API & Microservices Design Checklist for Scalability

Microservice Architecture in .NET — Industry Level Best Practices অনেকে Microservice শুনলেই ভাবে, সার্ভিস আলাদা মানেই Microservice। বাস্তব প্রোজেক্টে বিষয়টা এত সহজ না। ✅ Business driven boundary ✅ Independent deployability ✅ Own database ✅ Resilient communication যদি Product Team এবং Payment Team আলাদা হয়, তাহলে তাদের Service আলাদা হওয়া উচিত। Team structure মেলে Microservice boundary এর সাথে, একে বলা হয় Conway’s Law। Microservice Structure (Practical) প্রতিটি সার্ভিস একটি স্বাধীন Application: ProductService ┣ API (Minimal API / MVC) ┣ Application (Service, DTO, Validation) ┣ Domain (Entities, Rules) ┣ Infrastructure (EF Core, Repository) ┗ Database (Own Schema) OrderService ┣ API ┣ Application ┣ Domain ┣ Infrastructure ┗ Database (Own Schema) Shared database নয়। Data contract ভিত্তিক communication Communication Strategy (Real-world choice): Real-time stock check Sync (REST) Quick response needed Order update + Notify Async (Message Queue) Reliable and scalable High traffic events Event-driven Decoupling and replay .NET এ Messaging এর জন্য 1. MassTransit 2. NServiceBus 3. Azure Service Bus / RabbitMQ API Gateway Client কখনোই একাধিক service endpoint ঘেঁটে বেড়াবে না। Gateway handles: ✔ Routing ✔ Authentication ✔ Rate limit ✔ Response aggregation Tools: 1. YARP 2. Ocelot Resilience Pattern Production এ failure অবশ্যই ধরতে হবে ✔ Retry ✔ Circuit Breaker ✔ Timeout ✔ Bulkhead .NET এ এজন্য ✅ Polly অত্যন্ত জনপ্রিয় Realistic Example Flow User order করল 1️⃣ API Gateway → OrderService 2️⃣ OrderService → Stock check REST call to ProductService 3️⃣ Order Created 4️⃣ Event publish → PaymentService consumes 5️⃣ Successful payment → NotificationService consumes and sends SMS/Email এটাই Real world asynchronous consistency. Key Takeaways ✅ Microservice মানে স্বাধীনতা ✅ Boundaries driven by business ✅ Async-first mindset ✅ Own DB, own lifecycle ✅ Automation and monitoring compulsory #dotnet #aspnetcore #microservices #architecture #cloud #csharp #softwareengineering #scalability

🧩 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗟𝗲𝗮𝗱𝘀 𝘁𝗼 𝗖𝗵𝗮𝗼𝘀 — 𝗨𝗻𝗹𝗲𝘀𝘀 𝗗𝗲𝘀𝗶𝗴𝗻𝗲𝗱 𝗥𝗶𝗴𝗵𝘁 ✍️ By Abhishek Kumar | #FirstCrazyDeveloper Microservices promise scalability, agility, and faster delivery — but only if designed with the right patterns. Without structure, they quickly devolve into 🍝 spaghetti dependencies, fragile deployments, and debugging chaos. After years designing distributed systems, one truth stands out: “Microservices don’t fail because of code — they fail because of design decisions.” Let’s decode the 8 Core Patterns that turn chaos into clarity 👇 🧱 1. Decomposition Use Domain-Driven Design (DDD) and Bounded Context to ensure clear service ownership. Add Backend-for-Frontend (BFF) to optimize data flow for each client (web, mobile). 🕓 When: You see overlapping responsibilities or scaling teams. 🔗 2. Integration Centralize communication with API Gateway for routing, security, and versioning. Adopt Service Mesh for observability, mTLS, and traffic shaping. 🕓 When: You manage multiple microservices across teams and regions. ⚙️ 3. Configuration & Versioning Externalize configs via Azure App Configuration or Consul. Use Semantic & API Versioning to prevent breaking clients. 🕓 When: APIs evolve frequently or deploy across multiple environments. 💾 4. Database Patterns Each service should own its database. Adopt CQRS for performance and Saga / Compensating Transactions for consistency. 🕓 When: You handle distributed data or async business workflows. 💪 5. Resiliency Implement Retry, Circuit Breaker, Bulkhead, and Timeout patterns. They prevent cascading failures and improve reliability under pressure. 🕓 When: You rely on external APIs or network-heavy workloads. 🔍 6. Observability Use Distributed Tracing (OpenTelemetry), Health Checks, and Log Aggregation to understand system behavior. 🕓 When: Debugging feels like detective work. 🔐 7. Security Secure every endpoint with OAuth2, RBAC, Rate Limiting, and TLS 1.3. 🕓 When: Microservices exchange sensitive data or expose public APIs. 🚀 8. Deployment Use Blue-Green, Canary, and Feature Toggles for safe releases. 🕓 When: Continuous Delivery is part of your DevOps flow. 🎯 Dive deeper into full details, real-world examples, and C# + Python code here: 🔗 https://lnkd.in/gbe8veav #Microservices #Architecture #DesignPatterns #Azure #Cloud #CSharp #Python #DevOps #Resilience #Observability #FirstCrazyDeveloper #AbhishekKumar

