Meta System Design Interview
What Meta actually tests, how it evaluates, and how to think at Staff level. 27 episodes covering distributed systems fundamentals, 13 real Meta-style design problems, and capstone mock interviews.
About This Course
This is not another generic system design course.
Meta's system design interviews have specific evaluation criteria, specific signals interviewers look for, and specific problems drawn from their own infrastructure. This series teaches you exactly what those are.
We start with how Meta interviews actually work — the format, the rubric, the levels. Then we build a design framework and estimation toolkit calibrated to what Meta evaluates.
Before diving into design problems, we cover 5 foundational building blocks that appear in nearly every answer: consistent hashing, Redis (from basics to advanced patterns like distributed Top-K and rate limiting), Kafka and event-driven architecture, Meta's TAO graph storage internals, and leader election protocols.
From there, we tackle 12 real Meta-style design problems across four groups: social products (Instagram Feed, News Feed, Messenger, WhatsApp), real-time delivery (Notification System, Live Video Streaming), infrastructure (Distributed Cache, Social Graph/TAO, Rate Limiter), and data-intensive systems (Typeahead/Autocomplete, Trending Topics, Ad Click Analytics, Real-time Ranking, Experimentation Platform).
Finally, we cover product-system hybrid questions, Staff-level (IC6) thinking, tradeoff depth, and run a complete mock interview.
Every episode includes architecture diagrams, estimation practice, and explicit callouts for what an IC4, IC5, or IC6 answer looks like.
Original curriculum inspired by publicly available Meta engineering blog posts, InfoQ talks, and the experiences of Staff+ engineers.
Prerequisites
- Working knowledge of distributed systems basics (load balancers, databases, caching)
- Familiarity with at least one backend language (Python, Java, Go, C++)
- Understanding of basic data structures and algorithms
- No prior Meta interview experience required
What You Will Learn
- Understand the Meta system design interview format, rubric, and evaluation signals
- Apply a structured design framework calibrated to Meta's expectations
- Perform capacity estimation and back-of-envelope calculations confidently
- Master foundational building blocks: consistent hashing, Redis, Kafka, TAO, leader election
- Design core Meta systems: feeds, messaging, notifications, live streaming, caching, social graph
- Design data-intensive systems: typeahead search, trending topics, ad analytics, real-time ranking
- Articulate tradeoffs at Staff level (IC6) depth with quantitative reasoning
- Recognize what separates IC4, IC5, and IC6 answers on the same problem
- Handle streaming aggregation, approximate counting, and OLAP query patterns
- Execute a full 45-minute mock Meta system design interview
Terminology Mapping
How classic concepts map to the terminology used in this course.
| Classic | This Course (Meta) |
|---|---|
| Load Balancer | Proxygen / L7 LB |
| Cache | Memcache / TAO cache layer |
| Message Queue | Scribe / LogDevice |
| Database | MySQL + TAO / ZippyDB |
| Service Mesh | ServiceRouter / Thrift |
| CDN | Meta CDN / PoPs |
| Feature Flags | Gatekeeper |
| A/B Testing | Quick Experiments (QE) |
| Coordination Service | ZooKeeper / ShardManager |
| Stream Processing | Scribe + Stylus / Flink |
已发布产物溯源
这门课程是如何制作的
本记录来自已发布的课程清单和课件清单，既说明产物中有据可查的信息，也明确列出未被记录的部分。
- 来源说明
- Original curriculum inspired by publicly available Meta engineering blog posts, InfoQ talks, and the experiences of Staff+ engineers.
