Scalable Design Patterns

Design for Scale - 1: Control Plane and Data Plane Separation Over years of developing large-scale systems, we've discovered a fascinating pattern from networking and telecommunications that's transformed how we build systems today. It started when we noticed a recurring theme in our data flows - the people writing data often had completely different needs from those reading it. Sometimes we'd write data in one format, only to find other people needed it differently, with read operations dwarfing writes by factors of 5, 10, or even 100. We found event streams becoming our best friends in bridging these two worlds. Our control plane systems would emit events, and events propagate and transform, reaching all the places where our data plane needed them. In our data plane work, we treated information as immutable, focusing purely on read performance. Meanwhile, our control plane adventures involved wrestling with mutation conflicts and complex access control scenarios. We usually scale control plane and data plane at different orders of magnitude. If you are starting fresh projects today that share the similar read and write data flow patterns, I would strongly recommend beginning with two distinct codebases and teams, even if it seems like overkill. Think of it as planting seeds for future growth. You will pat yourself on the back if your systems become popular. If you're starting small - and most of us do - you can host both planes on the same server to save costs, but keep them as separate processes listening on different ports. This gives you clear boundaries from day one while being light on resources. For those facing existing monoliths where control plane and data plane are intertwined, start by identifying and separating those distinct responsibilities into different modules. Then gradually split the runtime components and consider forming specialized teams around each plane. It's like applying Conway's Law in reverse - reshape your organization to match the architecture you want. One of my favorite discoveries was how this pattern elegantly handled eventual consistency. By versioning mutations in the control plane, we could help data plane systems track exactly what they were consuming, making troubleshooting clearer. Through all of this, we've come to see this separation not just as an architectural choice, but as a reflection of the natural order in how data flows through our systems. Whether starting fresh or renovating an existing system, the extra effort of separation early on pays dividends when your system needs to scale. And you don’t need to be perfect in this separation. All large systems go through continuous data migration anyway. But as Edsger W. Dijkstra - the scientist who invented the shortest path algorithm that powers the google map - once said:“Separation of concerns, even if not perfectly possible, is yet the only available technique for effective ordering of one’s thoughts, that I know of.”

Don't overload a single AI Agent with a bunch of MCP Servers Use these multi-agent design patterns for clever orchestration... Cursor AI, MS Copilot, Harvey AI, and many other companies are now rapidly moving towards multi-agent development and execution. 📌 This is because of 4 core reasons: 1. Scalable automation through specialised agents 2. Improved decision-making via collaboration 3. Parallel Processing for Faster Results and 4. Real-Time Adaptation to Changing Inputs and Environments 📌 But why should you choose a multi-agent workflow? - A single-agent system handles all tasks alone, limiting scalability and specialisation, while a multi-agent system uses coordinated, specialised agents for modular, efficient, and smarter workflows. - Companies are shifting to multi-agent architectures to tackle complex problems faster, scale capabilities dynamically, and build systems that mimic real-world team collaboration. However, there are numerous ways to design a multi-agent system- which one to choose? 📌 Let me share 6 popular design patterns to help you move faster: 1. Sequential - Agents are chained one after another, where each agent refines or transforms the result in turn. Use-cases: Data processing / ETL pipelines and Automated Q&A verification. 2. Router Pattern - A central “router” agent delegates to the correct specialist based on the query. Use cases: Customer support agents and Service orchestration agents, where an API-gateway-style Router agent decides whether to call Authentication, User Profile, or Payment agents. 3. Parallel Pattern - A “Divisor” splits work into independent parallel subtasks, then aggregates results. Use-cases: Real-time Information retrieval and Financial risk analysis agents or legal agents. 4. Generator Pattern - An iterative “divisor → specialist agents → generator → feedback” cycle for draft–refine workflows. Use cases: Code generation agents, Automated design and documentation agents. 5. Network Pattern - A fully meshed “meta-agent → specialists ↔ specialists” collaboration model. Use Caes: Architectural design, with separate Design, Security-Review, and Compliance agents all able to call each other bidirectionally under the oversight of a Meta-Agent. 6. Autonomous Agents Pattern - Decentralised agents interact in loops without a central orchestrator—ideal for fully autonomous coordination. Use Cases: Autonomous embodied agents where multiple agents collaborate to sense and move around a certain path without human intervention. --- Need an AI Consultant or help building your career in AI? Message me now

