FAQ.md

Part 1

• Design a URL Shortener - Video

• Design a High Throughput Logging System- Video

• Design a Social Media platform

  • Twitter
  • Instagram
  • Facebook
  • TikTok

• Design a Chat System

  • Whatsapp
  • Slack

• Design a Web Crawler - Video

• Design a video streaming service

  • Youtube
  • Netflix

• Design an E-Commerce platform like Amazon - Video

• Design a Notification System - Video

• Design a ride sharing service like Uber/Lyft - Video

• Design a Key Value Store - Video

• Design a Scalable Logging and Monitoring System - Video

Part 2

• Design a UID Generator

• Design a S3 Object Storage - Video

• Design a Distributed Message Queue like Kafka - Video

• Design a Rate limiter - Video

• Design a Search Auto complete system - Video

• Design Google Drive

  • Dropbox
  • Google Docs
  • Google Maps

• Design a Stock Exchange - Video

• Design a News Feed System

• Design Proximity Service

• Design Nearby Friends

• Design an Ad Click Event Aggregation

• Design a Hotel Reservation System

• Design a Distributed Email Service

• Design a Real-time Gaming Leaderboard

CI/CD Pipeline Design

Question

Design a CI/CD pipeline for deploying machine learning models. How would you integrate automated testing and rollback mechanisms in the pipeline?

High-Level Design

• Source Control: Code repository (e.g., GitHub, GitLab).
• CI Server: Jenkins, AWS CodePipeline.
• Build Stage: Docker for containerization.
• Test Stage: Automated testing frameworks (e.g., pytest, TensorFlow Testing).
• Deploy Stage: Kubernetes, AWS ECS/EKS.
• Monitoring and Rollback: Prometheus, Grafana, AWS CloudWatch, and rollback scripts.

Low-Level Design

• Source Control:
  • Use branching strategies like GitFlow.
  • Implement code reviews and pull request checks.
• CI Server:
  • Jenkins with pipelines as code (Jenkinsfile).
  • AWS CodePipeline integrated with AWS CodeBuild and CodeDeploy.
• Build Stage:
  • Dockerfile to containerize the application.
  • Use multi-stage builds to optimize Docker images.
• Test Stage:
  • Unit tests, integration tests, and end-to-end tests.
  • Utilize frameworks like pytest for Python-based ML models.
• Deploy Stage:
  • Kubernetes manifests or Helm charts for deployment.
  • AWS ECS/EKS for scalable deployment.
• Monitoring and Rollback:
  • Prometheus for metrics collection.
  • Grafana for visualization.
  • AWS CloudWatch for logs and alerts.
  • Automated rollback using scripts triggered by monitoring alerts.

Automation for Operations

Question

Design a system to automate the deployment and monitoring of ML servers. How would you ensure the system is scalable and reliable?

High-Level Design

• Infrastructure Provisioning: Terraform, AWS CloudFormation.
• Configuration Management: Ansible, Chef.
• Deployment: Jenkins, AWS CodeDeploy.
• Monitoring: Prometheus, Grafana, AWS CloudWatch.
• Alerting: PagerDuty, AWS SNS.

Low-Level Design

• Infrastructure Provisioning:
  • Use Terraform scripts to provision EC2 instances, VPCs, subnets, security groups.
  • AWS CloudFormation templates for AWS-specific resources.
• Configuration Management:
  • Ansible playbooks to configure servers.
  • Use roles and tasks for modular configuration.
• Deployment:
  • Jenkins pipelines to trigger Ansible playbooks and deploy application code.
  • AWS CodeDeploy to manage deployment lifecycle.
• Monitoring:
  • Prometheus to collect metrics.
  • Grafana dashboards to visualize metrics.
  • AWS CloudWatch for custom logs and alarms.
• Alerting:
  • PagerDuty for on-call alerts.
  • AWS SNS for notification distribution.

Release Management System

Question

Design a release management system that handles multiple environments (dev, staging, production). Discuss strategies for handling version control, rollback, and auditing.

High-Level Design

• Version Control: Git branching strategy (GitFlow).
• Environment Management: Separate environments for dev, staging, and production.
• CI/CD Pipelines: Jenkins, AWS CodePipeline for each environment.
• Rollback Mechanism: Blue/Green Deployments, Canary Releases.
• Auditing: Logging, Monitoring, and Audit Trails.

Low-Level Design

• Version Control:
  • GitFlow strategy with branches for feature, develop, release, and hotfix.
  • Use tags for versioning releases.
• Environment Management:
  • Separate AWS accounts or VPCs for dev, staging, and production.
  • Use infrastructure as code (Terraform) for consistent environment setup.
• CI/CD Pipelines:
  • Jenkins pipelines for each environment.
  • AWS CodePipeline with stages for build, test, and deploy.
• Rollback Mechanism:
  • Implement Blue/Green deployment strategy to switch between versions with minimal downtime.
  • Canary releases to test new versions on a subset of users before full deployment.
• Auditing:
  • Use AWS CloudTrail for API activity logging.
  • Implement logging frameworks (e.g., ELK stack) for application logs.
  • Regular security audits and compliance checks.

AWS Step Functions Workflow

Question

How would you design a workflow using AWS Step Functions to automate a complex multi-step process? Integrate AWS Lambda, DynamoDB, and other AWS services in the design.

High-Level Design

• Step Functions Workflow: Define states for each step.
• Lambda Functions: Implement business logic.
• DynamoDB: Store intermediate and final results.
• S3: Input and output data storage.
• CloudWatch: Monitoring and logging.

Low-Level Design

• Step Functions Workflow:
  • Define states: Task, Choice, Parallel, and Catch.
  • Use JSON/YAML to define the workflow.
• Lambda Functions:
  • Implement functions for each task.
  • Use environment variables and IAM roles for configuration and permissions.
• DynamoDB:
  • Tables to store intermediate states and results.
  • Use partition keys for efficient queries.
• S3:
  • Buckets for input data, intermediate results, and output data.
  • Set up lifecycle policies for data management.
• CloudWatch:
  • Create metrics and alarms for monitoring Lambda functions and workflow execution.
  • Use CloudWatch Logs for debugging and analysis.

Machine Learning Infrastructure

Question

Design the infrastructure for deploying and managing machine learning models in production. Consider factors such as model versioning, monitoring, and scalability.

High-Level Design

• Model Versioning: MLflow, DVC.
• Deployment: Kubernetes, AWS SageMaker.
• Scalability: Auto-scaling with Kubernetes or AWS services.
• Monitoring: Prometheus, Grafana, AWS CloudWatch.
• CI/CD for Models: Jenkins, GitHub Actions.

Low-Level Design

• Model Versioning:
  • Use MLflow to track experiments and model versions.
  • DVC for data versioning and reproducibility.
• Deployment:
  • Kubernetes with Helm charts for model deployment.
  • AWS SageMaker for managed model deployment and scaling.
• Scalability:
  • Use Kubernetes Horizontal Pod Autoscaler.
  • AWS Auto Scaling Groups for EC2 instances.
• Monitoring:
  • Prometheus for custom metrics.
  • Grafana dashboards for visualization.
  • AWS CloudWatch for logs and alarms.
• CI/CD for Models:
  • Jenkins pipeline to automate model training, testing, and deployment.
  • GitHub Actions for integration with version control.