Abhishek Srivastava

🚀 **Building a Robust Production Microservice Application: Essential Components** 🚀 Creating a scalable, resilient microservice application involves several critical components. Here's a comprehensive overview with example tools: 1. **Service Discovery**: Manage service registration and discovery with tools like **Consul** and **Eureka**. 2. **API Gateway**: Ensure smooth routing, load balancing, security, and rate limiting using **Kong**, **Zuul**, or **NGINX**. 3. **Configuration Management**: Centralize and dynamically update configurations with **Spring Cloud Config** or **Consul**. 4. **Service Communication**: Use synchronous communication like **HTTP/REST** and **gRPC**, or asynchronous methods like **RabbitMQ** and **Kafka**. 5. **Data Management**: Employ a database per service for loose coupling, utilizing **MongoDB**, **PostgreSQL**, or **MySQL**. 6. **Logging and Monitoring**: Centralize logging with the **ELK Stack** (Elasticsearch, Logstash, Kibana), monitor with **Prometheus** and **Grafana**, and trace with **Jaeger** or **Zipkin**. 7. **Security**: Implement authentication and authorization with **OAuth2** and **OpenID Connect**, secure data with **TLS/SSL**,**RBAC/ABAC**, and manage secrets with **HashiCorp Vault/AWS Secrets Manager/Azure Key Vault/Google Secret Manager/Kubernetes Secrets**. 8. **Containerization and Orchestration**: Use **Docker** for containerization and **Kubernetes** for orchestration and scaling. 9. **CI/CD Pipeline**: Automate building, testing, and deployment using **Jenkins**, **GitLab CI**, or **ArgoCD**. 10. **Resilience and Fault Tolerance**: Ensure reliability with retries, circuit breakers like **Hystrix** or **Resilience4j**, and bulkheads. 11. **Scalability**: Achieve horizontal scaling and auto-scaling with **Kubernetes HPA (Horizontal Pod Autoscaler)**. 12. **Service Versioning**: Manage API versions effectively to ensure backward compatibility. 13. **Dependency Management**: Ensure proper startup order and connectivity among services. 14. **Health Checks and Self-Healing**: Implement liveness/readiness probes and automatic restarts with **Kubernetes Probes**. 15. **Documentation**: Maintain clear API documentation with **Swagger/OpenAPI** and comprehensive internal documentation. 16. **Optional - Event Sourcing and CQRS**: Capture all changes as events and separate read/write operations. Implementing these components ensures your microservice application is production-ready, offering high performance, reliability, and scalability. #Microservices #SoftwareArchitecture #DevOps #CloudComputing #Scalability #TechInnovation Please feel free to add more points!

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topmate.io is coming up with ZERO DAY on 30th May - the day where it will not charge any commission from its mentors. Doing my bit on this - whatever commission topmate.io is not charging me, I am giving it back to the community - not just on 30th May but this entire week. You can book 1:1 session with me or enroll in 'Cracking your next PM interview' using the code - TOPMATE and get 10% discount - yes, the commission saved is passed on to you. Here's the link for you to enroll: https://lnkd.in/dhQz8937 If you are a mentor looking to explore mentorship platforms, there is no better time to explore topmate.io than now. #Mentorship #Career #topmate #GivingBack

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Finsire has recently started its learning series - Ideally by chatting with the best folks in the Industry. Here, with Siddharth Shetty, Co-founder and CTO of Account Aggregator (AA) from DigiSahamati Foundation, we speak about the data exchange's inception, growth, and future in India. - We use AA for various reasons, including pushing boundaries in India's data exchange layer with various use cases. - It's been about three years since it went live, and over 70 million cumulative Indian accounts are linked. - One-of-a-kind data sharing network from India that is not seen anywhere across the globe, baring Singapore with https://lnkd.in/gyrCQtVQ - Different approach to protect consumers as compared to the previous models that existed to attain consumer data by various methods used by Plaid or Salt Edge Onemoney Finvu (Cookiejar Technologies) Protean eGov Technologies Anumati by Perfios AA NADL - Account Aggregator Computer Age Management Services Limited KFin Technologies Ltd. Enjoy - Please click on the link below and watch the video!

