{"id":829,"date":"2025-03-03T10:43:24","date_gmt":"2025-03-03T10:43:24","guid":{"rendered":"https:\/\/www.rajeshkumar.xyz\/blog\/?p=829"},"modified":"2025-03-03T10:43:26","modified_gmt":"2025-03-03T10:43:26","slug":"system-design-blueprint-the-ultimate-guide","status":"publish","type":"post","link":"https:\/\/www.rajeshkumar.xyz\/blog\/system-design-blueprint-the-ultimate-guide\/","title":{"rendered":"System Design Blueprint: The Ultimate Guide"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

Here’s a comprehensive tutorial on System Design Blueprint: The Ultimate Guide<\/strong>, covering all fundamental concepts, principles, and best practices.<\/p>\n\n\n\n

\n\n\n\n

System Design Blueprint: The Ultimate Guide<\/strong><\/h1>\n\n\n\n

Introduction<\/strong><\/h2>\n\n\n\n

System design is the process of defining the architecture, components, modules, and interfaces for a system to meet specific requirements. It involves understanding scalability, performance, security, and maintainability.<\/p>\n\n\n\n

This guide will cover:<\/p>\n\n\n\n

    \n
  • Basics of system design<\/li>\n\n\n\n
  • Architectural patterns<\/li>\n\n\n\n
  • Scaling strategies<\/li>\n\n\n\n
  • Data consistency models<\/li>\n\n\n\n
  • Best practices for designing robust, scalable systems<\/li>\n<\/ul>\n\n\n\n
    \n\n\n\n

    1. Fundamentals of System Design<\/strong><\/h2>\n\n\n\n

    Before diving into complex architectures, it’s essential to understand the key components:<\/p>\n\n\n\n

    1.1 Key Concepts<\/strong><\/h3>\n\n\n\n
      \n
    • Latency vs. Throughput<\/strong> \u2013 Lower latency means faster response time; higher throughput means handling more requests.<\/li>\n\n\n\n
    • CAP Theorem<\/strong> \u2013 A distributed system can only guarantee two out of three: Consistency<\/strong>, Availability<\/strong>, and Partition Tolerance<\/strong>.<\/li>\n\n\n\n
    • ACID vs. BASE<\/strong> \u2013 ACID ensures strong consistency, while BASE prioritizes scalability with eventual consistency.<\/li>\n<\/ul>\n\n\n\n

      1.2 Common Components in System Design<\/strong><\/h3>\n\n\n\n
        \n
      • Load Balancer<\/strong> \u2013 Distributes incoming traffic across multiple servers.<\/li>\n\n\n\n
      • Caching<\/strong> \u2013 Reduces latency by storing frequently accessed data.<\/li>\n\n\n\n
      • Message Queues<\/strong> \u2013 Enables asynchronous processing for better scalability.<\/li>\n\n\n\n
      • Database<\/strong> \u2013 Can be SQL (MySQL, PostgreSQL) or NoSQL (MongoDB, Cassandra).<\/li>\n\n\n\n
      • CDN (Content Delivery Network)<\/strong> \u2013 Delivers static content faster from edge locations.<\/li>\n\n\n\n
      • API Gateway<\/strong> \u2013 Manages API requests, authentication, and routing.<\/li>\n<\/ul>\n\n\n\n
        \n\n\n\n

        2. High-Level System Design Process<\/strong><\/h2>\n\n\n\n

        A structured approach to designing scalable systems:<\/p>\n\n\n\n

        2.1 Requirements Gathering<\/strong><\/h3>\n\n\n\n
          \n
        • Functional Requirements<\/strong> \u2013 What the system should do.<\/li>\n\n\n\n
        • Non-Functional Requirements<\/strong> \u2013 Performance, security, reliability.<\/li>\n\n\n\n
        • Constraints<\/strong> \u2013 Hardware, budget, regulatory compliance.<\/li>\n<\/ul>\n\n\n\n

