#Jad In Software Engineering

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Handling 1 Million RPS isn’t about code — it’s about smart architecture. 1️⃣ Traffic Distribution (Load Balancers) ➡️ Spreads incoming requests across many servers so nothing overloads. Example: 1M requests split across 200 servers = ~5K requests per server. ⸻ 2️⃣ Scale Out, Not Up (Horizontal Scaling) ➡️ Add more machines instead of making one server bigger. Example: Flash sale traffic? Instantly launch 50 new API instances. ⸻ 3️⃣ Fast Reads with Cache ➡️ Use Redis/Memcached to avoid hitting the database every time. Example: Cached user data = millions of DB calls saved daily. ⸻ 4️⃣ Edge Delivery with CDN ➡️ Static content loads from servers closest to the user. Example: Users in Delhi fetch images from a Delhi CDN node. ⸻ 5️⃣ Background Work with Queues ➡️ Heavy tasks run asynchronously so APIs respond instantly. Example: Payment succeeds now, email receipt sent in background. ⸻ 6️⃣ Split the Database (Sharding) ➡️ Divide data across multiple databases to handle scale. Example: Usernames A–M on one shard, N–Z on another. ⸻ 7️⃣ Rate Limiting ➡️ Prevent abuse and traffic spikes from taking the system down. Example: Limit clients to 100 requests/sec to block bots from killing the API. ⸻ 8️⃣ Lightweights Payloads ➡️ Smaller payloads = faster responses + less bandwidth. Example: Send only required fields instead of massive JSON blobs. Please follow for more such videos🙏 #systemdesign #softwaredevelopers #programming #tech #interview [API Design] [System Architecture] [API Scaling] [1 Million RPS] [Distributed Systems] [Load Balancing] [Database Sharding] [High Availability]

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Comment “HLD” to get the complete flow or just take the screenshot. One System Design Flow for every HLD interview. Use the flow in your next interview, scale application from 1 to million users and modify your application services in between before servers. Hope it helps! Follow for more! #systemdesign #datastructure #hld #reelitfeelit

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Java Developer → Agentic AI Engineer (Complete Roadmap) Most Java devs stop at calling ChatGPT APIs. That’s NOT Agentic AI. Comment "Link" for the perfect resource to learn from Zero to Hero Agentic AI. Here’s the real path 👇 1️⃣ Core Java stays (huge advantage) Concurrency • Spring Boot • System Design 2️⃣ AI basics (no heavy math) AI vs ML vs DL • LLMs • Transformers 3️⃣ Python (only what’s needed) Scripts • NumPy • Pandas • Requests 4️⃣ LLM fundamentals Tokens • Prompting • Context window • Hallucinations 5️⃣ Java + LLM integration Spring Boot APIs • Streaming • Cost optimization 6️⃣ Agentic AI core Goal → Plan → Act → Observe → Reflect 7️⃣ Tools & Memory (RAG) Embeddings • Vector DBs • Knowledge retrieval 8️⃣ Agent frameworks LangChain(Most Enterprise Ready) • AutoGen • CrewAI 9️⃣ Multi-agent systems Manager • Worker • Critic agents 🔟 Production-ready AI Guardrails • Monitoring • Security • Cost control 💡 Agentic AI = Distributed Systems + LLMs 💡 Java devs already know HALF of this Save this 📌 Share with your Java gang ☕ Comment "Link" for the perfect resource to learn from Zero to Hero Agentic AI. #systemdesign #engineers #developers #softwareengineering #genai [coding, system design, agentic ai, genAI , developers, software engineer, coders, java]

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