Mohit Gawande

If I were your interviewer in a System Design interview, here’s how I’d evaluate your answers — based on these 40 core fundamentals: 1. Scalability – Vertical vs horizontal; default to stateless horizontal scale 2. Latency & Throughput – Know P50/P95/P99 and tradeoffs 3. Capacity Estimation – Estimate QPS, storage, bandwidth 4. Networking Basics – TCP, HTTP/HTTPS, TLS, UDP, DNS 5. SQL vs NoSQL – Decide by access patterns, joins, consistency 6. Data Modeling – Structure to optimize hot queries 7. Indexing – Right indexes help reads, wrong ones hurt writes 8. Normalization – Normalize for writes, denormalize for reads 9. Caching – App/DB/CDN; write-through/back, TTLs 10. Cache Invalidation – TTLs, versioning, stampede protection 11. Load Balancing – L4/L7, RR, least-connections, hashing 12. CDN & Edge – Serve static content near users 13. Sharding – Hash/range/geo; handle hot keys, rebalancing 14. Replication – Sync/async, leader/follower, read replicas 15. Consistency Models – Strong, eventual, causal 16. CAP Theorem – In partition, pick A or C 17. Concurrency – Locks, MVCC, retries 18. Multithreading – Pools, contention, switching 19. Idempotency – Same request, same effect 20. Rate Limiting – Fairness and protection 21. Queues & Streams – Kafka, RabbitMQ; smooth spikes 22. Backpressure – Manage slow consumers/load shedding 23. Delivery Semantics – At-most/least/exactly-once 24. API Design – REST vs gRPC; human vs machine optimized 25. API Versioning – Additive only; never break clients 26. AuthN & AuthZ – OAuth2, JWT, RBAC/ABAC 27. Resilience – Circuit breakers, timeouts, retries 28. Observability – Logs, metrics, traces, SLI/SLO 29. Health Checks – Detect failures, auto-replace 30. Redundancy – Avoid SPOFs; use multi-AZ/region 31. Service Discovery – Dynamic endpoints, central config 32. Deployment – Canary, blue/green, rollbacks 33. Monolith vs Microservices – Split only if ops cost justified 34. Distributed Transactions – Use sagas, avoid 2PC 35. Event Sourcing/CQRS – Append-only logs, split models 36. Consensus – Raft, Paxos, quorum reads/writes 37. Data Privacy – Encryption, GDPR/CCPA 38. Disaster Recovery – Backups, RPO/RTO, test it 39. Serialization – JSON, Avro, Proto 40. Real-Time Delivery – Polling, SSE, WebSockets 📌Comment “Fundamentals” if you want access to this mindmap, I’ll send it you in the comments!!

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🚀 Multithreading in Java doesn’t have to be complicated! If you’ve ever struggled with manually creating and managing threads (new Thread() everywhere 😵), you know how messy things can get. That’s where ExecutorService comes in as your thread manager 🧑‍💻. With just a few lines of code, you can: ✅ Reuse threads for better performance ✅ Scale easily across hundreds of tasks ✅ Schedule jobs to run later or periodically ✅ Keep your code cleaner & safer In this attached carousel, I break down: 🔹 What ExecutorService is & why it matters 🔹 Different types (Single, Fixed, Cached, Scheduled) 🔹 Practical code snippets for each type 🔹 Best practices & pitfalls to avoid 👉 Whether you’re a beginner learning concurrency or a backend engineer managing enterprise workloads, this guide has you covered. 💬 I’d love to know: Which ExecutorService type do you use most often in your projects — and why? Drop your thoughts in the comments ⬇️ #Java #Multithreading #ExecutorService #SpringBoot #BackendDevelopment #Programming #SoftwareEngineering

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Ever faced issues with rollback hell in distributed systems? When services like Order → Inventory → Payment are tightly coupled in a synchronous flow, a single failure can break the entire transaction chain. Add in retry logic, rollbacks, and partial failures, and you're suddenly dealing with a fragile, monolithic-like setup inside a microservices world. Thankfully we have Saga Pattern: a better way to handle distributed transactions. In my latest video, I break down: ✅ Why traditional transaction models don’t scale in microservices ✅ How Saga Orchestration and Choreography work ✅ Step-by-step compensation logic for failures ✅ Comparison with 2-Phase Commit ✅ When to use which approach ✅ What’s next: a Spring Boot + Kafka implementation demo If you're building resilient, scalable systems, this one's for you. 📺 Watch now 👉 https://lnkd.in/gX2bdy86 How are you handling distributed transactions in your architecture? #Microservices #SagaPattern #DistributedTransactions #SystemDesign #Kafka #SpringBoot #SoftwareArchitecture #DevTalk #LazyProgrammer