          2.2 Define the High-Level Architecture<\/strong><\/h3>\n\n\n\n
            \n
          1. Identify components (frontend, backend, databases, caching).<\/li>\n\n\n\n
          2. Decide on monolithic vs. microservices architecture.<\/li>\n\n\n\n
          3. Consider synchronous vs. asynchronous communication.<\/li>\n<\/ol>\n\n\n\n

            2.3 Scaling Strategies<\/strong><\/h3>\n\n\n\n
              \n
            • Vertical Scaling<\/strong> \u2013 Adding more resources (CPU, RAM) to a single machine.<\/li>\n\n\n\n
            • Horizontal Scaling<\/strong> \u2013 Adding more machines (distributed systems).<\/li>\n\n\n\n
            • Load Balancing<\/strong> \u2013 Evenly distributing traffic among servers.<\/li>\n<\/ul>\n\n\n\n
              \n\n\n\n

              3. Architectural Patterns<\/strong><\/h2>\n\n\n\n

              Different design patterns help build scalable and maintainable systems.<\/p>\n\n\n\n

              3.1 Monolithic Architecture<\/strong><\/h3>\n\n\n\n
                \n
              • Single-codebase with tightly coupled components.<\/li>\n\n\n\n
              • Easy to develop and deploy but hard to scale.<\/li>\n<\/ul>\n\n\n\n

                3.2 Microservices Architecture<\/strong><\/h3>\n\n\n\n
                  \n
                • Breaks the system into loosely coupled, independently deployable services.<\/li>\n\n\n\n
                • Enables better scalability and fault isolation.<\/li>\n<\/ul>\n\n\n\n

                  3.3 Event-Driven Architecture<\/strong><\/h3>\n\n\n\n
                    \n
                  • Uses messaging queues (Kafka, RabbitMQ) for decoupled communication.<\/li>\n\n\n\n
                  • Ideal for real-time applications like stock trading or ride-sharing apps.<\/li>\n<\/ul>\n\n\n\n

                    3.4 Serverless Architecture<\/strong><\/h3>\n\n\n\n
                      \n
                    • Deploy code without managing servers (AWS Lambda, Azure Functions).<\/li>\n\n\n\n
                    • Best for lightweight, event-driven workloads.<\/li>\n<\/ul>\n\n\n\n
                      \n\n\n\n

                      4. Database Design in System Architecture<\/strong><\/h2>\n\n\n\n

                      Choosing the right database depends on system requirements.<\/p>\n\n\n\n

                      4.1 SQL vs. NoSQL<\/strong><\/h3>\n\n\n\n
                      Feature<\/th>SQL (MySQL, PostgreSQL)<\/th>NoSQL (MongoDB, Cassandra)<\/th><\/tr><\/thead>
                      Structure<\/td>Structured Tables<\/td>Key-Value, Document, Graph<\/td><\/tr>
                      Transactions<\/td>ACID-compliant<\/td>BASE (Eventual Consistency)<\/td><\/tr>
                      Scaling<\/td>Vertical Scaling<\/td>Horizontal Scaling<\/td><\/tr>
                      Use Case<\/td>Banking, E-commerce<\/td>Big Data, Social Media<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

                      4.2 Sharding & Replication<\/strong><\/h3>\n\n\n\n
                        \n
                      • Sharding<\/strong> \u2013 Distributing data across multiple nodes to improve performance.<\/li>\n\n\n\n
                      • Replication<\/strong> \u2013 Keeping multiple copies of data for redundancy.<\/li>\n<\/ul>\n\n\n\n
                        \n\n\n\n

                        5. Caching Strategies<\/strong><\/h2>\n\n\n\n

                        Caching reduces database load and improves response time.<\/p>\n\n\n\n

                        5.1 Types of Caching<\/strong><\/h3>\n\n\n\n
                          \n
                        • Application Caching<\/strong> \u2013 Local memory (e.g., Redis, Memcached).<\/li>\n\n\n\n
                        • Database Caching<\/strong> \u2013 Indexing, query optimization.<\/li>\n\n\n\n
                        • CDN Caching<\/strong> \u2013 Caching static assets on edge servers.<\/li>\n<\/ul>\n\n\n\n

