30. Design a Chat System (WhatsApp)

[!NOTE] A chat system like WhatsApp handles 100 billion messages per day with real-time delivery, offline support, and end-to-end encryption. This is one of the most popular system design interview questions because it covers WebSockets, message queuing, presence tracking, and persistence — a comprehensive systems design challenge.

Step 1: Requirements

Step 2: High-Level Design (v1)

                              ┌──────────────┐
  [User A] ──WebSocket──→     │              │
                              │  Chat Server  │
  [User B] ──WebSocket──→     │   (Stateful)  │
                              │              │
                              └──────┬───────┘
                                     │
                              ┌──────▼───────┐
                              │   Database    │
                              │(msg storage)  │
                              └──────────────┘

  

This works for a small scale, but a single server cannot handle millions of concurrent WebSocket connections. We need to scale out.

Step 3: Scaled Architecture (v2)

                    ┌──────────────────┐
                    │  Service Discovery │
                    │ (which server has  │
                    │  which user?)      │
                    └────────┬─────────┘
                             │
  [User A] ──WS──→ [Chat Server 1]
                         │
                    [Message Queue / Redis Pub/Sub]
                         │
  [User B] ──WS──→ [Chat Server 2]
                         │
                    ┌────▼─────┐    ┌──────────┐
                    │ Message   │    │  Push     │
                    │ Store     │    │  Service  │
                    │ (Cassandra)│   │ (FCM/APNs)│
                    └──────────┘    └──────────┘

  

Message Flow (1:1)

1. User A sends a message via WebSocket to Chat Server 1.
2. Chat Server 1 persists the message to the message store (status: SENT).
3. Chat Server 1 looks up which server User B is connected to (via service discovery / Redis).
4. If User B is online: route message via Redis pub/sub to Chat Server 2 → deliver via WebSocket → update status to DELIVERED.
5. If User B is offline: send a push notification via FCM/APNs. When User B comes online, fetch undelivered messages from the message store.

Message Flow (Group Chat)

For a group with 500 members:

• Fan-out on write: When User A sends a message to a group, write a copy to each member''s inbox. Expensive for large groups, but read is O(1).
• Fan-out on read: Store the message once, and when each member reads, they query the group''s message feed. Cheaper writes, but reads are more expensive.

WhatsApp uses fan-out on write for small groups (<256 members) and fan-out on read for larger groups. This is a common hybrid approach.

Step 4: Data Model

Messages table (Cassandra — partitioned by conversation_id):
┌───────────────────┬─────────────────┬──────────┬──────────┬─────────┐
│ conversation_id   │ message_id      │ sender_id│ content  │ status  │
│ (partition key)   │ (clustering key)│          │          │         │
├───────────────────┼─────────────────┼──────────┼──────────┼─────────┤
│ conv_ab_123       │ snowflake_001   │ user_a   │ "Hello"  │DELIVERED│
│ conv_ab_123       │ snowflake_002   │ user_b   │ "Hi!"    │ READ    │
└───────────────────┴─────────────────┴──────────┴──────────┴─────────┘

Sorting: message_id (Snowflake) sorts by time automatically.

  

Why Cassandra? Chat messages are write-heavy (100B writes/day), partitioned by conversation (each conversation is a natural partition), and queried in time order (Snowflake IDs sort naturally). Cassandra excels at all three.

Step 5: Presence System

Showing "online" / "last seen" for each user:

• Heartbeat approach: Each connected client sends a heartbeat every 30 seconds. If no heartbeat for 60 seconds, mark as offline.
• Store presence in Redis: presence:{user_id} → {server_id, last_heartbeat} with a TTL of 60s.
• When a friend opens a chat, check Redis for that user''s presence status.

Optimization for groups: Don''t broadcast individual presence updates to all 500 members. Instead, lazy-load presence when a user opens the group info screen.

Step 6: Read Receipts

Message lifecycle:
  SENT → DELIVERED → READ

  User A sends message (status: SENT)
  Server stores and delivers to User B (status: DELIVERED, notify User A)
  User B opens the chat (status: READ, notify User A)

  

Read receipts are sent back as lightweight WebSocket events. For group chats, aggregate: "Read by 15 of 20 members."

Message Delivery State Machine

Message Lifecycle:

  [Client A sends]
       ↓
  SENT (stored on server, ACK sent to Client A)
       ↓
  DELIVERED (pushed to Client B''s device, ACK sent back)
       ↓
  READ (Client B opens conversation, read receipt sent)

Server States per message:
  status: PENDING → SENT → DELIVERED → READ
  Each transition triggers a push to Client A (via WebSocket if online)

Offline handling:
  If Client B is offline → message stays as SENT
  When Client B reconnects → server pushes all SENT messages
  Client B ACKs each → status moves to DELIVERED

  

Cassandra Schema for Messages

CREATE TABLE messages (    conversation_id UUID,    message_id      TIMEUUID,   -- Snowflake-style, time-sortable    sender_id       UUID,    content         TEXT,    content_type    TEXT,       -- ''text'', ''image'', ''video''    created_at      TIMESTAMP,    PRIMARY KEY (conversation_id, message_id)) WITH CLUSTERING ORDER BY (message_id DESC);
-- Partition key: conversation_id--   → All messages in a conversation are on the same node--   → Efficient range queries: "get messages after ID X"
-- Query: Get latest 50 messages in a conversationSELECT

FROM messagesWHERE conversation_id = ?ORDER BY message_id DESCLIMIT 50;


  

Group Chat: Fan-Out Strategy

WhatsApp''s approach: Groups are limited to 1,024 members. They use fan-out on write for all groups, keeping the architecture simple. The cap avoids the complexity of large-group fan-out.

End-to-End Encryption (E2EE)

WhatsApp and Signal use the Signal Protocol for E2EE. Key insight: the server never sees plaintext messages. It only stores encrypted blobs. This means the server cannot search, moderate, or read message content. The tradeoff: server-side features like search are impossible without client-side indexing.

Message flow with E2EE:

Client A encrypts message with Client B''s public key
Encrypted message → Server (stores encrypted blob)
Server pushes encrypted blob to Client B
Client B decrypts with their private key  Server sees: "aGVsbG8gd29ybGQ=" (encrypted)  Server cannot: search, moderate, recommend, or read content


  

Common Mistakes

• ❌ Using a relational database for messages — SQL databases struggle with the write volume and partition requirements of chat. Use Cassandra, ScyllaDB, or similar.
• ❌ Storing messages only in memory — users expect to see message history when they reinstall the app. Always persist to durable storage.
• ❌ Broadcasting presence to all contacts — if a user has 1,000 contacts, going online sends 1,000 updates. Use lazy presence loading.
• ❌ Not handling message ordering — use Snowflake/TIMEUUID IDs for consistent ordering across distributed servers.
• ❌ No offline message queue — if a user is offline, messages must be queued and delivered on reconnect. Don''t drop them.

[!TIP] Key Takeaways:
• WebSocket for real-time delivery; push notifications for offline users.
• Redis pub/sub to route messages between chat servers.
• Cassandra for message storage: write-optimized, partitioned by conversation, time-sorted.
• Fan-out on write for small groups, fan-out on read for large groups.
• Message state machine: SENT → DELIVERED → READ. Each transition triggers a push update.
• E2EE means the server only stores encrypted blobs — cannot search or moderate.
• Presence via heartbeats + Redis TTL. Lazy-load for groups.