Repository Name: bigcommerce-scala-extensions
This repository hosts two Scala-based modules that integrate with an existing modular commerce platform. The overarching goal is to showcase modern Scala frameworks, real-time data analytics, and intelligent inventory optimization capabilities, all running on a Linux environment.
- Introduction
- System Overview
- Phase 1 (High Priority): Real-Time Data Analytics
- Phase 2 (Optional): Intelligent Inventory & Supply Chain Optimization
- Resources & Hosting (Linux Environment)
- Repository Structure
- License
This project extends an enterprise-level commerce platform—composed of various services in different languages (React, .NET, Go, NestJS, Python)—by adding two Scala-based modules.
- Phase 1 (High Priority): Focuses on real-time data ingestion and analytics.
- Phase 2 (Optional): Implements advanced inventory optimization and supply chain improvements.
By working through these phases, you’ll learn how to leverage Scala, Akka, Play Framework, and Apache Spark (or similar libraries) in a microservices ecosystem.
The existing platform includes:
- UI (React)
- Admin (.NET)
- Order Service (Go)
- Catalog (NestJS)
- Recommendation Engine (Python)
These services communicate via REST APIs, message queues (e.g., Kafka), and shared data stores. The Scala modules will:
- Ingest and analyze data from these services (Phase 1).
- Provide advanced inventory & supply chain insights (Phase 2).
The Real-Time Data Analytics Module gathers data from the platform (orders, inventory updates, user activities) and processes it in real time. It then exposes aggregated insights and metrics via RESTful APIs (or GraphQL) to the admin panel, dashboards, or any other interested service.
- Scala (2.13+ or 3.x) – Core language.
- Akka Streams or Kafka Streams – Real-time data processing.
- Play Framework – Building RESTful endpoints for analytics and dashboards.
- Apache Spark (optional) – For large-scale batch processing or machine learning pipelines.
- Kafka (or another messaging system) – For ingesting streaming data from other modules.
- Docker & Docker Compose – Containerizing the service for Linux deployment.
- Order Service (Go): Sends real-time order events via Kafka (or HTTP) to Scala Analytics.
- Catalog (NestJS): Provides product and inventory details via REST APIs or data streams.
- Recommendation Engine (Python): Can receive or share metrics (e.g., top products, user trends).
- Admin (.NET): Consumes analytics data for dashboards and reporting.
- Functional & Reactive Programming in Scala
- Data ingestion and streaming with Akka Streams or Kafka Streams
- API development with Play Framework
- Integration of a Scala microservice in a polyglot architecture (Go, Python, .NET, Node.js)
This Phase 2 module leverages Scala and modern frameworks to forecast inventory needs, detect low-stock situations, and optimize the flow of products across multiple warehouses and sales channels. It is an optional extension that builds on top of the core commerce platform, adding data-driven insights and automation.
The Inventory & Supply Chain Optimization Module processes data from orders, warehouses, supplier APIs, and product catalogs to generate predictions and recommendations. These insights help:
- Automatically trigger low-stock alerts or reorder requests.
- Improve supplier relationships by maintaining optimal stock levels.
- Reduce shipping delays and overall supply chain costs.
- Scala: Core programming language (2.13+ or 3.x).
- Play Framework: Builds REST or GraphQL endpoints to expose inventory and supply chain functionalities.
- Akka: Handles real-time, event-driven updates (e.g., stock changes, incoming shipments).
- Spark MLlib (Optional): Facilitates advanced demand forecasting and predictive analytics.
- Kafka (Optional): Manages continuous data streams (e.g., from order service, warehouse updates).
- Database (SQL or NoSQL): Stores inventory states, replenishment data, and historical usage.
-
Catalog (NestJS)
- Provides product and SKU metadata (e.g., names, categories).
- Supplies real-time updates for new or modified products.
-
Order Service (Go)
- Sends events for each new or updated order.
- Decrements inventory counts to reflect actual stock.
-
Warehouse/3rd-Party Logistics
- Receives reorder requests when thresholds are reached.
- Sends shipment and delivery confirmations back to the module.
-
Admin (.NET)
- Presents dashboards with stock levels, alerts, and supply chain metrics.
- Accepts user input for manual overrides, such as forced reorders or expedited shipments.
- Event-Driven Architecture: Use Akka or other event-driven paradigms to manage real-time changes in inventory.
- Predictive Analytics: Leverage Spark MLlib (or external ML models) for forecasting product demand based on sales trends and seasonality.
- Database Schema Design: Understand how to store fast-moving inventory states while preserving historical transaction data.
- Microservices Integration: See how Scala services coexist with existing modules in Go, .NET, Node.js, and Python.
-
Review Requirements
- Define the data flow for inventory updates (e.g., via Kafka or REST calls).
- Plan how reorder thresholds, supplier info, and other configurations will be managed.
-
Set Up Dependencies
- Confirm connectivity to the messaging system (Kafka) and databases (SQL or NoSQL).
- Ensure that other services (Order Service, Catalog) expose the necessary data endpoints.
-
Configure Environment
- Establish environment variables for DB connections, message broker URIs, and service URLs.
- Containerize the service (e.g., Docker) to run on a Linux host or cloud environment.
-
Integration & Testing
- Integrate with the existing platform by publishing and subscribing to relevant topics or API endpoints.
- Implement automated tests (unit, integration) to validate inventory logic and forecasting accuracy.
-
Deployment
- Deploy to a Linux machine or VM with Docker.
- Orchestrate multiple services (including this one) using Docker Compose or Kubernetes.
- Linux Distro: Ubuntu 20.04+ or CentOS 8+ are recommended for a stable deployment.
- Docker: Containerize this module for easier scaling and maintenance.
- Monitoring: Use Prometheus + Grafana (or similar) to track performance and inventory metrics in real time.
- Security: Consider network policies (firewalls, secure credentials) for communication between modules.
This module is part of the Martix project, licensed under the MIT License. Please see the main project repository for more details.