                          5.2 Cache Invalidation Strategies<\/strong><\/h3>\n\n\n\n
                            \n
                          • Write-through<\/strong> \u2013 Updates cache and database simultaneously.<\/li>\n\n\n\n
                          • Write-back<\/strong> \u2013 Updates cache first, then the database.<\/li>\n\n\n\n
                          • Write-around<\/strong> \u2013 Writes directly to the database, skipping the cache.<\/li>\n<\/ul>\n\n\n\n
                            \n\n\n\n

                            6. API Design Principles<\/strong><\/h2>\n\n\n\n

                            APIs enable communication between different system components.<\/p>\n\n\n\n

                            6.1 REST vs. GraphQL<\/strong><\/h3>\n\n\n\n
                            Feature<\/th>REST<\/th>GraphQL<\/th><\/tr><\/thead>
                            Data Fetching<\/td>Multiple endpoints<\/td>Single endpoint<\/td><\/tr>
                            Performance<\/td>Over-fetching possible<\/td>Only requested data returned<\/td><\/tr>
                            Use Case<\/td>Simple APIs<\/td>Complex queries & real-time apps<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

                            6.2 Rate Limiting<\/strong><\/h3>\n\n\n\n
                              \n
                            • Prevents abuse and protects system resources.<\/li>\n\n\n\n
                            • Implemented using token bucket, leaky bucket, sliding window<\/strong> algorithms.<\/li>\n<\/ul>\n\n\n\n
                              \n\n\n\n

                              7. Security Considerations<\/strong><\/h2>\n\n\n\n

                              A well-designed system must be secure.<\/p>\n\n\n\n

                              7.1 Authentication & Authorization<\/strong><\/h3>\n\n\n\n
                                \n
                              • OAuth 2.0<\/strong> \u2013 Token-based authentication.<\/li>\n\n\n\n
                              • JWT (JSON Web Token)<\/strong> \u2013 Stateless authentication.<\/li>\n\n\n\n
                              • RBAC (Role-Based Access Control)<\/strong> \u2013 Defines access permissions.<\/li>\n<\/ul>\n\n\n\n

                                7.2 Data Encryption<\/strong><\/h3>\n\n\n\n
                                  \n
                                • In-transit encryption<\/strong> (TLS, HTTPS).<\/li>\n\n\n\n
                                • At-rest encryption<\/strong> (AES, Hashing).<\/li>\n<\/ul>\n\n\n\n

                                  7.3 DDoS Protection<\/strong><\/h3>\n\n\n\n
                                    \n
                                  • Rate limiting<\/strong> and firewalls<\/strong> to prevent attacks.<\/li>\n\n\n\n
                                  • WAF (Web Application Firewall)<\/strong> to filter malicious traffic.<\/li>\n<\/ul>\n\n\n\n
                                    \n\n\n\n

                                    8. Monitoring and Logging<\/strong><\/h2>\n\n\n\n

                                    Observability ensures system reliability.<\/p>\n\n\n\n

                                    8.1 Monitoring Tools<\/strong><\/h3>\n\n\n\n
                                      \n
                                    • Prometheus<\/strong> \u2013 Metrics collection.<\/li>\n\n\n\n
                                    • Grafana<\/strong> \u2013 Data visualization.<\/li>\n\n\n\n
                                    • New Relic \/ Datadog<\/strong> \u2013 Full-stack monitoring.<\/li>\n<\/ul>\n\n\n\n

                                      8.2 Logging Best Practices<\/strong><\/h3>\n\n\n\n
                                        \n
                                      • Centralized logging with ELK Stack (Elasticsearch, Logstash, Kibana)<\/strong>.<\/li>\n\n\n\n
                                      • Use structured logging<\/strong> for better analysis.<\/li>\n<\/ul>\n\n\n\n
                                        \n\n\n\n

