Learn the 5 Best System Design Questions

>> System Design - Ultimate Cheat Sheet For Revisions & Interviews << [1] Networking ◦ DNS -- Resolves domain names to IP addresses for routing ◦ Load Balancer -- Distributes incoming traffic evenly across servers ◦ CDN -- Delivers cached content from edge servers near users ◦ Proxy -- Forwards and filters requests for security/performance ◦ VPN -- Encrypts and tunnels traffic for private access ◦ Firewall -- Blocks or allows traffic based on rules ◦ NAT -- Maps internal private IPs to public IPs ◦ Gateway -- Connects different network types or protocols [2] Storage ◦ SQL Database -- Structured, relational storage with ACID properties ◦ NoSQL Database -- Flexible, scalable storage for unstructured data ◦ Object Storage -- Stores large unstructured files as objects (e.g., S3) ◦ Block Storage -- Low-latency data blocks for VMs/DBs ◦ File System -- Shared access to files across network (e.g., NFS, HDFS) ◦ Caching -- Speeds up reads with in-memory data (e.g., Redis) ◦ Sharding -- Partitions data horizontally for scalability [3] Compute ◦ Bare Metal -- Dedicated hardware servers for raw performance ◦ VM -- Virtualized servers running isolated environments ◦ Container -- Lightweight, portable app environments (Docker/K8s) ◦ Serverless -- Runs code in response to events, no server management ◦ PaaS -- Platforms to deploy code without infra setup ◦ Edge Computing -- Processes data close to user for low latency [4] Communication ◦ API -- Interfaces for system-to-system communication (REST/gRPC) ◦ Message Queue -- Buffers and delivers async messages between services ◦ WebSocket -- Enables two-way real-time communication ◦ RPC -- Remotely invoke procedures between services ◦ Pub/Sub -- Scalable broadcast/subscribe for events ◦ Service Mesh -- Manages traffic and observability in microservices [5] Architecture Patterns ◦ Monolith -- Single deployable app with all components ◦ Microservices -- Independent, API-driven services ◦ Event-Driven -- Reacts to and processes events in real time ◦ Serverless -- Stateless, event-triggered functions ◦ Distributed Systems -- Components spread across multiple nodes Continued in comments — P.S: You can learn all of these concepts and how they play out in real-life scenarios by using our system design learning platform: layrs.me. I quit my job at Google to change how everyone studies system design, on Layrs, you can see the architecture, components, and how everyone works, and simulate a real-life situation, whether it is for 100 people or 1M. Go check it out: layrs.me Join our discord: https://lnkd.in/gJ3qhKDp

What is essential to design an application for 1000 users? You need to balance simplicity with smart architectural decisions that won't require complete rewriting later on. Key things to keep in mind: #Architecture_Basics: Go with monolith first - Though you might be hearing a lot about microservices, a properly structured monolith is much easier to develop, deploy, and debug for early-stage applications. You can always break it down later when scaling is required. Pick tried and tested technologies - Better to stick with mature, well-documented frameworks and dbs that your team already knows well. Don't experiment with the latest technologies. DB design - A well designed db with indexing from starting itself, normalized data in correct way, and design your schema keeping future growth in mind. Think about read replicas early if expecting read-heavy workloads. #Performance_Planning: Caching strategy - Put basic caching at multiple levels: db query results, API responses, and static assets also. Redis or Memcached are good options to go with. CDN for static files - Serve images, CSS, and JavaScript files through CDN to reduce server load and improve user experience across different locations. #Infrastructure_Things Horizontal scaling ready - Design your application to be stateless so you can add more servers whenever required. Store session data in Redis or db, not in server memory itself. Load balancing support - Structure your application so it can work behind load balancer. This means avoiding server-side state and ensuring db connections are handled properly. Monitoring and logging setup - Implement proper logging and basic performance monitoring from day one itself. #Code_Quality_Aspects: Clean architecture approach - Separate concerns properly with clear boundaries b/w business logic, data access, and presentation layers. This will make your codebase easier to modify and extend later. API design standards - APIs, design them to be consistent and versioned properly. RESTful principles or GraphQL both can work fine, but being consistent is more important than following any specific standard perfectly. Error handling mechanism - Implementing proper error handling and graceful degradation helps application to fail gracefully and give meaningful error messages to users. #Operational_Readiness: Automated deployment setup - Set up CI/CD pipelines from early stage itself. The ability to deploy quickly becomes very crucial as you start growing. Database migration system - Use a robust migration system that can handle schema changes safely in a prod environment. Config management - Keep configuration separate from code and use environment-specific settings that can be changed without doing code deployments. Focus on building something that works well for your current requirements while avoiding choices that would need complete rewriting to scale further.

