System Design ⚡ Recently updatedUpdated on Jun 30, 2026 — this content may still be evolving.

Design a Social Media App (Student-Level Walkthrough)

A complete interview-style answer for designing something like Instagram/Twitter at student scale. Practice saying this out loud with a whiteboard.

Step 1: Clarify Requirements (2–3 min)

Ask the interviewer:

Functional requirements:

1. Users can register and log in
2. Users can create posts with text and one image
3. Users can follow/unfollow other users
4. Home feed shows posts from followed users, newest first
5. Users can like/unlike posts

Non-functional requirements:

• Feed should feel fast (cache hot data)
• Images served via CDN
• System should handle 10x user growth without redesign

Step 2: Rough Estimates (2 min)

At student scale, a single Postgres + one API server works. Mention what breaks at 10x.

Step 3: High-Level Architecture

┌──────────┐     HTTPS      ┌─────────────┐
│  React   │ ──────────────►│  API Server │
│  Web App │ ◄──────────────│  (Node.js)  │
└──────────┘                └──────┬──────┘
                                   │
                    ┌──────────────┼──────────────┐
                    ▼              ▼              ▼
              ┌──────────┐  ┌──────────┐  ┌──────────┐
              │  Redis   │  │ Postgres │  │ S3 + CDN │
              │  (cache) │  │   (DB)   │  │ (images) │
              └──────────┘  └──────────┘  └──────────┘

Data flow — create post:

1. User uploads image → pre-signed S3 URL → direct upload to S3
2. User submits post text + image URL → POST /api/v1/posts
3. API validates, inserts into posts table
4. Invalidate user’s followers’ feed caches (or let TTL expire)

Data flow — read feed:

1. GET /api/v1/feed?cursor=...&limit=20
2. Check Redis: feed:userId:page1 → cache hit? return
3. Cache miss → query DB → store in Redis (60s TTL) → return

Step 4: Database Schema

users (
  id            UUID PK,
  username      VARCHAR(50) UNIQUE,
  email         VARCHAR(255) UNIQUE,
  password_hash VARCHAR(255),
  avatar_url    VARCHAR(500),
  created_at    TIMESTAMPTZ
)

posts (
  id            UUID PK,
  user_id       UUID FK → users,
  caption       TEXT,
  image_url     VARCHAR(500),
  created_at    TIMESTAMPTZ
)
-- INDEX: (user_id, created_at DESC) for profile page

follows (
  follower_id   UUID FK → users,
  following_id  UUID FK → users,
  created_at    TIMESTAMPTZ,
  PRIMARY KEY (follower_id, following_id)
)
-- INDEX: (follower_id) for "who I follow" list

likes (
  user_id       UUID FK → users,
  post_id       UUID FK → posts,
  created_at    TIMESTAMPTZ,
  PRIMARY KEY (user_id, post_id)
)

-- Denormalized counter (optional, for performance)
posts.like_count INT DEFAULT 0

Step 5: API Endpoints

Step 6: Deep Dive — Home Feed

This is where interviewers spend the most time.

Approach 1: Pull Model (Start Here)

On feed request, query posts from users you follow:

SELECT p.*, u.username, u.avatar_url
FROM posts p
JOIN users u ON p.user_id = u.id
WHERE p.user_id IN (
  SELECT following_id FROM follows WHERE follower_id = $currentUserId
)
ORDER BY p.created_at DESC
LIMIT 20;

Pros: Simple, always fresh, easy to build Cons: Slow if you follow 5,000 people; DB does heavy work per request

Optimizations:

• Index on follows(follower_id) and posts(created_at DESC)
• Cache feed in Redis (60-second TTL)
• Cursor pagination, not offset

Approach 2: Fan-Out on Write (Scale Path)

When user posts → push post ID into each follower’s feed cache (Redis list).

Pros: Feed read is O(1) — just read pre-built list Cons: Slow post if user has 1M followers (“celebrity problem”)

Hybrid (what real systems do):

• Fan-out for normal users (< 10K followers)
• Pull model for celebrities

Frontend Feed

• Infinite scroll with cursor pagination
• React Query useInfiniteQuery
• Virtualized list for performance
• Optimistic UI for likes (update count immediately, rollback on error)

Step 7: Image Handling

1. Client requests pre-signed S3 upload URL from API
2. Client uploads directly to S3 (doesn’t burden API server)
3. API stores image_url in post record
4. CloudFront CDN serves images globally
5. Store thumbnails (200px) for feed; full size on detail page

Why not store images in Postgres? Blobs bloat the DB, slow backups, expensive.

Step 8: Tradeoffs Summary

Step 9: Security & Reliability

• HTTPS everywhere
• bcrypt password hashing
• Auth middleware on all write endpoints
• Users can only delete their own posts (authorization check)
• Rate limit: 10 posts/hour, 100 feed requests/minute
• Validate image type and size server-side
• Pre-signed S3 URLs expire in 15 minutes

What to Draw on a Whiteboard

[Browser] → [API] → [Postgres]
              ↓
           [Redis]
              ↓
           [S3/CDN]

Tables: users, posts, follows, likes
Feed query: pull model + cache

Common Follow-Up Questions

Q: How do you handle a celebrity with 10M followers? A: Don’t fan-out on write. Pull model for their posts only. Cache aggressively.

Q: How do you add real-time notifications for likes? A: WebSocket or SSE connection. On like → insert notification → push to post author’s socket.

Q: How do you rank feed beyond chronological? A: Score = recency + engagement (likes/comments) + relationship strength. Precompute scores in a background job. (Mention as future improvement.)

Practice tip: Time yourself — 25 minutes for the full walkthrough, leaving 20 minutes for follow-ups.