                                        9. Case Study: Designing a Scalable URL Shortener<\/strong><\/h2>\n\n\n\n

                                        9.1 Requirements<\/strong><\/h3>\n\n\n\n
                                          \n
                                        • Shorten long URLs and provide redirection.<\/li>\n\n\n\n
                                        • Handle 100M requests\/day<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                                          9.2 High-Level Design<\/strong><\/h3>\n\n\n\n
                                            \n
                                          • Load Balancer<\/strong> \u2013 Distributes requests.<\/li>\n\n\n\n
                                          • Application Servers<\/strong> \u2013 Processes requests.<\/li>\n\n\n\n
                                          • Database (NoSQL – Redis)<\/strong> \u2013 Stores URL mappings.<\/li>\n\n\n\n
                                          • CDN<\/strong> \u2013 Caches frequent requests.<\/li>\n\n\n\n
                                          • Hashing Function<\/strong> \u2013 Converts URLs into unique keys.<\/li>\n<\/ul>\n\n\n\n

                                            9.3 Scaling<\/strong><\/h3>\n\n\n\n
                                              \n
                                            • Database Partitioning<\/strong> \u2013 Distribute URLs across multiple servers.<\/li>\n\n\n\n
                                            • Caching<\/strong> \u2013 Use Redis for faster lookups.<\/li>\n\n\n\n
                                            • Asynchronous Processing<\/strong> \u2013 Log analytics data in background jobs.<\/li>\n<\/ul>\n\n\n\n
                                              \n\n\n\n

                                              10. System Design Interview Tips<\/strong><\/h2>\n\n\n\n
                                                \n
                                              • Clarify requirements<\/strong> before designing.<\/li>\n\n\n\n
                                              • Break down the system<\/strong> into components.<\/li>\n\n\n\n
                                              • Discuss trade-offs<\/strong> between different approaches.<\/li>\n\n\n\n
                                              • Sketch a diagram<\/strong> to visualize the architecture.<\/li>\n<\/ul>\n\n\n\n

                                                10.1 Example System Design Questions<\/strong><\/h3>\n\n\n\n
                                                  \n
                                                • Design YouTube<\/strong><\/li>\n\n\n\n
                                                • Design WhatsApp<\/strong><\/li>\n\n\n\n
                                                • Design an E-commerce System<\/strong><\/li>\n\n\n\n
                                                • Design Google Docs<\/strong><\/li>\n<\/ul>\n\n\n\n
                                                  \n\n\n\n

                                                  Case Study: Designing a Scalable Ride-Sharing System (Uber\/Lyft) \ud83d\ude96<\/strong><\/h2>\n\n\n\n

                                                  1. Introduction<\/strong><\/h3>\n\n\n\n

                                                  Ride-sharing platforms like Uber<\/strong> and Lyft<\/strong> connect passengers with drivers in real-time, handling millions of concurrent users<\/strong> globally. Designing such a system requires considerations for high availability, low latency, real-time tracking, and scalability<\/strong>.<\/p>\n\n\n\n

                                                  \n\n\n\n

                                                  2. Requirements Gathering<\/strong><\/h2>\n\n\n\n

                                                  Before designing, we break down the system into:<\/p>\n\n\n\n

                                                  2.1 Functional Requirements<\/strong><\/h3>\n\n\n\n

                                                  \u2705 Allow passengers to request a ride.
                                                  \u2705 Match passengers with nearby drivers.
                                                  \u2705 Track drivers and passengers in real time.
                                                  \u2705 Support payments and ride history.
                                                  \u2705 Provide estimated fare and arrival time.<\/p>\n\n\n\n

                                                  2.2 Non-Functional Requirements<\/strong><\/h3>\n\n\n\n

                                                  \u26a1 Low Latency<\/strong> \u2013 Instant ride-matching.
                                                  \u26a1 Scalability<\/strong> \u2013 Handle millions of concurrent users<\/strong>.
                                                  \u26a1 Fault Tolerance<\/strong> \u2013 No downtime.
                                                  \u26a1 Security<\/strong> \u2013 Secure transactions and user authentication.<\/p>\n\n\n\n