🚀 15 Mini System Design Problems Every Engineer Should Practice Building large systems starts with small, focused problems. Here’s a curated list of real-life micro problems with the most useful techniques you’ll need to solve them: 1️⃣ URL Shortener Problem: Convert long URLs to short, unique ones. Techniques: Hashing, collision handling, database indexing. 2️⃣ Like/Upvote System Problem: Track likes/upvotes on posts. Techniques: Counters, caching (Redis), DB vs in-memory trade-offs. 3️⃣ Recent Search History Problem: Show last N searches per user. Techniques: LRU cache, persistence, key-value stores. 4️⃣ Session Tracker Problem: Track active users in a web app. Techniques: Session storage, expiration policies, concurrency control. 5️⃣ File Upload Service Problem: Upload small/large files efficiently. Techniques: Chunking, resumable uploads, object storage (S3/GCS). 6️⃣ Notification System Problem: Send emails or push notifications. Techniques: Queues (Kafka/RabbitMQ), retry logic, delivery guarantees. 7️⃣ Rate Limiter Problem: Limit API requests per user. Techniques: Counters, sliding window, token bucket algorithms, caching. 8️⃣ Auto-suggest / Autocomplete Problem: Suggest search terms as users type. Techniques: Trie / prefix tree, fast lookup, caching frequent queries. 9️⃣ Tagging System Problem: Add/remove tags to items (many-to-many relationships). Techniques: Relational DB design, indexing, junction tables. 🔟 Simple Key-Value Store Problem: Store and retrieve key-value pairs efficiently. Techniques: Hash maps, TTL/expiration, in-memory caching. 1️⃣1️⃣ Image Thumbnail Generator Problem: Generate small previews for uploaded images. Techniques: Image processing libraries, async/background jobs. 1️⃣2️⃣ Simple Chat Room Problem: Users can send/receive messages in a room. Techniques: WebSockets, real-time messaging, message storage. 1️⃣3️⃣ Leaderboard Problem: Show top N users by score. Techniques: Sorting algorithms, priority queues, caching top results. 1️⃣4️⃣ Countdown Timer / Reminder Problem: Trigger alerts after N minutes/hours. Techniques: Scheduling, background jobs, persistence. 1️⃣5️⃣ Clipboard Sharing Problem: Sync small text/snippets across devices. Techniques: Key-value stores, real-time sync, conflict resolution. 💡 Why practice these? They’re small enough to design in 15–30 minutes, but teach core system design concepts: caching, storage, scheduling, concurrency, real-time updates, and more. Mastering these micro systems builds the foundation for larger, scalable systems.

