Balaji Gundle

Writing modular code isn’t just for clean code points, it makes collaboration and scaling so much easier. Here’s how. ➊ Design ➟ Single Responsibility Each module/class/function should do one thing. This is the core of the Single Responsibility Principle (SRP), a pillar of SOLID design. ➟ Loose Coupling Modules should have minimal dependencies. This ensures easier reuse and lower maintenance cost. ➟ High Cohesion Related functionality should be grouped together for better clarity and maintainability. ➋ Structure ➟ Small Functions Functions should do one thing and do it well. This improves readability and testability. ➟ Clear Interfaces Well-defined APIs/interfaces enable easy integration and replacement. ➟ Consistent Naming Using consistent, descriptive names helps other developers understand the code quickly. ➌ Reuse ➟ Libraries Common functionality should be abstracted into libraries/modules. ➟ Generic Code Use parameterization/generics/templates where appropriate to maximize reuse. ➟ Configuration Avoid hardcoding values, use configs for flexibility. ➍ Testing ➟ Unit Tests Essential for verifying small, isolated pieces of code. ➟ Mocking Facilitates testing in isolation, independent of dependencies. ➟ Coverage Strive for high test coverage to ensure reliability. ➎ Documentation ➟ Comments Explain why (not just what) in the code. ➟ Readme Every module/library should have clear usage instructions. ➟ Examples Usage examples are extremely helpful for onboarding and adoption. ➏ Maintainability ➟ Refactor Regularly Tackle technical debt before it grows. ➟ Code Reviews Peer reviews catch issues and spread knowledge. ➟ Follow Standards Consistency (via code style guides and conventions) prevents confusion.

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14 Microservices Design Patterns Every Engineer Should Master Building scalable microservices isn’t just about APIs and containers, it’s about creating systems that can handle real-world failures, coordination, and growth. Here are 14 design patterns that have stood the test of production: 1. Gateway Pattern Acts as the single entry point — handles routing, rate limiting, auth, and observability. 2. Service Registry Pattern Enables dynamic service discovery without hardcoded URLs. 3. Circuit Breaker Pattern Prevents cascading failures by halting requests to unhealthy services. 4. Saga Pattern Splits distributed transactions into smaller, coordinated local transactions for eventual consistency. 5. CQRS Pattern Separates read/write paths for performance tuning and domain clarity. 6. Bulkhead Pattern Isolates failures so one crashing service doesn’t bring down the rest. 7. Sidecar Pattern Pairs services with “helper” containers for logging, proxying, or monitoring. 8. API Composition Pattern Combines multiple service responses into a single aggregated result. 9. Event-Driven Pattern Services interact via async events for loose coupling and scale. 10. Database per Service Pattern Each microservice owns its data, supporting independence and modularity. 11. Retry Pattern Retries failed operations with backoff to reduce transient errors. 12. Externalized Configuration Pattern Moves config out of code for easier updates and environment flexibility. 13. Strangler Fig Pattern Wraps legacy systems with modern services for gradual replacement. 14. Leader Election Pattern Elects a leader in distributed clusters to coordinate shared tasks. 15. Manual Scaling Fallback Keeps human controls in place when auto-scaling misfires. These patterns aren’t theoretical, they’re what separates resilient systems from brittle ones. Which one have you leaned on lately? ♻️ Repost this to help your network get started ➕ Follow Sathish Kumar Subramani for more #Microservices #SoftwareArchitecture #SystemDesign #DevOps #Resilience #EventDriven