Brain Boost Drop #16 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝗳𝗼𝗿 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝘀𝗶𝗴𝗻 Over the years, I’ve learned that building truly scalable and resilient systems isn't just about breaking things into services, it’s about how you connect, manage, and recover from failures between them. Microservices patterns offer battle-tested strategies for dealing with everything from data ownership to distributed transactions and fault isolation. Here’s a breakdown of the top patterns I often refer to when designing or reviewing microservices-based systems: 🔹 Decomposition Pattern – Split monoliths into focused services for better scalability. 🔹 API Gateway Pattern – Centralized entry point for routing, auth, and throttling. 🔹 Service Discovery Pattern – Dynamically locate services without hardcoded IPs. 🔹 Database per Service Pattern – Give each service its own DB for better isolation. 🔹 CQRS Pattern – Separate read/write operations to handle complexity at scale. 🔹 Event Sourcing Pattern – Store event logs instead of current state; great for audits. 🔹 Strangler Pattern – Gradually replace monolith components with microservices. 🔹 Circuit Breaker Pattern – Block calls to failing services to avoid cascading failures. 🔹 Bulkhead Pattern – Isolate workloads to contain failures and increase resilience. 🔹 Sidecar Pattern – Attach shared tools (e.g., logging/monitoring) beside services. 🔹 Saga Pattern – Handle distributed transactions without global locks. 🔹 Message Queue Pattern – Use async queues to decouple services and improve load handling. Each of these solves a specific class of problems. The key is knowing when (and when not) to apply them. 💬 Which of these have you used recently—or struggled with? Let’s discuss! Follow Nikhil Kassetty for more Brain Boost Drops.

💡 7 Layers of Scalable System Design – A Blueprint for Modern Engineers Scalability isn’t a feature — it’s an architecture. Whether you're building SaaS, e-commerce platforms, or real-time apps, your system design choices define your product’s reliability and growth. Here’s a practical breakdown of the 7 essential layers in modern scalable architecture: 1. Client Layer – Responsive UI, optimized data fetching, local storage, and lazy loading. 2. API Gateway Layer – Manages traffic, routes requests, handles rate limits, and provides monitoring. 3. Application Layer – Microservices encapsulating business logic with frameworks like Spring Boot, Node.js, etc. 4. Caching Layer – Reduces load and latency using Redis, CDN, or Memcached. 5. Database Layer – Ensures reliable, scalable storage using SQL/NoSQL, sharding, and replication. 6. Data Processing Layer – Supports real-time ETL, event pipelines, analytics using Kafka, Spark, and Flink. 7. Infrastructure Layer – Manages containerized workloads, CI/CD, observability, and failover strategies. 🔧 Tools like Docker, Kubernetes, Terraform, NGINX, and PostgreSQL power these layers. 📈 Scenarios like billing systems, recommendation engines, or real-time dashboards bring this design to life. A well-architected system isn’t built in a day—but knowing what to build and why gives you a head start.

If you're working in cloud, here are 7 architecture patterns you need to know.. (plus when each one makes sense to use) Before we start — why should you care about architecture patterns? Because building in the cloud is all about designing systems that are scalable, resilient, and easy to evolve. These patterns help you do exactly that. They’re battle-tested strategies used by modern teams to solve real-world challenges — like handling millions of users, avoiding downtime, or replacing legacy systems without a total rewrite. Here are 7 patterns worth knowing: 1. Microservices Architecture ↳ Break large apps into small, independent services. ↳ Each service handles a specific task and communicates via APIs. ↳ When to use ~ When you want independent deployment, scaling, or teams owning different parts of the system. 2. Event-Driven Architecture ↳ Let components react to events asynchronously. ↳ Events (like user sign-ups or file uploads) trigger workflows automatically. ↳ When to use ~ For loosely coupled systems that require real-time or reactive flows. 3. Sidecar Pattern ↳ Pair helper containers with your main app container. ↳ Offloads tasks like logging, service discovery, or networking. ↳ When to use ~ In Kubernetes/microservices for clean separation of concerns. 4. Strangler Fig Pattern ↳ Replace legacy systems one piece at a time. ↳ Gradually route functionality from the old system to new microservices. ↳ When to use ~ During legacy modernization with minimal disruption. 5. Database Sharding / Horizontal Scaling ↳ Split data across multiple databases or nodes. ↳ Each shard holds a subset of data for better performance. ↳ When to use ~ For high-throughput systems and global-scale applications. 6. Serverless Architecture ↳ Run code on-demand without managing servers. ↳ Functions are triggered by events (like HTTP calls or file uploads). ↳ When to use ~ For unpredictable workloads, quick development, or cost-efficiency. 7. API Gateway Pattern ↳ Centralize all client requests through one gateway. ↳ Acts as a single entry point, handling routing, auth, throttling, etc. ↳ When to use ~ To simplify client-server communication and secure backend services. Now know this — these patterns are the backbone of high-performing, cloud-native apps at top tech companies. Understanding their pros, cons, and use cases can give you a real edge in interviews. Want detailed use cases and interview questions to prep? I’ll be sharing them this Thursday in my newsletter — subscribe here: https://lnkd.in/dBNJPv9U What did I miss? • • • If you found this useful.. 🔔 Follow me (Vishakha) for more Cloud & DevOps insights ♻️ Share so others can learn as well!