Your Notion workspace started as a simple note-taking app. Now it's a slow, cluttered mess where finding anything takes forever. Sound familiar? Here's how to architect Notion systems that actually scale. The problem isn't Notion. It's database architecture. Most users jump from basic usage to complex systems without proper planning. The result? A digital junk drawer that works against you instead of for you. Scalable Notion systems share 3 characteristics: 1️⃣ Consistent data relationships (info flows naturally) 2️⃣ Predictable patterns (anyone can navigate easily) 3️⃣ Flexible foundations (accommodate growth without breaking) Nail these, and your system can handle anything. The 3 deadly sins of Notion architecture: 1️⃣ "Everything Everywhere" trap → Too many disconnected databases 2️⃣ Property proliferation → Dozens of columns nobody uses 3️⃣ Single mega-database → Cramming everything into one place Avoid these or suffer the consequences. When to use pages vs databases: Pages = Structured content (meeting notes, briefs, docs) Databases = Tracking multiple items of the same type Golden rule: If you're creating multiple pages with similar structures, you need a database instead. Properties are your database DNA. Start with 5-7 select options, expand based on usage. Use specific property types (not just text for everything). Relations = where Notion's true power emerges. Relations & rollups are Notion's secret weapon. But keep chains short. Performance degrades fast with complex chains. Every relation should serve a clear purpose: help users find info or enable useful calculations. If you need data 3 relationships away, restructure. Single database vs multiple? Single → Items share most properties and need unified views Multiple → Items have different properties or lifecycles Example: Projects, Tasks, and Clients seem related but need separate databases. Views make or break usability. Create views based on user roles and workflows, not just data organization. Project managers need cross-project deadlines. Contributors need focused views of their tasks. Filter everything. Users should rarely see all data at once. Performance optimization 101: - Limit visible items in views - Reduce simultaneous rollup calculations - Use linked databases instead of embedded - Break complex formulas into multiple properties - Use select properties instead of formula-generated categories Long-term maintenance tips: 1️⃣ Schedule regular architecture reviews 2️⃣ Document why you made design decisions 3️⃣ Gather user feedback constantly 4️⃣ Keep systems modular Systems that work at 10 items might need restructuring at 100. The best Notion architectures adapt gracefully to new requirements without complete rebuilds. Build with intention. Design for scale. Document your decisions. Your future team (and future you) will thank you. Read more here: https://lnkd.in/gX8ig65b

🌸 Day 15 of 100 Days of System Design: Bloom Filters Picture this: You’re running a massive database with billions of entries. A user searches for a record that doesn’t exist. Instead of hitting the database every time, wouldn’t it be great to know quickly if the item is definitely not there? That’s exactly what Bloom Filters do. --- What is a Bloom Filter? A Bloom filter is a space-efficient probabilistic data structure that tells you if an element is possibly in a set or definitely not in the set. It trades perfect accuracy for speed and memory efficiency. --- Why Bloom Filters Matter 1. Efficiency → Saves expensive database lookups. 2. Scalability → Handles billions of elements with very little memory. 3. Performance → Great for high-traffic systems where quick checks are needed. --- How It Works (Simplified) 1. Use multiple hash functions to map an item to different positions in a bit array. 2. To check if an item exists, see if all its corresponding bits are set. 3. If any bit is missing → the item is definitely not there. If all bits are set → the item is probably there. --- Real-World Examples 1. Google Bigtable & HBase → Use Bloom filters to avoid unnecessary disk lookups. 2. Email Spam Filters → Quickly check if an email address is in a blacklist. 3. Web Caches → Avoid cache misses by predicting if content is stored. --- Benefits 1. Very memory-efficient compared to traditional sets. 2. Extremely fast lookups. 3. Useful for large-scale distributed systems. --- Challenges 1. False positives → It may say an item exists when it doesn’t. 2. No deletions (unless using variants like Counting Bloom Filters). 3. Requires good choice of hash functions to minimize errors. --- In Short Bloom filters don’t guarantee perfect answers, but they guarantee speed and efficiency. They shine in systems where “definitely not” is more important than “definitely yes.” --- 💡 Question for you: If you were designing a search system, would you prioritize absolute accuracy or speed with small error margin—and why? --- 📚 Resources Bloom Filters Explained: https://lnkd.in/gya9apzC Bigtable Paper: https://lnkd.in/g9Wpexux Counting Bloom Filters: https://lnkd.in/gSUnsuMK --- That’s Day 15: Bloom Filters ✅. Tomorrow → Day 16: Distributed Consensus ⚖️

System design core concepts checklist: Venting: When I was younger, I wish someone had explained things in this clear and simple way, instead of repeating the same words over and over. Unfortunately, nobody did, and I had to go through the traditional long-winded answers in my country. People there expect elaborate, flowery responses, and if I answer in this straightforward style, they say I’m unqualified. Load Balancer → Distributes traffic across multiple servers for availability. 🖥 Example: Nginx balancing requests to 5 backend API servers. Cache → Stores frequently used data in memory for fast access. 🖥 Example: Redis caching product details to avoid hitting the database. Database Replication → Keeps multiple database copies for reliability and scaling reads. 🖥 Example: MySQL master-slave setup for an e-commerce site. Sharding → Splits data across databases to handle very large datasets. 🖥 Example: User accounts split by region in a social media app. Message Queue → Buffers and distributes tasks between services asynchronously. 🖥 Example: Kafka processing orders from an online marketplace. CDN (Content Delivery Network) → Delivers static content closer to users worldwide. 🖥 Example: Cloudflare caching images for a global news website. Rate Limiting → Controls how many requests a client can make in a time window. 🖥 Example: API Gateway limiting login attempts to prevent brute-force attacks. Circuit Breaker → Stops calls to a failing service to prevent cascading failures. 🖥 Example: Hystrix blocking requests to a slow payment service. CAP Theorem → You can’t have consistency, availability, and partition tolerance all at once. 🖥 Example: MongoDB prioritizing availability over strict consistency. Event Sourcing → Stores every change as an event rather than just current state. 🖥 Example: Axon Framework keeping all order events in an e-commerce system. CQRS → Separate models for writes and reads to optimize performance. 🖥 Example: Write orders into one database, read from a denormalized view store. Saga Pattern → Splits long transactions into steps with compensations if one fails. 🖥 Example: Cancel flight + refund hotel if payment for a travel booking fails.

