Skip to content

Latest commit

 

History

History
41 lines (33 loc) · 1.96 KB

README.md

File metadata and controls

41 lines (33 loc) · 1.96 KB

Event Sourcing Kanban Example

This example project is an effort to address the desired documentation objective outlined in the eventsourcing library

Quickstart

$ python3 -m venv .venv
$ source .venv/bin/activate
$ pip install -r requirements.txt
$ apistar create_tables
$ apistar run
$ http :8080/NewUser name="Name" password="Mk91Q^U%" email="[email protected]"

TODO

First Priority

  • [] active record classes (stored event schema, indexes and performance of the queries);
  • [] active record strategy (database management system);
  • [] the JSON object encoder and decoder;
  • [] the optional cipher strategy; and
  • [] the event store object which holds it all together.

Second Priority

  • [] the layers (interface, application, domain, infrastructure);
  • [] how an aggregate can respond to commands by constructing and applying and publishing events;
  • [] how to replay events to get current state using a mutator function; how an entity factory can work;
  • [] how a repository can provide a dictionary-like interface for accessing domain entities by ID;
  • [] how domain services can work;
  • [] how an aggregate root can work;
  • [] how to use entities within an aggregate;
  • [] how to use value objects within an aggregate;
  • [] how to use an application object both to bind the domain layer and infrastructure layer, and to present application services;
  • [] application policies and publish-subscribe mechanisms;
  • [] how interfaces can use an application object;
  • [] how application logs can allow projections to update themselves from the application state; and
  • [] how notification logs can allow remote projections to update another application.

Third Priority

  • [] how traversing historical events can be used to answer common "BI" questions like user behavior and detailed utilization trends
  • [] how historical events can be used as a rich supply of data for use in forecasting and machine learning