🧩 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗟𝗲𝗮𝗱𝘀 𝘁𝗼 𝗖𝗵𝗮𝗼𝘀 — 𝗨𝗻𝗹𝗲𝘀𝘀 𝗗𝗲𝘀𝗶𝗴𝗻𝗲𝗱 𝗥𝗶𝗴𝗵𝘁 ✍️ By Abhishek Kumar | #FirstCrazyDeveloper Microservices promise scalability, agility, and faster delivery — but only if designed with the right patterns. Without structure, they quickly devolve into 🍝 spaghetti dependencies, fragile deployments, and debugging chaos. After years designing distributed systems, one truth stands out: “Microservices don’t fail because of code — they fail because of design decisions.” Let’s decode the 8 Core Patterns that turn chaos into clarity 👇 🧱 1. Decomposition Use Domain-Driven Design (DDD) and Bounded Context to ensure clear service ownership. Add Backend-for-Frontend (BFF) to optimize data flow for each client (web, mobile). 🕓 When: You see overlapping responsibilities or scaling teams. 🔗 2. Integration Centralize communication with API Gateway for routing, security, and versioning. Adopt Service Mesh for observability, mTLS, and traffic shaping. 🕓 When: You manage multiple microservices across teams and regions. ⚙️ 3. Configuration & Versioning Externalize configs via Azure App Configuration or Consul. Use Semantic & API Versioning to prevent breaking clients. 🕓 When: APIs evolve frequently or deploy across multiple environments. 💾 4. Database Patterns Each service should own its database. Adopt CQRS for performance and Saga / Compensating Transactions for consistency. 🕓 When: You handle distributed data or async business workflows. 💪 5. Resiliency Implement Retry, Circuit Breaker, Bulkhead, and Timeout patterns. They prevent cascading failures and improve reliability under pressure. 🕓 When: You rely on external APIs or network-heavy workloads. 🔍 6. Observability Use Distributed Tracing (OpenTelemetry), Health Checks, and Log Aggregation to understand system behavior. 🕓 When: Debugging feels like detective work. 🔐 7. Security Secure every endpoint with OAuth2, RBAC, Rate Limiting, and TLS 1.3. 🕓 When: Microservices exchange sensitive data or expose public APIs. 🚀 8. Deployment Use Blue-Green, Canary, and Feature Toggles for safe releases. 🕓 When: Continuous Delivery is part of your DevOps flow. 🎯 Dive deeper into full details, real-world examples, and C# + Python code here: 🔗 https://lnkd.in/gbe8veav #Microservices #Architecture #DesignPatterns #Azure #Cloud #CSharp #Python #DevOps #Resilience #Observability #FirstCrazyDeveloper #AbhishekKumar

MicroServices DesignPatterns when to use: 1. ✔️ ServiceMesh Pattern-  to setup protecting layers to MicroService pods in cluster environment like LoadBalancers, Security, Proxy and etcetra. Most of the clusters got this Pattern. 2. ✔️ SideCar Pattern - to have most common logic to all MicroServices in one single Parent service,   Deployment example- before service startup in cluster we may need some common logics like - monitoring operators(Grafana) and others   Coding example - common logic can be maintained in parent service but make sure to FINISH IT BEFORE USE and NO code adding in frequent releases because it violates MicroServices Autonomous DesignPrinciple- kills build, development activities time.  3. ✔️ CQRS(Command Query Responsibility Segregation) - write(save, update, delete) data in One DB1 and replicate it in another DB2 using dataBaseAutoTriggers or event triggers, now through DB2 only get data should happen.   Important: It’s used to resolve subqueries and joins problem whenever schemaPerService Db pattern is used   Tip - We can avoid this CQRS pattern through event service calls whenever other schema details are needed also through this we avoid duplicate data in two DBs. Make sure to use based on requirements. 4. ✔️ SAGA pattern - in MicroServices environment if DB operation depends on more than 2 services then this is KING of all DB patterns. So simple to implement use Transaction Management with ACID properties which will do either all Db operation units ROLLBACK or COMMIT.   Tip - Whenever there’s another Service call to do DB operation make sure to handle error perfectly to ROLLBACK all prior DB operations & terminate Transaction. 5. ✔️ Data consistency DB patterns - it’s used only in distributed cluster DBs like Ignite & others, when client request data reached MasterNode then it will auto replicate it to other slaveNodes in cluster. We got sub patterns here   5.1 Event consistency Pattern- when client request data reaches MasterNode then immediately response will be sent back so here if replication isn’t happened perfectly with other SlaveNode(s) then there’s loss of data.   5.2 Strict consistency Pattern- response will be sent back to client after successful replication of data to all slaveNodes by MasterNode. 6. ✔️ TwoPhaseCommit pattern - we got 2 phases here, most of the DBs have this pattern by default but to active this we should do prior configurations.   6.1 Phase-1 execution- There'll be a controller who sends Db operation request to sub-systems let’s say we got 3 sub-systems. All these 3 sub-systems sends back acknowledgment to Controller as either YES or NO. If successful then YES otherwise NO.   6.2 Phase-2: we got here COMMIT and ROLLBACK         COMMIT - If all 3 sub-systems acknowledgement is YES then transaction will be committed.       ROLLBACK- if any of 3 sub-systems acknowledged NO then transaction will be rollbacked by terminating other sub-systems Db operations. TBD - Other patterns yet to be posted.

