Riddhi Gupta

System Design interviews don’t reward memorization. They reward clarity. Because today, system design is no longer “for seniors only.” Even SDE1/SDE2 interviews test whether you can think at scale - users, latency, failures, cost, and reliability. That’s exactly what this guide covers. It breaks down 10 must-know system design architectures that show up again and again in interviews - along with the core components, scaling tricks, and the exact “interview gold points” for each system. Instead of random theory, it trains you to explain systems like an engineer. How requests flow, where bottlenecks happen, what components matter most, and what tradeoffs interviewers actually care about. The 10 Designs Covered Inside : 1. It starts with fundamentals like a URL Shortener - then expands into real-world architectures like: 2. A Messaging System (WhatsApp) that handles delivery, retries, ordering, read receipts, and offline users. 3. An Instagram Feed that teaches fanout-on-write vs fanout-on-read and ranking tradeoffs. 4. A Video Streaming system (YouTube/Netflix) that shows the encoding pipeline, chunking, and why CDNs dominate scale. 5. A Ride Sharing system (Uber) built on real-time GPS streams, ETA accuracy, and geospatial indexing. 6. A Web Crawler (Google) designed as a pipeline: discover → fetch → parse → index with deduplication. 7. An E-commerce system (Amazon) showing inventory consistency, idempotent orders, and event-driven flows. 8. A Food Delivery system powered by workflow engines and state machines for reliable tracking. 9. A Spotify architecture where streaming + personalization becomes the retention engine. 10. And a Dropbox file sharing system where metadata + versioning is the real product. If you can explain these 10 clearly, you’re already ahead of most candidates - because these designs cover almost every interview pattern: caching, queues, sharding, consistency, fanout, CDN, state machines, and observability.

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35 practical thoughts on system design Core Principles 1. Every system is a trade-off -> you never get speed, cost, and simplicity all at once. 2. Latency compounds -> every millisecond added across layers adds up to user pain. 3. Scalability ≠ performance -> one handles growth, the other handles speed. 4. Read vs. write paths -> scaling each requires completely different strategies. 5. Design for change, not perfection -> requirements will shift. Databases & Storage 6. Indexes are levers -> high-selectivity columns are worth indexing, low ones often aren’t. 7. Replication helps reads, partitioning helps writes -> don’t confuse the two. 8. Dual writes are a lie -> without coordination, you will see drift. 9. Event stores > queues (sometimes) -> better traceability, worse simplicity. 10. Cache invalidation is still the hardest problem -> freshness vs. performance is the eternal fight. Reliability & Consistency 11. Idempotency saves you -> retries without it will hurt. 12. Fail fast, fail loud -> silent failures sink systems. 13. Eventual consistency is a feature -> not a bug, but only if the business allows it. 14. Conflict resolution in active-active is business logic, not infra. 15. Durability isn’t free -> syncing across regions has cost and latency. Architecture Patterns 16. Microservices are an org structure, not a tech goal. 17. Monolith first, modular second, microservices last -> don’t jump too early. 18. Choreography scales, orchestration simplifies -> pick based on team maturity. 19. Serverless buys you focus, costs you control. 20. Queues don’t remove work, they smooth it. Observability & Operations 21. Tracing > logging -> logs tell you “what”, traces tell you “why”. 22. Metrics rot without ownership -> measure what someone actually uses. 23. Retries without backoff = DDoS on yourself. 24. Dead-letter queues aren’t optional -> every system has poison messages. 25. Levers > knobs -> design quick kill switches and controls to contain blast radius. Performance & Cost 26. Optimize the hot path, not the cold path. 27. Most bottlenecks live in the database, not the code. 28. Horizontal scaling beats vertical scaling ->until coordination kills it. 29. Warm caches mask bad queries -> measure against cold starts too. 30. The cheapest resource is disk, the most expensive is time. People & Process 31. The best architecture dies without documentation. 32. System design reviews aren’t about diagrams, they’re about trade-offs. 33. Small PRs are for speed, big PRs are for context -> balance both. 34. A senior engineer’s role in design is asking the uncomfortable “what if”. 35. No design survives first contact with production -> but good ones bend instead of break. Want to sharpen your instincts with daily practice? Check out joinenginuity.com One system design question every day, with streaks, badges, and leaderboards, and a dashboard to keep you sharp. Enjoy the rabbit hole!