                                                  2.3 Constraints<\/strong><\/h3>\n\n\n\n
                                                    \n
                                                  • Response Time<\/strong>: <1 second for ride-matching.<\/li>\n\n\n\n
                                                  • User Base<\/strong>: Millions of concurrent users.<\/li>\n\n\n\n
                                                  • Data Storage<\/strong>: Location updates every 5 seconds<\/strong>.<\/li>\n<\/ul>\n\n\n\n
                                                    \n\n\n\n

                                                    3. High-Level System Design<\/strong><\/h2>\n\n\n\n

                                                    3.1 Architecture Overview<\/strong><\/h3>\n\n\n\n

                                                    The system consists of:<\/p>\n\n\n\n

                                                      \n
                                                    1. API Gateway<\/strong> (Entry point for all requests).<\/li>\n\n\n\n
                                                    2. Load Balancer<\/strong> (Distributes traffic).<\/li>\n\n\n\n
                                                    3. Microservices<\/strong>:\n
                                                        \n
                                                      • Passenger Service<\/strong> (Handles ride requests).<\/li>\n\n\n\n
                                                      • Driver Service<\/strong> (Handles driver availability).<\/li>\n\n\n\n
                                                      • Matching Service<\/strong> (Matches drivers to passengers).<\/li>\n\n\n\n
                                                      • Ride Management Service<\/strong> (Tracks rides, fares, etc.).<\/li>\n<\/ul>\n<\/li>\n\n\n\n
                                                      • Real-Time Location Tracking<\/strong> (Uses WebSockets & Kafka).<\/li>\n\n\n\n
                                                      • Data Storage<\/strong>:\n
                                                          \n
                                                        • Relational DB<\/strong> (User details, payments).<\/li>\n\n\n\n
                                                        • NoSQL DB<\/strong> (Ride history, location data).<\/li>\n\n\n\n
                                                        • Caching Layer<\/strong> (Redis for frequent lookups).<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n
                                                          \n\n\n\n

                                                          4. Key Components<\/strong><\/h2>\n\n\n\n

                                                          4.1 Ride Matching Algorithm<\/strong><\/h3>\n\n\n\n
                                                            \n
                                                          • Nearest Driver First<\/strong>: Selects drivers based on distance.<\/li>\n\n\n\n
                                                          • Load Balancing<\/strong>: If all nearby drivers are busy, expand the search radius.<\/li>\n\n\n\n
                                                          • Surge Pricing<\/strong>: Increases fares when demand is high.<\/li>\n<\/ul>\n\n\n\n

                                                            Implementation:<\/strong><\/p>\n\n\n\n

                                                              \n
                                                            1. Get passenger\u2019s location<\/strong> from the request.<\/li>\n\n\n\n
                                                            2. Query the real-time driver location cache<\/strong> (Redis).<\/li>\n\n\n\n
                                                            3. Apply Haversine Formula<\/strong> to compute distance.<\/li>\n\n\n\n
                                                            4. Select the nearest available driver<\/strong>.<\/li>\n<\/ol>\n\n\n\n

                                                              \ud83d\udd39 Tech Stack<\/strong>: Redis + Geospatial Queries + Kafka.<\/p>\n\n\n\n

                                                              \n\n\n\n

                                                              4.2 Real-Time Location Tracking<\/strong><\/h3>\n\n\n\n
                                                                \n
                                                              • WebSockets<\/strong> for real-time updates.<\/li>\n\n\n\n
                                                              • Kafka<\/strong> for event streaming (driver locations).<\/li>\n\n\n\n
                                                              • Redis<\/strong> for storing the latest locations.<\/li>\n<\/ul>\n\n\n\n

