Prashanth Ramadass

Software engineering isn’t just React or Angular. Software engineering isn't just Claude or ChatGPT. Software engineering isn’t just Docker or Kubernetes. Software engineering isn’t just building APIs or consuming them. Software engineering isn’t just pushing code to GitHub. Software engineering isn’t just fixing CI/CD pipelines. It’s solving real-world problems with (or without) code. It’s understanding systems, trade-offs, and constraints. It’s about creating scalable, maintainable, and efficient solutions. It’s designing for customers, anticipating edge cases, and building for the future. Tools come and go. Frameworks rise and fade. But the mindset of solving problems stays forever. #softwareengineering #ai #llm

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🔥Kafka can handle 2+ MILLION messages per second, but it's overkill for 90% of applications - ZeroMQ has ZERO broker overhead it's just a library that makes our app lightning fast. - RabbitMQ's routing capabilities are so advanced, we can build entire microservice orchestration with just message patterns. - Redis Streams gives us Kafka-like features at a fraction of the complexity. - Amazon SQS costs more but saves weeks of infrastructure headaches. - ActiveMQ has been quietly powering enterprise systems for 20+ years. The Real Question: Are we choosing based on hype or actual requirements???☺️ Connect Sabari Balaji for Tech Insights 💡

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Ever faced issues with rollback hell in distributed systems? When services like Order → Inventory → Payment are tightly coupled in a synchronous flow, a single failure can break the entire transaction chain. Add in retry logic, rollbacks, and partial failures, and you're suddenly dealing with a fragile, monolithic-like setup inside a microservices world. Thankfully we have Saga Pattern: a better way to handle distributed transactions. In my latest video, I break down: ✅ Why traditional transaction models don’t scale in microservices ✅ How Saga Orchestration and Choreography work ✅ Step-by-step compensation logic for failures ✅ Comparison with 2-Phase Commit ✅ When to use which approach ✅ What’s next: a Spring Boot + Kafka implementation demo If you're building resilient, scalable systems, this one's for you. 📺 Watch now 👉 https://lnkd.in/gX2bdy86 How are you handling distributed transactions in your architecture? #Microservices #SagaPattern #DistributedTransactions #SystemDesign #Kafka #SpringBoot #SoftwareArchitecture #DevTalk #LazyProgrammer

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📌 𝗥𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝗶𝘀 𝗮𝘀 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗮𝘀 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗰𝗼𝗱𝗲. At Zepto, we kept hitting a wall with 𝘁𝗵𝗶𝗿𝗱-𝗽𝗮𝗿𝘁𝘆 𝗔𝗣𝗜 𝗽𝗿𝗼𝘃𝗶𝗱𝗲𝗿𝘀' 𝘀𝗮𝗻𝗱𝗯𝗼𝘅𝗲𝘀 that were slow, unreliable, or simply unavailable - 𝗯𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝗮𝗻𝗱 𝘄𝗮𝘀𝘁𝗶𝗻𝗴 𝘃𝗮𝗹𝘂𝗮𝗯𝗹𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗵𝗼𝘂𝗿𝘀. So we built our own: 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗹 𝗠𝗼𝗰𝗸 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 🚀 A self-hosted simulation engine for 𝗛𝗧𝗧𝗣, 𝗴𝗥𝗣𝗖, 𝗞𝗮𝗳𝗸𝗮 & 𝘄𝗲𝗯𝗵𝗼𝗼𝗸𝘀 that lets teams:  • Mock without writing code  • Proxy + mock selectively for hybrid testing  • Record & replay real responses  • Simulate event-driven workflows The outcome? 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝘁 𝘁𝗲𝘀𝘁 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀, 𝗳𝗮𝘀𝘁𝗲𝗿 𝗤𝗔, 𝗮𝗻𝗱 𝘀𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁 𝗯𝗮𝗻𝗱𝘄𝗶𝗱𝘁𝗵 𝘀𝗮𝘃𝗶𝗻𝗴𝘀 - while giving teams complete control over their testing ecosystem. Rich, one of our rockstar Engineering Managers, has detailed it in one of our latest Tech Blogs. The link is in the first comment. #ZeptoTechXPress

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Uber’s fault-tolerant, highly available, Schemaless Datastore using MySQL Functional Requirements - We should be able to linearly add capacity by adding more servers i.e. adding servers should increase the available disk storage + decrease the system response times - We need write availability over read-your-write semantics - We need to notify downstream dependencies of data changes in a isolated multi step system - We need secondary indexes to search for trips - We should have operational knowledge to quickly fix DB Outages High Level Design Uber built a key-value store which save any JSON data without schema validation They called it Schemaless Behind the scene, - It is append-only sharded MySQL - Writes are buffered to support failing MySQL masters - It has a publish-subscribe feature for data change notification which they call triggers - It supports global indexes over the data. Data Model - Schemaless is three dimensional persistent hash map - The smallest data entity is called a cell which is immutable - Cell is a JSON Blob, Referenced by (row key, column name, ref key) - For e.g. in the diagram, a Cell is Referenced by (trip_uuid_1, BASE, 0) - row key = primary key - column name = String Column Name - ref key = integer for cell versioning i.e. a cell is updated by writing a new cell with a higher ref key - Schemaless has no predefined schema and rows do not need to share column names. The column names are completely defined by the application The Trip Data Model As shown in the diagram, - We have two trips i.e. trip_uuid1 and trip_uuid2 and four columns (BASE, STATUS, NOTES, and FARE ADJUSTMENT) - A cell is represented by a box, with a number and a JSON blob (abbreviated with {…}). - The boxes are overlaid to represent the versioning (i.e., the differing ref keys) - trip_uuid1 has 3 cells: 1 in the BASE column, 2 in the STATUS column and none in the FARE ADJUSTMENTs column - trip_uuid2 has 3 cells: 2 in the BASE column, 1 in the NOTES column and none in the FARE ADJUSTMENTS column 🤔 over to you, is this data a complicate data model? Let me know in the comments! 👇 --- You've got System Design skills but showcasing them in 60 minutes interview is a completely different challenge for e.g. where to start? How much time to spend on HLD vs LLD etc Let me share a FREE 7-day email course that provides a step-by-step framework for acing your 60-minute System Design interview 📣 Join 1700+ subscribers and sign up for the course to learn time allocation to demonstrate full range of design skills (link in comments 👇)

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