Gaurav Kumar Singh

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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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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𝗢𝗻𝗲 𝗛𝗟𝗗 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗺𝗶𝘀𝘁𝗮𝗸𝗲 𝘁𝗵𝗮𝘁 𝗰𝗼𝘀𝘁 𝗺𝗲 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀 𝗮𝘁 Amazon 𝗮𝗻𝗱 Atlassian 🚨 HLD (High-Level Design) rounds are all about building scalable systems that meet both functional and non-functional requirements. One big mistake I made was trying to stay too ahead of time. In doing so, I rushed my decisions — and that only led to more mistakes piling up. ❌ Rushing never helps. Take time to think things through and make informed choices. A solid design with fewer flaws is always better than a fast, shaky one. ⏳ Time management is important, but never at the cost of making careless decisions. Mock interviews helped me a lot in improving my approach. If you're preparing for system design interviews, I highly recommend practicing as much as you can. Feel free to book a topmate.io session if you'd like help preparing! 💬 Link: https://lnkd.in/gZkAR64w

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What Are Design Patterns & Why Should You Care? Design patterns are proven, reusable solutions to common software design problems. They bring clarity, flexibility, and scalability to your code—especially in complex enterprise or microservices architectures. 🔍 Types of Design Patterns 🧠 Creational – e.g., Singleton, Factory, Builder 🔗 Structural – e.g., Adapter, Decorator, Proxy 🎯 Behavioral – e.g., Strategy, Observer, Template Method 🏗️ Architectural – e.g., CQRS, Event Sourcing, Saga Using the right pattern at the right time helps enforce clean architecture, reduce technical debt, and drive developer productivity. I've shared 75+ real-world interview questions and answers across experience levels (0–20 yrs) covering design pattern usage in REST APIs, microservices, UI responsiveness, and cloud-native systems. 👇 Like, comment, repost, and follow Sai Reddy for more such tech insights. 💬 If I’m wrong, correct me in the comments—always open to learning! 📩 Reach me here for collab, questions, or mentoring. https://lnkd.in/gYvf3_N3 Or https://lnkd.in/gsrnePyD #DesignPatterns #SoftwareEngineering #SystemDesign #CleanArchitecture #Microservices #TechLeadership #DevCommunity #SoftwareDesign #CodingBestPractices #EnterpriseSoftware #DeveloperTips #SaiTeachesTech

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🎯 Frontend Development Skills for Product-Based Companies 🔥 Ready to stand out in top tech teams? Join Ng Delhi Community: https://lnkd.in/gZfR9exS ✅ A well-structured roadmap is your best friend if you’re aiming to become a high-performing frontend developer. 📌 𝗕𝗮𝘀𝗶𝗰 𝗝𝗮𝘃𝗮𝗦𝗰𝗿𝗶𝗽𝘁 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 • Data types • Functions • Scope • Closures • Event loop • Prototype & prototype chain • Classes & inheritance • DOM manipulation • bind / call / apply • Promises • Web APIs • Task queue & call stack • Async/await • Generators • TypeScript 📌 𝗕𝗮𝘀𝗶𝗰 𝗛𝗧𝗠𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 • Block vs Inline elements • Semantic HTML • Imports and includes • Forms & accessibility • Infinite questions, infinite possibilities! 📌 𝗕𝗮𝘀𝗶𝗰 𝗖𝗦𝗦 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 • Selectors (Class, ID, Universal) • Pseudo Classes & Elements • Box Model • Display types: flex, grid, block • Centering techniques • Element states: active, hover, focus • Media queries • Preprocessors: SCSS/LESS • Mixins, variables, constants • BEM naming convention • Importing styles 📌 𝗕𝗮𝘀𝗶𝗰 𝗪𝗲𝗯 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 • Page rendering lifecycle • HTTP, HTTPS, HTTP/2 • CORS • LocalStorage / SessionStorage • Cookies • JWT • XMLHttpRequest • Micro Frontends • REST / GraphQL / WebSocket • Browser internals • Debugging techniques • Chrome DevTools mastery 📌 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗝𝗮𝘃𝗮𝗦𝗰𝗿𝗶𝗽𝘁 • OOP principles • Design Patterns: ▸ Singleton, Provider, Prototype ▸ Observer, Module, HOC • V8 Engine insights: ▸ JIT, Interpreter, Execution • Currying 📌 𝗕𝗮𝘀𝗶𝗰 𝗥𝗲𝗮𝗰𝘁𝗝𝗦 (𝗕𝗼𝗻𝘂𝘀) • JSX • Components (Functional & Class) • State, Props, Hooks • Lifecycle methods • Virtual DOM • Context API • Redux / Flux • Routing • Testing with Jest & RTL • Webpack, Babel, ESLint, Prettier 📌 𝗕𝗮𝘀𝗶𝗰 𝗔𝗻𝗴𝘂𝗹𝗮𝗿 (𝗕𝗼𝗻𝘂𝘀) • Angular architecture & modules • Components, Directives, Pipes • Services & Dependency Injection • Lifecycle Hooks • Routing & Lazy Loading • Template vs Reactive Forms • RxJS & Observables • Change Detection Strategies • Angular Signals (latest) • State Management (NgRx / Signals) • Testing with Jasmine & Karma • Angular CLI, Environment configs • SSR with Angular Universal • Angular Material & CDK Like, save, and share if this helped you! Enjoy learning — one topic at a time! #frontend #javascript #html #css #webdevelopment #reactjs #angular #typescript #roadmap #developers #ngdelhi #codingjourney #techskills

