Design WhatsApp

Design a large-scale mobile messaging platform where billions of users send text messages, media, and voice messages in 1-on-1 and group chats, with end-to-end encryption, delivery and read receipts, typing indicators, message/user search, and offline message delivery — similar to WhatsApp.

Functional requirements

  • Users send and receive text messages in 1-on-1 chats with sub-second delivery when both parties are online
  • Users send and receive images, video, and voice messages; media is compressed and delivered efficiently
  • Messages are end-to-end encrypted — the server must never have access to plaintext message content
  • Offline users receive all missed messages in order when they come back online
  • Delivery receipts: single tick (sent to server), double tick (delivered to device), blue tick (read by recipient)
  • Typing indicators and online/last-seen presence are shown to contacts in real time
  • Group chats support up to 1,024 members with the same delivery and receipt guarantees
  • Users can search their message history, contacts, and groups by keyword
  • The system captures usage analytics and exposes operational metrics/logs for monitoring, without impacting message delivery latency

Non-functional requirements

  • Message delivery latency < 200ms p99 end-to-end when both users are online
  • Support 2 billion registered users; 100 million concurrent online users
  • Messages must be delivered at-least-once; deduplication on the client side
  • End-to-end encryption: server stores only ciphertext; keys never leave the device
  • Message history stored for 90 days; media stored for 30 days with CDN caching
  • Search queries return results within 300ms p99 via a dedicated, asynchronously-updated search index
  • All services and workers emit metrics/logs to a centralized observability pipeline for production monitoring and alerting
  • 99.99% uptime — users must be able to send messages even during partial outages

How the design evolves

Stage 1: MVP: Single Chat Server

Start with the simplest possible design — one server handles all connections and message delivery. Understand why a single server with in-memory routing immediately breaks down at scale and what the core challenges of messaging systems are.

Stage 2: DNS Routing + Chat Gateway + Presence Cache + Message Store

Split the monolith into a stateless Chat Gateway (WebSocket connections only) and a separate Message Service. Introduce a DNS/routing layer so clients are geo-routed to the nearest region before hitting a Load Balancer. Externalise presence (who is online and on which server) to Redis. Use Cassandra as the Message Store for write-heavy time-series message data.

Stage 3: Kafka Fan-out + Group Messaging

Introduce a multi-topic Kafka queue to decouple message ingestion from delivery. Add a Fan-out Worker to handle group message delivery to N recipients asynchronously. Understand the write amplification problem in group chats and how to manage it.

Stage 4: Media Service + Object Storage + Delivery Receipts

Add media upload and delivery via Object Storage and CDN with pre-signed URLs. Route media processing through its own Kafka topic so transcoding never blocks message delivery. Implement delivery and read receipts as a separate lightweight event stream. Understand why media must never flow through the messaging server.

Stage 5: Full Production: DNS Routing, Specialized Worker Services, Search & Analytics

Assemble the complete production architecture: a DNS/routing layer geo-routes clients; the Chat Server (WebSocket) and a merged Application Server (REST API — auth, send, group/user management, search) sit behind the Load Balancer; a dedicated Online Presence Service and a general-purpose Cache both run on their own Redis Clusters; a multi-topic Kafka queue (backed by a Kafka Cluster) feeds a pool of specialized Worker Services — Message Delivery, Push Notification, Media Processing, Search Indexing, and Analytics — each scaled and failing independently; a Search Index (Elasticsearch) enables message/user/group search; a User Database (MySQL) and Message Database (Cassandra) each run with read replicas; and Rate Limiting, Monitoring & Logging, and an Analytics/Data Warehouse round out the operational picture. Understand why splitting one fan-out worker into five single-purpose workers is the final step in isolating failure domains.

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