🏗️ System Design Guides for FAANG Interviews

In-depth walkthroughs of the most common system design problems asked at Google, Meta, Amazon, Apple, and Microsoft. Each guide covers requirements gathering, capacity estimation, data modeling, scalability trade-offs, and architecture decisions — with answers calibrated from L3 to L8.

• URL Shortener System Design: FAANG Interview Guide (L3–L8)

  URL shortener system design walkthrough: requirements, encoding, caching, data model, scalability. Level-calibrated answers from L3 to L8 at FAANG companies.

• Rate Limiter System Design: FAANG Interview Guide (L3–L8)

  Design a rate limiter for a system design interview. Covers token bucket vs leaky bucket, sliding window, distributed counters, Redis Lua scripts, failure modes.

• Twitter/X Feed System Design: FAANG Interview Guide (L3–L8)

  Design a Twitter/X news feed for your system design interview. Covers fan-out strategies, timeline generation, ranking, caching, and real-time delivery. Used at Meta, Twitter, LinkedIn interviews.

• Key-Value Store & Distributed Cache System Design

  Design a distributed key-value store or cache (Redis, Memcached). Covers consistent hashing, eviction policies, replication, partitioning, and failure modes for FAANG interviews L3–L8.

• Search Autocomplete System Design: FAANG Interview Guide (L3–L8)

  Design a Search Autocomplete system: trie vs inverted index, prefix ranking, typeahead at scale. Full FAANG/MAANG interview guide covering Google, Amazon, Twitter, LinkedIn — L3 to L8.

• Web Crawler System Design: FAANG Interview Guide (L3–L8)

  Design a Web Crawler for your system design interview. Covers BFS frontier, politeness, URL deduplication, and distributed crawl at Google/Bing scale.

• Notification Service System Design: FAANG Interview Guide (L3–L8)

  Design a Notification Service: push, email, and SMS fan-out at scale. Priority queues, delivery guarantees, rate limiting, and multi-channel routing.

• Distributed Message Queue System Design: FAANG Interview Guide (L3–L8)

  Step-by-step guide to designing a distributed message queue (Kafka) for FAANG interviews. Covers partitioning, ISR, acks, exactly-once delivery, and tiered storage.

• YouTube System Design: FAANG Interview Guide (L3–L8)

  Step-by-step guide to designing YouTube for FAANG interviews. Covers adaptive bitrate streaming (HLS/DASH), async transcoding pipeline, CDN caching, and scalability.

• Chat System (WhatsApp) System Design: FAANG Interview Guide (L3–L8)

  Design a Chat System like WhatsApp for FAANG interviews. Covers WebSocket architecture, message delivery guarantees, fan-out, and multi-device sync.

• File Storage System Design (Dropbox / Google Drive): FAANG Interview Guide (L3–L8)

  Step-by-step guide to designing a file storage and sync system for FAANG interviews. Covers chunking, delta sync, global deduplication, conflict resolution, and petabyte-scale storage strategy.

• Design a Booking & Reservation System like Airbnb

  A deep dive into double-booking prevention, availability staleness, geo-partitioning, and capacity estimators for travel system design.

• Design a Payment Processing System

  A comprehensive guide to designing a payment system for a system design interview. Covers idempotency, exactly-once semantics, ledger models, and multi-region scaling.

• Online Judge System Design

  Design an online judge like LeetCode or HackerRank. Covers secure sandboxing, execution queues, test case storage, worker scaling, and fairness.

• Photo-Sharing Feed System Design (Instagram)

  Design a photo-sharing feed like Instagram: image upload pipeline, CDN delivery, social graph at scale, and feed generation strategies.

• Proximity Search System Design

  Deceptively simple to describe, surprisingly hard to scale: finding the nearest relevant business in under 200 ms across 200 million listings worldwide.

• Ride-Sharing System Design (Uber / Lyft)

  Real-time driver matching at global scale. Geohashing, quadtrees, WebSocket-driven location tracking, ETA prediction, and surge pricing — the core algorithms that make Uber and Lyft work.

• Top-K Leaderboard System Design

  Design a Top-K Leaderboard at scale — Redis sorted sets, stream aggregation, approximate counting with Count-Min Sketch. Full system design guide for Google, Meta, Amazon interviews (L3–L8).