#3 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 > 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 | 𝗪𝗵𝘆 𝗔𝗹𝗶𝗯𝗮𝗯𝗮'𝘀 𝟰𝟬-𝗖𝗮𝗹𝗹 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗪𝗮𝘀𝗻'𝘁 𝗙𝗶𝘅𝗲𝗱 𝗯𝘆 𝗡𝗲𝘄 𝗦𝗲𝗿𝘃𝗲𝗿𝘀 3-𝒎𝙞𝒏𝙪𝒕𝙚 𝙧𝒆𝙖𝒅 1: 𝗧𝗵𝗲 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗧𝗿𝗮𝗽 Your app is slow. Users are complaining. What's the first thing everyone says? "Let's switch to PostgreSQL." "Add more servers." "Tune the JVM settings." 𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: 𝗡𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗮𝘁 𝘄𝗶𝗹𝗹 𝗳𝗶𝘅 𝗶𝘁. Performance isn't about your tech stack. It's about how you designed your system. 2: 𝗧𝗵𝗲 𝗧𝗿𝘂𝘁𝗵 𝗡𝗼𝗯𝗼𝗱𝘆 𝗪𝗮𝗻𝘁𝘀 𝘁𝗼 𝗛𝗲𝗮𝗿 You can't tune your way out of bad design. Think about it: If your app makes 50 unnecessary network calls for every user click, no database on earth will make that fast. Every extra API call. Every unnecessary database trip. Every service talking to 10 other services. These aren't configuration problems. They're design problems. 𝘐𝘵'𝘴 𝘭𝘪𝘬𝘦 𝘣𝘶𝘺𝘪𝘯𝘨 𝘢 𝘧𝘢𝘴𝘵𝘦𝘳 𝘦𝘯𝘨𝘪𝘯𝘦 𝘧𝘰𝘳 𝘢 𝘤𝘢𝘳 𝘴𝘵𝘶𝘤𝘬 𝘪𝘯 𝘮𝘶𝘥. 𝘛𝘩𝘦 𝘦𝘯𝘨𝘪𝘯𝘦 𝘪𝘴𝘯'𝘵 𝘵𝘩𝘦 𝘱𝘳𝘰𝘣𝘭𝘦𝘮. 𝟯: 𝗥𝗲𝗮𝗹 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: 𝗔𝗹𝗶𝗯𝗮𝗯𝗮'𝘀 𝟰𝟬-𝗖𝗮𝗹𝗹 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 Alibaba had a massive performance issue. Every user request triggered 40+ service calls. Each call added network time. Each hop added delay. 𝗪𝗵𝗮𝘁 𝗱𝗶𝗱𝗻'𝘁 𝘄𝗼𝗿𝗸: ❌Adding servers ❌Switching databases ❌Tuning configurations 𝗪𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝗲𝗱: They redesigned the architecture: ✔️Cut 40+ calls down to just 3 ✔️Added smart caching ✔️Fixed service boundaries ✔️Used async messaging 𝘚𝘢𝘮𝘦 𝘪𝘯𝘧𝘳𝘢𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦. 𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘥𝘦𝘴𝘪𝘨𝘯. 𝘖𝘳𝘥𝘦𝘳-𝘰𝘧-𝘮𝘢𝘨𝘯𝘪𝘵𝘶𝘥𝘦 𝘧𝘢𝘴𝘵𝘦𝘳. 𝟰: 𝗪𝗵𝘆 𝗧𝗲𝗮𝗺𝘀 𝗞𝗲𝗲𝗽 𝗠𝗮𝗸𝗶𝗻𝗴 𝗧𝗵𝗶𝘀 𝗠𝗶𝘀𝘁𝗮𝗸𝗲 𝗜𝘁'𝘀 𝗲𝗮𝘀𝗶𝗲𝗿 𝘁𝗼 𝗯𝗹𝗮𝗺𝗲 𝘁𝗵𝗲 𝘁𝗼𝗼𝗹𝘀. Switching databases feels like progress. But you're just moving deck chairs. 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝗮𝗿𝗲 𝗶𝗻𝘃𝗶𝘀𝗶𝗯𝗹𝗲. A slow query shows up in logs. But 40 service calls? That's spread across your whole system. 𝗥𝗲𝗱𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗵𝘂𝗿𝘁𝘀. It means admitting the original design was wrong. 𝟱:𝗪𝗵𝗮𝘁 𝗬𝗼𝘂 𝗦𝗵𝗼𝘂𝗹𝗱 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗗𝗼 ✔ 𝗙𝗶𝘅 𝘆𝗼𝘂𝗿 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗳𝗶𝗿𝘀𝘁 : Architecture sets the speed limit. Tuning just helps you reach it. You can't tune past it. ✔ 𝗦𝘁𝗼𝗽 𝘁𝗵𝗲 𝗰𝗵𝗮𝘁𝘁𝘆 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿 : 10 API calls for one page = architecture problem ✔ 𝗞𝗲𝗲𝗽 𝗱𝗮𝘁𝗮 𝗰𝗹𝗼𝘀𝗲 : Every network hop costs time. ✔ 𝗧𝗲𝘀𝘁 𝗲𝗮𝗿𝗹𝘆 : Don't wait until customers are complaining. Measure from day one. #SoftwareArchitecture #TechLeadership #Microservices #SystemDesign #Engineering 𝙁𝙧𝙤𝙢 𝙢𝙮 𝙨𝙚𝙧𝙞𝙚𝙨: 𝘼𝙧𝙘𝙝𝙞𝙩𝙚𝙘𝙩𝙪𝙧𝙚 𝙋𝙡𝙖𝙮𝙗𝙤𝙤𝙠 - 𝗪𝗵𝗲𝗿𝗲 𝗿𝗲𝗮𝗹 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗺𝗲𝗲𝘁𝘀 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀. 𝙄𝙣𝙨𝙥𝙞𝙧𝙚𝙙 𝙗𝙮: 97 𝙏𝙝𝙞𝙣𝙜𝙨 𝙀𝙫𝙚𝙧𝙮 𝙎𝙤𝙛𝙩𝙬𝙖𝙧𝙚 𝘼𝙧𝙘𝙝𝙞𝙩𝙚𝙘𝙩 𝙎𝙝𝙤𝙪𝙡𝙙 𝙆𝙣𝙤𝙬