- 已发布课件
- 27 节课中已有 27 节发布课件
- 幻灯片
- 27 份课件共 387 页（每份 11–17 页）
- 已测量的旁白时长
- 2 小时 39 分钟；27 节课中有 10 节记录了时长
- 预计学习时间
- 约 7 小时学习时间，并非视频时长
- 课件生成时间
- 2026年2月22日 至 2026年2月28日
- 课程清单生成时间
- 2026年3月3日
已记录的讲述者配置
- Lena Brookshost · curious
语音 ID：English_CaptivatingStoryteller - Dr. Weiteacher · socratic
语音 ID：Weidong2025 - Samstudent · engaged
语音 ID：English_CalmWoman - Avery Chenassistant · engaged
语音 ID：English_CalmWoman
逐课构建记录
| 课程 | 已发布 | 幻灯片 | 生成时间 | 已记录版本 | 已测量的旁白时长 |
|---|---|---|---|---|---|
| How Meta System Design Interviews Work01-how-meta-interviews-work | 是 | 13 | 2026年2月28日 | 课程 2026-02-28 · 课件架构 v3 | 未记录 |
| The Meta Design Framework02-meta-design-framework | 是 | 13 | 2026年2月28日 | 课程 2026-02-28 · 课件架构 v3 | 未记录 |
| Estimation for Meta03-estimation-for-meta | 是 | 12 | 2026年2月28日 | 课程 2026-02-28 · 课件架构 v3 | 未记录 |
| Consistent Hashing & Partitioning04-consistent-hashing | 是 | 15 | 2026年2月28日 | 课程 2026-02-28 · 课件架构 v3 | 未记录 |
| Redis — Crash Course to Advanced Patterns05-redis-crash-course | 是 | 17 | 2026年2月28日 | 课程 2026-02-28 · 课件架构 v3 | 未记录 |
| Kafka & Event-Driven Architecture06-kafka-event-streaming | 是 | 14 | 2026年2月28日 | 课程 2026-02-28 · 课件架构 v3 | 未记录 |
| Graph Storage & TAO Internals07-graph-storage-tao-internals | 是 | 15 | 2026年2月28日 | 课程 2026-02-28 · 课件架构 v3 | 未记录 |
| Leader Election & Distributed Coordination08-leader-election | 是 | 14 | 2026年2月28日 | 课程 2026-02-28 · 课件架构 v3 | 未记录 |
| Design Instagram Feed09-design-instagram-feed | 是 | 14 | 2026年2月24日 | 课程 2026-02-24 · 课件架构 v3 | 17 分钟 |
| Design Facebook News Feed10-design-facebook-newsfeed | 是 | 15 | 2026年2月24日 | 课程 2026-02-24 · 课件架构 v3 | 21 分钟 |
| Design Messenger11-design-messenger | 是 | 15 | 2026年2月22日 | 课程 2026-02-22 · 课件架构 v3 | 未记录 |
| Design WhatsApp at Scale12-design-whatsapp | 是 | 14 | 2026年2月24日 | 课程 2026-02-24 · 课件架构 v3 | 12 分钟 |
| Design Notification System13-design-notification-system | 是 | 14 | 2026年2月28日 | 课程 2026-02-28 · 课件架构 v3 | 未记录 |
| Design Live Video Streaming14-design-live-video-streaming | 是 | 16 | 2026年2月28日 | 课程 2026-02-28 · 课件架构 v3 | 未记录 |
| Design Distributed Cache15-design-distributed-cache | 是 | 15 | 2026年2月24日 | 课程 2026-02-24 · 课件架构 v3 | 15 分钟 |
| Design Social Graph (TAO)16-design-social-graph-tao | 是 | 15 | 2026年2月24日 | 课程 2026-02-24 · 课件架构 v3 | 16 分钟 |
| Design Rate Limiter17-design-rate-limiter | 是 | 15 | 2026年2月24日 | 课程 2026-02-24 · 课件架构 v3 | 16 分钟 |
| Design Typeahead / Autocomplete18-design-typeahead-autocomplete | 是 | 15 | 2026年2月28日 | 课程 2026-02-28 · 课件架构 v3 | 未记录 |
| Design Trending Topics (Top-K)19-design-trending-topics-topk | 是 | 14 | 2026年2月28日 | 课程 2026-02-28 · 课件架构 v3 | 未记录 |
| Design Ad Click Analytics20-design-ad-click-analytics | 是 | 13 | 2026年2月28日 | 课程 2026-02-28 · 课件架构 v3 | 未记录 |
| Design Real-Time Ranking System21-design-realtime-ranking | 是 | 14 | 2026年2月24日 | 课程 2026-02-24 · 课件架构 v3 | 12 分钟 |
| Design Experimentation Platform22-design-experimentation-platform | 是 | 15 | 2026年2月24日 | 课程 2026-02-24 · 课件架构 v3 | 19 分钟 |
| Design WeChat Steps Ranking23-design-wechat-steps-ranking | 是 | 15 | 2026年2月28日 | 课程 2026-02-28 · 课件架构 v3 | 未记录 |
| Product-System Design: The Meta Hybrid Question24-product-system-design | 是 | 15 | 2026年2月22日 | 课程 2026-02-22 · 课件架构 v3 | 未记录 |
| Staff-Level Thinking (IC6)25-staff-level-thinking | 是 | 12 | 2026年2月24日 | 课程 2026-02-24 · 课件架构 v3 | 11 分钟 |
| Tradeoff Deep Dive26-tradeoff-deep-dive | 是 | 11 | 2026年2月22日 | 课程 2026-02-22 · 课件架构 v3 | 未记录 |
| Mock Full Meta System Design Interview27-mock-meta-interview | 是 | 17 | 2026年2月24日 | 课程 2026-02-24 · 课件架构 v3 | 19 分钟 |
Your Learning Path
Each module builds on the last. Take your time—the AI tutor is with you at every step.
