Malik Faizan Ahmed

I asked 50+ software engineers this question during system design interviews, and only 40% gave a satisfactory answer. The question?  "Why is Kafka faster than RabbitMQ when multiple consumers need independent log traversal?"  ► Why did I ask this?  1. It tests depth of knowledge.     Anyone can say “Kafka is faster,” but can they explain why?  2. It highlights trade-off awareness.     Engineers need to understand when to use Kafka vs RabbitMQ—not just pick one blindly.  3. It evaluates scalability thinking.     Modern systems handle massive traffic and real-time data, so engineers must know how tools scale.  4. It reveals clarity in communication.     Good engineers can explain complex systems simply, a skill that matters as much as technical depth.  ► Now, let’s break down the answer:   1. Message Handling Models  - RabbitMQ (Message Broker):    - Uses queues with shared cursors, delivering each message to one consumer at a time.    - Tracks locks on messages to manage “in-progress” status.    - Deletes messages after they’re acknowledged or unlocks them for redelivery.  - Kafka (Log Ingestor):    - Stores messages in a log and lets consumers manage their own cursors (offsets).    - No lock management,consumers just pull messages based on offsets.    - Keeps all messages until explicitly deleted, allowing multiple consumers to traverse logs independently.   2. Compute Overhead  - RabbitMQ:    - Supports filtering, deduplication, scheduling, and expiration.    - These features require additional compute work and indexing, slowing throughput.  - Kafka:    - Focuses purely on log storage and retrieval.    - No filtering, locks, or deduplication,just sequential reads for maximum performance.   3. Performance Bottlenecks  - RabbitMQ’s bottleneck:    - Shared cursors and lock management create contention at the head of the queue.    - This limits scaling when multiple consumers need the same data.  - Kafka’s advantage:    - No locks, no shared cursors, just clients reading directly from the log.    - Faster, parallel consumption for high-throughput scenarios.  Key Takeaways?  - RabbitMQ is great for task distribution, dispatching jobs to workers.   - Kafka shines in real-time data pipelines where multiple consumers need independent access to the same data.  The difference isn’t just “Kafka is faster.” It’s about how they handle data, manage state, and distribute work.  ► Another question for you:  If your system needed deduplication and message ordering guarantees, would Kafka still be the right choice? Or would you rethink the architecture?  Think about it and don’t just memorize answers from the internet. Understand why.

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A good engineer understands the limitations of the software they use (database, proxy, etc.) and build their workflows to flow with them as opposed of against them. If you are tasked with picking the software, it is critical to deeply understand one’s workflows intimately and pick the software with limitations that doesn’t clash with them. Because at the end of the day, every software has limitations.

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Some engineering principles I follow: 1. Under engineering is preferable to over-engineering 2. Don’t be on the bleeding edge of tech because it’s you that will be bleeding 3. Start with a modular monolith 4. Default to Postgres unless you have a very good reason to pick something different 5. Favor maintainability over performance

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Take breaks. The compiler isn’t going anywhere. Identify and solve stress issues : 👉Unrealistic Deadlines → Estimate better, set buffers, communicate blockers. 👉 Unclear Requirements → Ask questions, document agreements, validate with prototypes. 👉 Technical Debt → Prioritize refactoring, use best practices, document fixes. 👉Interruptions → Set focus hours, batch tasks, use async communication. 👉 Burnout → Set boundaries, take breaks, balance workload.

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Senior engineers doesn't know everything They Google things daily, review documentation, and ask questions. What sets them apart is knowing what to look for, how to approach problems, and avoiding common traps. They don’t know every answer, but they just investigate better. --- ♻️ Share so more people can understand #SoftwareEngineering #Programming #Coding #Tech

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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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The System Design Iceberg What you think it is vs. what it actually is What most people think: - Just add more servers = faster system - Caching? Nice to have, not must-have - Cloud will handle everything - Downtime is okay, we’ll fix it - It’s just drawing boxes and arrows What it actually is: - Every design choice has trade-offs (speed, consistency, availability) - Things will fail — and you have to plan for it Security should be part of the foundation, not a patch later - You’ll deal with old systems, versioning, and breaking changes - Microservices make things more complex, not easier Logs are not just for bugs — you need real monitoring - Scaling properly needs smart caching, stateless services, and resilience Real system design isn’t about making things work. It’s about making things not break when stuff hits the fan.