Here is the list of system design building blocks that are important irrespective of whether you are designing a system or preparing for a system design interview ✔️ Distributed message queue: A producer puts a message Or work in a line, which the consumers pick up for further processing or work defined. This is an important concept to explain how you can scale your system, perform asynchronous processing, ✔️ DNS: Translates website names to IP address. DNS service works much like a phone directory. Services like Route53 help configure the domain and routing, and one needs to understand common types of resource records like A, CNAME, NS, MX, etc. ✔️ CDN: The content delivery network is a server between the origin and the client. It stores and delivers content to the end users, reducing latency and improving the system's overall performance. ✔️ PUB SUB: Publish-subscribe messaging works asynchronously, involving publishers, topics, subscribers, and messages. It helps to decouple the system, scalability, Durability, etc. ✔️ Load Balancer: Load balancer helps divide and divert traffic across the system. It helps drive the overall system Availability and Performance. ✔️ Distributed Caching: Caching servers store the data in memory and return it to the end users, improving the overall performance and end-user experience. A fleet of servers works together to keep and serve the data in a distributed caching system, enhancing availability. ✔️ Database: Understanding data storage, storage, types, and choosing a Database, RDMBS Vs Nosql, is vital when designing a system. It is essential to deeply understand for what requirements RDBMS is the right fit, and nosql is the right fit. ✔️ Observability: It's important to know what is happening in the system. How do we make the system easier to debug and fix? Observability helps the system to drive maintainability, resiliency, and reliability. ✔️ Task Scheduler: This is an essential component that helps one decide how the tasks must be prioritized, delegated and how much resources need to be allocated ✔️ Unstructured Data Storage: Storing photos, videos, audio, and other unstructured data is essential when designing a system since these cannot be stored in traditional databases. One needs to create a highly available, consistent, reliable solution. and efficient ✔️ Scaling Services: Scalability is an essential building block of any robust system. Understanding various scaling options and what options should be used is critical. From Serverless to ID generators, one must understand the multiple options and how to design them. ✔️ Distributed Search: Search is the core of many system designs. Understating how to build a search system and the core building blocks of a search system, including crawling and indexing, is essential. #engineering #systemdesign #interview #productdevelopment

