This section is not a gallery of pretty diagrams. It trains architectural judgment on problems where latency, consistency, cost, and product constraints matter in different proportions.
The chapter turns the case list into a route: which problems sharpen infrastructure thinking, which build data-flow intuition, and which train correctness under concurrency and peak load.
For interviews and architecture discussions, it provides a stable vocabulary for solving cases: framing, invariants, critical path, trade-offs, and evolution.
Coverage
This chapter maps the case-study domain and gives a practical route through all case categories.
Prioritization
Prioritize cases by risk profile: latency, consistency, throughput, and operating cost.
Transferability
The section is designed to transfer architectural patterns across different domains.
Interview Focus
Practice is aligned to a structured answer: framing, architecture, trade-offs, evolution.
Related chapter
System Design Interviews: A 7-Step Approach
A seven-step framework for working through system design problems in a structured way.
This section is not a gallery of pretty diagrams. It trains architectural judgment on problems where latency, throughput, consistency, cost, and product constraints matter in different proportions. It contains 30 full case studies, from infrastructure primitives to product systems with many dependencies and user flows.
The goal is to learn how to isolate requirements, choose architectural primitives, make explicit trade-offs, and explain how a solution should evolve as load grows.
If you move through the section in order, it reinforces the same engineering habit every time: start with the system goal and service expectations, then move into architecture, bottlenecks, growth, and operational consequences.
Coverage breadth
30 cases span infrastructure primitives, product scenarios, data flows, and transaction-heavy domains.
Depth of reasoning
Each case trains explicit trade-off articulation, risk framing, and solution evolution under growth.
Practical focus
The section mirrors real engineering workflow: framing requirements, naming NFRs, and making operational decisions.
Interview readiness
The path helps you keep structure under time pressure and defend architecture choices clearly.
How to work through the section in 4 phases
Core infrastructure primitives
Phase 1Start with Rate Limiter, API Gateway, Object Storage, and CDN to build solid infrastructure instincts.
Product and domain-heavy systems
Phase 2Then add booking, real-time, search, recommendation, and fintech flows with richer constraints.
Trade-off and reliability drills
Phase 3In every solution, explicitly cover SLA/SLO, bottlenecks, operating cost, and failure risks.
Timed interview simulation
Phase 4Solve cases under strict timing and keep a stable structure: framing -> design -> deep dive -> evolution.
Scale and coverage
Total in section
30 cases
From core infrastructure problems to domain-specific product systems.
Infrastructure case studies
11 cases
Gateways, storage systems, CDNs, rate limiters, and other universal infrastructure building blocks.
Product case studies
19 cases
Marketplaces, real-time systems, search, fintech, communications, and geoservices.
Learning focus
NFRs and trade-offs
Latency, throughput, consistency, availability, reliability and cost.
Case catalog
Infrastructure tasks
Fundamental services and platform components that are found in any system.
Product cases
Domain-specific tasks where user scenarios, business constraints and complex data flows are important.
Marketplace and bookings
Content and communications
Platforms and search
Fintech and transactions
Diversity map: what the section actually trains
Latency and real-time scenarios
Tasks where low-latency paths, fan-out, push mechanisms and predictable response time are critical.
Data-intensive and indexing
Ingestion flows, deduplication, indexing, ranking and high throughput requirements.
Storage, durability and delivery
Scenarios with metadata/data split, replication, fault tolerance and data distribution.
Transactions and business correctness
Cases where consistency boundaries, idempotency, anti-fraud and the correct state of orders are important.
Recommended training trajectories
Interview sprint (7-10 days)
A compact route for covering the core patterns and the most common interview prompts.
Platform / infrastructure focus
For platform and backend engineers: storage, data paths, resilience, and control-plane thinking.
Product systems focus
For designing consumer products with many user journeys and business constraints.
Key trade-offs in case solving
Answer speed vs reasoning quality
A quick high-level answer is useful, but without explicit assumptions and trade-offs the depth of engineering thinking is unclear.
Reusable patterns vs domain context
The same architecture building blocks behave differently in fintech, real-time and search due to different risk and SLO profiles.
Technical optimality vs operating cost
An elegant design may still be too expensive in support, on-call load and long-term operational ownership.
Deep dive depth vs end-to-end coverage
One strong deep dive is essential, but you still need a coherent end-to-end picture: data flow, bottlenecks, evolution and risks.
Why infrastructure first, then business
The order is structured as a learning path: first, universal primitives, then their application in products with more complex scenarios and domain restrictions.
1. Basic blocks
Rate Limiter, API Gateway, Object Storage, and CDN form the foundation. Without them, business systems do not scale.
2. Pattern combination
Product cases teach you how to combine cache, queues, sharding, consistency, and degradation modes.
3. Real restrictions
Domain cases add UX, service expectations, anti-fraud concerns, compliance, and cost constraints. That is much closer to real work.
How to work with tasks
- First, clarify the requirements and specify the assumptions.
- Highlight the key NFRs: latency, throughput, consistency, availability and cost.
- Build a high-level architecture and identify critical components.
- Dive into the hardest subsystem and explain the trade-offs you chose.
- Talk through evolution: from MVP to scale and sustained operations.
If you need a reference framework, take a look at Design principles for scalable systems and System Design Interviews: A 7-Step Approach.
For additional hands-on practice on interview-style problem framing and depth, use System Design Primer (problem set) and High Scalability.
How to know the section is improving your skill
- You consistently fit the interview timebox while keeping response structure stable.
- You can explain 2-3 alternatives and justify why the selected approach fits this exact context.
- In each case you name SLO/NFR explicitly and map them to concrete architecture decisions.
- You can describe an evolution plan for 10x and 100x growth without hand-waving.
How to lock in case-study progress
Common pitfalls
Recommendations
Related chapters
- System Design Primer (short summary) - acts as an external problem bank and checklist source to extend practice beyond the local case catalog.
- A/B Testing platform - trains the engineering side of experimentation: event ingestion, metric quality, and statistical correctness.
- Airbnb - shows multi-domain product complexity: search, booking, ranking, and anti-fraud concerns inside one architecture.
- API Gateway - covers foundational edge responsibilities: routing, authentication, rate limiting, and observability.
- Content Delivery Network (CDN) - adds practical latency/throughput trade-offs for global content delivery and caching.
- Chat System - strengthens real-time design skills around latency pressure, fan-out, and offline delivery paths.
- Distributed File System (GFS/HDFS) - deepens storage architecture reasoning: separation of metadata and data, replication strategy, and recovery behavior.
- Google Maps / Proximity Service - introduces geospatial search and spatial indexing as a distinct class of design challenges.
- Hotel reservation system - focuses on transactional correctness and idempotency in booking-critical flows.
- Interplanetary Distributed Computing System - extends system design thinking to extreme constraints: high latency, autonomous nodes and eventual sync.
- Feature Store & Model Serving - adds practice in offline/online parity, point-in-time correctness, and training-serving skew guardrails.
- ML Ops Pipeline - adds a dedicated AI/ML case class: feature pipelines, model rollout safety, drift monitoring, and operational guardrails.