Day 1 of my DSA Journey in C++! 💻 📚 Today I learned : • Algorithms & Programming Basics. • Problem-Solving Approach. • Flowcharts & Pseudocode. • Basic Logic, Loops & Decision-Making. ✓ DSA is not just about coding, it's about thinking logically and breaking complex problems into smaller steps. #DSA #CPlusPlus #Cpp #BuildInPublic #CodingJourney

If you are in tech Let's connect 🙂

Hey @X 👋 I'm Arun, a developer learning, building, and sharing my journey in public. I'm looking to connect with people interested in : 💻 Full-Stack Development 🤖 AI / ML 🧠 DSA & System Design 🚀 Startups & SaaS 🛠️ Building real-world projects 📈 #BuildInPublic Whether you're a student, fresher, developer, or founder, let's learn from each other, share ideas, and grow together ! 🤝 What are you currently building or learning? 👇 #TechTwitter #Developers #WebDevelopment

My X circle! The people I talk with most on here: 🥇 @Kiara_X_X1 🥈 @Shreyaazvp 🥉 @Big_Yawn3 Make yours: wishkit.io/x-circle

HLD Day 16/17 - Google Docs, WhatsApp & API Gateway ✅ Today, I explored how real-world applications handle real-time collaboration, messaging, and API requests. Here’s what I learned 👇 1. Google Docs - Collaborative Editing • Learned how multiple users can edit the same document in real time. • Explored WebSockets for instant, two-way communication. • Understood how Operational Transformation (OT) helps handle editing conflicts. 2. WhatsApp - Messaging System • Learned how WebSockets enable real-time message delivery. • Explored how servers manage online and offline users. • Understood how server mapping helps route messages to the right users. 3. API Gateway • Routes API requests to the correct microservice. • Can handle authentication and rate limiting. • Learned how it differs from a Load Balancer, which distributes traffic across server instances. 4. Service Discovery • Explored how services find and communicate with each other. • Learned about tools like ZooKeeper and Eureka. 💡 My biggest takeaway : Real-time applications aren't just about sending data. They need efficient communication, conflict handling, and smart request routing to work at scale. #HLD #SystemDesign #WebSockets #Microservices #APIGateway #BackendDevelopment #LearningInPublic

HLD Day 15/17 : Understanding SQL Database Internals ✅ Today, I explored how SQL databases store data and make queries faster. Here’s what I learned 👇 1. SQL Architecture • Understood Web Tier and Database Tier. • Explored Master-Slave architecture for handling reads and writes. 2. Database Scaling • Learned about sharding to distribute millions of records across multiple databases. • Explored how data is stored in rows, columns, data pages, and blocks. 3. Database Indexing • Learned how indexing helps retrieve data faster. • Explored how B+ Trees reduce search complexity from O(N) to O(log N). 4. Clustered vs Non-Clustered Index • Clustered index stores complete row data at the leaf level. • Non-clustered index stores indexed values and references to the data. 5. B-Trees & B+ Trees • Understood how tree-based structures help databases search and retrieve records efficiently. • Learned why B+ Trees are useful for range queries. 6. Storage Internals • Explored how DBMS manages data pages and maps them to disk blocks. • Learned that indexes improve query speed but also consume extra storage. 💡 My biggest takeaway : Writing a query is easy, but understanding how the database finds the data efficiently is what helps build faster applications. #HLD #SystemDesign #DatabaseIndexing #SQL #BackendDevelopment #LearningInPublic

💡 One thing I learned while studying System Design : Scaling a database isn't just about adding more servers. The real challenge is keeping data available, handling failures, and maintaining the right level of consistency. 🤔 Question for the tech community : If you were designing a distributed database, what would you prioritize - Consistency or Availability ? And why ? I'd love to hear your thoughts ! 👇 #SystemDesign #HLD #DistributedSystems #DatabaseDesign #NoSQL #BackendDevelopment #SoftwareEngineering #BuildInPublic #LearningInPublic

HLD Day 14/17 : Understanding NoSQL & Distributed Databases ✅ Today, I explored how distributed databases store data and scale across multiple servers. Here’s what I learned 👇 1. SQL vs NoSQL • SQL stores data in tables; NoSQL supports different data models like key-value and documents. • Explored DynamoDB, Cassandra, and MongoDB. 2. Key-Value Stores • Store data as key-value pairs. • Basic operations: put(key, value) and get(key). 3. Sharding & Consistent Hashing • Sharding distributes data across multiple servers. • Consistent hashing helps make scaling easier when servers are added or removed. 4. Replication & Quorum • Replication keeps copies of data across different servers. • Learned how read and write quorums balance consistency and latency. 5. Consistency Models • Strong, weak, and eventual consistency. • Understood the trade-offs between consistency and availability. 6. Failure Handling • Explored temporary and permanent server failures. • Learned about gossip protocols, heartbeats, and data recovery. 💡 My biggest takeaway : Scaling a database isn't just about adding servers. The real challenge is keeping data available, handling failures, and maintaining the right level of consistency. #HLD #SystemDesign #NoSQL #DistributedSystems #DatabaseDesign #LearningInPublic

