MasterSystem Design

Design a Ride-Sharing App (Uber/Lyft) Design and architect a scalable ride-sharing platform similar to Uber or Lyft that connects riders with drivers in real-time. This lab challenges you to build a system handling millions of concurrent users, GPS tracking, dynamic pricing, and payment processing. Learning Objectives Design distributed systems for real-time geospatial applications Implement efficient location-based matching algorithms Handle high-throughput data streams and WebSocket connections Build scalable microservices architecture Design fault-tolerant payment and transaction systems Core Features to Implement 1. User Management Rider and driver registration with profile management Authentication and authorization (OAuth, JWT) Rating and review system for both riders and drivers Background checks and verification for drivers 2. Real-Time Location Tracking GPS coordinate streaming from driver devices Geospatial indexing using quadtrees or geohashing Nearby driver discovery within configurable radius Live trip tracking and ETA calculations 3. Ride Matching System Intelligent driver-rider matching algorithm Dispatch optimization based on proximity and availability Handle concurrent ride requests efficiently Support for ride cancellations and timeouts 4. Pricing Engine Base fare calculation with distance and time factors Dynamic surge pricing during high demand Promotional codes and discounts Multiple ride types (economy, premium, shared) 5. Payment Processing Integration with payment gateways (Stripe, PayPal) Support for multiple payment methods Transaction history and invoicing Driver payout management Technical Requirements Architecture Components API Gateway: Handle authentication, rate limiting, and routing Location Service: Manage GPS updates and geospatial queries Matching Service: Connect riders with optimal drivers Trip Service: Manage ride lifecycle and state transitions Notification Service: Push notifications and SMS alerts Analytics Service: Real-time metrics and business intelligence Data Storage Relational DB: User profiles, trips, payments (PostgreSQL) Geospatial DB: Location data and queries (PostGIS, Redis Geo) Cache Layer: Active drivers, session data (Redis/Memcached) Message Queue: Asynchronous processing (Kafka, RabbitMQ) Performance & Scale Handle 10,000+ location updates per second Sub-second response time for driver searches 99.99% uptime for critical services Horizontal scaling for all microservices Load balancing and auto-scaling policies Bonus Challenges Implement ride pooling/sharing with route optimization Add predictive demand forecasting using ML Build driver heatmaps showing high-demand areas Create an admin dashboard for operations monitoring Implement multi-city and multi-country support Add accessibility features (wheelchair-accessible vehicles) Deliverables System architecture diagram with component interactions Database schema and data models API documentation for core endpoints Working implementation of matching algorithm Performance benchmarks and scaling strategy

AWS: Build a Serverless API In this hands-on lab, you'll learn to build a fully functional serverless API using AWS services. You'll create a scalable, cost-effective REST API that requires no server management, leveraging the power of AWS Lambda, API Gateway, and DynamoDB. Lab Objectives By the end of this lab, you will be able to: Create and configure AWS Lambda functions to handle API requests Set up Amazon API Gateway to expose your Lambda functions as RESTful endpoints Design and implement a DynamoDB table for data persistence Implement CRUD operations (Create, Read, Update, Delete) in a serverless architecture Configure IAM roles and policies for secure service-to-service communication Test and debug your API using AWS console tools and API clients Lab Overview This lab guides you through building a complete serverless API from scratch. You'll start by creating a DynamoDB table to store your data, then develop Lambda functions in Python or Node.js to process API requests. Next, you'll configure API Gateway to create HTTP endpoints that trigger your Lambda functions, establishing a complete request-response cycle without managing any servers. Architecture Components Amazon API Gateway: Serves as the entry point for HTTP requests, handling routing, request validation, and response formatting AWS Lambda: Executes your business logic in response to API calls, automatically scaling based on demand Amazon DynamoDB: Provides fast, consistent NoSQL database storage for your application data AWS IAM: Manages permissions and security between services Key Tasks Create a DynamoDB Table: Set up a NoSQL table with appropriate primary keys and indexes for your data model Develop Lambda Functions: Write serverless functions to handle GET, POST, PUT, and DELETE operations Configure IAM Roles: Create execution roles granting Lambda permissions to access DynamoDB Build API Gateway: Design REST API resources, methods, and integrations with Lambda functions Implement Error Handling: Add proper status codes, error messages, and input validation Deploy and Test: Deploy your API to a stage and test all endpoints using various tools Prerequisites Basic understanding of REST API concepts Familiarity with JSON data format Basic knowledge of Python or Node.js (code samples provided) AWS account with appropriate permissions Use Cases The serverless API architecture you'll build is ideal for: Mobile and web application backends Microservices architectures IoT data collection endpoints Webhook handlers for third-party integrations Rapid prototyping and MVPs with minimal infrastructure overhead Benefits of Serverless APIs No server management: Focus on code, not infrastructure Automatic scaling: Handle one request or one million with the same architecture Cost-effective: Pay only for actual usage with no idle capacity costs High availability: Built-in redundancy across multiple availability zones Fast deployment: Iterate quickly with instant updates Estimated completion time: 60-90 minutes

