System Design Home

Introduction

Welcome to System Design: From First Principles to FAANG Interview Mastery — a mentor-led course for engineers who need to design systems that survive real traffic, real failures, and real interview whiteboards.

The story

Forty-five minutes into a staff loop, the interviewer says: "Design Twitter." Most candidates draw a box labeled "Database" and stall. One candidate asks: "What's the read/write ratio? Do we need strong consistency on likes? What's the SLA?" — then sketches clients → CDN → load balancer → write API / read API → cache → sharded DB, naming trade-offs at each hop. Same question, different outcome. That structured curiosity is what this course builds.

The business problem

Teams that skip disciplined system design thinking pay in outages, cost overruns, and failed interviews:

• Outages: components chosen without scale math fail at peak.
• Cost: over-engineered microservices for 100-user products.
• Latency: missing cache/CDN/replica on read-heavy paths.
• Interviews: boxes without numbers and trade-offs don't hire.

The problem teams faced

This course addresses the gap between "I know what a load balancer is" and "I can design Twitter in 45 minutes with trade-offs:"

• How to clarify requirements and estimate scale before drawing boxes.
• Which building blocks compose into real production architectures.
• Classic case studies asked at every major tech company.
• Interview framework, mocks, and cheat sheet for loop readiness.

Understanding the topic

Your arc across 48 mentor-led lessons:

• Foundations — requirements, estimation, APIs, trade-offs.
• Building blocks — load balancers, cache, CDN, queues, DBs.
• Distributed systems — CAP, consistency, sharding, idempotency.
• Scalability — read/write split, indexing, search, object storage.
• Reliability — observability, DR, SLAs, deployment, security.
• Case studies — URL shortener, Twitter, WhatsApp, Uber, Netflix…
• Interview mastery — framework, mocks, cheat sheet.

Internal architecture

System Design Home — system view:

Clients
   ↓
System Design Home layer (this lesson's focus)
   ↓
Supporting infrastructure
   ↓
Data & observability

Visual explanation

Three diagrams: architecture flow, design process, and scaling lens:

Informative example

Example — System Design Home:

// Capacity sketch — URL shortener
// 100M URLs/month ≈ 40 writes/sec peak (~100 with 2.5× burst)
// Read:write = 100:1 → ~4,000 reads/sec peak
// Storage: 100M × 500 bytes ≈ 50 GB/year
// → Single region OK initially; plan sharding at 1B URLs

Execution workflow

Real-world use

Used in production at major tech companies and every FAANG system design interview loop. Patterns align with AWS/GCP well-architected frameworks and Google SRE practice.

Production case study

A Series B startup's API p99 hit 8 seconds during a product launch — not from code bugs, but from missing cache and read replica on a hot dashboard query. A one-week architecture fix (Redis + read replica + query rewrite) dropped p99 to 120ms without rewriting the app. System design is often the cheapest performance win.

• Context: production or interview scenario from this lesson.
• Decision: component and trade-off choices explained.
• Outcome: measurable latency, availability, or cost impact.

Trade-offs

• Pro: structured approach reduces outages and interview failures.
• Con: upfront thinking takes time — faster than wrong rebuild.
• Con: every product has unique constraints — adapt templates.

Decision framework

• Always estimate before drawing microservices.
• Match consistency model to business domain (money vs likes).
• Document rejected alternatives — ADR or interview closing.

Best practices

• State assumptions explicitly (DAU, read:write ratio).
• Draw async vs sync paths with different line styles.
• Close with monitoring and on-call failure modes.

Anti-patterns to avoid

• Jumping to Kafka and microservices without scale justification.
• Single DB box with no read replica or cache on read-heavy design.
• Ignoring idempotency on write APIs with retries.

Common mistakes

• Underestimating peak QPS (forgetting peak factor).
• Hot shard from poor partition key choice.

Debugging tips

• Trace one request ID through diagram — find missing component.
• Ask "what fails first at 10×?" for every design.

Optimization strategies

• Cache + CDN first for read-heavy; shard when single DB saturates.
• Async queue for slow side effects (email, analytics).

Common misconceptions

• More components ≠ better design — simplest meeting NFRs wins.
• Interviews test process — not memorizing Netflix architecture.

Advanced interview questions

Summary

You can explain System Design Home in a 45-minute system design interview with numbers, diagrams, and trade-offs. Teach it back without notes — you own this piece.