Metadata-Version: 2.1
Name: django-kck
Version: 0.0.8
Summary: Data orchestration for Django
Home-page: https://gitlab.com/frameworklabs/django-kck
Author: Framework Labs
Author-email: fred@frameworklabs.us
License: BSD
Description: # Django KCK
        Django KCK is data orchestration for Django.  It can be used for:
        * scheduled data imports from remote sources
        * ensuring each data product kept fresh, either by updating at a regular
          interval or when there is a change in source data on upon which it
          depends
        * preparing complex data products in advance of a likely request
        * simplifying and optimizing complex data flows
        
        The development pattern Django KCK encourages for data products
        emphasizes compartmentalization and simplification over complexity,
        cached data with configurable refresh routines over real-time
        computation, and common-sense optimizations over sprawling distributed
        parallelism.
        
        ## History
        Django KCK is a simplified version of KCK that targets the Django
        environment exclusively.  It also uses PostgreSQL as the cache backend,
        instead of Cassandra.
        
        ## Quick Install
        
        ## Basic Usage
        
        ```
        # myapp/primers.py
        
        from kck import Primer
        
        
        class TitleListPrimer(Primer):
            key = 'title_list'
            parameters = [
                {"name": "id", "from_str": int}
            ]
        
            def compute(self, key):
                param_dict = self.key_to_param_dict(key)
                results = [{ 'title': lkp_title(id) } for id in param_dict['id_list']]
                return results
        ```
        
        ```
        # myapp/views.py
        
        from kck import Cache
        from django.http import JsonResponse
        
        def first_data_product_view(request, author_id):
            cache = Cache.get_instance()
            title_list = cache.get(f'title_list/{author_id}')
            return JsonResponse(title_list)
        
        ```
        
        ## Theory
        Essentially, Django KCK is a lazy-loading cache.  Instead of warming the
        cache in advance, Django KCK lets a developer tell the cache how to
        prime itself in the event of a cache miss.
        
        If we don't warm the cache in advance and we ask the cache for a data
        product that depends on a hundred other data products in the cache, each
        of which either gathers or computes data from other sources, then this
        design will only generate or request the data that is absolutely
        necessary for the computation.  In this way, Django KCK is able to do
        the last amount of work possible to accomplish the task.
        
        To further expedite the process or building derivative data products,
        Django KCK includes mechanisms that allow for periodic or triggered
        updates of data upon which a data product depends, such that it will be
        immediately available when a request is made.
        
        It also makes it possible to "augment" derivative data products with
        new information so that, for workloads that can take advantage of the
        optimization, a data product can be updated in place, without
        regenerating the product in its entirety.  Where it works, this approach
        can turn minutes of computation into milliseconds.
        
Keywords: data orchestration framework
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Framework :: Django
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
