WhizStream Web Application Architecture

This project deploys a multi-tier web application architecture on Amazon Web Services (AWS) using Amazon Elastic Compute Cloud (EC2) instances. The architecture leverages various AWS services to achieve high availability, scalability, and security.

Code

1. Frontend (React) : https:/youtubeMini/WhizStream_Frontend
2. Backend (Nodejs) : https:/youtubeMini/WhizStream_Backend
3. S3 Presigned URL (Lmabda) : https:/youtubeMini/s3-signed-url-lambda
4. Video Processing (Ec2) : https:/youtubeMini/video_processing

Key Components:

• Regions: The application is deployed across multiple isolated AWS regions for redundancy.
• Public and Private Subnets: Public subnets host resources accessible from the internet, while private subnets hold application logic and databases for security.
• Auto Scaling Groups: These groups automatically manage EC2 instances to handle varying traffic loads.
• Web Application Tiers: The architecture follows a three-tier structure:
  • Frontend tier (public subnet) for handling user requests and delivering content.
  • Backend tier (private subnet) for processing requests and interacting with the database.
  • Database tier (private subnet) with a master-read replica configuration for data management.
• API Gateway: Acts as a single entry point for API requests, routing them to backend services.
• Load Balancers: Distribute traffic across multiple EC2 instances for high availability.

Uploading and Processing Videos

This section outlines the process for uploading videos to Amazon S3 storage and processing them using AWS Lambda functions.

Workflow:

1. Lambda (generate URL): An AWS Lambda function is invoked, perhaps to generate a pre-signed URL for uploading the video.
2. Upload video to S3 directly: Videos are uploaded directly to an S3 bucket using a presigned URL which is generated by the Lambda function.
3. Request Pre-Signed URL: An S3 pre-signed URL is generated to control access permissions for uploading the video.
4. Send message to Queue: A message is sent to an SQS queue, and EC2 autoscaling, EC2 process the uploaded video.
5. Send Email Notification: An email notification is sent, upon successful video processing.
6. Sent Push Notification to App: A push notification is sent to an app to inform users about video processing completion.

CI/CD Pipeline with AWS

This section describes the continuous integration and continuous delivery (CI/CD) pipeline implemented for this project using Github Action. The pipeline automates the process of building, testing, and deploying the application to Amazon EC2 instances.

Pipeline Stages:

1. Github: The pipeline starts with code changes being pushed to master branch of github.
2. Build: A build stage is triggered, by the github action. This stage builds the docker image and it will be pushed to the Docker Hub.
3. Build succeeded (Yes): If the build is successful, the pipeline progresses to the deployment stage.
4. Build failed (No): In case of build failures, the pipeline halts, and notifications are sent to developers for troubleshooting.
5. Deploy: The deployment stage utilizes Github action to deploy the application to EC2 instances.
6. Deployment succeeded (Yes): Upon successful deployment, the pipeline completes.
7. Deployment failed (No): If deployment fails, notifications are sent, and rollback mechanisms might be initiated to revert to a previous version.