Design Reddit - Scaling Considerations

Horizontal Scaling

• Application Servers: 5,000 stateless servers
• Database Sharding: 500 shards by subreddit_id
• Read Replicas: 5 replicas per shard
• Auto-scaling: Based on CPU/memory

Caching Strategy

• Hot Posts: Redis (1 hour TTL, 90% hit rate)
• Comment Trees: Redis (30 min TTL)
• Vote Counts: Redis (5 min TTL)
• Subreddit Info: Redis (1 day TTL)

Ranking Algorithms

Hot Algorithm

def hot(ups, downs, date):
    score = ups - downs
    order = log10(max(abs(score), 1))
    sign = 1 if score > 0 else -1 if score < 0 else 0
    seconds = date - 1134028003
    return round(sign * order + seconds / 45000, 7)

Best Algorithm (Wilson Score)

def confidence(ups, downs):
    n = ups + downs
    if n == 0:
        return 0
    z = 1.96  # 95% confidence
    phat = ups / n
    return (phat + z*z/(2*n) - z * sqrt((phat*(1-phat)+z*z/(4*n))/n))/(1+z*z/n)

Vote Counting

• Eventual Consistency: Vote counts updated asynchronously
• Batch Updates: Aggregate votes every 5 seconds
• Cache Invalidation: Invalidate on vote
• Anti-Cheating: Rate limiting, vote fuzzing

Comment Threading

• Nested Storage: parent_comment_id for hierarchy
• Depth Limit: Max 10 levels deep
• Load More: Pagination for large threads
• Caching: Cache entire comment trees

Performance Optimization

• Database Indexes: Composite indexes on (subreddit_id, score)
• Query Optimization: Limit result sets, use covering indexes
• CDN: Serve static content from CDN
• Lazy Loading: Load comments on demand

Auto-Scaling

• Triggers: CPU >70%, Memory >80%, Queue depth >1000
• Policies: Target tracking at 60% CPU
• Scheduled: Scale for peak hours (6-11 PM)

Monitoring

• Metrics: Response time, error rate, cache hit rate
• Alerting: Critical >5% error rate, Warning >1%
• Dashboards: Real-time system health

This scaling strategy ensures Reddit can handle millions of communities and billions of votes.