Reusable Architecture Templates

Most system design questions map to one of a few fundamental architecture patterns. A social media feed, a news aggregator, and a notification timeline are all "feed systems." An e-commerce checkout, a booking platform, and a ticketing system are all "transactional platforms." This page gives you 5 templates that cover the vast majority of interview questions. Learn the template, then customize per question.

How to Use Templates

1. Identify which template fits the question (most map to one of five)
2. Customize the components for the specific problem
3. Never say you are using a template — present it as your design
4. Modify trade-offs based on the specific requirements

Template	Applies To
Social Media Feed	Twitter, Instagram, Facebook, Reddit, LinkedIn, TikTok, news feed
E-Commerce Platform	Amazon, Shopify, booking systems, ticketing, food delivery
Real-Time Messaging	WhatsApp, Slack, Discord, Telegram, comments, notifications
Video Streaming	YouTube, Netflix, TikTok, live streaming, Twitch
Ride-Sharing	Uber, Lyft, food delivery matching, logistics, delivery tracking

Template 1: Social Media Feed

Applies to: Twitter, Instagram, Facebook feed, Reddit, LinkedIn, TikTok, any system where users create content and others consume a personalized feed.

Template Placeholders to Fill

Placeholder	Options to Consider	Decision Criteria
Post Store	PostgreSQL, Cassandra, DynamoDB	Write volume, query patterns
Feed Cache	Redis sorted sets	Timeline read latency requirement
Message Queue	Kafka, SQS, RabbitMQ	Fanout volume, ordering needs
Fanout Strategy	Write (push), Read (pull), Hybrid	Follower distribution
Media Storage	S3, GCS, Azure Blob	Cost, CDN integration
Search Index	Elasticsearch	Full-text search needs
Social Graph	Redis, Neo4j, adjacency list in SQL	Graph query complexity
CDN	CloudFront, Cloudflare	Media delivery latency

Key Decisions to Justify

1. Fanout strategy: Push (fanout-on-write) vs Pull (fanout-on-read)
   - Push: Fast reads, high write amplification
   - Pull: Slow reads, low write amplification
   - Hybrid: Push for normal users, pull for celebrities

2. Feed ranking: Chronological vs Algorithmic
   - Chronological: Simple, real-time, user-controlled
   - Algorithmic: Better engagement, requires ML pipeline

3. Content delivery: Presigned URLs vs CDN
   - Presigned: Access control, temporary links
   - CDN: Low latency, global distribution

4. Engagement counts: Real-time vs Approximate
   - Real-time: Redis counters, expensive at scale
   - Approximate: Periodic aggregation, good enough for most cases

Template 2: E-Commerce Platform

Applies to: Amazon, Shopify, booking systems (hotels, flights), food ordering, ticketing platforms, any system with inventory, checkout, and payments.

Key Decisions

1. Inventory management: Pessimistic vs Optimistic locking
   - Pessimistic: Reserve inventory on "add to cart" (prevents oversell)
   - Optimistic: Check inventory at checkout (higher conversion)
   - Hybrid: Soft reserve for 15 min, hard lock at payment

2. Cart storage: Database vs Redis
   - Database: Persistent, survives crashes, slower
   - Redis: Fast, temporary, needs persistence strategy

3. Payment flow: Synchronous vs Two-phase
   - Sync: Charge immediately, refund on failure
   - Two-phase: Authorize first, capture after fulfillment

4. Search: Elasticsearch vs Database queries
   - ES: Fast faceted search, fuzzy matching, relevance ranking
   - DB: Simple queries, strong consistency, fewer moving parts

5. Pricing: Cache vs Real-time
   - Cached: Fast, might show stale prices (handle at checkout)
   - Real-time: Always accurate, slower

Template 3: Real-Time Messaging

Applies to: WhatsApp, Slack, Discord, Telegram, Facebook Messenger, in-app chat, live comments, collaborative tools.

Key Decisions

1. Protocol: WebSocket vs Long Polling vs SSE
   - WebSocket: Full duplex, lowest latency, connection overhead
   - Long Polling: Simpler, works through any proxy
   - SSE: Server-to-client only, auto-reconnect

2. Message ordering: Total order vs Causal order
   - Total: All users see messages in exact same order (expensive)
   - Causal: Messages respect causality (reply after original)

3. Delivery guarantee: At-most-once vs At-least-once
   - At-most-once: Fast, might lose messages
   - At-least-once: Reliable, need deduplication on client

4. Group messaging: Fan-out at sender vs Fan-out at server
   - Sender: Client sends N copies (wastes bandwidth)
   - Server: Client sends once, server fans out (standard)

5. Offline messages: Store-and-forward vs Push notification
   - Store: Queue messages, deliver when user reconnects
   - Push: Send push notification, user opens app to read

6. End-to-end encryption: Client-side vs Server-side
   - E2E: Server cannot read messages, complex key management
   - Server-side: TLS in transit, server can read (moderation possible)

Template 4: Video Streaming

Applies to: YouTube, Netflix, TikTok, Twitch, video conferencing, live streaming, any media-heavy platform.

