mjh @nothingmjh - Twitter Profile

Your system design knowledge, explaining: - gRPC - CDNs - WebSockets - Rate Limiting - API Gateways - Microservices - Redis Caching - Load Balancers - Message Queues - Database Sharding - Consistent Hashing - Eventual Consistency - Distributed Tracing - Horizontal Scaling - Circuit Breakers - Event Sourcing - Reverse Proxy - CAP Theorem - Service Mesh - Saga Pattern - CQRS - Kafka Interview decision: Sorry, we need someone who can explain why they'd use a database. Knowing the name of a pattern doesn't mean you understand when to use it. I've seen engineers confidently explain microservices architecture, then suggest splitting a 3-table CRUD app into 15 services. They knew the pattern. They didn't know the problem it solves. Understanding fundamentals means asking better questions, for example: 1. Why would I choose PostgreSQL over MongoDB? Not "what are the differences" but what specific requirements in my system make one a better fit. Is it the transaction guarantees? The query flexibility? The team's expertise? 2. When should I NOT use Redis caching? Maybe your data changes too frequently. Maybe cache invalidation becomes more complex than just hitting the database. Maybe you're solving a performance problem that doesn't exist yet. 3. What's the real cost of event sourcing? Sure, you get an audit trail and time travel. But now you're debugging by replaying events, your storage grows unbounded, and your queries need projections. Is that tradeoff worth it for your use case? 4. Do I actually need Kafka? Or would a simple queue work fine for my 1000 requests per day? Are you building for scale you have or scale you imagine? The best engineers I know can explain why they'd choose a boring SQL database over the latest distributed system. They understand the problems these patterns solve AND the problems they create. That's what separates someone who memorized a list from someone who can actually architect systems.

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Aditya

@_adityaa21

4 months ago

Most important Backend topics to revise before interviews (save this) : 1. API Design & HTTP Fundamentals - REST principles, CRUD mapping - HTTP methods (GET, POST, PUT, DELETE, PATCH) - Status codes (200, 201, 400, 401, 403, 404, 500) - Idempotency, headers, query vs params - Pagination, filtering, versioning 2. Authentication & Authorization - JWT (access vs refresh tokens) - Sessions vs cookies - OAuth basics (Google login flow) - Role-based access control (RBAC) - Password hashing (bcrypt) 3. Databases (VERY IMPORTANT) - SQL vs NoSQL (when to use what) - Joins, indexing, normalization - Transactions & ACID properties - Query optimization (avoid N+1 queries) - Schema design & relationships 4. Caching & Performance - Why caching is used - Redis basics (key-value, TTL) - CDN concept - Cache invalidation strategies - Reducing DB calls 5. System Design Basics - How to design scalable systems - Load balancing (horizontal vs vertical scaling) - Rate limiting - CAP theorem basics - High availability vs consistency 6. Server & Runtime Concepts - How backend actually works (request → server → DB → response) - Node.js event loop (callbacks, promises, async/await) - Blocking vs non-blocking I/O - Thread vs process basics 7. Error Handling & Logging - Try/catch, global error handlers - Logging levels (info, warn, error) - Debugging production issues - Monitoring basics 8. Security Fundamentals - Hashing vs encryption - SQL Injection, XSS, CSRF - Rate limiting & brute-force protection - HTTPS, CORS basics - Environment variables & secrets 9. Architecture Patterns - Monolith vs Microservices MVC pattern - Service layer, controllers, routes - Clean code & separation of concerns 10. Message Queues & Async Processing - Why queues are needed - Basics of Kafka / RabbitMQ - Background jobs (emails, notifications) 11. File Handling & Storage - Uploading files - Cloud storage (S3 basics) - Handling large files efficiently 12. Testing & Deployment - Unit testing vs integration testing - API testing (Postman) - CI/CD basics - Docker basics (containers) - Deployment flow (build → test → deploy) Most candidates prepare frameworks. Top candidates understand how systems actually work. That’s the difference between: “Can build APIs” vs “Can build scalable products.”

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Neo Kim

@systemdesignone

4 months ago

If you want to become good at AI engineering, learn these 12 concepts: 1 How RAG Works ↳ https://t.co/cGmunPTUlb 2 LLM Concepts - A Deep Dive ↳ https://t.co/5lCKxq2g4N 3 How to Design an AI Agent ↳ https://t.co/JvnPd9773A 4 What is Reinforcement Learning ↳ https://t.co/AVpl9j1oit 5 AI concepts 101 ↳ https://t.co/y1pZpCehda 6 Context Engineering vs Prompt Engineering ↳ https://t.co/9h8q9F2i57 7 Context Engineering 101 ↳ https://t.co/OMkiZhkODL 8 AI Coding Workflow 101 ↳ https://t.co/paIf9ksIU9 9 How ChatGPT Apps Work ↳ https://t.co/BJTYYnAwO1 10 How AI Agents Work ↳ https://t.co/tk3zkCjRvg 11 How MCP Works ↳ https://t.co/wgf8gHnnkn 12 Generative AI 101 ↳ https://t.co/Njr7qcq0Q6 What else should make this list? === 👋 PS - Want my System Design Playbook for FREE? Join my newsletter with 200K+ software engineers now: → https://t.co/ByOFTtOihX === 💾 Save & RT to help others learn AI engineering. 👤 Follow @systemdesignone + turn on notifications.

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Dhanian 🗯️

@e_opore

4 months ago

System Design Roadmap │ ├── 1. Introduction to System Design │ ├── What system design is │ ├── Importance in real-world applications │ └── Key goals: scalability, reliability, performance │ ├── 2. Fundamental Concepts │ ├── Networking basics (HTTP, HTTPS, DNS) │ ├── Client-server architecture │ └── Latency vs throughput │ ├── 3. Databases & Storage │ ├── SQL vs NoSQL │ ├── Data modeling │ ├── Indexing │ └── Replication and sharding │ ├── 4. Scalability Principles │ ├── Vertical scaling │ ├── Horizontal scaling │ └── Load distribution │ ├── 5. System Components │ ├── Load balancers │ ├── Application servers │ ├── Databases │ ├── Cache (Redis, Memcached) │ └── CDN │ ├── 6. Caching Strategies │ ├── Client-side caching │ ├── Server-side caching │ └── Cache invalidation │ ├── 7. Distributed Systems │ ├── CAP theorem │ ├── Consistency models │ ├── Fault tolerance │ └── Consensus basics │ ├── 8. API Design │ ├── RESTful APIs │ ├── Rate limiting │ └── Authentication & authorization │ ├── 9. Message Queues & Async Systems │ ├── Event-driven architecture │ ├── Queues (Kafka, RabbitMQ) │ └── Background processing │ ├── 10. Designing Real Systems │ ├── URL shortener │ ├── Chat system │ ├── File storage system │ └── News feed system │ ├── 11. Monitoring & Reliability │ ├── Logging │ ├── Metrics and alerts │ └── High availability │ └── 12. Interview Strategy ├── Clarify requirements ├── High-level design first ├── Deep dive into components └── Discuss trade-offs →Grab the System Design Roadmap Handbook → https://t.co/WIMretQFPE