🧠 The Ultimate System Design Cheat Sheet (2025 Edition) 🚀 Stop memorizing system design questions. Start mastering the concepts that power every scalable system. Here are the core pillars of modern backend design 👇 ⚙️ A. Data Distribution & Scalability Sharding / Partitioning → break massive data across DB nodes. Consistent Hashing → distribute keys evenly, handle node changes smoothly. Replication → ensure fault tolerance with data copies. Leader–Follower Architecture → manage consistency in writes. Load Balancing → smart traffic routing across servers. ⚡ B. Caching & Performance Caching Strategies → LRU / LFU / TTL. CDN (Content Delivery Network) → global edge delivery. App-Level Cache → Redis / Memcached. Cache Invalidation → write-through / write-back / write-around. Compression & Serialization → reduce latency & bandwidth. 🧾 C. Messaging & Asynchronous Systems Message Queues → Kafka, RabbitMQ, SQS. Pub/Sub Pattern → async notifications. Backpressure & Flow Control → balance producers & consumers. Dead-Letter Queues → capture failures safely. Delivery Guarantees → at-least-once / exactly-once. 🧮 D. Storage & Indexing Object Storage (S3 Model) → separate data & metadata. Chunking & Resumable Uploads → handle large files. Time-Series DBs → for metrics. Geospatial Indexing → Geohash, QuadTree. Search Indexing → Trie, Inverted Index. 💰 E. Payments, Transactions & Ledgers Idempotency Keys → avoid double payments. Double-Entry Ledger → track debits & credits. State Machine → initiated → success/failure. Reconciliation Jobs → sync internal & external records. 🧩 F. Real-Time Systems WebSockets / Long Polling → live communication. Real-Time Analytics → Flink, Spark Streaming. Leaderboards → Redis Sorted Sets. Adaptive Bitrate (ABR) → dynamic video streaming. 🛡️ G. Reliability & Fault Tolerance Idempotent Operations → safe retries. Retry with Backoff → avoid downstream overloads. Circuit Breakers → stop cascading failures. Replication + Quorum → ensure availability. Consensus (Raft, Paxos) → elect leaders safely. 🧠 H. Algorithms for Scale Rate Limiting → Token / Leaky Bucket. Priority Queues → for scheduling. Bloom Filters → fast membership checks. Tries → autocomplete & search. Ranking Functions → personalized feeds. 🔐 I. Security & Access Control OAuth 2.0, JWT → user identity & sessions. Encryption → at-rest & in-transit. Tokenization → protect card data. Abuse Prevention → rate limits, auth walls. 📈 J. Observability & Monitoring Metrics → Prometheus, Grafana. Logging & Tracing → ELK, OpenTelemetry. Alerting Rules → real-time failure detection. Health Checks & Circuit Breakers → resilience. 🧰 K. Patterns & Trade-offs CAP Theorem → Consistency vs Availability. CQRS + Event Sourcing → separate read & write models. Eventual Consistency → async updates at scale. Backpressure & Throttling → control system load. Horizontal Scaling → scale out, not up. #SystemDesign #BackendDevelopment #SoftwareEngineering #DistributedSystems