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𝐏𝐫𝐞𝐩𝐚𝐫𝐢𝐧𝐠 𝐟𝐨𝐫 𝐚 𝐒𝐲𝐬𝐭𝐞𝐦 𝐃𝐞𝐬𝐢𝐠𝐧 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰? 𝐃𝐨𝐧’𝐭 𝐒𝐤𝐢𝐩 𝐓𝐡𝐞𝐬𝐞! Before jumping into complex architectures like Uber, Netflix, or WhatsApp — 𝐦𝐚𝐬𝐭𝐞𝐫 𝐭𝐡𝐞 𝐟𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬. These 𝐓𝐨𝐩 𝟗 𝐃𝐚𝐭𝐚 & 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐅𝐥𝐨𝐰 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬 are the real building blocks behind every scalable, reliable system. They’re also 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐟𝐚𝐯𝐨𝐫𝐢𝐭𝐞𝐬 at 𝐆𝐨𝐨𝐠𝐥𝐞, 𝐀𝐦𝐚𝐳𝐨𝐧, 𝐌𝐞𝐭𝐚 & other top product companies. 🔹 𝐏𝐞𝐞𝐫-𝐭𝐨-𝐏𝐞𝐞𝐫 – Direct service calls 🔹 𝐀𝐏𝐈 𝐆𝐚𝐭𝐞𝐰𝐚𝐲 – One gateway, many services 🔹 𝐏𝐮𝐛-𝐒𝐮𝐛 – Decoupled, async messaging 🔹 𝐑𝐞𝐪𝐮𝐞𝐬𝐭-𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐞 – Classic but still gold 🔹 𝐄𝐯𝐞𝐧𝐭 𝐒𝐨𝐮𝐫𝐜𝐢𝐧𝐠 – Store every action, not just the result 🔹 𝐄𝐓𝐋 – Raw to ready in 3 steps 🔹 𝐁𝐚𝐭𝐜𝐡𝐢𝐧𝐠 – Efficiency through grouping 🔹 𝐒𝐭𝐫𝐞𝐚𝐦 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 – Handle real-time like a pro 🔹 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 – Your system’s master conductor 🔍 These patterns may look simple — but 𝐭𝐡𝐞𝐲 𝐡𝐨𝐥𝐝 𝐭𝐡𝐞 𝐤𝐞𝐲 to answering “How would you design XYZ?” 📚 𝐏𝐫𝐨 𝐭𝐢𝐩: Nail the concepts, not just the code. If you’re serious about system design, 𝐭𝐡𝐢𝐬 𝐢𝐬 𝐲𝐨𝐮𝐫 𝐜𝐡𝐞𝐜𝐤𝐥𝐢𝐬𝐭. 💡 𝐒𝐚𝐯𝐞 𝐭𝐡𝐢𝐬. 𝐒𝐡𝐚𝐫𝐞 𝐭𝐡𝐢𝐬. 𝐑𝐞𝐯𝐢𝐬𝐢𝐭 𝐭𝐡𝐢𝐬.

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𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗦𝗲𝗿𝗶𝗲𝘀: Behind every blazing-fast read, there is a cache write strategy working in the background. Every cache write strategy comes with its own trade-offs between performance, consistency, and complexity: Let us discuss some of the most commonly used cache write strategies. 𝗪𝗿𝗶𝘁𝗲-𝗔𝗿𝗼𝘂𝗻𝗱 𝗖𝗮𝗰𝗵𝗲 In this cache write strategy, the data is directly stored in the database, bypassing the cache, and is loaded into the cache only on a subsequent read. 𝗪𝗿𝗶𝘁𝗲-𝗕𝗮𝗰𝗸 𝗖𝗮𝗰𝗵𝗲 In write-back cache, the data is written first to the cache, and then the database is updated asynchronously once in a while. By doing so, we are improving write performance but also risking data loss in case of cache failure. 𝗪𝗿𝗶𝘁𝗲-𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝗖𝗮𝗰𝗵𝗲 In this caching strategy, the data is first written to the cache, but unlike the write-back cache strategy, the data is also immediately written to the database.  For my previous posts in the Caching series, you can check them out here: 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗰𝗮𝗰𝗵𝗲𝘀: https://lnkd.in/gzERggJ6 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗖𝗮𝗰𝗵𝗲 𝘀𝘆𝘀𝘁𝗲𝗺: https://lnkd.in/gwfqWR_V 𝗖𝗮𝗰𝗵𝗲 𝗘𝘃𝗶𝗰𝘁𝗶𝗼𝗻 𝗣𝗼𝗹𝗶𝗰𝗶𝗲𝘀: https://lnkd.in/g9HQiHKv 𝗖𝗮𝗰𝗵𝗲 𝗥𝗲𝗮𝗱 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀: https://lnkd.in/gS38wgF9 In my next post in the caching series, I will be talking about how CDN works.

