Project Assignment: Online Imtiaz Mart API Using Event-Driven Microservices Architecture

Project Overview

This project aims to develop an online mart API using an event-driven microservices architecture. The API will leverage various technologies such as FastAPI, Docker, DevContainers, Docker Compose, PostgreSQL, Kafka, Protocol Buffers (Protobuf), and Kong for API gateway management. The goal is to create a scalable, maintainable, and efficient system that handles high volumes of transactions and data in a distributed manner.

Additionally, Test-Driven Development (TDD) and Behavior-Driven Development (BDD) practices will be incorporated to ensure high code quality and alignment with business requirements.

Objectives

• Develop a scalable and efficient API for an online mart using microservices.
• Implement an event-driven architecture to handle asynchronous communication between services.
• Utilize modern technologies such as FastAPI for API development, Docker for containerization, and Kafka for event streaming.
• Ensure smooth development and deployment using DevContainers and Docker Compose.
• Manage and route API requests through Kong API Gateway.
• Use Protocol Buffers (Protobuf) for efficient data serialization.
• Persist data using PostgreSQL.
• Incorporate TDD and BDD to enhance code quality and ensure the application meets business requirements.

Technologies

• FastAPI: A modern, fast (high-performance) web framework for building APIs with Python.
• Docker: For containerizing the microservices, ensuring consistency across different environments.
• DevContainers: To provide a consistent development environment.
• Docker Compose: For orchestrating multi-container Docker applications.
• PostgreSQL: A powerful, open-source relational database system.
• SQLModel: For interacting with the PostgreSQL database using Python.
• Kafka: A distributed event streaming platform for building real-time data pipelines and streaming applications.
• Protocol Buffers (Protobuf): A method developed by Google for serializing structured data, similar to XML or JSON but smaller, faster, and simpler.
• Kong: An open-source API Gateway and Microservices Management Layer.
• Github Actions: For CI/CD pipeline.
• Pytest: For unit testing and TDD.
• Behave: For BDD.

Architecture

Microservices

1. User Service: Manages user authentication, registration, and profiles.
2. Product Service: Manages product catalog, including CRUD operations for products.
3. Order Service: Handles order creation, updating, and tracking.
4. Inventory Service: Manages stock levels and inventory updates.
5. Notification Service: Sends notifications (email, SMS) to users about order statuses and other updates.
6. Payment Service: Processes payments and manages transaction records.

Event-Driven Communication

• Kafka: Acts as the event bus, facilitating communication between microservices. Each service can produce and consume messages (events) such as user registration, order placement, and inventory updates.
• Protobuf: Used for defining the structure of messages exchanged between services, ensuring efficient and compact serialization.

Data Storage

• PostgreSQL: Each microservice with data persistence needs will have its own PostgreSQL database instance, following the database-per-service pattern.

API Gateway

• Kong: Manages API request routing, authentication, rate limiting, and oth \\er cross-cutting concerns.

Development Environment

• DevContainers: Provide consistent development environments using VSCode DevContainers, ensuring that all team members work in identical environments.
• Docker Compose: Orchestrates the various microservices and dependencies (PostgreSQL, Kafka, etc.) during development and testing.

Development Methodologies

Test-Driven Development (TDD):

TDD involves writing tests before writing the actual code. This ensures that the code meets the required functionality and helps prevent bugs. In this project, Pytest will be used for unit testing.

Behavior-Driven Development (BDD):

BDD extends TDD by writing test cases in a natural language that non-programmers can read. This ensures that the software development process aligns closely with business requirements. In this project, Behave will be used for BDD.

As mentioned above, Behavior-driven development is an extension of Test-driven development (TDD) that emphasizes collaboration among project stakeholders. Behave operates on the principle of scenarios, which are written in a natural language that non-programmers can understand. These scenarios describe how a feature should work from the end user’s perspective.

Behave is a BDD framework for Python that follows the principles of writing tests in a human-readable format. It uses Gherkin language to describe software behaviors without detailing how that functionality is implemented.

Key Features of Behave

• Gherkin Language: Enables the definition of application behavior in natural language, which stakeholders can easily understand.
• Scenario Outline: Facilitates data-driven tests, allowing the same scenario to be run multiple times with different data sets.
• Hooks: Offers setup and teardown operations for scenarios or features, improving test management.

Implementation Plan

Phase 1: Setup and Initial Development

1. Setup Development Environment

   • Configure DevContainers with necessary dependencies and tools.
   • Create Docker Compose file for orchestrating services and dependencies.

2. Develop Microservices with TDD

   • Implement the User Service, Product Service, Order Service, Payment Service, and Notification Service using FastAPI and SQLModel.
   • Write unit tests using Pytest before writing the actual code.
   • Containerize each microservice using Docker.

3. Setup Kafka and Protobuf

   • Configure Kafka for event streaming.
   • Define Protobuf schemas for messages exchanged between services.

4. Write BDD Scenarios

   • Define user stories and acceptance criteria.
   • Write BDD scenarios in Gherkin language using Behave.
   • Implement step definitions to automate the BDD scenarios.

5. Testing

   • Ensure all unit tests and BDD scenarios pass.
   • Use Docker Compose to run the entire application locally for integration testing.

Phase 2: Expand Functionality

1. Develop Additional Microservices

   • Inventory Service: Manage stock levels and inventory updates.
   • Notification Service: Send notifications about order statuses and other events.
   • Payment Service: Process payments and manage transactions.

2. Integrate Event-Driven Communication

   • Ensure all services can produce and consume relevant Kafka messages.
   • Use Protobuf for message serialization.