                                                                \ud83d\udd39 How it Works:<\/strong><\/p>\n\n\n\n

                                                                  \n
                                                                1. Drivers send GPS updates every 5 seconds<\/strong>.<\/li>\n\n\n\n
                                                                2. Updates are streamed via Kafka<\/strong>.<\/li>\n\n\n\n
                                                                3. Redis stores the latest driver coordinates<\/strong>.<\/li>\n\n\n\n
                                                                4. Passenger apps receive live updates<\/strong> via WebSockets.<\/li>\n<\/ol>\n\n\n\n
                                                                  \n\n\n\n

                                                                  4.3 Payment & Fare Calculation<\/strong><\/h3>\n\n\n\n
                                                                    \n
                                                                  • Payment Gateway<\/strong> (Stripe, PayPal, Razorpay).<\/li>\n\n\n\n
                                                                  • Fare Calculation<\/strong>:\n
                                                                      \n
                                                                    • Base Fare + Distance Fare + Surge Pricing.<\/li>\n\n\n\n
                                                                    • Uses Google Maps API<\/strong> for distance estimation.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n

                                                                      \ud83d\udd39 Tech Stack<\/strong>: PostgreSQL for transactions, Redis for caching fare calculations.<\/p>\n\n\n\n

                                                                      \n\n\n\n

                                                                      4.4 Database Schema<\/strong><\/h3>\n\n\n\n

                                                                      Users Table<\/strong><\/h4>\n\n\n\n
                                                                      Field<\/th>Type<\/th>Description<\/th><\/tr><\/thead>
                                                                      user_id<\/td>UUID<\/td>Unique user ID<\/td><\/tr>
                                                                      name<\/td>String<\/td>User’s name<\/td><\/tr>
                                                                      phone<\/td>String<\/td>Contact number<\/td><\/tr>
                                                                      type<\/td>Enum<\/td>Passenger \/ Driver<\/code><\/td><\/tr>
                                                                      location<\/td>GeoJSON<\/td>Current GPS coordinates<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

                                                                      Rides Table<\/strong><\/h4>\n\n\n\n
                                                                      Field<\/th>Type<\/th>Description<\/th><\/tr><\/thead>
                                                                      ride_id<\/td>UUID<\/td>Unique Ride ID<\/td><\/tr>
                                                                      passenger_id<\/td>UUID<\/td>User requesting the ride<\/td><\/tr>
                                                                      driver_id<\/td>UUID<\/td>Assigned driver ID<\/td><\/tr>
                                                                      status<\/td>Enum<\/td>Requested, Ongoing, Completed<\/code><\/td><\/tr>
                                                                      fare<\/td>Float<\/td>Total ride cost<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                                                                      \n\n\n\n

                                                                      5. Scalability & Performance Optimization<\/strong><\/h2>\n\n\n\n

                                                                      5.1 Load Balancing<\/strong><\/h3>\n\n\n\n
                                                                        \n
                                                                      • API Gateway + Nginx Load Balancer<\/strong> to handle millions of requests.<\/li>\n\n\n\n
                                                                      • Round-robin & Weighted Load Balancing<\/strong> for distributing traffic.<\/li>\n<\/ul>\n\n\n\n

                                                                        5.2 Caching Strategies<\/strong><\/h3>\n\n\n\n
                                                                          \n
                                                                        • Redis for hot data<\/strong> (e.g., driver locations, ride status).<\/li>\n\n\n\n
                                                                        • CDN for static content<\/strong> (e.g., images, maps).<\/li>\n<\/ul>\n\n\n\n

                                                                          5.3 Sharding & Replication<\/strong><\/h3>\n\n\n\n
                                                                            \n
                                                                          • Geo-based Sharding<\/strong>: Users in different cities store data in different database clusters.<\/li>\n\n\n\n
                                                                          • Read Replicas<\/strong>: PostgreSQL replicas for handling high read traffic.<\/li>\n<\/ul>\n\n\n\n
                                                                            \n\n\n\n

