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Menu close menu icon System Design Interview Questions Adobe System Design Interview Questions Top Atlassian System Design Interview Questions Top Amazon System Design Interview Questions Top Microsoft System Design Interview Questions Top Meta (Facebook) System Design Interview Questions Top Netflix System Design Interview Questions Top Uber System Design Interview Questions Top Google System Design Interview Questions Top Apple System Design Interview Questions Top Airbnb System Design Interview Questions Top 10 System Design Interview Questions Mobile App System Design Interview Questions Top 20 Stripe System Design Interview Questions Top Shopify System Design Interview Questions Top 20 System Design Interview Questions Top Advanced System Design Questions Most-Frequented System Design Questions in Big Tech Interviews What Interviewers Look for in System Design Questions Critical System Design Questions to Crack Any Tech Interview Top 20 API Design Questions for System Design Interviews Top 10 Steps to Create a System Design Portfolio for Developers Introduction to High-Level System Design What is System Design? Difference Between HLD and LLD Key Considerations in HLD System Design Fundamentals Functional vs. Non-Functional Requirements Scalability, Availability, and Reliability Latency and Throughput Considerations Load Balancing Strategies Architectural Patterns Monolithic vs. Microservices Architecture Layered Architecture Event-Driven Architecture Serverless Architecture Model-View-Controller (MVC) Pattern CQRS (Command Query Responsibility Segregation) Scaling Strategies Vertical Scaling vs. Horizontal Scaling Sharding and Partitioning Data Replication and Consistency Models Load Balancing Strategies CDN and Edge Computing Database Design in HLD SQL vs. NoSQL Databases CAP Theorem and its Impact on System Design Database Indexing and Query Optimization Database Sharding and Partitioning Replication Strategies API Design and Communication REST vs. GraphQL vs. gRPC Designing Scalable APIs Rate Limiting and Throttling Authentication & Authorization (OAuth, JWT) Caching Strategies Types of Caching Cache Invalidation Strategies Redis vs. Memcached Cache-Aside, Write-Through, and Write-Behind Strategies Message Queues and Event-Driven Systems Kafka vs. RabbitMQ vs. SQS Pub-Sub vs. Point-to-Point Messaging Handling Asynchronous Workloads Eventual Consistency in Distributed Systems Security in System Design Encryption (AES, RSA, TLS) API Security Best Practices Secure Data Storage and Access Control DDoS Protection and Rate Limiting Observability and Monitoring Logging Strategies (ELK Stack, Prometheus, Grafana) API Security Best Practices Secure Data Storage and Access Control DDoS Protection and Rate Limiting Real-World System Design Case Studies Distributed locking (Locking and its Types) Memory leaks and Out of memory issues HLD of YouTube HLD of WhatsApp close menu icon High-Level Design of Facebook Messenger IntroductionFacebook Messenger powers real-time conversations for over 500 million active users daily. Building such a system demands expertise in scalable architectures, distributed data stores, and low-latency communication. If you’re preparing for a system design interview, our complete system design training provides hands-on projects to master these concepts.Core Requirements and GoalsFunctional RequirementsUser Management: Registration, authentication, profile updates, and contact management.Messaging: 1:1 and group chat support for text, images, videos, and files.Real‑Time Delivery: Instant message updates via persistent connections.Notifications: In-app and push alerts for new messages.End‑to‑End Security: Encryption to ensure privacy.For a quick primer on system design fundamentals, explore our system design crash course.Non‑Functional RequirementsScalability: Seamlessly handle millions of concurrent users.Low Latency: Deliver messages in under 100 ms.High Availability: Fault‑tolerant services with multi‑region failover.Data Consistency: Prevent message loss or duplication.Storage Efficiency: Optimize for petabytes of text and media.Capacity Planning and ConstraintsWhat traffic can Facebook Messenger handle?Assuming 25 billion messages per day, with an average of 2.33 reads per message:Write Requests: 25 BRead Requests: 58 BTotal Requests: 83 B per dayThat translates to an average of 960 K queries per second (QPS) and a peak of approximately 4.8 M QPS.How much storage is needed?Daily Text Storage: 25 TBAnnual Text Storage: 9 PB5‑Year Projection (Text + Media): 450 PBBandwidth and Cache RequirementsDaily Bandwidth: 1.3 PB (text + media) ≈ 15 GB/secCaching: 10 % of messages (2.5 TB) stored in Redis for hot reads, plus 5 TB for metadata/indexing.For insights into data analysis at scale, check our data science specialization. System APIs OverviewA RESTful and WebSocket‑based API surface handles user interactions:Authentication (POST /api/login)Issues JWT tokens for session management.Send Message (POST /api/messages)Persists to NoSQL, updates SQL conversation metadata, and pushes via Kafka/WebSockets.Fetch Messages (GET /api/messages?chat_id={id})Queries Cassandra/DynamoDB with Redis caching and pagination.Fetch Conversations (GET /api/conversations?user_id={id})Joins SQL tables for recent chat summaries and unread counts.User Presence (GET /api/user-presence?user_id={id})Retrieves online/offline status from Redis.Group Chat: Create, send, fetch, and manage participants via /api/groups endpoints.For a hands‑on introduction