Chat System

Cloud Continuous Delivery of Flask Microservices: online chatting system

Services used

• AWS App runner
• AWS CloudFormation
• MongoDB Atlas

Introduction

This online chatting system is designed to be straightforward and user-friendly. It is built using Flask, a popular Python-based microservice framework, and is deployed on the AWS App Runner platform. The system leverages the power of MongoDB Atlas, a highly scalable and reliable cloud-based database service, to store user profile and credentials. The chat microservice is deployed using AWS CloudFormation, a tool that enables users to create, manage, and update AWS infrastructure resources in a safe and predictable manner. This combination of powerful technologies ensures that the online chatting system is able to handle a large number of concurrent users and provide a fast and responsive user experience.

Architecture

• Flask Microservice: The main application logic is implemented using the Flask framework. It is responsible for handling incoming requests from users, processing chat messages, and interacting with the database.

• MongoDB Atlas Database: The system uses MongoDB Atlas, a cloud-based NoSQL database service, to store chat messages and other data. This database is deployed on the AWS Cloud and can be easily scaled as the number of users grows.

• AWS App Runner: The Flask microservice is deployed on the AWS App Runner platform, which provides a highly available and scalable infrastructure for running web applications. The App Runner automatically manages the deployment, scaling, and monitoring of the application.

• AWS CloudFormation: The MongoDB Atlas database is deployed using AWS CloudFormation, which provides a safe and predictable way to manage cloud infrastructure. This allows for easy deployment and maintenance of the database and ensures that the infrastructure is in a known state.

• Load Balancer: To ensure high availability and scalability, the system uses an AWS load balancer to distribute incoming traffic across multiple instances of the Flask microservice.

Getting started

1. Clone the repository and cd into the project directory
2. Install needed dependencies using pip install -r app/requirements.txt
3. Create a MongoDB Atlas database and add the connection string to the MONGO_URI environment variable in the app/dao/db.py file
4. Run the application using gunicorn --worker-class eventlet -w 1 app.server:app to start a production server, or python app/server.py to start a development server

Demo

Application is deployed on AWS

Done

• Create a Microservice in Flask or Fast API
• Push source code to Github
• Configure Build System to Deploy changes
• Use IaC (Infrastructure as Code) to deploy code
• Use either AWS, Azure, GCP (recommended services include Google App Engine, AWS App Runner or Azure App Services) Containerization is optional, but recommended

License

MIT License