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𝐓𝐨𝐩 𝟏𝟎 𝐉𝐚𝐯𝐚 𝐂𝐨𝐧𝐜𝐮𝐫𝐫𝐞𝐧𝐜𝐲 𝐌𝐢𝐬𝐭𝐚𝐤𝐞𝐬 (𝐘𝐨𝐮 𝐌𝐢𝐠𝐡𝐭 𝐁𝐞 𝐌𝐚𝐤𝐢𝐧𝐠 𝐑𝐢𝐠𝐡𝐭 𝐍𝐨𝐰) Java concurrency is tricky. Even seasoned developers fall into these traps: ❌ 𝐈𝐠𝐧𝐨𝐫𝐢𝐧𝐠 𝐯𝐨𝐥𝐚𝐭𝐢𝐥𝐞 𝐤𝐞𝐲𝐰𝐨𝐫𝐝 → stale data between threads ❌ 𝐔𝐬𝐢𝐧𝐠 𝐬𝐲𝐧𝐜𝐡𝐫𝐨𝐧𝐢𝐳𝐞𝐝 𝐞𝐯𝐞𝐫𝐲𝐰𝐡𝐞𝐫𝐞 → contention & slowdowns ❌ 𝐅𝐨𝐫𝐠𝐞𝐭𝐭𝐢𝐧𝐠 𝐭𝐨 𝐜𝐥𝐨𝐬𝐞 𝐄𝐱𝐞𝐜𝐮𝐭𝐨𝐫𝐒𝐞𝐫𝐯𝐢𝐜𝐞 → resource leaks ❌ 𝐍𝐨𝐭 𝐡𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐭𝐡𝐫𝐞𝐚𝐝 𝐢𝐧𝐭𝐞𝐫𝐫𝐮𝐩𝐭𝐢𝐨𝐧 → no graceful shutdowns ❌ 𝐀𝐬𝐬𝐮𝐦𝐢𝐧𝐠 𝐇𝐚𝐬𝐡𝐌𝐚𝐩 𝐢𝐬 𝐭𝐡𝐫𝐞𝐚𝐝-𝐬𝐚𝐟𝐞 → data corruption ❌ 𝐎𝐯𝐞𝐫𝐮𝐬𝐢𝐧𝐠 𝐓𝐡𝐫𝐞𝐚𝐝.𝐬𝐥𝐞𝐞𝐩() → unpredictable behavior ❌ 𝐒𝐩𝐚𝐰𝐧𝐢𝐧𝐠 𝐭𝐨𝐨 𝐦𝐚𝐧𝐲 𝐭𝐡𝐫𝐞𝐚𝐝𝐬 → CPU thrashing ❌ 𝐃𝐞𝐚𝐝𝐥𝐨𝐜𝐤𝐬 𝐟𝐫𝐨𝐦 𝐩𝐨𝐨𝐫 𝐥𝐨𝐜𝐤 𝐨𝐫𝐝𝐞𝐫𝐢𝐧𝐠 → stuck threads ❌ 𝐀𝐯𝐨𝐢𝐝𝐢𝐧𝐠 𝐜𝐨𝐧𝐜𝐮𝐫𝐫𝐞𝐧𝐭 𝐜𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧𝐬 → race conditions ❌ 𝐁𝐥𝐨𝐜𝐤𝐢𝐧𝐠 𝐈/𝐎 𝐢𝐧 𝐩𝐚𝐫𝐚𝐥𝐥𝐞𝐥 𝐬𝐭𝐫𝐞𝐚𝐦𝐬 → thread starvation 💡 Mastering concurrency isn’t about more threads—it’s about right threads, safe access, and clean shutdowns. 🔗 Follow Madhu K. for more Java tips, best practices, and real-world coding insights! #Java #Concurrency #Multithreading #ProgrammingTips #CleanCode #JavaDeveloper #SoftwareEngineering