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Found another gem of a resource for Low Level Design / Machine Coding. Git: https://lnkd.in/gBFEXSAU Most FAANG asked problems like: 1. Elevator Design 2. Uber/Ola Design (Driver, Rider flows) 3. Splitwise 4. Rate Limiter 5. TicTacToe 6. Facebook etc. are well designed wrt SOLID principles and design patterns. (Not a paid promotion!) #lld #machinecoding #lowleveldesign

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iOS Architecture: 1. Refers to the high-level structure of an iOS app. 2. Determines how different components of the app interact and how the app is organised. 3. Example patterns: MVC, MVVM, VIPER, Clean Architecture. 4. Concerned with scalability, maintainability, testability, and separation of concerns. iOS Design: 1. Refers to the implementation details within components. 2. Focuses on how a specific feature or module works internally. 3. Includes UI/UX design, class design, method interactions, data flow, and design patterns like Singleton, Observer, Delegate, Strategy. 4. Concerned with code quality, reusability, and efficiency. iOS Example: Architecture Example: Using MVVM Model → Holds data View → Displays UI ViewModel → Binds data to the view Design Example: Inside ViewModel Using Singleton for network manager Using Delegate to communicate from a service to ViewModel

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Transactions are a protocol on top of a storage layer. It might be integrated into the storage layer. But you can also always build your own transaction layer on top of a storage layer that doesn't support transactions. You can also even ignore the transaction layer of any storage layer that has an integrated transaction layer and build your own transaction layer on top. Convex did this, reimplementing MVCC on top of Postgres (or MySQL or something). And the Delta Lake protocol for transactions on blob storage is even simpler.

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80% of system design problems can be tackled with these 20% of distributed systems patterns  System design interviews can feel overwhelming but A small set of core distributed systems patterns show up again and again.  Here are the 5 most important patterns you need to know:   1️⃣ Leader Election  🔹 What does it do?   Ensures that in a distributed system, one node is designated as the leader to make decisions and coordinate tasks.  🔹 Use case:   - Managing a single point of coordination in databases (e.g., primary-replica setups).   - Preventing split-brain scenarios in distributed clusters.  🔹 Real-life example:   - Apache Zookeeper uses leader election to ensure a single master node coordinates tasks.   - Kubernetes’ Raft-based control plane elects a leader among control nodes to manage workloads.   2️⃣ Write-Ahead Log (WAL)  🔹 What does it do?   Ensures durability by logging changes before they are committed, so they can be replayed in case of failure.  🔹 Use case:   - Prevents data loss in databases and distributed file systems.   - Guarantees that transactions are replayable after a crash.  🔹 Real-life example:   - PostgreSQL & MySQL use WAL to maintain consistency.   - Kafka implements an immutable log for fault tolerance.   3️⃣ Consensus (Paxos & Raft)   🔹 What does it do?   Allows multiple nodes in a distributed system to agree on a single truth, even in the presence of failures.  🔹 Use case:   - Keeping replicated databases consistent.   - Ensuring only one leader exists at a time in clusters.  🔹 Real-life example:   - Raft is used in etcd and Kubernetes for ensuring leader consistency.   - Paxos is used in Google Spanner for transaction coordination across data centers.   4️⃣ High-Watermark Pattern   🔹 What does it do?   Ensures that only processed and confirmed data is committed, reducing inconsistencies in distributed data pipelines.  🔹 Use case:   - Managing event processing systems where failures can lead to duplicate or missing messages.   - Helps checkpoint progress in stream processing.  🔹 Real-life example:   - Apache Flink & Spark Streaming use high-watermark for exactly-once processing guarantees.   - Kafka consumers track offsets to avoid reprocessing already committed messages.   5️⃣ Event Sourcing   🔹 What does it do?   Stores changes to system state as a sequence of immutable events, rather than just maintaining the current state.  🔹 Use case:   - Provides a complete audit log of all changes.   - Enables time-travel debugging and rollback to any previous state.  🔹 Real-life example:   - EventStoreDB & Kafka use event sourcing to track state changes over time.   - Financial systems use event sourcing to maintain audit trails. 

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