django-kck 0.0.4

Data orchestration for Django

Django KCK

Django KCK is a flexible data pipeline framework for Django that makes it easy to write code that periodically imports data from remote sources.

It's easy to define data products that depend on those updates and to make sure they refresh as soon as possible after an update.

It's easy to make sure that some data products refresh every five minutes while others refresh on demand, returning fresh data every time they're requested.

Django KCK is designed to manage data products and keep them up-to-date, whether they reflect authoritative sources like the daily reports from a system of cash registers or conglomerate data products assembled from assortments of authoritative and derivative data products, with uniquely configured refresh schedules and triggers.

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 FirstDataProduct(Primer):
    key = 'first_data_product'
    parameters = [
        {"name": "id", "from_str": int}
    ]

    def compute(self, key):
        param_dict = self.key_to_param_dict(key)
        .
        .
        .
        return result

# myapp/views.py

from kck import Cache

cache = Cache.get_instance()

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.