M0 How Meta System Design Interviews Work — Format, rubric, levels, and what interviewers actually evaluate
Understand the Meta system design interview format: 45-minute structure, the 4 evaluation signals (Problem Exploration, Design & Architecture, Communication, Tradeoffs), how IC4/IC5/IC6 answers differ, and common mistakes that tank interviews.
M1 The Meta Design Framework — A repeatable 4-phase approach calibrated to Meta's rubric
Learn a structured framework for Meta SD interviews: Scope → Estimate → Design → Evaluate. Each phase maps directly to Meta's evaluation signals. Practice applying it to a warm-up problem.
M1 Estimation for Meta — Back-of-envelope math that impresses interviewers
Master capacity estimation for Meta-scale systems. Learn the key numbers (DAU, QPS, storage ratios), practice estimation workflows, and understand how to use estimates to drive design decisions — not just as a checkbox exercise.
F1 Consistent Hashing & Partitioning — The backbone of every distributed system at Meta scale
Master consistent hashing: the hash ring, virtual nodes, rebalancing on node add/remove, and bounded-load variants. Then connect it to real Meta use cases — Memcache shard routing, TAO partition assignment, rate limiter bucket distribution, and Kafka partition assignment.
F1 Redis — Crash Course to Advanced Patterns — From basic caching to distributed rate limiting, Top-K, and leaderboards
Redis fundamentals and advanced patterns for system design interviews. Cover data structures (strings, hashes, sorted sets, HyperLogLog, streams), persistence modes (RDB/AOF), replication and Redis Cluster, then dive into advanced use cases: distributed rate limiting (token bucket via Lua scripts), real-time Top-K (sorted sets + probabilistic structures), leaderboards, session stores, and pub/sub.
F1 Kafka & Event-Driven Architecture — The event backbone behind notifications, analytics, and real-time pipelines
Kafka fundamentals and event-driven architecture for system design interviews. Cover topics, partitions, consumer groups, offset management, and delivery guarantees (at-least-once, exactly-once). Then connect to Meta patterns: Scribe/LogDevice as Meta's Kafka equivalent, event sourcing for notifications, click-stream pipelines for ad analytics, and CDC for cache invalidation.
F1 Graph Storage & TAO Internals — How Meta stores and queries the social graph for 3 billion users
Deep dive into Meta's TAO (The Associations and Objects): the objects-and-associations data model, read-after-write consistency via cache invalidation, the MySQL + cache architecture, association queries (assoc_get, assoc_count, assoc_range), write-through caching, and cross-region replication. Builds the foundation for the Design Social Graph module.
F1 Leader Election & Distributed Coordination — ZooKeeper, Raft consensus, and coordination primitives at Meta scale
Leader election protocols and distributed coordination for system design interviews. Cover why leader election is needed, the Raft consensus algorithm (leader election, log replication, safety), ZooKeeper's coordination primitives (ephemeral nodes, watches, sequential nodes), and practical applications: Kafka controller election, distributed locks, config management, and service discovery.
M2 Design Instagram Feed — Fan-out, ranking, and serving 2 billion users their personalized feed
Design Instagram's home feed end-to-end: post ingestion, fan-out strategies (push vs pull vs hybrid), feed ranking, and timeline serving. Understand how Meta handles celebrity accounts, cold-start users, and real-time updates.
M2 Design Facebook News Feed — Aggregation, EdgeRank, and multi-source feed merging
Design the Facebook News Feed, which differs from Instagram by aggregating content from friends, pages, groups, and ads. Cover the evolution from EdgeRank to ML-based ranking, story aggregation, and the unique challenges of multi-source feeds.
M2 Design Messenger — Real-time messaging, presence, and delivery guarantees at Meta scale
Design Facebook Messenger: real-time 1:1 and group messaging, online presence, message delivery guarantees (sent/delivered/read), offline message queuing, and end-to-end encryption considerations.
M2 Design WhatsApp at Scale — E2E encryption, media delivery, and extreme engineering efficiency
Design WhatsApp, which handles 2B users with a famously small engineering team. Focus on what differentiates WhatsApp from Messenger: end-to-end encryption by default, media message handling, status/stories, and the Erlang-based backend architecture.
M2 Design Notification System — Cross-platform push delivery, deduplication, and priority ranking at Meta scale
Design Meta's notification system: real-time push delivery across iOS, Android, and web; notification ranking and priority; deduplication and rate limiting to avoid spamming users; offline queuing and batching; notification preferences and muting.
