ddd-leaven-akka-v2
Sample e-commerce application built on top of Akka and EventStore following a CQRS/DDDD-based approach.
Overview
This sample e-commerce system has unique set of properties. It is:
- responsive, resilient, elastic
👏 , - incorporates a SOA, EDA, and Microservice architecture
👌 , - incorporates CQRS/DDDD architectural patterns
👍 , - supporting long-running business processes (eg. payment deadlines)
💪 , and - developer-friendly (implemented in Scala, ~1500 lines of code)
😄 .
All these capabilities are obviously supported by the underlying technology stack, which includes:
-
Akka - actor-based, reactive middleware implemented in Scala,
-
Akka HTTP - HTTP server build upon Akka Stream (Akka's implementation of Reactive Streams Specification),
-
Akka Persistence - infrastructure for building durable (event sourced) actors, which has a pluggable journal,
-
Event Store - scalable, highly available event store with akka-persistence journal implementation. Provides engine for running user-defined projections (javascript functions) over single or multiple event streams. Projections allow the system to group or combine events into new event streams that can represent domain-level journals such as office journals (events grouped by emitter (
Aggregate Root) class) or business process journals (events related to concrete business process). Domain journals are topic of interest for services such as:- view updaters - responsible for updating the read side of the system
- receptors - allow event-driven interaction between subsystems (event choreography), including long-running processes (sagas),
- Akka-DDD - framework containing glue-code and all building blocks
Subsystems
The system currently consists of the following subsystems:
- Sales/Reservation - responsible for creating and confirming Reservations
- Invoicing - responsible for the invoicing
- Shipping - responsible for the goods delivery
- Headquarters - executes the Ordering Process (see below)
Ordering Process
Subsystem components
Each subsystem is divided into write and read side, each side containing back-end and front-end application:
write-back
Backend cluster node hosting Aggregate Roots, Receptors and Process Managers (Sagas).
write-front
HTTP server forwarding commands to backend cluster.
read-back
View update service that consumes events from event store and updates view store (PostgreSQL database).
read-front
HTTP server providing rest endpoint for accessing view store.