                                                                            6. Security Measures<\/strong><\/h2>\n\n\n\n

                                                                            \ud83d\udd12 Authentication<\/strong> \u2013 OAuth 2.0, JWT tokens.
                                                                            \ud83d\udd12 Data Encryption<\/strong> \u2013 AES for at-rest, TLS for in-transit data.
                                                                            \ud83d\udd12 Fraud Detection<\/strong> \u2013 Machine Learning to detect fake rides.<\/p>\n\n\n\n

                                                                            \n\n\n\n

                                                                            7. Monitoring & Logging<\/strong><\/h2>\n\n\n\n

                                                                            \ud83d\udcca Prometheus + Grafana<\/strong> for real-time monitoring.
                                                                            \ud83d\udcca Elasticsearch + Kibana (ELK Stack)<\/strong> for logs.
                                                                            \ud83d\udcca Sentry<\/strong> for error tracking.<\/p>\n\n\n\n

                                                                            \n\n\n\n

                                                                            8. Tech Stack<\/strong><\/h2>\n\n\n\n
                                                                            Component<\/strong><\/th>Technology<\/strong><\/th><\/tr><\/thead>
                                                                            Backend API<\/td>Node.js \/ Go \/ Python<\/strong><\/td><\/tr>
                                                                            Database<\/td>PostgreSQL + MongoDB<\/strong><\/td><\/tr>
                                                                            Caching<\/td>Redis \/ Memcached<\/strong><\/td><\/tr>
                                                                            Load Balancing<\/td>NGINX \/ AWS ALB<\/strong><\/td><\/tr>
                                                                            Real-Time Tracking<\/td>Kafka + WebSockets<\/strong><\/td><\/tr>
                                                                            Payments<\/td>Stripe \/ Razorpay<\/strong><\/td><\/tr>
                                                                            Monitoring<\/td>Prometheus + Grafana<\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                                                                            \n\n\n\n

                                                                            9. Future Enhancements<\/strong><\/h2>\n\n\n\n

                                                                            \ud83d\ude80 AI-based Driver Matching<\/strong> \u2013 Predicting ETAs using ML.
                                                                            \ud83d\ude80 Dynamic Pricing Optimization<\/strong> \u2013 AI-driven surge pricing.
                                                                            \ud83d\ude80 Blockchain-based Ride History<\/strong> \u2013 Immutable ride records.<\/p>\n\n\n\n

                                                                            \n\n\n\n

                                                                            Conclusion<\/strong><\/h2>\n\n\n\n

                                                                            This case study breaks down the system design of Uber\/Lyft<\/strong> into functional, architectural, and scalability considerations. By leveraging caching, real-time updates, distributed storage, and security measures<\/strong>, we can design an efficient ride-sharing platform.<\/p>\n\n\n\n

                                                                            <\/p>\n\n\n\n

                                                                            <\/p>\n\n\n\n

                                                                            <\/p>\n","protected":false},"excerpt":{"rendered":"

                                                                            Here’s a comprehensive tutorial on System Design Blueprint: The Ultimate Guide, covering all fundamental concepts, principles, and best practices. System […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-829","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/www.rajeshkumar.xyz\/blog\/wp-json\/wp\/v2\/posts\/829","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.rajeshkumar.xyz\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.rajeshkumar.xyz\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.rajeshkumar.xyz\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.rajeshkumar.xyz\/blog\/wp-json\/wp\/v2\/comments?post=829"}],"version-history":[{"count":1,"href":"https:\/\/www.rajeshkumar.xyz\/blog\/wp-json\/wp\/v2\/posts\/829\/revisions"}],"predecessor-version":[{"id":831,"href":"https:\/\/www.rajeshkumar.xyz\/blog\/wp-json\/wp\/v2\/posts\/829\/revisions\/831"}],"wp:attachment":[{"href":"https:\/\/www.rajeshkumar.xyz\/blog\/wp-json\/wp\/v2\/media?parent=829"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.rajeshkumar.xyz\/blog\/wp-json\/wp\/v2\/categories?post=829"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.rajeshkumar.xyz\/blog\/wp-json\/wp\/v2\/tags?post=829"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}