System Design Course

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 failure…

System design is the discipline of structuring components — servers, databases, caches, queues — so a product meets functional requirements under real-world constraints: scale,…

Non-functional requirements (NFRs) — latency, throughput, availability, durability, consistency — usually determine architecture more than feature lists.

Requirements clarification is the first 5 minutes of every system design interview and every production RFC.

Back-of-envelope estimation translates vague "millions of users" into QPS, storage, and bandwidth — so you pick right-sized components before drawing boxes.

API design is the contract between clients and your system.

Every architecture is a trade-off.

Load balancers distribute traffic across servers for throughput and fault tolerance.

Caching trades freshness for speed at every layer: browser, CDN, application, database query cache, and distributed cache (Redis).

CDNs cache static and dynamic content at edge PoPs close to users — cutting latency and origin load for global products.

SQL vs NoSQL is not a moral choice — it's a data model fit question.

Replication copies data for read scale and fault tolerance.

Message queues decouple producers and consumers — absorbing traffic spikes, enabling async workflows, and improving failure isolation.

Microservices split a system into independently deployable services aligned (ideally) to business capabilities — trading monolith simplicity for team autonomy and isolated failu…

DNS and networking are the invisible foundation — routing users to healthy endpoints, enabling TLS, and defining security perimeters (VPC, subnets, security groups).

CAP theorem: during a network partition, a distributed system must choose between consistency (C) and availability (A).

Consistency models — strong, eventual, causal, read-your-writes — define what clients can assume after a write.

Sharding (horizontal partitioning) splits data across nodes by a shard key — scaling writes and storage beyond one machine.

Replication strategies — single-leader, multi-leader, leaderless — balance write scalability, conflict handling, and read consistency.

Distributed consensus (Raft, Paxos) elects leaders and agrees on ordered logs — foundation for strongly consistent metadata and coordination services.

Idempotency ensures repeating the same request has the same effect as once — essential for retries, queues, and payment APIs.

Rate limiting protects services from abuse and overload — token bucket, sliding window, and leaky bucket at gateway or service level.

Event-Driven Architecture (EDA) replaces synchronous "call and hope" chains with immutable facts published to a log.

Vertical scaling (bigger machine) is simpler until you hit hardware limits.

Read/write splitting routes mutations to primary and queries to replicas — the most common database scale pattern before sharding.

Indexes trade write amplification for read speed.

Data warehouses (Snowflake, BigQuery, Redshift) optimize analytical queries via columnar storage and ETL from OLTP systems — never run BI on production primary.

Search at scale uses inverted indexes (Elasticsearch, OpenSearch) — full-text, faceted, fuzzy — separate from transactional DB.

Object storage (S3, GCS) stores unstructured blobs — images, videos, backups — with durability, CDN integration, and pay-per-GB economics.

Observability — metrics, logs, traces — answers "what broke?" and "why slow?" in distributed systems where printf debugging fails.

Fault tolerance designs for component failure as normal — redundancy, health checks, automatic failover, and graceful degradation.

Disaster recovery (DR) plans for region loss — RPO (data loss tolerance) and RTO (recovery time) drive backup, replication, and runbook investment.

SLI, SLO, SLA — measure reliability (SLI), set internal targets (SLO), contract with users (SLA).

Deployment strategies — blue-green, canary, rolling — balance release speed with blast radius control.

Security at scale — authentication, authorization, encryption in transit/at rest, secrets management, and zero-trust between services.

URL shortener — the classic system design interview.

Twitter / news feed — fan-out on write vs fan-out on read, celebrity problem, and timeline ranking at scale.

WhatsApp-style messaging — real-time delivery, offline sync, end-to-end encryption, and presence at billion-user scale.

Uber / ride-sharing — geospatial indexing, matching supply and demand, surge pricing, and real-time location updates.

Netflix-style streaming — transcoding pipeline, adaptive bitrate, CDN delivery, and playback analytics at global scale.

Instagram-style photo sharing — upload pipeline, feed generation, likes/comments, and object storage for media.

Dropbox-style file storage — chunking, deduplication, sync protocol, and metadata vs blob separation.

E-commerce at scale composes checkout, inventory, payment, search, and recommendations — the full system design capstone.

System design interview framework — structured 45 minutes: clarify, estimate, design, deep-dive, trade-offs.

Mock interview: URL shortener — walk through full 45-minute loop with junior, mid, and senior answer levels.

Mock interview: chat system — 1:1 and group messaging, online presence, read receipts, and push notifications.

Mock interview: distributed rate limiter — token bucket at scale across multiple API servers with Redis.

System design cheat sheet — quick reference for interviews: components, CAP, estimation formulas, and classic problem patterns.