Meeting 01 Jan 16th 2022

Meeting 1 Discussion:

Frontend/UI:

React.js

• More closer to core JavaScript than Angular.js.
• React.js is library, Angular is a framework. Hence, React.js would provide more flexibility.
• We will mostly use React.js.

Angular.js

• More complex, less flexibility.

Backend

3 languages to be used for microservices:

• TypeScript: Major tasks like authentication
• Python: Flask or Django. Mostly we will use Flask since it is a lightweight Python micro-framework.
• Java: For CPU-intensive or tasks related to database interaction

Above considerations are tentative and are subject to change based on new requirements.

Communication between UI and backend:

• REST API: Most popular option.
• GraphQL: As a middleware between client and server for specific requirements.

Communication between microservices:

Options:

• For synchronous tasks. Example: Interaction between gateway service and auth service.
  • gRPC
• To distribute data between microservices
  • Apache Kafka
  • Rabbit-MQ

DevOps:

Containerizing the services:

• Docker

CI/CD:

• Continuous Integration:
  • For this available tools are: Jenkins and ANSIBLE
  • We will be using containerized Docker images to build and integrate applications, using above tools.
• Continuous Deployment:
  • GitHub Actions
  • Jenkins
  • ANSIBLE

Memory, Load-balancing, Instance Configuration:

• To Be Decided

Testing:

• Frontend:
  • Functional Testing
  • Component-based unit testing
  • Integration testing
• Backend:
  • Unit Testing
  • Load Testing
  • Integration Testing

Action Items:

• Rajdeep:
  • Learn gRPC, Kafka, revise Docker, build project with microservice architecture with different languages, Java core concepts w.r.t. microservices.
• Nirav:
  • Learn gPRC, learn how to build microservice architecture with Python, Java core concepts, containerizing services with Docker (overview), Database interaction with Python/Java (ORM optional)
• Shashank:
  • Learn gRPC, learn how to build microservice architecture with Python, REST APIs in Python with Flask, Data ingestion from S3 and data processing).