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🚀 𝗠𝗮𝘃𝗲𝗻 + 𝗗𝗲𝘃𝗢𝗽𝘀 = 𝗣𝗮𝗶𝗻𝗹𝗲𝘀𝘀 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀 🎯 🔹 𝗗𝗲𝘃𝗢𝗽𝘀 → a culture + practice that bridges development & operations, enabling faster builds, testing, and deployments with automation at its core. ⚡ 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 → compile ➝ test ➝ package ➝ deploy ⚡ 𝗗𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝗶𝗲𝘀 → auto-resolved from central repos ⚡ 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗮𝘁𝗶𝗼𝗻 → same structure across teams ⚡ 𝗣𝗿𝗼𝗳𝗶𝗹𝗲𝘀 → dev / QA / prod builds in one command ⚡ 𝗣𝗹𝘂𝗴𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 → test, package, code quality, reporting ⚡ 𝗦𝘆𝘀𝘁𝗲𝗺𝗰𝘁𝗹 → run apps as services, restart on failure 💻 Few Maven commands every DevOps engineer should know: ✅ 𝒎𝒗𝒏 𝒄𝒍𝒆𝒂𝒏 𝒊𝒏𝒔𝒕𝒂𝒍𝒍 → cleans old builds + compiles + installs locally ✅ 𝒎𝒗𝒏 𝒕𝒆𝒔𝒕 → runs unit tests ✅ 𝒎𝒗𝒏 𝒅𝒆𝒑𝒍𝒐𝒚 → pushes artifact to remote repository ⚙️ 𝗕𝘂𝗶𝗹𝗱. 𝗧𝗲𝘀𝘁. 𝗗𝗲𝗽𝗹𝗼𝘆. 𝗥𝘂𝗻. 🔥 𝑪𝒖𝒓𝒊𝒐𝒖𝒔 𝒉𝒐𝒘 𝑴𝒂𝒗𝒆𝒏 𝒔𝒖𝒑𝒆𝒓𝒄𝒉𝒂𝒓𝒈𝒆𝒔 𝑫𝒆𝒗𝑶𝒑𝒔 𝒑𝒊𝒑𝒆𝒍𝒊𝒏𝒆𝒔? From lifecycle phases to systemctl deployments — I’ve broken it all down in my blog. 🚨𝐂𝐡𝐞𝐜𝐤 𝐢𝐭 𝐨𝐮𝐭 𝐡𝐞𝐫𝐞 📖:https://lnkd.in/gQMBU9vM 🔖 #DevOps #Maven #Java #BuildAutomation #CI/CD #CloudEngineering #Systemctl #SoftwareEngineering #CloudComputing #InfrastructureAsCode #ContinuousDelivery #AutomationTesting #DevSecOps #SoftwareArchitecture #SRE #Linux #Programming #Technology #Innovation #joindevops

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🟣API Design : From Basics to Best Practices ❓️What is an API? An API (Application Programming Interface) defines how software components should interact. It’s essentially a contract between systems that specifies: 🔹️What requests can be made (endpoints, methods, payloads) 🔹️How to make those requests 🔹️What responses to expect (status codes, response body) It’s an abstraction mechanism because it hides implementation details while exposing functionality. APIs also set service boundaries by defining clear interfaces between system components APIs enable different systems to communicate regardless of their underlying implementation. Like client browsers with servers, servers with another server, and so on. 🌳Core API Styles Let’s focus on the most important API styles you’ll encounter: 1️⃣REST (Representational State Transfer): 🔹️A resource-based approach using HTTP methods 🔹️Stateless interactions 🔹️The most common API style today 2️⃣GraphQL 🔹️Query language allowing clients to request exactly what they need 🔹️Single endpoint for all operations 🔹️Ideal for complex data requirements with minimal round trips 3️⃣gRPC 🔹️High-performance RPC framework using Protocol Buffers 🔹️Excellent for microservices and internal system communication 🔹️Supports streaming and bidirectional communication ✅️Key Design Principles Having covered the most common types of APIs, let’s now turn our attention to the four key design principles (best practices) we typically adhere to when creating APIs. 1️⃣Consistency: 🔹️Use consistent naming, casing, and patterns across your API 🔹️Example: If you use camelCase in one endpoint, use it throughout 2️⃣Simplicity 🔹️Focus on core use cases and minimize complexity 🔹️Aim for intuitive designs that developers can understand quickly 🔹️Remember: The best API is one that developers can use without reading documentation 3️⃣Security: 🔹️Authentication: Verify who is making the request 🔹️Authorization: Determine what they’re allowed to do 🔹️Input validation: Never trust client data 4️⃣Performance 🔹️Design for efficiency with appropriate caching strategies 🔹️Consider pagination for large data sets 🔹️Minimize payload sizes and network round trips 🌳How application protocols influence API design decisions Application protocols also play an important role in influencing our choices regarding API design. For example: 🔹️HTTP features directly enable RESTful capabilities 🔹️WebSockets enable real-time, bidirectional APIs Your protocol choice fundamentally shapes your API design options. Protocol constraints and their influence on design decisions. (I’ll discuss the API protocols in more detail in the next post, so stay tuned) #Java #Developer #resume #fresher #interview #spring #program #questionforgroup #Java8features #Springboot

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