Key Decisions

1. Upload: Direct to storage vs Through server
   - Direct (presigned URL): Offloads bandwidth, complex auth
   - Through server: Simple, server is bandwidth bottleneck

2. Transcoding: Eager vs Lazy
   - Eager: Transcode all resolutions on upload (storage cost)
   - Lazy: Transcode on first request (latency on first view)
   - Hybrid: Transcode popular resolutions eagerly, rest lazily

3. Streaming protocol: HLS vs DASH vs CMAF
   - HLS: Apple ecosystem, widest support
   - DASH: Open standard, more flexible
   - CMAF: Common format for both HLS and DASH

4. Adaptive bitrate: How many resolutions?
   - Typical: 240p, 360p, 480p, 720p, 1080p, 4K
   - Each resolution = separate transcoding job + storage

5. View counting: Real-time vs Batch
   - Real-time: Redis counter, accurate, expensive at scale
   - Batch: Aggregate every minute, approximate, cheap
   - YouTube approach: Real-time up to 300 views, then batch

6. Recommendations: Collaborative filtering vs Content-based
   - Collaborative: "Users who watched X also watched Y"
   - Content-based: Similar tags, categories, creators
   - Hybrid: Both, weighted by engagement signals

Template 5: Ride-Sharing / Location-Based

Applies to: Uber, Lyft, DoorDash, food delivery, logistics, delivery tracking, any system matching supply with demand based on location.

Key Decisions

1. Geospatial index: Quadtree vs Geohash vs H3
   - Quadtree: Dynamic splitting, good for uneven distribution
   - Geohash: String prefix matching, easy to implement
   - H3 (Uber): Hexagonal grid, uniform area, no distortion

2. Location updates: Frequency vs Battery
   - High frequency (every 1s): Accurate tracking, drains battery
   - Low frequency (every 5s): Saves battery, less accurate
   - Adaptive: High during trip, low when idle

3. Matching algorithm: Nearest driver vs Optimal assignment
   - Nearest: Simple, fast, suboptimal globally
   - Optimal: Better globally, computationally expensive
   - Batch matching: Collect requests for 5s, solve assignment problem

4. Dynamic pricing (surge): Real-time vs Zone-based
   - Real-time: Price changes per request based on supply/demand
   - Zone-based: Divide city into zones, set multiplier per zone
   - Uber approach: Zone-based with smooth transitions

5. ETA estimation: Historical average vs ML model
   - Historical: Simple, based on past trips in same area/time
   - ML: Uses traffic, weather, events, more accurate
   - Hybrid: ML model with historical fallback

Template Mapping Cheat Sheet

Interview Question	Primary Template	Customize
Design Twitter	Social Media Feed	Tweets, timeline, search
Design Instagram	Social Media Feed	Photos, stories, explore
Design Reddit	Social Media Feed	Subreddits, voting, comments
Design TikTok	Social Media Feed + Video	Short video, recommendation-heavy
Design Amazon	E-Commerce	Product catalog, reviews, recommendations
Design a hotel booking system	E-Commerce	Inventory = rooms, dates as dimensions
Design a food delivery app	E-Commerce + Ride-Sharing	Menu, restaurant matching, delivery tracking
Design WhatsApp	Real-Time Messaging	E2E encryption, groups, voice/video
Design Slack	Real-Time Messaging	Channels, threads, search, integrations
Design YouTube	Video Streaming	Upload, transcode, recommendations
Design Netflix	Video Streaming	Licensing, personalization, download
Design Uber	Ride-Sharing	Real-time matching, surge pricing
Design Google Maps	Ride-Sharing (Location)	Routing, traffic, POI search

Cross-References

• System Design Interview Framework — how to use these templates in an interview
• Practice Questions: Easy — apply templates to easy problems
• Practice Questions: Medium — apply templates to medium problems
• Practice Questions: Hard — apply templates to hard problems
• Scalability Patterns — scaling the template
• Communication Patterns — choosing protocols

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Templates are starting points, not answers. The value is in how you customize them for the specific problem, justify your choices, and discuss trade-offs. An interviewer can tell the difference between someone who memorized an architecture and someone who understands why each component exists.