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Software Architecture Patterns for Product Managers: PMs in the Tech space need to understand the basic architecture patterns for building robust products. The top six types are as follows : 1. Layered Architecture 2. Client-Server Architecture 3. Event-Driven Architecture 4. Microservices Architecture 5. Monolithic Architecture 6. Microkernel Architecture Let us expand on the same and understand them a bit better : 1. Layered Architecture : - This mechanism focuses on organizing the system into different layers - Each layer will have a specific task at hand and assists the above layer - Ex : In Web Apps Interface we typically have an MVC(Model-View-Controller) layer approach The changes made in one layer will not affect the other 2. Client-Server Architecture: - The system is bifurcated into two components: Client (Interface) & Server (Request processer) - The client usually sends the requests to the server where the computation happens and reverts with a response - Generally, a scalable mechanism can manage multiple clients simultaneously - Variations include : 1-Tier(Logic on Client), 2-Tier(Client speaks with database), 3-Tier (Client-Server with Business logic layer), N-Tier(scalable N layers) 3. Event-Driven Architecture : - The components communicate with each other by exchanging events asynchronously - When an event occurs anywhere in the system and is published as a message can be consumed by any subscriber (A Pub-Sub Model) - This is a loosely coupled architecture and flexible one, real-time collab works very well 4. Microservices Architecture: - Made functional parts as services that are very much independent - Every service will have its functional cause and can be developed deployed and scaled accordingly - APIs (Application Programming Interface) is the medium of communication 5. Monolithic Architecture : - Unlike Microservices, a monolith is bundled into one single entity of a large codebase - The easiest way to get started but managing it while it keeps growing is difficult - A lot of interconnected dependencies to work upon for future development works 6. Microkernel Architecture - Core functionalities reside in the kernel and the rest all work as add-ons or plugins How does one choose an architecture? It depends on a few parameters like : 1. Complexity of the system 2. Performance requirements 3. Maintainability and scalability needs #Engineering #ProductManagement #KPKR

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Everytime I come back to work in the start-up ecosystem, there are 5 positives that just make it worth it - ✅ Pace of learning The number of launches, production issues, customer feedback and overall iterations on a software are happening at 10x the pace of any mid -> large sized company. Only you yourself limit your uptake. ✅ Ownership There is enough on everyone's plate. You can literally pick up any work and people will cheer for you. ✅ Collaboration There is no way to move forward unless you have folks around you partnering with you. ✅ Forging Ahead The flywheel will trudge you along, better find a way to avoid that bump for yourself and people around you. ✅ Self-actualization You have good days, you have bad days but at end of every day you end up knowing that you have picked up either a domain skill or a lifeskill. On a shitty day, you might pick up both :) #musings #startups #indianstartups

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For Techno-functional leaders, System Designers and Developers: As we acquire consumers, the load is bound to increase. At times, it surpasses our initial thoughts (a good problem to have). Before panic sets in for a re-architecture, consider these important points: 🤔 - Will caching help? - Will read-replicas help? Can some queries be moved to read replicas? - Should database sharding be used (while using the same DB technology)? Is it a good choice? - What all invalidation points are required? 🤦‍♂️ My problem is more involved if the above points can't provide breathing space. I must have made some blunders earlier. Let's evolve together as a community. Follow for more frequent doses of tech-shot and content to help Engineering Leaders move up the ladder and for System Designers to design with more confidence. Join our Discord https://lnkd.in/dbhHahQr #agiledcoders

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Building a composable solution requires a robust orchestration layer to avoid creating too much dependency on one solution or any one vendor. Similarly, using vendor-provided solutions will ultimately tie you up with their solution and defeat composability. At Assurex, we built Assure.Mart, a comprehensive solution which works along with your chosen vendors, be it CMS, Commerce, CDPs, Marketing Automation platforms, and Digital Asset Management platforms. Best of all, it provides a robust plug-and-play solution without compromising on SaaS-capable features offered by the platform. Don't let your implementation defeat composability. And if you want to see all this in action, drop a note at -> bit.ly/assure-me and request a customized demo. . . . . . . . #Assurex #CRO #MarketingStrategy #Innovation #DigitalTransformation #commerce #cdp #personlize #search #accelerators #dxp #delhi #dubai #delaware #london #uae #uk #india #usa #seamless #saudiarabia #middleeast #microsoft #google #aws #technology #experience #azure #composable #startups

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Leveraging Docker containers for efficient application deployment? Here are three common challenges and solutions: 1.    Complex Configuration Management: Managing configuration settings across multiple environments can be challenging with Docker containers. Solution: Use Docker Compose to define and share multi-container applications with configuration files. Implement environment-specific configuration using environment variables and Docker secrets for sensitive data, ensuring consistency and security across development, testing, and production environments. 2.    Resource Overhead: Running numerous containers on a single host can lead to resource contention and performance issues. Solution: Use orchestration tools like Kubernetes to manage and scale your containers efficiently. Kubernetes can automatically distribute workloads across nodes, ensuring optimal resource utilization. Additionally, implement resource limits and quotas for containers to prevent any single container from monopolizing system resources. 3.    Persistent Storage: Managing persistent storage for stateful applications in a containerized environment can be complex. Solution: Utilize Docker volumes to manage persistent data storage. For more advanced needs, use Kubernetes' Persistent Volume (PV) and Persistent Volume Claim (PVC) mechanisms to abstract storage management, providing reliable and consistent storage solutions across different nodes and clusters. Please let me know in comment and let us engage to discuss more. ********** DM | Follow Nageswara Rao Korrapolu for Building a Better Technology for a Better Future. Thank you very much.  #microservices #DigitalTransformation #Migration #TechnologySolutions #cgm