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𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗧𝗮𝗹𝗸𝘀 #𝟭 — 𝗛𝗼𝘄 𝗡𝗲𝘁𝗳𝗹𝗶𝘅 𝗦𝗰𝗮𝗹𝗲𝘀? If someone asks you to design Netflix in a system design interview, saying “𝗺𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 + 𝗖𝗗𝗡” is not enough. 𝗟𝗲𝘁’𝘀 𝘁𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝗶𝘁 𝗽𝗿𝗼𝗽𝗲𝗿𝗹𝘆. 𝗧𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗡𝗲𝘁𝗳𝗹𝗶𝘅 𝗳𝗮𝗰𝗲𝘀 𝗶𝘀 𝗯𝗮𝗻𝗱𝘄𝗶𝗱𝘁𝗵 𝗮𝗻𝗱 𝗹𝗮𝘁𝗲𝗻𝗰𝘆. Video streaming is extremely heavy compared to typical web traffic. If every stream came from a centralized data center, network congestion and latency would kill performance. That’s why Netflix uses a global CDN. Video content is pre-replicated across edge locations. When you press play, your request is routed to the nearest edge server. The key idea here is understanding that data transfer, not compute, which becomes your primary bottleneck in media systems. 𝗧𝗵𝗲 𝘀𝗲𝗰𝗼𝗻𝗱 𝗺𝗮𝗷𝗼𝗿 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝗶𝘀 𝗵𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝘂𝗻𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗮𝗯𝗹𝗲 𝘁𝗿𝗮𝗳𝗳𝗶𝗰 𝘀𝗽𝗶𝗸𝗲𝘀. Evening peak hours can multiply traffic several times. Instead of scaling a monolithic backend, Netflix isolates responsibilities into independent services (eg. playback, recommendations, user profiles, billing.) Why is that important? Because different components scale differently. Playback scales with concurrent users. Billing scales monthly. Recommendations scale per user session. 𝗧𝗵𝗲 𝘁𝗵𝗶𝗿𝗱 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗽𝗶𝗲𝗰𝗲 𝗶𝘀 𝗰𝗮𝗰𝗵𝗶𝗻𝗴. Imagine millions of users requesting metadata for the same trending show. Without caching, your database becomes the single point of failure. Netflix aggressively caches popular titles, frequently accessed metadata and session data. A note to take here: At scale, database optimization is not enough. You must reduce database dependency. 𝗔𝗻𝗼𝘁𝗵𝗲𝗿 𝘂𝗻𝗱𝗲𝗿-𝗱𝗶𝘀𝗰𝘂𝘀𝘀𝗲𝗱 𝗰𝗼𝗻𝗰𝗲𝗽𝘁 𝗶𝘀 𝗳𝗮𝗶𝗹𝘂𝗿𝗲 𝗵𝗮𝗻𝗱𝗹𝗶𝗻𝗴. Distributed systems fail in subtle ways- -> Network partitions -> Partial outages -> Slow downstream services Netflix practices chaos engineering to simulate failures intentionally. Why? Because large-scale systems must be designed assuming failure, not assuming stability. In interviews, mentioning retries, circuit breakers, and graceful degradation shows maturity. 𝗙𝗶𝗻𝗮𝗹𝗹𝘆, 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻. Your Netflix homepage isn’t static. It’s generated based on: -> User history -> Real-time engagement -> Ongoing A/B experiments That means large-scale data processing pipelines run continuously behind the scenes. So scaling Netflix is more about 𝙨𝙩𝙧𝙚𝙖𝙢𝙞𝙣𝙜 + 𝙙𝙞𝙨𝙩𝙧𝙞𝙗𝙪𝙩𝙚𝙙 𝙘𝙤𝙢𝙥𝙪𝙩𝙖𝙩𝙞𝙤𝙣 + 𝙙𝙖𝙩𝙖 𝙥𝙞𝙥𝙚𝙡𝙞𝙣𝙚𝙨 running simultaneously. It’s about identifying your real bottleneck instead of adding just more servers. Good system design is constraint-driven thinking. If you were asked to design Netflix in an interview, what would you optimize first? #SystemDesign #SoftwareEngineering #Netflix

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Is CDN a reverse proxy, just like a load balancer or an API gateway? Yes. While LB and API gateway fit the classic definition of a Reverse Proxy, CDN is also a reverse proxy with caching as its primary purpose. Like any other reverse proxy, 1. CDN acts as a single point of entry for client requests, 2. CDN abstracts out different origins configured behind it 3. CDN can also route the requests across multiple origins depending on the route-matching configuration. Hence, the deduction is on point, and LB, API Gateway, and CDN are all examples of Reverse Proxies. I elaborated on CDN, as it is an unconventional example. ps: I love how the comments on my YouTube videos are also no-fluff and filled with thought-provoking questions. Thank you for asking such great questions. By the way, the question was asked in my recent video on Forward and Reverse Proxy. In the video, I went into highly practical details with real-world examples and stories from my life. give it a watch and subscribe - youtu.be/HrG0MHkSsCA btw, enrollments open for sys design June cohort, about 30 seats left - arpitbhayani.me/course

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