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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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📌 𝗥𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝗶𝘀 𝗮𝘀 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗮𝘀 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗰𝗼𝗱𝗲. At Zepto, we kept hitting a wall with 𝘁𝗵𝗶𝗿𝗱-𝗽𝗮𝗿𝘁𝘆 𝗔𝗣𝗜 𝗽𝗿𝗼𝘃𝗶𝗱𝗲𝗿𝘀' 𝘀𝗮𝗻𝗱𝗯𝗼𝘅𝗲𝘀 that were slow, unreliable, or simply unavailable - 𝗯𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝗮𝗻𝗱 𝘄𝗮𝘀𝘁𝗶𝗻𝗴 𝘃𝗮𝗹𝘂𝗮𝗯𝗹𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗵𝗼𝘂𝗿𝘀. So we built our own: 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗹 𝗠𝗼𝗰𝗸 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 🚀 A self-hosted simulation engine for 𝗛𝗧𝗧𝗣, 𝗴𝗥𝗣𝗖, 𝗞𝗮𝗳𝗸𝗮 & 𝘄𝗲𝗯𝗵𝗼𝗼𝗸𝘀 that lets teams:  • Mock without writing code  • Proxy + mock selectively for hybrid testing  • Record & replay real responses  • Simulate event-driven workflows The outcome? 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝘁 𝘁𝗲𝘀𝘁 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀, 𝗳𝗮𝘀𝘁𝗲𝗿 𝗤𝗔, 𝗮𝗻𝗱 𝘀𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁 𝗯𝗮𝗻𝗱𝘄𝗶𝗱𝘁𝗵 𝘀𝗮𝘃𝗶𝗻𝗴𝘀 - while giving teams complete control over their testing ecosystem. Rich, one of our rockstar Engineering Managers, has detailed it in one of our latest Tech Blogs. The link is in the first comment. #ZeptoTechXPress

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Spinner froze post "Confirm Purchase" Second click could mean double charges and duplicate orders, wonder if I should click it again? Solution: Idempotent APIs What makes an API idempotent? 1. Multiple identical requests produce the same result as a single request 2. System state remains unchanged after the first successful operation 3. Safe to retry without unintended side effects Key benefits for your distributed architecture: 1. Prevent duplicate operations — No more double charges or repeated actions 2. Ensure consistent results, even if the same request is sent multiple times 3. Simplify error handling by making retries safe and predictable 4. Boost system reliability and user trust by avoiding unintended side effects 5. Enhance fault tolerance, allowing systems to recover gracefully from failures 6. Reduce client-side complexity - Developers can focus on features, not defensive code 🤔 over to you, how to implement idempotent APIs? Let me know in the comments! 👇 --- You've got System Design skills but showcasing them in 60 minutes interview is a completely different challenge for e.g. where to start? How much time to spend on HLD vs LLD etc Let me share a FREE 7-day email course that provides a step-by-step framework for acing your 60-minute System Design interview 📣 Sign up for the course at systemdesign.engineer, to learn time allocation to demonstrate full range of design skills

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DAY 1 of Posting Elixir Content 🚀 1 Billion Loops in Elixir — in Just 0.3 Seconds! Recently, I conducted a simple yet insightful experiment — how long does it take to execute 1 billion iterations using different methods in Elixir? What started as a curiosity turned into an optimization challenge... and I ended up getting the loop down to ~0.3 seconds! 😱 🔗 Repo: https://lnkd.in/d4g2jihD 🔍 What I Found: - Writing tight, tail-recursive functions in Elixir can be surprisingly efficient. - Leveraging pattern matching and tail call optimization allowed me to bring execution time down significantly. - Compared naive recursion vs tail recursion vs for-loops — and the results were eye-opening. ✅ Why Elixir Worked So Well: - Immutable data structures and functional purity allow for more predictable optimizations. - BEAM VM (Erlang’s virtual machine) handles recursion and process scheduling incredibly well — especially for long-running or heavy computational tasks. - Code clarity made it easy to iterate and experiment without introducing bugs. This wasn’t about beating C or Rust in performance — it was about understanding Elixir’s limits and strengths for CPU-intensive tasks and optimizing within its paradigm. 💡 If you're into Elixir, benchmarking, or curious how a functional language deals with a billion-iteration challenge, check out the repo and try your own variations! #elixir #functionalprogramming #beamvm #performance #opensource #erlang #tech

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"JEP draft: Automatic Heap Sizing for G1" Now that's Java enhancement which can't ship soon enough! It always is a hassle to find suitable values for min and max heap size, and oftentimes folks wouldn't care to do so to begin with and just apply the defaults, thus leaving memory unused, which is particularly painful in containerized environments. Can't wait to see this one! 👉 https://lnkd.in/ewgjx3cz

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