HLD Day 13/17 : Designing a Video Streaming Service ✅ Ever wondered how YouTube handles millions of video uploads and streams ? Today, I explored the architecture behind a video streaming system. Here’s what I learned 👇 1. Video Storage • Store videos in object storage like Amazon S3. • Keep video metadata separately in a database. 2. Direct Uploads • Let clients upload videos directly to S3 using a pre-signed URL. • This reduces the load on application servers. 3. Video Processing • Use codecs to compress videos and reduce file size. • Split large videos into smaller chunks for easier processing and streaming. 4. Transcoding • Convert videos into multiple resolutions like 240p, 480p, 720p, and 1080p. • Helps users watch videos based on their internet speed. 5. Adaptive Bitrate Streaming • Automatically adjust video quality based on network conditions. • Reduces buffering and improves the viewing experience. 6. CDN & Scalability • Use a CDN to deliver videos from servers closer to users. • Helps reduce latency and handle high traffic. 💡 My biggest takeaway : Video streaming isn't just about storing and playing videos. It's about processing, delivering, and adapting them efficiently for millions of users. #HLD #SystemDesign #VideoStreaming #BackendDevelopment #LearningInPublic

HLD Day 12/17 : Designing an Instagram Feed ✅ How does Instagram deliver posts to millions of users ? Today, I explored how to design a scalable social media feed. Here’s what I learned 👇 1. Requirements & Estimation Create posts, follow/unfollow users, and view feeds. Designed for 20 million Daily Active Users (DAU). 2. Database Design Amazon S3 for storing images and videos. DynamoDB for post metadata. Neo4j graph database for follow relationships. 3. Feed Generation Fetch posts from followed users. Sort posts in reverse chronological order. 4. Push vs Pull Model Push : Precompute feeds when someone posts. Pull : Fetch posts when a user opens their feed. 5. Celebrity Problem Pushing posts to millions of followers can be expensive. A hybrid approach combines Push and Pull based on follower count. 6. Caching & Kafka Cache feed data for faster reads. Kafka and background workers process feed updates asynchronously. 💡 My biggest takeaway : There’s no one-size-fits-all solution. A hybrid feed strategy helps balance fast reads, write costs, and scalability. #HLD #SystemDesign #Instagram #BackendDevelopment #LearningInPublic

Some people come into your life unexpectedly, and some leave when you need them the most.

HLD Day 11/17 : Designing an Autocomplete System ✅ Ever wondered how Google suggests search queries while you type ? Today, I explored how to design an autocomplete system. Here’s what I learned 👇 1. System Requirements • Show the top 6 most popular suggestions for a search prefix. • Suggestions are ranked by search frequency and are case-insensitive. 2. Trie Data Structure • Learned how a Trie stores prefixes and helps find matching queries. • Explored how caching the top 6 suggestions at each node makes searches faster. 3. Data Processing • Analytics logs collect search queries. • Aggregator services calculate query frequency, and workers build updated Tries asynchronously. 4. Caching & Database • Trie cache keeps frequently accessed suggestions in memory. • Trie DB stores the generated data for persistent access. 5. Scaling & Optimization • Explored AJAX requests, browser-side caching, database sharding, and CDN. • Learned why autocomplete suggestions can use periodic updates instead of rebuilding everything for every keystroke. 💡 My biggest takeaway : Fast search suggestions aren't just about searching quickly. They require smart data structures, precomputed results, caching, and efficient data processing. #HLD #SystemDesign #DataStructures #Autocomplete #LearningInPublic

HLD Day 10/17 : Authentication & Authorization ✅ Today, I explored how applications verify users and control access securely. Here’s what I learned 👇 1. Authentication vs Authorization • Authentication : Who are you? • Authorization : What are you allowed to do? 2. Login & Session Management • Learned about session-based authentication and JWT-based authentication. • Explored access tokens and refresh tokens. 3. Password Security • Understood password hashing, salts, and peppers. • Learned why slow hashing algorithms like bcrypt and Argon2 help protect passwords. 4. Access Control • Explored RBAC, PBAC, and ACL to manage user permissions. • Understood how roles and policies control access to resources. 5. System Design • Covered registration, login, logout, token validation, and password recovery. • Explored databases for users, credentials, passwords, and sessions. 💡 My biggest takeaway : A secure application doesn't just verify who you are; it also makes sure you can access only what you're allowed to. #HLD #SystemDesign #Authentication #CyberSecurity #LearningInPublic

