How Reddit Works: Architecture Behind the Front Page of the Internet

Reddit's Scale and Challenges

Reddit is the "front page of the internet" with 50+ million daily active users, 100,000+ active communities, and billions of pageviews monthly. The platform faces unique challenges: viral content, real-time voting, and complex ranking algorithms.

Core Architecture

Reddit has evolved from a monolithic Python application to a microservices architecture:

Key Components:

• **r2**: The legacy Python monolith (being decomposed)
• **Snooserv**: New services in Go and Node.js
• **Reddit's infrastructure**: AWS-based with custom tooling

The Voting System

Reddit's upvote/downvote system is central to the platform:

Challenges:

• Millions of votes per minute during peak times
• Real-time score updates
• Vote fuzzing for anti-manipulation
• Historical vote data for users

Implementation:

1. Votes written to Cassandra (high write throughput)
2. Scores cached in Redis with TTL
3. Batch processing for historical data
4. Vote counts deliberately fuzzed to prevent manipulation

Hot vs Controversy Rankings:

Hot ranking considers:

• Net votes (upvotes - downvotes)
• Time decay (newer content ranked higher)
• Engagement velocity

Controversy ranking identifies divisive content:

• High total votes
• Close to 50/50 split

Content Storage and Delivery

Posts and Comments:

• PostgreSQL for core data
• Cassandra for high-volume writes
• Redis for caching hot content
• CDN for static assets and images

The Comment Tree Problem:

Reddit's nested comments are complex:

• Recursive data structure
• Must handle deep nesting (sometimes 10+ levels)
• Sorting options (Best, Top, New, Controversial)
• Collapse/expand state

Solution:

• Materialized paths for efficient tree queries
• Pre-computed "best" comments
• Lazy loading for deep threads
• Client-side rendering optimization

Feed Generation

Home Feed Algorithm:

1. Identify subscribed subreddits
2. Fetch top posts from each (time-weighted)
3. Personalization based on engagement history
4. Remove already-seen content
5. Interleave and rank

Performance Optimizations:

• Feed pre-computation for active users
• Caching at multiple levels
• Pagination with cursor-based approach
• Background refresh of stale feeds

Real-time Features

Reddit supports real-time updates:

WebSocket Infrastructure:

• Connection management at scale
• Pub/sub for live updates
• Graceful degradation when disconnected
• Mobile push notification fallback

Use Cases:

• Live comment updates
• Vote count changes
• Award notifications
• Chat messages

Handling Viral Content

When content goes viral:

Challenges:

• Thundering herd on database
• CDN cache invalidation
• Real-time vote counting
• Comment flood

Solutions:

• Aggressive caching with short TTL
• Rate limiting per user/IP
• Queue-based write buffering
• Gradual rollout of viral detection

Search and Discovery

Reddit's search has historically been weak but improving:

Current Stack:

• Elasticsearch for text search
• Lucene-based indexing
• Real-time index updates via Kafka
• Faceted search by subreddit, time, type

Key Technical Decisions

1. Eventual Consistency

Vote counts are eventually consistent - exact numbers aren't critical.

2. Denormalization

Popular data is denormalized for read performance.

3. Feature Flags

Extensive use of feature flags for gradual rollouts.

4. Caching Everything

Multiple cache layers with different TTLs.

Interview Application

When designing a Reddit-like platform:

Must-Have Features:

• Post/comment CRUD operations
• Voting system with real-time updates
• Nested comments with efficient retrieval
• Feed generation (home, subreddit, popular)
• Search functionality

Key Considerations:

• Read-heavy workload (optimize for reads)
• Viral content handling
• Anti-abuse measures
• Mobile experience

Reddit's architecture demonstrates handling user-generated content at scale, complex ranking algorithms, and community moderation systems.