Design: Twitter/X

Design: Twitter/X

Requirements

Functional: Post tweets, Follow users, View home timeline, Like/Retweet Scale: 300M DAU, 500M tweets/day, 100B timeline reads/day

The Core Problem: Home Timeline

Generating a user's timeline = aggregating tweets from everyone they follow. If a user follows 1000 people, a naive query SELECT * FROM tweets WHERE user_id IN (followees) ORDER BY created_at is extremely expensive at scale.

Two Approaches

Fan-out on Write (Push model)

When User A tweets, push the tweet ID to every follower's timeline cache.

Tweet posted by User A
  → Message queue
  → Fan-out worker reads follower list (500 followers)
  → Writes tweet_id to each follower's timeline in Redis

Timeline read:
  → Read from Redis cache (O(1) per user)
  → Extremely fast!

✅ Fast reads ❌ Write amplification for celebrities (Katy Perry has 150M followers → 150M Redis writes per tweet)

Fan-out on Read (Pull model)

When user views timeline, query tweets from all followees.

Timeline read for User B (follows 1000 people):
  → Fetch last 20 tweet IDs from each of 1000 followees
  → Merge-sort 20,000 tweets → top 20
  → Cache result per user

✅ No write amplification ❌ Very slow for users following many people; complex to cache

Hybrid (Twitter's actual approach):

• Regular users (< ~10K followers): fan-out on write
• Celebrities (> ~10K followers): fan-out on read, merged at query time

Data Model

-- Tweets: Cassandra (time-series, high write)
tweets: tweet_id (Snowflake), user_id, text, media_urls, created_at

-- Follow graph: Graph DB or Cassandra
follows: follower_id, followee_id, followed_at

-- Timeline cache: Redis sorted set per user
-- key = "timeline:{userId}"
-- score = tweet timestamp, value = tweet_id

Architecture

API Gateway → Load Balancer
  ↓
[Tweet Service] → Cassandra (tweets)
  ↓
[Fanout Service] ← message queue
  ↓
[Redis Timeline Cache]  ← ZADD timeline:{userId} score=ts value=tweetId
  ↓
[Timeline Service] → hydrate tweet IDs → return tweets

Interview Tips

1. The fan-out problem is the crux — explain both approaches and the hybrid.
2. Use Snowflake IDs (time-sortable) so tweets are naturally ordered by ID.
3. Celebrity handling is the key insight that separates strong candidates.