Deploy Application on Kubernetes Overview This hands-on lab guides you through deploying a containerized application on a Kubernetes cluster. You'll learn fundamental Kubernetes concepts including pods, deployments, services, and how to manage application lifecycles in a production-grade container orchestration platform. Objectives Create and configure Kubernetes deployments for containerized applications Expose applications using Kubernetes services Scale applications horizontally to handle increased load Perform rolling updates and rollbacks Implement basic health checks with liveness and readiness probes Manage application configuration using ConfigMaps and Secrets Prerequisites Basic understanding of Docker and containerization concepts Familiarity with YAML syntax Command-line interface experience Basic knowledge of networking concepts Lab Environment You will work with a pre-configured Kubernetes cluster and have access to kubectl , the Kubernetes command-line tool. A sample containerized application image will be provided for deployment exercises. Key Concepts Covered Deployments Learn to create deployment manifests that define your application's desired state, including container images, replica counts, and update strategies. Understand how Kubernetes maintains the specified number of pod replicas. Services Discover how to expose your application using different service types including ClusterIP, NodePort, and LoadBalancer. Configure service selectors to route traffic to appropriate pods. Scaling Practice manual scaling by adjusting replica counts and observe how Kubernetes automatically distributes workloads across available nodes. Updates and Rollbacks Implement rolling updates to deploy new application versions with zero downtime. Learn to monitor update progress and perform rollbacks when issues arise. Lab Tasks Create a deployment manifest for a web application Deploy the application to the Kubernetes cluster Verify pod creation and examine pod details Create a service to expose the application Access the application through the service endpoint Scale the deployment to multiple replicas Update the application to a new version using rolling updates Monitor the rollout status and verify the update Perform a rollback to the previous version Configure health checks using probes Create ConfigMaps for application configuration Clean up resources Expected Duration 60-90 minutes Learning Outcomes By completing this lab, you will gain practical experience with core Kubernetes operations essential for deploying and managing containerized applications. You'll understand how to leverage Kubernetes features for high availability, scalability, and seamless updates in production environments.

Create Pub/Sub Notification System Overview In this lab, you will build a real-time notification system using a publish-subscribe (Pub/Sub) messaging pattern. You'll create publishers that send messages to topics, and subscribers that receive and process those messages asynchronously. This architecture enables scalable, decoupled communication between different components of an application. Objectives Understand the Pub/Sub messaging pattern and its use cases Set up a message broker or cloud-based Pub/Sub service Create topics and manage subscriptions Implement publishers to send notifications Build subscribers to consume and process messages Handle message delivery guarantees and error scenarios Test the system with multiple publishers and subscribers Prerequisites Basic understanding of asynchronous programming concepts Familiarity with REST APIs or command-line tools Experience with at least one programming language (Python, Java, Node.js, or Go) Access to a cloud platform account (GCP, AWS, or Azure) or local message broker Lab Tasks Task 1: Set Up the Pub/Sub Infrastructure Create a Pub/Sub topic for notifications. Configure the topic with appropriate settings such as message retention, ordering keys, and schema validation if needed. Task 2: Implement a Publisher Build a publisher application that sends notification messages to the topic. Include metadata such as timestamp, message type, and priority. Implement batch publishing for efficiency when sending multiple messages. Task 3: Create Subscriptions Set up one or more subscriptions to the topic. Configure delivery types (push or pull), acknowledgment deadlines, and retry policies. Create filters to route specific message types to different subscribers. Task 4: Build Subscriber Applications Develop subscriber applications that consume messages from subscriptions. Implement proper message acknowledgment to ensure reliable delivery. Add error handling and logging for failed message processing. Task 5: Test End-to-End Flow Send test notifications through your publisher and verify that subscribers receive and process them correctly. Test scenarios including multiple subscribers, message ordering, and failure recovery. Task 6: Monitor and Optimize Set up monitoring for message throughput, delivery latency, and undelivered messages. Implement dead-letter topics for messages that fail processing repeatedly. Optimize subscriber configuration for better performance. Key Concepts Topic A named resource to which publishers send messages Subscription A configuration that determines how messages are delivered to subscribers Message Acknowledgment A signal from subscriber to broker confirming successful message processing Push vs Pull Delivery Push sends messages to an endpoint; pull requires subscribers to request messages Expected Outcomes By completing this lab, you will have a functional notification system capable of handling asynchronous message delivery at scale. You'll understand how to decouple system components using Pub/Sub patterns and implement reliable message processing with proper error handling. Best Practices Always acknowledge messages after successful processing Implement idempotent message handlers to handle duplicate deliveries Use message attributes for routing and filtering Set appropriate acknowledgment deadlines based on processing time Monitor dead-letter queues and investigate failed messages Design messages to be self-contained with all necessary context

