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.
Flask-FileAlchemy alternatives and similar packages
Based on the "Flask" category.
Alternatively, view Flask-FileAlchemy alternatives based on common mentions on social networks and blogs.
-
-
-
-
-
-
-
pycord
Pycord is a modern, easy to use, feature-rich, and async ready API wrapper for Discord written in Python -
-
-
flask-restless
NO LONGER MAINTAINED - A Flask extension for creating simple ReSTful JSON APIs from SQLAlchemy models. -
apispec
A pluggable API specification generator. Currently supports the OpenAPI Specification (f.k.a. the Swagger specification).. -
-
-
-
-
-
-
-
-
Flask-Muck
🧹 Flask REST framework for generating CRUD APIs and OpenAPI specs in the SQLAlchemy, Marshmallow/Pydantic application stack. -
-
-
-
-
-
Cilantropy
DISCONTINUED. :four_leaf_clover: Cilantropy is a Python Package Manager interface created to provide an "easy-to-use" visual and also a command-line interface for Pythonistas. Works great on windows, linux, macos :star: -
-
-
-
-
-
-
-
-
argsv
argsv is a library for validating arguments passed to callables. With this library, you can validate arguments sent to callables in a simpler, more readable, and well-defined context.
InfluxDB – Built for High-Performance Time Series Workloads
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
Do you think we are missing an alternative of Flask-FileAlchemy or a related project?
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.
Git clone the repository -
git clone https://github.com/siddhantgoel/flask-filealchemy.Install the packages required for development -
poetry install.That's basically it. You should now be able to run the test suite -
poetry run py.test.