Suyash Soni

💼 Addressing Mass Layoffs in India: A Personal Perspective The recent surge in mass layoffs across various sectors in India, including IT, startups, and established enterprises, has left a significant impact on the job market. Skilled professionals of all levels, from newcomers to seasoned leaders, are facing sudden unemployment. It's crucial to recognize that behind each statistic of a layoff is an individual with responsibilities, ambitions, and aspirations. The repercussions extend beyond mere economic implications; they delve into the personal realm. 👉 While companies often cite "cost-cutting" or "restructuring" to justify layoffs, the emotional toll on employees remains consistent. 👉 For those affected, this period brings about anxiety, self-doubt, and a sense of uncertainty. 👉 Industry-wide, it serves as a stark reminder to reevaluate sustainable growth strategies, financial planning, and compassionate employment practices. 🔹 To those navigating this challenging phase: Your skills, expertise, and value transcend any single employer. This moment signifies a redirection, not a conclusion. 🔹 Employers looking to expand their teams should consider offering opportunities to those impacted by recent layoffs. A gesture of inclusion can profoundly impact someone's life. The Indian workforce showcases resilience, talent, and adaptability. What is imperative now is a more compassionate ecosystem that provides empathy, avenues for growth, and unwavering support. ✨ Let's leverage LinkedIn not just as a networking platform but as a catalyst for uplifting those in need. #Layoffs #IndiaJobs #Workforce #Hiring #CareerGrowth #Support

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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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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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Why is Redis so fast? There are many design decisions that make Redis blisteringly quick. We’ll focus on two that carry most of the weight. - First is Redis’s in-memory design + single-threaded event loop. Keeping data in RAM and processing commands in one core loop avoids locks and random I/O. - Second is its purpose-built data structures and lean wire protocol. Most commands hit O(1) or O(log N) paths with tiny per-op overhead. What happens when you read from Redis👇 Step 1: A client sends a command using RESP (a simple, compact text/binary protocol). Minimal parsing and small payloads reduce CPU and network overhead. No big JSON/XML blobs. Step 2: Redis’s event loop accepts the socket and queues work. Single-threaded command execution means no lock contention or context-switch thrash. If you want to use more CPUs you need to run more Redis instances in a cluster. Step 3: The command is parsed and routed via a hash table lookup to the target keyspace entry.  Lookups are O(1) on average thanks to efficient dictionaries and cache-friendly memory layouts. Step 4: The operation runs entirely in memory using specialized structures:   Strings, lists (quicklist), hashes (compact encodings), sets/intsets, sorted sets (skiplist + hash), streams, etc. They're are engineered for predictable, fast operations with tight CPU caches. Step 5: The response is written back through the same event loop.   Pipelining and batching can amortize syscalls and round trips, pushing throughput even higher. Step 6: Persistence and replication are off the critical path.   AOF uses append-only, sequential writes with configurable fsync; RDB snapshots happen in a child process.  Basically: we can lose data! This is a tradeoff Redis makes. Replication is async by default. The slow stuff is handled in the background so the hot path stays hot. Because Redis keeps data in RAM, executes commands in a single, lock-free event loop, and uses highly optimized data structures and a lean protocol, it avoids the latency traps of disk I/O, heavy parsing, and lock contention. That’s why it’s fast. Reed more about Redis here! https://lnkd.in/g2TEVzvx

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I'm a Software Engineer at AWS with 7+ years of industry experience. I've designed systems across CloudWatch, EC2, and Region Services. Some scaled well. Some broke at 2AM. Here are 15 system design lessons I learned the hard way: ► Before you build anything → Understand what you're solving before you start designing → There is no perfect solution — only tradeoffs you can live with → Assume everything will fail. Design for it. → Don't build what you don't need yet ► Data decisions will make or break you → SQL when you need ACID. NoSQL when you need flexibility. → Index for fast reads. Denormalise for read-heavy workloads. → Shard when one database isn't enough. Replicate so one failure isn't fatal. → Choose your partition key carefully — a bad one creates hot spots that ruin everything ► Scaling isn't magic → Scale horizontally. Vertical has a ceiling. → Put load balancers in front. Put CDNs at the edge. → Cache aggressively but know when your cache is lying to you. → Keep systems decoupled → Use queues between services. Sync calls create cascading failures. → Make operations idempotent. Retries will happen. → Async everything that doesn't need an immediate response ► The one that matters most → Design for the engineer debugging this at 3AM, not the one building it today --- ♻️ Repost to help someone with system design  ➕ Follow Abdirahman Jama for software engineering tips

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🚀 Day 5/60 – #geekstreak60 Challenge Continuing my 60 Days POTD consistency journey powered by National Payments Corporation Of India (NPCI) on GeeksforGeeks. Day 5 Problem: Missing Range 🧠 Concept Used: Hashing + Range Scanning ⏱ Time Complexity: O(N + (high − low)) 🔎 Problem Summary: Given an array and a range [low, high], the task is to find all numbers within this range that are not present in the array. 💡 Approach Insight: To efficiently detect which numbers are missing: Insert all array elements into a HashSet for O(1) lookup Iterate from low to high and add numbers that are not found in the set This avoids nested loops and gives a clean, readable solution. #geekstreak60 #npci #DSA #Hashing #ProblemSolving #Java #CodingChallenge #geeksforgeeks #npci

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🤖 Swiggy integrates #MCP to enable #AI-native ordering 🍔 Swiggy becomes one of the first Indian consumer platforms to adopt Model Context Protocol (MCP) across food delivery, #quickcommerce, and dining out. 🧠 Users can now discover and place orders directly from AI tools like OpenAI ChatGPT, Anthropic Claude, and Google Gemini using natural language — without opening the app. ⚡ Instamart becomes the first quick-commerce platform globally to support MCP, enabling AI-driven access to 40,000+ SKUs in real time. 🔗 MCP acts as a secure, open bridge between LLMs and live systems, allowing AI models to interact with catalogs, availability, and workflows — not just static data. 🏗️ This marks a shift from surface-level AI features to deep infra-level AI integration, where apps become services callable by AI agents. 🇮🇳 After fintech and logistics, Indian consumer internet companies are now building AI-native foundations. 🚀 Commerce is quietly moving from apps → APIs → agents. https://lnkd.in/gTBquHk8

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