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Nice....another pattern that we can apply is a cache-through pattern, which is a combination of 2 cache patterns, read-through and write-through. In a read-through cache pattern, when a cache miss occurs (i.e., the requested data is not found in the cache), the cache automatically fetches the data from the backend data source. The fetched data is then stored in the cache before being returned to the requester. Subsequent requests for the same data can be served directly from the cache until the cached data expires or is invalidated. A write-through cache pattern involves writing data to both the cache and the backend data source simultaneously. When data is updated or inserted, the cache is updated first, followed by updating or inserting the data in the backend data source. This ensures that the cache and the backend remain consistent, reducing the risk of stale data in the cache. In the cache-through pattern, the application delegates the responsibility of managing the cache to the underlying data access layer or caching framework. The data is fetched from the backend data source on cache misses (read-through), and updates are propagated to the backend data source immediately (write-through). This pattern ensures that the cache remains consistent with the backend data source while minimising the impact on performance. The application code is not directly involved in managing the cache. Instead, it interacts with the cache indirectly through the data access layer. In cache-aside pattern, the application has more control over caching behaviour, it also carries the responsibility of ensuring cache consistency and managing cache invalidation.

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Why isn't GraphQL seeing much wider adoption? To me it seems like the perfect next step from REST and Webhooks. Callers can define their own queries and relationships of interest relatively easily (Which they can't really do with REST without it basically devolving into something like GraphQL anyway). Plus with subscriptions you get Webhook like behaviour, observability doesn't become an aferthought.

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Devprime 💡 Explore Devprime as a tool to accelerate software developer productivity and application architecture governance. One of the major challenges in designing modern applications at digital-native companies is understanding the fundamentals and applying them according to the scenarios. Try it now : Devprime #devprime #microservices #startup #cloudnative #digitalnative #distributedsystems #software #developer #csharp #visualstudio

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Unexpected object mutations? Tedious equality checks? Redundant boilerplate code? 🚀 Embrace immutability with Record Classes! 🚀 Record classes—introduced in C# 9—allow us to create immutable reference types using lightweight code. They are the best of both worlds! 👍Advantages - Simplifies creating immutable types with minimal code. - Supports `with` keyword for non-destructive mutation. - Auto-implements `Equals`, `GetHashCode`, and `ToString` methods. - The `Equals` and `GetHashCode` methods compare the properties' value instead of the object's reference. - The `ToString` method outputs a readable format that includes property values. - Supports deconstruction similar to tuples. 👎Disadvantages - Not suitable for scenarios requiring frequent mutations of objects since the immutable nature of record classes requires creating a new object for every mutation. ✅When to Use - When you need immutable data structures. - For creating value-like types without the overhead of value type copying—classes are reference types. - In scenarios where you need built-in equality and comparison logic. - For Data Transfer Objects (DTOs) to ensure data consistency. - To define events that capture state changes immutably. - For value objects in Domain-Driven Design (DDD). - For state objects where immutability is crucial for maintaining integrity. ❌When Not to Use - When you need mutable objects. - For performance-critical applications where object creation overhead is a concern. 💡Takeaways Record classes offer a concise way to define immutable types with built-in equality and string representation features, making them ideal for value-type behaviors in reference types. Their streamlined syntax makes them a great time and space saver in a codebase! 💬Comments Have you tried record classes in your projects? How have record classes simplified your code? Do you have any tips or tricks for using record classes? Share your experience and let us know in the comments below! #ASPNETCore #dotnet #csharp #CleanCode #CodeDesign #ProgrammingTips #SoftwareArchitecture #DesignAndArchitecture

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Hosted Leaders' Roundtable: "AI in the Software Development Lifecycle" We gathered leaders who have built or run massive-scale companies: ✨Shailendra Sharma, Ex - Co-Founder @ dailyhunt, Founder @ NebulaIQ.AI ✨Naresh Agarwal, Head of R&D @ Traceable ✨Kaushik Mukherjee, Ex-Head of Engineering at udaan.com, Founder @ Raptorise Key Insights: 1️⃣ For enterprise use-cases today, AI needs to be paired with a deterministic approach to reduce hallucinations and ensure accuracy. 2️⃣ AI reduces the mean time to detect errors and identify the root cause of downtime, greatly enhancing developer productivity. 3️⃣ Developers can maximize AI's value by delegating mundane tasks. Many use GenAI to write test cases, making it like an augmented coworker. Today, 70% of software costs are maintenance related (including security and code quality) - AI can bring about a transformation here on cost, outcome quality & developer experience. Kudos to DeVC- Divyanshi, Aakash, and Rahul for planning the roundtable and making this happen! 🙌

