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Flask integration with DataTables and peewee.

Project description

As DataTables is a quite power and useful JavaScript library for manipulating and displaying data, we intended to make integration of the client-side DataTables scripts with the server-side processing based on Flask and peewee.

Installation

Note

As we have noticed, there’s already a Flask-DataTables library available on PyPI. However, this package was intended for integration with SQLAlchemy instead of peewee.

To start with, simply install the Flask-DataTables package from PyPI:

pip install Flask-DataTables-peewee

or should you prefer the bleeding edge version:

git clone https://github.com/JarryShaw/Flask-DataTables.git
cd Flask-DataTables
pip install .

Usage

Flask-DataTables is quite simple to use, just declare your data model in the preferable way from peewee and voilà, that’s it.

Taking examples from the peewee documentation, we can have a DataTables integrated data model just as below:

from flask import Flask
from flask_datatables import (CharField, DataTable, DateTimeField,
                              ForeignKeyField, Metadata, TextField)

DATABASE = 'postgresql://postgres:password@localhost:5432/my_database'

app = Flask(__name__)
app.config.from_object(__name__)

db_wrapper = DataTable(app)


class User(db_wrapper.Model):
    username = CharField(unique=True)

    class Meta(Metadata):
        datatables = True


class Tweet(db_wrapper.Model):
    user = ForeignKeyField(User, backref='tweets')
    content = TextField()
    timestamp = DateTimeField(default=datetime.datetime.now)

    class Meta(Metadata):
        datatables = True

And now, you may simply call Tweet.search() to perform the server-side processing queries.

See Also

It is also possible to customise the orderable and/or searchable fields through flask_datatables.fields.Field parameters, and their corresponding behaviours by subclassing the flask_datatables.fields.Field classes.

Project details


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