" models which don't have more than 2-3 fields, Flask-FileAlchemy supports reading from an _all.yml file. In such a case, instead of adding one file for every row, simply add all the rows in the _all.yml file inside the table directory.

For the "blog" example, this would look like the following.

- slug: first-post-ever
  title: First post ever!
  content: This blog post talks about how it's the first post ever!

- slug: second-post-ever
  title: second post ever!
  content: This blog post talks about how it's the second post ever!

3. Markdown/Frontmatter

It's also possible to load data from Jekyll-style Markdown files containing Frontmatter metadata.

In case of the blog example, it's possible to create a new BlogPost record by defining a data/blog_posts/first-post-ever.md file with the following content.

---
slug: first-post-ever
title: First post ever!
---

This blog post talks about how it's the first post ever!

Please note that when defining data using markdown, the name of the column associated with the main markdown body needs to be content.

4. Configure and load

Finally, configure Flask-FileAlchemy with your setup and ask it to load all your data.

# configure Flask-FileAlchemy
app.config['FILEALCHEMY_DATA_DIR'] = os.path.join(
   os.path.dirname(os.path.realpath(__file__)), 'data'
)
app.config['FILEALCHEMY_MODELS'] = (BlogPost,)

# load tables
FileAlchemy(app, db).load_tables()

Flask-FileAlchemy then reads your data from the given directory, and stores them in the data store of your choice that you configured Flask-FileAlchemy with (the preference being sqlite:///:memory:).

Please note that it's not possible to write to this database using db.session. Well, technically it's allowed, but the changes your app makes will only be reflected in the in-memory data store but won't be persisted to disk.

Contributing

Contributions are most welcome!

Please make sure you have Python 3.5+ and Poetry installed.

1. Git clone the repository - git clone https://github.com/siddhantgoel/flask-filealchemy.

2. Install the packages required for development - poetry install.

3. That's basically it. You should now be able to run the test suite - poetry run py.test.