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With our mobile app, it takes less than a minute to audit an asset. Visit capex.t2win.io to understand more about our AssetZ application. Happy to connect for a detailed demo of product 😊 Track2win Naveen Katti #assets #audit #finance #pv #physicalverification #mobileapp

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Hot take: Building a business around open-source software is a lost battle. In last few months, we have seen how companies such as HashiCorp, Redis labs have faced the heat when they adopted business source licensing model. It resulted in their alternatives popping up within few days like OpenTofu, Dragonfly etc. There is no moat for such businesses because the underlying software is open-sourced which means new entrants can easily replicate what they are doing. They can sell it at a cheaper price or with a better ecosystem(if they are cloud vendors) or both. These businesses also face the dilemma of whether to merge a major improvement in the open-source or put it behind the SaaS offering. An alternative to this model is to build the business around consulting & training. The business keeps the software open sourced & provide consulting services to clients who want to use it in the most efficient manner. Pivotal has done a great job with Spring framework using this model. This way your revenue stream is not marginalized by new players easily & the core team can focus on improving the software without worrying whether it makes their core revenue model i.e. SaaS offering less attractive. Especially in the non-ZIRP era, companies are bound to reconsider where they are spending. Hence they will choose the option that burns the smallest hole in their pocket. #opensource

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🎉 WordPress to Static Site Migration 🎉 I've recently migrated my personal blog and portfolio website, https://tusharkant.in, from WordPress on a LEMP stack (hosted over GoDaddy) to a more efficient and cost-effective setup! Here’s a breakdown of the process: Jekyll for Static Site Generation I switched to Jekyll, an open-source framework that uses Markdown and Liquid templates. Jekyll simplifies content management and generates static HTML files. With Jekyll, my site is now faster, more secure, and easier to maintain. WordPress Plugin: Jekyll Exporter Using this free plugin I converted all posts, pages, and settings from WordPress to Markdown and YAML for use in Jekyll Netlify for Hosting Netlify makes deployment a breeze. Here’s how I did it: 1. Installed Jekyll locally with Ruby and RubyGems. 2. Created a new Jekyll site using the jekyll new command. 3. Pushed my site to GitHub (hello, continuous deployment!). 4. Connected my GitHub repo to Netlify. Now, every time I update my site, Netlify automatically rebuilds and deploys it. Cloudflare for DNS and SSL I set up my custom domain on Cloudflare. Cloudflare provides free SSL certificates, ensuring secure communication, and their global CDN improves site performance. Cost Savings The best part? It’s all free (except for the domain registrar fee)! No more server costs or complex setups. 🚀 If you’re curious, check out my revamped site at tusharkant.in. Let’s connect! 😊 #WebDevelopment #Jekyll #Netlify #Cloudflare #StaticSites #PersonalPortfolio #TusharKant #Godaddy

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How do systems handle increasing load? The answer is to make the system "Scalable" Here are 5 key aspects you should know : 1. Horizontal vs. Vertical Scaling: when to add more machines or upgrade existing hardware. 2. Load Balancing: Distribute workloads effectively to prevent bottlenecks. 3. Database Sharding: Improve performance by dividing your database into smaller, more manageable pieces. 4. Caching Strategies: Accelerate data retrieval by storing frequently accessed information, reducing latency and load times. 5. Monitoring and Optimization: Continuously track and optimize system performance. Understanding these essential aspects of scalability can significantly enhance your system's resilience and performance

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Hey Gang. Shall we talk about the recent CrowdStrike issue and "lessons learned" from an Embedded SW developers perspective where our products are used daily in consumer's homes to provide both Broadband services (internet connectivity) and carrier-grade voice services including E911 capabilities? Imagine what would happen if an embedded device failed to reboot after the upgrade or did not have a failsafe validation of the upgrade.... All of the suggestions below are great, but most important "lesson" in my mind is definitely #5 and ensuring that any SW updates are rolled out in a phased deterministic manner with an appropriate amount of "soak" time and confirmation via customer telemetry and monitoring of field call-in rates that all is going smoothly. This is of course after extensive automated testing protocols but that is another topic. It still blows my mind here that this bad patch got rolled out to 8.5million PCs within an hour of being published. Was the update really that critical? What are your thoughts? Any other suggestions to add in addition to below?

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