Key Actions
- โนRehearse your opening 60 seconds (scope, clarifying questions, success metric).
- โนKnow the evaluation axes: requirements, API contracts, data model, scale math, resiliency.
- โนPractice narrating trade-offs out loud with a timer.
System Design Interview Labs
21 real case studies โ URL shorteners to LLM inference serving โ each with interviewer dialogue, an architecture blueprint, and a flow diagram you can narrate out loud.
21
Case studies
5
Categories
15
Flow blueprints
21
Free to start
URL Shortener
Design a highly scalable service like Bitly to shorten long URLs into manageable links.
OpenRide-Hailing Service
Architect a real-time platform like Uber to connect riders with drivers and manage trips.
OpenVideo Streaming Platform
Design a massive-scale system like YouTube for video uploading, processing, and streaming.
OpenFree access
100%
of case studies open without membership
Playbook Section
Understand expectations before touching diagrams.
Review the FAANG evaluation rubric, pacing, and storytelling patterns. The interviewer wants clarity, not perfection.
Key Actions
Suggested Resources
The method
You don't need 15 memorized architectures โ you need one repeatable way to attack a problem you've never seen. Every case study on this site follows these ten steps, so the pattern transfers to whatever your interviewer asks tomorrow.
01
Clarify
What are we building? Restate the problem in one sentence.
02
Scope
What are we NOT building? Cut features out loud.
03
Estimate
Users โ QPS โ storage โ bandwidth. Let one number drive the design.
04
Model
Entities and API contracts before any boxes.
05
Architect
Draw the simplest system that satisfies the requirements.
06
Deep dive
Pick the 2โ3 hardest problems and go deep only there.
07
Scale
Find the bottleneck. Fix that one, not everything.
08
Failure
What happens when each component breaks? Blast radius and recovery.
09
Trade-offs
Explain why this design โ and what you gave up for it.
10
Summarize
Walk the interviewer through the final design end to end.
Learning journey
Follow the stages in order. Each stage assumes the muscles built by the one before it.
Progression, not memorization
Never open with the final diagram. Start simple and let each new component earn its place โ interviewers score the reasoning between levels, not the boxes.
Level 1 โ Simple
Client โ API โ DB
Correct for thousands of users. Start here out loud โ it proves you scale on evidence, not habit.
Level 2 โ Production
Client โ Load Balancer โ API (n instances) โ Cache โ DB
The LB exists for deploys and instance failure; the cache exists because reads dominate. Each box has a stated reason.
Level 3 โ Scale
Client โ CDN / LB โ Services (split by domain) โ Cache tier โ Partitioned DB
Partitioning appears only when one primary provably cannot hold the write volume. Services split along team and failure boundaries.
Level 4 โ Global
Users โ Global routing (GeoDNS/Anycast) โ Regional services โ Regional caches โ Replicated data
Regions exist for latency and jurisdiction, not vanity. Now consistency between regions is your hardest problem โ say so.
Case Study
Design a highly scalable service like Bitly to shorten long URLs into manageable links.
Client
Edge POP
Redis
Case Study
Architect a real-time platform like Uber to connect riders with drivers and manage trips.
Clients
API Gateway
Case Study
Design a massive-scale system like YouTube for video uploading, processing, and streaming.
client
upload gateway
signed URL (S3/GCS)
Case Study
Ship a critical OS update to 500M devices within 5 days.
Device
Update Gateway (global
API service
Case Study
Design Spotify-scale music streaming with personalized playlists and offline sync.
Ingest pipeline
object storage +
CDN distribution
Case Study
Design Twitter-scale microblogging with timelines, fan-out, and moderation.
Tweet ingest
metadata DB (Cassandra) + search index
Case Study
Design a platform to train and serve large language models as an API.
Global API gateway
auth
safety policy
Case Study
Design Windsurf/Codex: an AI coding assistant integrated with IDEs.
IDE plugin
context service
storage (object store
Case Study
Design LinkedIn-scale networking, feeds, and recruiting tools.
Profile service writes to graph DB
search index
Case Study
Design GitHub-scale repositories, pull requests, and CI integrations.
Git front end
repo storage
blob store
Case Study
Design Netflix-scale SVOD with personalization and Open Connect.
Ingest
encoding
DRM
Case Study
Explain Google's Borg cluster manager handling billions of containers.
Job submit
Borg master
scheduler
Case Study
Design a platform that coordinates fleets of autonomous LLM agents โ planning, tool use, memory, and guardrails at scale.
Planner agent
Orchestrator
Worker agents
Case Study
Design a retrieval-augmented generation system: chunking, embeddings, vector search, and grounding an LLM's answers in your own data.
Ingestion pipeline
Vector index
Hybrid retrieval
Case Study
Design the serving layer that runs a large language model in production: batching, KV cache, GPU autoscaling, and latency SLOs.
Router/gateway
Continuous-batching scheduler
KV cache manager
Case Study
Design a messaging service delivering billions of messages a day with end-to-end delivery guarantees.
Case Study
Design a ticket-booking system that survives 10 million fans hitting one on-sale at the same second.
Case Study
Design a file sync service: upload once, appear everywhere, survive laptops that lie and networks that die.
Case Study
Design a distributed rate limiter โ the beginner-friendly question that still exposes senior-level thinking.
Case Study
Design a polite, distributed crawler that fetches billions of pages without DDoSing the internet.
Case Study
Design real-time collaborative editing โ concurrent cursors, conflict-free merging, and offline edits.
URL Shortener
Design a highly scalable service like Bitly to shorten long URLs into manageable links.
Ride-Hailing Service
Architect a real-time platform like Uber to connect riders with drivers and manage trips.
Video Streaming Platform
Design a massive-scale system like YouTube for video uploading, processing, and streaming.