System Design Experience Guide - From a Senior FAANG Engineer My perspective on building system design skills through work experience and effective interview preparation. Building System Design Experience at Work The Reality: System Design is Broad and Deep Let me be honest - system design can't be mastered quickly. The best way to build these skills is through real work experience: doing projects and learning from other teams' designs. These are battle-tested industrial solutions that actually work. Personal Example: I've led several system designs that happened to be classic interview questions. During the design phase, I dove deep into: - Implementation details of various solutions - Pros and cons of each approach - Hardware parameters and load test results - Real-world pitfalls and lessons learned This hands-on experience is far more valuable than online tutorials or YouTube videos. When these topics come up in interviews, I can lead the discussion and cover aspects even interviewers haven't considered. Career Strategy for Building Design Skills For Mid-Level Engineers: - System design interviews test breadth more than depth - You can pass without knowing every technology detail - Focus on providing sensible solutions For Senior+ Engineers: - Both breadth AND depth matter - You need data and experience to support your solutions - Not knowing a key area can fail the interview Career Progression Tips: - Early career: Switch teams/projects to build breadth - Later career: Stay in one domain to build depth - You'll discover many concepts are interconnected - If your company doesn't offer growth opportunities, consider if it's the right fit Learning and Preparing for System Design Interviews Foundation Resources 1. Designing Data-Intensive Applications (DDIA) - Often called the "bible" but really it's an entry-level foundation book - Excellent for understanding how industry handles distributed data - Core focus on data - which is the heart of system design - Won't directly solve interview problems but builds crucial understanding - Weak on batch/stream processing - needs supplementation 2. System Design Primer - Good for beginners learning concepts - Warning: Some content has errors since it's crowd-sourced - Don't memorize - think critically about the content 3. Essential Papers - Google's Big Three: GFS, MapReduce, Bigtable - Amazon's DynamoDB - Various summaries and notes available online 4. Other Books - "Designing Distributed Systems" - decent but not as good as above - For batch/stream processing: Consider Kafka, Flink specific books - "Real-Time Analytics" Learning from Real Cases Best Sources: - If you're at a big tech company, study other teams' production designs - Google, Meta, Amazon are goldmines for real-world examples - Industrial solutions vs. online guesses - huge difference Resource Quality Guide Watchable but Limited: - Grokking: Basic concepts and ID generator example are okay, skip the rest - Alex Xu's Books: - Book 1: Too shallow, similar to Grokking - Book 2: Better with more content but inconsistent quality - Feels like different authors or compiled from various sources - Some sections pad word count while skipping important discussions - Still worth buying due to lack of better resources System Design Interview (YouTube): - Rate Limiter video is good - Skip the Top K solution - outdated approach that will fail interviews - Recently selling courses - will update after reviewing Interview Tips Core Understanding Most system design interviews revolve around: 1. Requirements → What data do you need? 2. Data characteristics → Size, patterns, access methods 3. Data handling → Storage, retrieval, integrity This is why DDIA is valuable - it's all about data-centric design. Drawing and Explaining Interviewers want to see: - Reasonable infrastructure choices - Correct data flow - Clear thinking process Pattern Recognition Many design problems share common patterns: - Group chat ≈ Multiplayer card games (similar data handling) - With experience, you'll see recurring patterns - Market may introduce new problems as it gets more competitive Capacity Estimation In real work: Calculate precisely for scalability and cost In interviews: Order of magnitude is usually enough - TB vs GB? - Million vs thousand QPS? - These determine your technical choices Senior+ tip: If you can casually calculate exact numbers and provide specific infrastructure choices with cost analysis, it's a major plus. Luck Factor Sometimes you get difficult or egotistical interviewers - no technique helps there 😅 Recommended Case Studies Good examples should clearly address schema design - if they gloss over data modeling, the entire architecture is questionable. Quality Examples 1. Rate Limiter - Popular YouTube tutorial on rate limiting - Solid for interviews - Schema is simple: store numbers and identity - Missing: Design is not one-way - can use local rate limiters as safeguards - Think deeper: CPU/memory-based ra