नवरात्रि में माँ दुर्गा के नौ रूपों की पूजा करते हैं, कन्या पूजन के लिए बेटियों के पैर छूते हैं… 🙏❤️ लेकिन जब घर में बेटी जन्म लेती है, तो वही चेहरे क्यों उतर जाते हैं? 💔 जिस बेटी में माँ दुर्गा का रूप देखते हो, उसे जन्म लेते ही बोझ क्यों समझते हो? बेटियाँ सिर्फ़ पूजने के लिए नहीं, बल्कि प्यार, सम्मान और बराबरी का अधिकार पाने के लिए भी हैं। माँ दुर्गा की सच्ची पूजा तभी है, जब हर बेटी को जन्म से ही सम्मान मिले। 🥺🙏 #Navratri #BetiBachaoBetiPadhao #RespectDaughters #SaveTheGirlChild

Hey @X @grok Algorithm I’m looking to #connect with people interested in: • Full-stack dev • Solo founder • App Development • SaaS • AI / ML • Data Science • Freelancing • Startups • Building in public If that’s you, let’s connect!

HLD Day 9/17 - Learn Caching & Messaging Systems ✅ Today, I explored how applications handle data efficiently and reduce database load. Here’s what I learned 👇 1. Kafka vs RabbitMQ • Learned how Kafka and RabbitMQ handle messages between producers and consumers. • Explored exchanges, routing keys, queues, and Dead Letter Queues (DLQ). • Understood Kafka’s pull-based and RabbitMQ’s push-based message delivery. 2. Caching Basics • Learned how caching stores frequently accessed data temporarily to reduce response time and database queries. • Explored TTL (Time To Live) and distributed caching. 3. Caching Strategies • Cache Aside and Read Through for fetching data. • Write Through, Write Back, and Write Around for handling updates. 4. Cache Eviction Policies • LRU, LFU, FIFO, MRU, and Random. • Learned how caches remove data when storage becomes limited. 5. Real-World Applications • Explored Redis, CDN, proxies, API gateways, and client-side caching. 💡 My biggest takeaway : A faster system isn't just about a powerful database. Smart caching and the right messaging strategy can make a huge difference in performance and scalability. #HLD #SystemDesign #Kafka #RabbitMQ #Redis #LearningInPublic

HLD Day 8/17 - Learn about Messaging Queues & Kafka ✅ Today, I explored how systems handle a huge number of requests without overwhelming the database. Here’s what I learned 👇 1. Messaging Queues • Producers send messages, and consumers process them independently. • Both can work at their own pace without waiting for each other. 2. Sync vs Async Communication • Synchronous communication makes services wait for responses. • Asynchronous communication lets services continue working without waiting for every task to finish. 3. Why Kafka? • Handles high volumes of messages efficiently. • Helps manage traffic spikes and reduces pressure on databases through batch processing. 4. Kafka Basics • Learned about topics, partitions, brokers, offsets, and consumer groups. • Understood how partitions allow multiple consumers to process messages in parallel. 5. Queue vs Pub/Sub • Queue : Messages are processed by consumers. • Pub/Sub : One published message can reach multiple subscribers. 6. Real-World Use Case • Explored how ride-sharing apps can handle millions of driver location updates using Kafka and background workers. My biggest takeaway : A system doesn't always need to process everything immediately. Sometimes, buffering requests and processing them asynchronously is the key to handling traffic at scale. #HLD #SystemDesign #ApacheKafka #BackendDevelopment #LearningInPublic #Linkedin

Excited to reach 200 followers on X! Thank you to everyone who’s been part of my journey. Looking forward to learning, building projects, sharing knowledge, and growing with this amazing tech community. 💻 Thank you, @X, for bringing developers together. #BuildInPublic #WebDevelopment #MERNStack #DSA

HLD Day 7/17 ✅ Designing a Notification System How do apps send millions of notifications to users ? Today, I explored how to design a scalable notification system. • Notification Types : Push, SMS, Email, and In-App notifications. • System Design : Learned about APIs, notification templates, and user preferences. • Third-Party Services : Explored APNs, FCM, Twilio, and email providers. • Scalability : Understood how message queues and workers help process notifications asynchronously. • Reliability : Learned about retries, rate limiting, authentication, and monitoring. • Storage & Analytics : Explored notification logs, caching, and tracking delivery activity. > My biggest takeaway : Sending a notification is easy, but delivering millions of them reliably requires proper architecture, asynchronous processing, and failure handling. #HLD #SystemDesign #LearningInPublic #BackendDevelopment

Hey @X Algorithm I’m looking to #connect with people interested in: • Full-stack dev • Solo founder • App Development • SaaS • AI / ML • Data Science • Freelancing • Startups • Building in public If that’s you, let’s connect!