M2 Design Live Video Streaming — Real-time ingest, adaptive bitrate, and live comments at Meta scale
Design a live video streaming platform like Facebook Live / Instagram Live: real-time video ingest and transcoding, adaptive bitrate streaming, CDN distribution, live comments and reactions overlay, viewer count tracking, and recording for replay.
M3 Design Distributed Cache — Memcache at Meta scale: consistency, eviction, and thundering herds
Design a distributed caching system inspired by Meta's Memcache deployment. Cover cache topologies, consistency models, eviction strategies, thundering herd mitigation, and cross-datacenter cache invalidation.
M3 Design Social Graph (TAO) — Meta's graph database: objects, associations, and read-after-write consistency
Design a social graph storage system inspired by Meta's TAO. Cover the objects-and-associations data model, read-after-write consistency, caching graph queries, and handling the friend-of-friend query pattern at billions of nodes.
M3 Design Rate Limiter — Token buckets, sliding windows, and distributed rate limiting
Design a distributed rate limiting system for Meta's API platform. Cover rate limiting algorithms (token bucket, sliding window, fixed window), distributed coordination, per-user and per-API limits, and graceful degradation under load.
M4 Design Typeahead / Autocomplete — Low-latency prefix search and ranking at Meta scale
Design a typeahead autocomplete system for Meta search: prefix-based suggestion with sub-100ms latency, personalized ranking, multi-entity results (people, pages, groups, posts), real-time index updates, and handling of trending queries.
M4 Design Trending Topics (Top-K) — Real-time streaming aggregation and approximate counting at Meta scale
Design a trending topics system that identifies the top-K most discussed topics across Meta's platform in real time. Cover streaming aggregation, approximate counting algorithms (Count-Min Sketch, HeavyKeeper), time-windowed rankings, spam/bot filtering, and geographic trending.
M4 Design Ad Click Analytics — Real-time event aggregation, exactly-once counting, and OLAP at Meta scale
Design Meta's ad click analytics system: real-time ingestion of click/impression events, exactly-once counting for billing accuracy, multi-dimensional aggregation (by campaign, audience, geo, time), near-real-time dashboards for advertisers, and fraud detection.
M4 Design Real-Time Ranking System — Feature stores, model serving, and ranking at 100K QPS
Design a real-time ranking system for feed, search, and recommendations at Meta scale. Cover feature stores, online vs offline features, model serving infrastructure, and the candidate generation → ranking → re-ranking pipeline.
M4 Design Experimentation Platform — A/B testing, feature flags, and statistical rigor at Meta scale
Design Meta's experimentation platform (Quick Experiments / Gatekeeper). Cover experiment configuration, traffic splitting, metric collection, statistical analysis, and the challenges of running thousands of concurrent experiments without interference.
M5 Design WeChat Steps Ranking — Social leaderboards, write amplification, and friend-scoped ranking at scale
Design a daily fitness step ranking system among friends (WeChat-style). Cover data ingestion from mobile health APIs, friend-scoped leaderboard design, on-demand vs precomputed ranking tradeoffs, write amplification analysis, Redis sorted sets for real-time ranking, midnight reset strategies, anti-cheating heuristics, and privacy controls.
M5 Product-System Design: The Meta Hybrid Question — When the interviewer says 'design a feature to increase engagement' — bridging product thinking and system architecture
Meta increasingly asks hybrid questions that blend product intuition with system design. Learn the PMAS framework (Product goal → Metric → Architecture capability → System design) through three worked examples: Stories engagement, Messenger reply rate, and Marketplace trust. Understand what separates IC5 from IC6 on these questions.
M5 Staff-Level Thinking (IC6) — What separates Senior from Staff in Meta's system design interviews
Understand what Meta expects from Staff-level (IC6) system design answers. Cover driving ambiguity, quantitative tradeoff analysis, phased rollout planning, cross-team impact awareness, and demonstrating technical leadership through design decisions.
M5 Tradeoff Deep Dive — Quantitative analysis of the hard choices in system design
Deep dive into the most common system design tradeoffs with quantitative analysis. Cover consistency vs availability, latency vs throughput, storage vs computation, and how to articulate tradeoff decisions at Staff level.
M5 Mock Full Meta System Design Interview — 45-minute simulation with real-time coaching
A full 45-minute mock Meta system design interview. Sam tackles a novel problem (not covered in earlier episodes) while Dr. Wei acts as the interviewer, providing real-time coaching and IC6-level feedback at each phase.