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I just built a distributed database from scratch! 🚀 I recently completed a months-long project building ConureDB 🦜, a distributed key-value database entirely from the ground up, using minimal external dependencies to truly understand database internals. This was a 0-to-1 build - implementing everything from the storage layer to distributed consensus. The goal was to understand how these systems actually work rather than just integrating existing solutions. ✨ Key features of ConureDB: • Persistent Storage: B-tree engine with copy-on-write concurrency for lock-free reads and node pooling for reduced allocation overhead and better performance. • Distributed Consensus: Raft algorithm ensuring data consistency across multiple nodes, surviving up to 50% failures. • Client Interfaces: RESTful HTTP API and interactive CLI for database operations with automatic write routing. • Kubernetes Deployment: Cloud-native orchestration with StatefulSets for stable identities and Helm charts for easy deployment. • Fault Tolerance: Nodes automatically discover and join clusters on startup, failed nodes seamlessly rejoin when recovered, preventing data loss and service interruption. The hardest part? Understanding the intricate trade-offs between consistency, performance, and operational complexity. Every design decision - from storage engines to bootstrap strategies - required deep research into how systems like etcd and PostgreSQL actually work under the hood. I have captured this whole account of building a distributed database from scratch in a blog, do check it out! https://lnkd.in/gbhXUxnz And, here's the GitHub project link, feel free to play around with it and give it a ⭐️ if you like it: https://lnkd.in/gRhaawXr

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Develop like monolith and run like microservices Google recently introduced an Open source framework for distributed applications. Service Weaver, offers the flexibility to write your application as a modular monolith, a term we define as a monolithic application that is structured in a modular way, and deploy it as a microservice, a feature that sets it apart from other frameworks. Service Weaver libraries allow you to write your application as a single module binary using native data structures and method calls. These can be effortlessly deployed as a set of microservices on the cloud or on-premise, providing the best of both worlds of monolith and microservices. With Service Weaver, developers can harness the simplicity of monolithic application development while reaping the scalability and reliability benefits of microservices. By decoupling application development from deployment topology, it empowers you with a flexible and efficient development process, making it easier to build and maintain distributed applications

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🐞 Spring Boot + Kafka: A Silent Bug That Taught Me a Big Lesson While working on a Spring Boot microservice that consumed Kafka messages, everything worked perfectly in dev. But in production—no logs, no errors, just... silence. After hours of debugging, I found the culprit: ❌ Misconfigured consumer group ID Kafka treated the service as a new group every time it restarted, reprocessing old messages and causing duplicate entries downstream. 🔍 What I learned: ✅ Always explicitly set the consumer group ID in your application.yml or application.properties. 🧭 Use offset monitoring tools like Confluent Control Center or Burrow. 📝 Add structured logging around message consumption to catch silent failures. These small fixes saved us from a major data integrity issue. 💬 Have you faced similar issues or other with Kafka or Spring Boot? Let’s share and learn together! #SpringBoot #Kafka #Microservices #JavaDeveloper #DebuggingTips #BackendEngineering #TechStories #SoftwareDevelopment

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🔥 Clean Architecture & KMP – A Perfect Match! ❤️ Clean architecture’s main focus? Separation of concerns. KMP’s core requirement? Isolating business logic from the framework (Android, iOS). They complement each other perfectly! 🚀 ✅ Shared business logic stays modular & testable ✅ Platform-specific UI remains independent ✅ Scalable, maintainable, and future-proof apps By combining KMP + Clean Architecture, we build high-quality, cross-platform apps with clean, reusable code! 🔹 How are you structuring your KMP projects? Let’s discuss! 👇 #KotlinMultiplatform #KMP #CleanArchitecture #AndroidDevelopment #iOSDevelopment