to building real‑time web applications, explore our web development bootcamp.Database Schema: SQL and NoSQL HybridTo balance consistency and performance, Messenger uses:SQL (PostgreSQL/MySQL)Users, conversations, attachments, and group metadata.NoSQL (Cassandra/DynamoDB)High‑write message storage with partition keys for fast retrieval.RedisIn‑memory storage for presence, typing indicators, and hot messages.If you’re brushing up on algorithms and data modeling, our essential DSA and web development courses cover these fundamentals.When to Use SQL vs NoSQL?Use CaseRecommended DatabaseRationaleUser ProfilesSQLTransactional integrity and relationsChat MessagesNoSQLHigh throughput and horizontal scalabilityConversation IndexesSQLConsistent metadata joinsPresence TrackingRedisLow‑latency reads and writesFile MetadataSQLReferential integrityFor a deep dive into data structures, check out our DSA fundamentals course.High‑Level Architecture and Message FlowSystem Component PlacementAPI Gateway: Authentication and routingMicroservices: Authentication, Messaging, Notifications, Presence, Media StorageWebSocket Servers: Real‑time message pushMessage Broker (Kafka): Durable queueing for offline deliveryLoad Balancers (Nginx): Distribute HTTP and WebSocket trafficCaching (Redis): Hot data and presenceCDN/S3: Media storage and delivery How are messages routed?User A sends a message to the API Gateway.Message Service checks recipient presence.Online: Push via WebSocket.Offline: Queue in Kafka.Delivery: Update status from sent → delivered → read.Group Chats: Fan‑out to all online members or queue for offline users.Our system design crash course covers similar patterns end to end for a quicker path to building this yourself.Enabling Real‑Time CommunicationWhat is Polling?Clients periodically request new messages. High latency and wasted requests make this unsuitable at scale.What is Long Polling?Servers hold connections open until new data arrives, reducing empty responses but still requiring reconnects.Why WebSockets?Persistent two‑way connections deliver instant updates with minimal overhead. Fallbacks to long polling can handle firewalls or unsupported clients.For real‑time application best practices, see our web development bootcamp.Data Retention and CleanupArchival: Move messages older than one year to cold storage (S3/Glacier).Soft Deletion: Mark records for removal, then purge in background jobs (Airflow/Kafka Streams).Media Cleanup: Separate lifecycle policies for attachments in CDN/S3.Partitioning, Replication, and CachingSharding: Horizontal partitioning by user or conversation ID.Replication: Leader‑follower for writes and reads, multi‑region for global availability.Cache Strategies: Redis for recent messages, CDN for static assets. Understanding these patterns is critical for distributed systems engineers preparing for roles at top tech companies.Future Enhancements: Voice and Video CallingWebRTC for peer‑to‑peer media streams.TURN/STUN Servers for NAT traversal.Selective Forwarding Unit (SFU) for group calls.Adaptive Bitrate (ABR) for network QoS.End‑to‑End Encryption with DTLS‑SRTP.Common Q&A on Messenger’s System Design How does Messenger handle real‑time messaging? WebSockets provide low‑latency delivery. For additional context, review our Netflix DSA interview guide on streaming systems. How are messages stored? Messages land in a NoSQL store optimized for writes, then a SQL store tracks metadata. Prepare with our top 20 DSA interview questions. How is push notification managed? A Notification Service uses FCM/APNs with Kafka to queue messages. If you’re interviewing at Meta, check our Meta/Facebook DSA prep. How does Messenger scale globally? Load balancers, sharding, and multi‑region replication ensure users connect to the nearest data center. How is message deletion handled? Soft deletion flags records before background cleanup. For enterprise‑grade cleanup strategies, see our Atlassian DSA interview guide. This insightful blog post is authored by Somya Rajput, who brings his expertise and deep understanding of the topic to provide valuable perspectives. Somya Rajput Software Engineer At Thomson Reuters , Product Engineering - Research Products WebSockets provide low‑latency delivery. For additional context, review our Netflix DSA interview guide on streaming systems.Messages land in a NoSQL store optimized for writes, then a SQL store tracks metadata. Prepare with our top 20 DSA interview questions. A Notification Service uses FCM/APNs with Kafka to queue messages. If you’re interviewing at Meta, check our Meta/Facebook DSA prep. Load balancers, sharding, and multi‑region replication ensure users connect to the nearest data center. Soft deletion flags records before background cleanup. For enterprise‑grade cleanup strategies, see our Atlassian DSA interview guide. This insightful blog post is authored by Somya Rajput, who brings his expertise and deep understanding of the topic to provide valuable perspectives. Somya Rajput Software Engineer At Thomson Reuters , Product Engineering - Research Products Got Confusion ? Accelerate your Path to a Product based Career Boost your career or get hired at top product-based companies by joining our expertly crafted courses. Gain practical skills and real-world knowledge to help you succeed. Explore our flagship courses Reach Out Now If you have any queries, please fill out this form. We will surely reach out to you. 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Author: Rajput Somya

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