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Fundamentals of System Design

Course Contents

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What Is System Design? (And Why It Runs the World)The Trade-off Mindset: There Are No Right Answers From One User to One Million: How a System Grows System Design in 30 Concepts: The Aerial View How This Course Works + Your Study Plan Diagnostic: Where Should You Start?One System, Five Passes: Meet Your Capstone Checkpoint: Welcome & How to Think

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Anatomy of a Server: One Request, Start to Finish The Memory Hierarchy: Disk, RAM, CPU Cache Sequential vs Random Access: Why Order Matters Processes vs Threads: Units of Work The 10K Connections Problem Connection Pools: Reuse Beats Re-open Concurrency vs Parallelism Shared State & Race Conditions: Why Locks Exist The Four Bottlenecks: CPU, Memory, Disk, Network Checkpoint: Computer & OS Foundations

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Scalability: Vertical vs Horizontal Stateful vs Stateless: Where Does State Live?Availability & the Nines Reliability & Fault Tolerance: Failing Without Falling Single Point of Failure — and Redundancy, the Antidote Latency vs Throughput vs Bandwidth Performance vs Scalability Consistency: First Taste of the Hardest Trade-off Maintainability, Simplicity & Cost Checkpoint: Core Concepts & Non-Functional Requirements

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IP & Addressing (OSI, the Useful Parts)TCP vs UDP DNS: The Internet's Phonebook HTTP/1.1 to HTTP/2 to HTTP/3 (QUIC)TLS: Security's Latency Bill Proxies: Forward vs Reverse Anycast: One IP, Many Places Checksums: Trust but Verify Checkpoint: Networking

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Load Balancers: The Traffic Directors Load Balancing Algorithms L4 vs L7 Load Balancing (+ Sticky Sessions)Health Checks & Failover Global Load Balancing: GeoDNS & Anycast Rate Limiting: Token Bucket, Leaky Bucket, Windows Codelab: Load-Balance Two Servers with nginx Checkpoint: Load Balancing & Traffic

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API Design & REST: Contracts Between Strangers Pagination, Filtering & Sorting Idempotency: Safe to Retry Data Formats & Serialization: JSON, Protobuf, Avro Schema Evolution & API Versioning GraphQL: Ask for Exactly What You Need gRPC & Streaming API Gateways: The Front Door AuthN vs AuthZ: Sessions & JWT OAuth2 & SSO Checkpoint: APIs

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Polling & Long-Polling WebSockets: The Two-Way Street Server-Sent Events Webhooks: We'll Call You WebRTC in 15 Minutes (+ How Zoom Scales)Sync vs Async: The Decoupling Decision Calling Services Safely: Timeouts, Retries & Backoff Message Queues: Absorbing the Burst Queue Models: Broker (RabbitMQ) vs Log (Kafka)Pub/Sub: Broadcasting Events Delivery Semantics: At-Most, At-Least, Exactly-Once Codelab: Produce & Consume with Kafka Checkpoint: Real-time & Async Communication

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Why Caching Wins Caching Patterns: Aside, Read-Through, Write-Through, Write-Behind Cache Eviction: LRU, LFU, TTL, FIFO Cache Invalidation: The Second-Hardest Problem Stampedes, Thundering Herds & Cache Warming Distributed Caching (+ Consistent-Hashing Intuition)CDNs: Caching at the Edge CDN Invalidation & Edge Strategies Codelab: Cache a Slow Endpoint with Redis Checkpoint: Caching

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How Engineers Measure Systems Back-of-the-Envelope: Estimate Like an Engineer Numbers to Know Estimation Walkthroughs: QPS, Storage, Bandwidth Estimation Practice From Numbers to Architecture Checkpoint: Estimation & Numbers Capstone Pass 1: The Marketplace MVP

Delivery Semantics: At-Most, At-Least, Exactly-Once

Stop charging customers twice by choosing when to acknowledge — at-most-once, at-least-once and exactly-once delivery, why exactly-once is both a lie and the goal, the dual-write trap, and dead-letter queues.

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