Phase 3: Implement API Gateway and Finalize

1. Setup Kong API Gateway

   • Configure Kong for routing, authentication, and rate limiting.
   • Create routes for each microservice.

2. Testing and Quality Assurance

   • Write unit, integration, and end-to-end tests for all services.
   • Perform load testing to ensure scalability and performance.

3. Deployment (Optional)

   • Deploy services to a cloud platform (e.g., AWS, GCP, Azure) using Kubernetes for orchestration locally or the following Cloud Services:

   Deploying containers in the cloud is a popular approach for managing and scaling applications. Various cloud providers offer a range of services to facilitate container deployment, management, and orchestration. Here are some of the prominent services available for deploying containers in the cloud:

• Amazon Web Services (AWS)

1. Amazon Elastic Kubernetes Service (EKS)

   • Managed Kubernetes service that simplifies running Kubernetes on AWS without needing to install and operate your own Kubernetes control plane or nodes.

2. Amazon Elastic Container Service (ECS)

   • Fully managed container orchestration service that supports Docker containers. ECS can run applications on a managed cluster of Amazon EC2 instances or using AWS Fargate.

3. AWS Fargate

   • Serverless compute engine for containers that works with ECS and EKS. It allows you to run containers without having to manage servers or clusters.

4. Amazon Lightsail

   • Simplified service for small-scale applications that need easy deployment and management of containers, virtual servers, and more.

• Microsoft Azure

1. Azure Kubernetes Service (AKS)

   • Managed Kubernetes service that simplifies Kubernetes cluster management, scaling, and upgrades.

2. Azure Container Instances (ACI)

   • Offers a quick and easy way to run containers without managing servers, using a pay-per-second billing model.

3. Azure App Service

   • Platform as a Service (PaaS) offering that enables easy deployment and scaling of web apps, APIs, and mobile backends, including support for Docker containers.

4. Azure Red Hat OpenShift

   • Managed OpenShift service for running containerized applications using Kubernetes with integrated DevOps tools.

• Google Cloud Platform (GCP)

1. Google Kubernetes Engine (GKE)

   • Managed Kubernetes service that offers easy, reliable, and scalable Kubernetes cluster management on GCP.

2. Google Cloud Run

   • Fully managed compute platform that automatically scales your stateless containers, billed only for the resources you use.

3. Google Cloud Functions

   • Serverless execution environment for building and connecting cloud services. You can deploy containerized functions as well.

4. Google App Engine

   • PaaS offering that allows you to build and deploy applications using containers, without managing the underlying infrastructure.

• IBM Cloud

1. IBM Cloud Kubernetes Service

   • Managed Kubernetes service that provides advanced tools and security to deploy, manage, and scale containerized applications.

2. IBM Cloud Code Engine

   • Serverless platform for running containerized workloads, including web apps, microservices, and batch jobs.

• Oracle Cloud Infrastructure (OCI)

1. Oracle Container Engine for Kubernetes (OKE)

   • Managed Kubernetes service that allows you to deploy and manage containerized applications with ease on Oracle Cloud.

2. Oracle Cloud Infrastructure Container Instances

   • Service for running containers directly on OCI without managing underlying infrastructure.

• Other Cloud Providers and Services

1. DigitalOcean Kubernetes

   • Managed Kubernetes service that simplifies container orchestration on DigitalOcean's cloud.

2. Linode Kubernetes Engine (LKE)

   • Managed Kubernetes service that provides high-performance Kubernetes clusters on Linode's infrastructure.

3. Red Hat OpenShift on IBM Cloud

   • Managed OpenShift service that provides enterprise Kubernetes orchestration and DevOps capabilities.

4. VMware Tanzu

   • Suite of products for building, running, and managing containerized applications on Kubernetes across multi-cloud environments.

• Container Management Platforms

1. Rancher

   • Open-source container management platform that simplifies deploying and managing Kubernetes clusters across various cloud providers and on-premises.

2. Portainer

   • Lightweight management UI that helps manage Docker and Kubernetes environments.

Phase 4: Monitoring and Continues Delivery (Optional)

1. Implement Monitoring and Logging

   • Set up monitoring tools (e.g., Prometheus, Grafana) to track service health and performance.
   • Implement centralized logging for troubleshooting and analysis.

2. Continuous Delivery with GitHub Actions (Options)

   • Use Github Actions to continuesly deliver code for deployment to the the cloud. Using GitHub Actions in your Online Mart API project can greatly enhance your development workflow by automating building, testing, and deployment processes. This not only saves time but also ensures consistency and reliability in your deployment pipeline. Below is a comprehensive conclusion to the setup:

• Consistency and Reliability

  • Automated workflows ensure that every code change is consistently built, tested, and deployed, reducing the risk of human error.

• Early Error Detection

  • Running tests on every push helps detect issues early in the development process, making it easier to fix bugs before they reach production.

• Scalability

  • Automated workflows can handle multiple microservices and scale with your project. As your application grows, you can easily add more workflows for additional services.

• Efficiency

  • Automating the build and deployment process reduces the time developers spend on manual tasks, allowing them to focus on writing code and developing features.

• Integration with Other Tools

  • GitHub Actions integrates well with other tools and services, such as container registries.

Conclusion

This project aims to create a robust, scalable, and efficient online mart API using an event-driven microservices architecture. By leveraging modern technologies such as FastAPI, Docker, Kafka, Protobuf, and Kong, we ensure a high-performance and maintainable system capable of handling large-scale operations. The development will follow a phased approach, ensuring thorough testing, quality assurance, and continuous improvement.