Understanding Microservices Complexity

Building Strong and adaptable Microservices with Java and Spring While building robust and scalable microservices can seem complex, understanding essential concepts empowers you for success. This post explores crucial elements for designing reliable distributed systems using Java and Spring frameworks. 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗹 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 𝗳𝗼𝗿 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: The core principles of planning for failure, instrumentation, and automation are crucial across different technologies. While this specific implementation focuses on Java, these learnings are generally applicable when architecting distributed systems with other languages and frameworks as well. 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗳𝗼𝗿 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: A typical microservices architecture involves: Multiple Microservices (MS) communicating via APIs: Services interact through well-defined Application Programming Interfaces (APIs). API Gateway for routing and security: An API Gateway acts as a single entry point, managing traffic routing and security for the microservices. Load Balancer for traffic management: A Load Balancer distributes incoming traffic efficiently across various service instances. Service Discovery for finding MS instances: Service Discovery helps locate and connect to specific microservices within the distributed system. Fault Tolerance with retries, circuit breakers etc.: Strategies like retries and circuit breakers ensure system resilience by handling failures gracefully. Distributed Tracing to monitor requests: Distributed tracing allows tracking requests across different microservices for better monitoring and debugging. Message Queues for asynchronous tasks: Message queues enable asynchronous communication, decoupling tasks and improving performance. Centralized Logging for debugging: Centralized logging simplifies troubleshooting by aggregating logs from all services in one place. Database per service (optional): Each microservice can have its own database for data ownership and isolation. CI/CD pipelines for rapid delivery: Continuous Integration (CI) and Continuous Delivery (CD) pipelines automate building, testing, and deploying microservices efficiently. 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴 𝗦𝗽𝗿𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗳𝗼𝗿 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: Frameworks like Spring Boot, Spring Cloud, and Resilience4j streamline the implementation of: Service Registration with Eureka Declarative REST APIs Client-Side Load Balancing with Ribbon Circuit Breakers with Hystrix Distributed Tracing with Sleuth + Zipkin 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 𝗳𝗼𝗿 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗥𝗼𝗯𝘂𝘀𝘁 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀: Adopt a services-first approach Plan for failure Instrument everything Automate deployment

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.

In 2019, Doordash re-engineered its systems and moved to a microservice architecture from its previous monolith architecture. They were rapidly scaling, and microservices were the best option for them to scale their backend service; however, it wasn’t an easy journey. They faced many challenges during the process. Here’s how they rebuilt the entire service with microservices:  1. The Problem with Monolith Architecture   ↳ Initial monolith built on Django was great for quick development and scaling early on.   ↳ As DoorDash grew, the monolith created bottlenecks, increased bugs, and developer frustration.   ↳ Rolling back changes became expensive due to tightly coupled code and complex dependencies.  2. Why the Move to Microservices   ↳ Monolith systems couldn’t scale with rising demand and traffic.   ↳ Microservices offer isolation, agility, and resilience—minimizing impact from service failures.   ↳ Allowed teams to focus on individual domains, reducing cognitive load and increasing efficiency.  3. The Roadmap for Transition  ↳ Prehistory (2014-2019): Initial services were extracted without a cohesive plan, still dependent on the monolith.  ↳ Project Reach (2019): Standardized tech stack (Kotlin, gRPC) and began structured code extraction.  ↳ Project Metronome: Prioritized extraction of core workflows with management buy-in and team involvement.  ↳ Project Phoenix: Final planning and execution of a fully microservice-based platform.  4. Challenges Faced During Migration   ↳ Convincing teams to let go of the monolith due to cognitive bias (IKEA effect).   ↳ Developing new reliability patterns while avoiding temporary anti-patterns (like direct DB access).   ↳ Handling gradual service adoption and database migrations without disrupting operations.   ↳ Managing technical debt as old and new systems ran in parallel during the transition.  5. The New Architecture   ↳ Frontend Layer: Different apps for customers, Dashers, and merchants.   ↳ BFF (Backend-for-Frontend) Layer: Orchestrates backend services for smoother frontend integration.   ↳ Backend Layer: Manages core logic like order, delivery, and payments.   ↳ Platform Layer: Provides reusable services like identity management.   ↳ Infrastructure Layer: Powers databases and cloud operations with seamless scaling.  6. Results and Takeaways   ↳ 50+ microservices now support DoorDash’s platform, 20 of which are business-critical.   ↳ Achieved better uptime, agility, and isolation—allowing rapid development of new features.   ↳ Smaller, focused teams now manage services efficiently, reducing cognitive load.   ↳ Lessons: Transitioning takes time, but aligning tech and business goals ensures success.  

Amazon deploys code every 11.7 seconds. Netflix handles over 7 billion requests per day. You don’t ship at that scale with a monolith. Here’s what real-world microservices architecture actually looks like, and why it works. Most people think microservices are just about “splitting the app.” They’re not. They’re about designing for change, complexity, and chaos and doing it at scale. Let’s walk through a standard e-commerce flow. It looks simple: user adds to cart → places order → gets shipment. But under the hood? You’re dealing with: - Inventory syncing in real time - Payment authorization across providers - Shipping via external logistics - Analytics updates - Supplier backorder events - Compliance logs And this all has to happen asynchronously, in parallel, across fault-tolerant systems. Here’s how we make it work: Each domain is isolated: Cart, Orders, Inventory, Payments, Shipping, Reporting. They communicate via events, not direct calls. APIs wrap around them, but they’re not the core. The event log is the truth. Third-party systems (shipping, suppliers) are plug-and-play, wired via adapters. This lets you: - Deploy independently - Debug locally - Scale horizontally - Replace without downtime You design it like Lego, independent units that work together under stress. So no, microservices aren’t just about scale. They’re about resilience. And when your business depends on uptime and iteration speed, there’s no alternative. ♻️ Repost if you find this insightful 🛎️ Follow me for more such content #softwareengineering #tech #job #linkedin