AWS: Set up Kafka Cluster Overview This hands-on lab guides you through setting up a production-ready Apache Kafka cluster on Amazon Web Services (AWS). You'll deploy a multi-broker Kafka cluster with ZooKeeper ensemble, configure essential security settings, and validate the installation by producing and consuming messages. By the end of this lab, you'll have practical experience with distributed streaming infrastructure on AWS. Learning Objectives Deploy and configure EC2 instances for Kafka brokers and ZooKeeper nodes Install and configure Apache Kafka and ZooKeeper on AWS infrastructure Set up security groups and network configurations for cluster communication Create Kafka topics with appropriate replication and partition settings Produce and consume messages to verify cluster functionality Implement basic monitoring and health checks for the cluster Prerequisites Basic understanding of distributed systems concepts Familiarity with AWS EC2, VPC, and security groups Command-line experience with Linux systems Basic knowledge of messaging systems or event streaming SSH client installed on your local machine Lab Environment You'll work with the following AWS resources: Region: us-east-1 EC2 Instances: 3 t3.medium instances for Kafka brokers, 3 t3.small instances for ZooKeeper Operating System: Amazon Linux 2 VPC: Pre-configured VPC with public and private subnets Software Versions: Apache Kafka 3.5.x, ZooKeeper 3.8.x Key Tasks 1. Infrastructure Setup Launch EC2 instances across multiple availability zones for high availability. Configure security groups to allow necessary ports: 2181 for ZooKeeper, 9092 for Kafka brokers, and 22 for SSH access. 2. ZooKeeper Ensemble Configuration Install ZooKeeper on three nodes and configure them as an ensemble. Set up the zoo.cfg file with proper server definitions and establish quorum communication. 3. Kafka Broker Installation Download and install Apache Kafka on broker nodes. Configure server.properties with unique broker IDs, ZooKeeper connection strings, and appropriate log directories. 4. Cluster Validation Create test topics with replication factor of 3. Use Kafka console producer and consumer to verify message flow. Check broker metadata and partition assignments using Kafka command-line tools. 5. Basic Monitoring Setup Configure JMX monitoring on brokers. Set up basic CloudWatch metrics collection for instance-level monitoring. Estimated Completion Time 90 minutes Challenge Elements Configure inter-broker communication using private IPs Optimize Java heap settings for your instance types Implement log retention policies based on storage constraints Test failure scenarios by stopping one broker Deliverables Fully functional 3-node Kafka cluster with ZooKeeper ensemble Documentation of broker configuration settings Successfully created topics with verified replication Producer and consumer test results demonstrating message flow

In this hands-on lab, you'll design and deploy a custom Amazon Virtual Private Cloud (VPC) from scratch, configuring subnets, route tables, and internet gateways to create a secure, isolated network environment. You'll practice implementing network segmentation with public and private subnets, configure Network ACLs and security groups for layered security, and establish connectivity between your VPC and the internet. By the end of this lab, you'll have practical experience architecting cloud networks that follow AWS best practices for scalability, security, and high availability.