Metadata-Version: 2.1
Name: cerberedis
Version: 1.0.3
Summary: A Cerberus powered frontend for Redis
Home-page: https://github.com/adamlwgriffiths/cerberedis/
Author: Adam Griffiths
Author-email: adam.lw.griffiths@gmail.com
License: BSD 2-Clause License
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.5
Requires-Dist: cerberus
Requires-Dist: redis

CerbeRedis
==========

.. image:: https://raw.githubusercontent.com/greyli/flask-share/master/images/cerberedis.png

De/serialise `Cerberus <https://github.com/pyeve/cerberus>`_ data to a `Redis <http://redis.io/>`_ database.

Installation
------------

.. code-block:: bash

    $ pip install cerberedis


Limitations
-----------

* Containers cannot be nested. Ie. lists and sets cannot contain lists, sets, or dicts.

Example
-------

A basic example:

.. code-block:: python

    from cerberus import Validator, TypeDefinition
    from redis import Redis
    from cerberedis import CerbeRedis

    schema = {
        'name': {'type': 'string', 'required': True},
        'email': {'type': 'string', 'required': True},
    }
    data = {'name': 'Bob', 'email': 'bob@example.com'}

    # connect to our redis server
    r = Redis()
    db = CerbeRedis(r)

    # save the schema under the type name 'User'
    # with a document id of 1
    model_name, id = 'User', 1
    db.save(model_name, schema, id, document)

    # print the key the model is stored under

    print(db.key(model_name, id))

    # reload the document
    loaded_document = db.load(model_name, schema, id)


A full featured example, including custom types:

.. code-block:: python

    from ipaddress import ip_address, IPv4Address, IPv6Address
    from datetime import date, datetime
    from cerberus import Validator, TypeDefinition
    from redis import Redis
    # alternatively use redis_mock (https://github.com/adamlwgriffiths/redis-mock)
    # from redis_mock import Redis
    from cerberedis import CerbeRedis

    # Add custom types to both Cerberus
    # https://docs.python-cerberus.org/en/stable/customize.html
    Validator.types_mapping.update({
        'ipaddress': TypeDefinition('ipaddress', (IPv4Address, IPv6Address), ()),
        'ipv4address': TypeDefinition('ipv4address', (IPv4Address,), ()),
        'ipv6address': TypeDefinition('ipv6address', (IPv6Address,), ()),
    })
    # Provide CerbeRedis with information on how to handle these new Cerberus types
    CerbeRedis.rules.update({
        # dictionary of: <cerberus type name>: [to redis function, from redis function]
        'ipaddress': [lambda x: str(x), lambda x: ip_address(x.decode('utf-8'))],
        'ipv4address': [lambda x: str(x), lambda x: IPv4Address(x.decode('utf-8'))],
        'ipv6address': [lambda x: str(x), lambda x: IPv6Address(x.decode('utf-8'))],
    })

    # An example Cerberus schema that uses all the built-in Cerberus types
    # and the custom ones we defined above
    schema = {
        'boolean': {'type': 'boolean', 'required': True},
        'binary': {'type': 'binary', 'required': True},
        'date': {'type': 'date', 'required': True},
        'datetime': {'type': 'datetime', 'required': True},
        'float': {'type': 'float', 'required': True},
        'integer': {'type': 'integer', 'required': True},
        'number': {'type': 'number', 'required': True},
        'string': {'type': 'string', 'required': True},
        'dict': {'type': 'dict', 'schema': {
            'dict_a': {'type': 'string', 'required': True},
            'dict_b': {'type': 'integer', 'required': True},
        }},
        'list': {'type': 'list', 'schema': {'type': 'integer'}},
        'set': {'type': 'set', 'schema': {'type': 'string'}},
        'ipv4address': {'type': 'ipv4address', 'required': True},
    }

    data = {
        'boolean': True,
        'binary': b'123',
        'date': date.today(),
        'datetime': datetime.now(),
        'float': 1.23,
        'integer': 456,
        'number': 789.0,
        'string': 'abcdefg',
        'dict': {
            'dict_a': 'dict_a_value',
            'dict_b': 9999,
        },
        'list': [1,2,3,4,5],
        'set': {'a', 'b', 'c'},
        'ipv4address': IPv4Address('127.0.0.1'),
    }

    # Use cerberus to validate and normalise the data
    validator = Validator(schema)
    document = validator.normalized(data)
    if not document:
        raise ValueError(str(validator.errors))

    # connect to our redis server
    r = Redis()
    db = CerbeRedis(r)
    model_name, id = 'TestModel', 1
    db.save(model_name, schema, id, document)

    # reload the document
    loaded_document = db.load(model_name, schema, id)

    # verify the data is laid out how we expect
    assert document == loaded_document

Future Work
-----------

* Use a Redis Pipeline for the load function

Changelog
=========

1.0.3
-----

* Fix handling of None values for non-required fields

1.0.2
-----

* Expose key function

1.0.1
-----

* Version bump due to PyPi version pedantry

1.0.0
-----

* Initial release

BSD 2-Clause License
====================

Copyright (c) 2021, Adam Griffiths
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this
   list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice,
   this list of conditions and the following disclaimer in the documentation
   and/or other materials provided with the distribution.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