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As a Principal Engineer, I don't mind if a junior engineer isn't the best at coding-yet. What matters far more to me is how they think, communicate, and collaborate because those qualities determine how quickly they will grow into a strong engineer. 𝗪𝗵𝗮𝘁 𝗜 𝘃𝗮𝗹𝘂𝗲 𝗺𝗼𝘀𝘁 𝗶𝗻 𝗷𝘂𝗻𝗶𝗼𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀: 𝟭. 𝗖𝗹𝗲𝗮𝗿 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻: You should be able to articulate your thoughts, explain your reasoning, and ask good questions. If you’re struggling with something, say so. If you have an idea, share it. 𝟮. 𝗦𝘁𝗿𝗼𝗻𝗴 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Engineering is a team sport. No one succeeds in isolation. I want to see you working well with designers, product managers, and other engineers. Listen actively, give thoughtful feedback, and be a reliable teammate. 𝟯. 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗠𝗲𝗻𝘁𝗮𝗹𝗶𝘁𝘆: You don’t have to know everything, but I expect you to take responsibility for your work. If something goes wrong, don’t point fingers—own it, learn from it, and improve. 𝟰. 𝗚𝗿𝗼𝘄𝘁𝗵 𝗠𝗶𝗻𝗱𝘀𝗲𝘁: Your current skill level matters far less than your willingness to improve. Are you eager to learn? Do you seek out feedback? Do you push yourself outside your comfort zone? 𝗪𝗵𝘆 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝘀𝗸𝗶𝗹𝗹𝘀 𝗺𝗮𝘁𝘁𝗲𝗿 𝗹𝗲𝘀𝘀 𝗮𝘁 𝗳𝗶𝗿𝘀𝘁: Coding is a skill that anyone can improve with deliberate practice. You have unlimited resources at your disposal: • ChatGPT, Stack Overflow, and YouTube can provide instant explanations. • Leetcode and personal projects can help sharpen your problem-solving. • Pair programming and code reviews will accelerate your learning. But soft skills? Those are much harder to teach. You can have the sharpest technical mind in the room, but if you can’t collaborate, communicate, or take feedback well, you’ll struggle in your career. 𝗪𝗵𝗮𝘁 𝘀𝗲𝘁𝘀 𝗮𝗽𝗮𝗿𝘁 𝗮 𝘁𝗿𝘂𝗹𝘆 𝗴𝗿𝗲𝗮𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿: At the principal level, I’ve seen that the best engineers aren’t just the ones who write brilliant code-they’re the ones who: • Elevate the people around them. • Mentor and guide others. • Think holistically about architecture, scalability, and user impact. • Align technical decisions with business goals. • Influence without authority and bring people together toward a common vision. So if you’re a junior engineer, focus less on being a “𝟭𝟬𝘅 𝗰𝗼𝗱𝗲𝗿” 𝗮𝗻𝗱 𝗺𝗼𝗿𝗲 𝗼𝗻 𝗯𝗲𝗶𝗻𝗴 𝗮 𝗵𝗶𝗴𝗵-𝗶𝗺𝗽𝗮𝗰𝘁 𝘁𝗲𝗮𝗺𝗺𝗮𝘁𝗲. The technical skills will come with time-but the way you show up, work with others, and take ownership will define your success far more than any single line of code you write.

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A crucial realization for software engineers and engineering leaders is that coding, frameworks, infrastructure, etc., are just parts of engineering. Actual engineering lies in recognizing that problem-solving addresses customer needs. Every other engineering component (namely backend, DBs, frontend, DevOps and so on) is merely a tool; their trade-offs are utilized to solve these problems effectively. Anyone who realizes this early and stays grounded in their career will distinguish themselves as engineers rather than just programmers. They remain engineers no matter what their titles are! #engineering #problemsolving

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One of the most difficult parts of any system design process is choosing your trade-offs. You compromise on the wrong thing and set yourself up for failure. This post will teach you how to choose your trade-offs in distributed systems. These are my takeaways after spending the whole of 2024 studying system design and distributed architectures ► The CAP Theorem at a Glance   CAP states that distributed systems can guarantee only two out of three properties:  - Consistency (C): All nodes have the same data at any given time.   - Availability (A): Every request gets a successful or failed response.   - Partition Tolerance (P): The system works despite network failures.  You can’t have all three. Distributed systems must choose what to optimize for based on their use case.     ► Stream processing complements CAP by enabling real-time event handling. It processes data as it arrives, ensuring low latency.  - Handle failures through retries and replication.   - Guarantees order and delivery even during partitions.   - Balances throughput and latency.  Together, CAP and stream processing force decisions on performance, fault tolerance, and scalability.  ► Trade-offs Based on Requirements  1/ When consistency is non-negotiable, design for CP systems.   - Use databases like MongoDB or PostgreSQL with quorum reads and writes.   - Focus on transaction integrity and locking mechanisms to maintain correctness.   - Be ready to sacrifice availability during network failures to protect data accuracy.  2/ When availability is the priority, design for AP systems.   - Use eventually consistent databases like DynamoDB or Cassandra.   - Prioritize replication and asynchronous messaging to handle high traffic.   - Accept temporary inconsistencies but ensure updates synchronize later.  3/ When both consistency and availability are required, design for CA systems.   - Use relational databases like SQL Server for local, non-distributed setups.   - Focus on low-latency queries with strong guarantees for small-scale applications.   - Work well when network partitions are not a concern. ►  Stream Processing Trade-offs  4/ When low latency is a must, optimize for performance.   - Use frameworks like Kafka or Apache Flink for real-time pipelines.   - Focus on windowing and batching to balance speed and accuracy.  5/ When scalability matters most, prioritize AP designs.   - Use distributed messaging queues and horizontal scaling to handle spikes.   - Accept eventual consistency and rely on sync jobs to update data later.  6/ When a hybrid approach is needed, combine real-time and batch processing.   - Use Kafka for streaming and Spark for batch analytics.   - Implement event sourcing to replay data and ensure consistency.  CAP theorem tells you what’s impossible. Stream processing tells you how to handle the consequences of that impossibility. Your job is to choose the trade-offs that let your system succeed when things go wrong. 

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Don't be too serious about yourself in the interview. I have seen it very often during senior developers interviews. 1. They start their introduction in the base voice. 2. Explain so many things that they are proud of. 3. Mention scenarios how they held the ground together during earthquake 🫨😂 Finally I ask them "if javascript is interpreted how hoisting works" ? they struggle to answer and feel very low. My suggestion is, don't go to an interview with a baggage. Don't go to an interview with an attitude you know everything, be open to fail, that's the way to succeed. what do you think ? follow me and I will help you to find your dream job: Vasanth Bhat

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My favorite record feature from is: Deconstruction. When records instance is created, compiler automatically creates a deconstruct method that corresponds to the primary constructor parameters. The must here is to define positional records, because for traditional definition you need to define it manually. This was useful for me at one project where I need to attach few user properties in email template. And also for event handling feature in MAUI applications.

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Software engineering usually comes down to two not-so-secret rules: Kidlin’s Law: If you can write the problem clearly, you’re halfway to the solution. Pareto’s Law: 80% of results come from 20% of effort. The projects that actually work? They nail two things: 1. They define the problem in plain English before anyone writes a line of code. 2. They put focus on the handful of features, bugs, or fixes that actually move the needle. Engineers love complexity. Stakeholders love “more.” But clarity + focus will beat complexity every single time. So the next time your team complains about velocity… Don’t ask them to work longer hours. Ask if the problem is even clear. Ask if they’re solving the 20% that matters. Because software success isn’t about writing more code. It’s about solving the right problem, as simply as possible.

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