Microservice Roadmap API Management 🔔 Focuses on managing APIs, which are essential for communication between microservices. Includes tools like MULESOFT for API handling. Service Registration 🔔 Service Registration ensures services are discoverable within the ecosystem. Tools like RUN SCOPE facilitate service registration and discovery. Application Gateway 🔔 Acts as a gateway for external and internal service communication. Includes popular services like NETFLIX Eureka and Zookeeper for registry and coordination. Load Balancer 🔔 Distributes incoming network traffic across multiple servers. Examples include TRAFFIC, NGINX, and OCELOT. Caching 🔔 Improves performance by temporarily storing data. Tools include TRAFFIK, SEESAW, HAZELCAST, REDIS, and MEMCACHED. Cloud Provider 🔔 Cloud services that host microservices. Includes AZURE, GCP, AWS, and DYNAMO DB. Database 🔔 Stores persistent data. Technologies listed are MySQL, MongoDB, Cassandra, PostgreSQL, Oracle, Microsoft SQL Server, Dynamo DB, and HBase. Distributed Tracing 🔔 Tracks the flow of requests through various microservices for debugging and monitoring. Tools like OpenTelemetry help visualize request paths. Container Orchestration 🔔 Manages deployment, scaling, and operation of containers. Includes Kubernetes and Zippkin. Monitoring & Alerting 🔔 Observes system performance and sends alerts when needed. Prometheus, HashiCorp, OpenShift, and Grafana are key tools. Message Broker 🔔 Facilitates asynchronous communication between services. Uses Kafka and RabbitMQ. Languages 🔔 Programming languages used for developing microservices: .net ,Java, Go, Python, Node.js, Kotlin, JavaScript, and Golang. Security 🔔 Secures communications and data. Implements security protocols like JWT (JSON Web Tokens). Container 🔔 Encompasses the core element of microservices – containers. Technologies such as Docker, LXC, Podman, and TLS for secure communication. Message Brokers 🔔 Handle asynchronous messaging between services (repeated in a different context). Summary: 🎤 This roadmap captures the comprehensive landscape of microservice architecture, starting from API management at the top, progressing through service discovery, load balancing, data storage, container orchestration, and monitoring, down to security and messaging. It highlights common tools, platforms, and languages used throughout the microservices ecosystem to build scalable, reliable, and manageable distributed systems. Want to know more? Follow me or connect🥂

#Microservices: Built for Scale, Prone to Chaos Without Discipline. Many teams proudly say: “We’ve moved to microservices.” But when production breaks, they realize: -Services are tightly coupled -Debugging is painful -Deployments are risky -Monitoring is missing -Microservices aren’t just about splitting code. They’re about designing systems that communicate, recover, and evolve — reliably. Real-World Microservices Issues and How to Handle Them: 1. Random 500 Errors Across Services Issue: Users report intermittent failures. Logs show nothing useful. Cause: No trace IDs, no correlation between services. Fix: Implement distributed tracing using OpenTelemetry or Zipkin. Add trace IDs to every log line. Use structured logging to connect the dots. 2. One Service Change Breaks Five Others Issue: A small change in the inventory service breaks checkout, cart, and analytics. Cause: No API versioning, no contract testing. Fix: Use backward-compatible APIs. Implement consumer-driven contract tests with tools like Pact. Version your endpoints and document changes clearly. 3. Kafka Consumer Lag Builds Up Rapidly Issue: A consumer falls behind by millions of messages. Cause: Slow processing logic, poor partitioning, no alerting. Fix: Monitor lag metrics. Scale consumers horizontally. Optimize processing logic. Use backpressure strategies and alert when lag crosses thresholds. 4. Shared Database Across Services Issue: Services are split, but they all hit the same database. Cause: Tight coupling at the data layer. Fix: Apply domain-driven design. Give each service ownership of its data. Use APIs or events for cross-service communication. 5. Deployment Chaos Issue: Deploying one service causes cascading failures. Cause: No health checks, no rollback strategy, no canary releases. Fix: Use Kubernetes readiness and liveness probes. Implement blue-green or canary deployments. Automate rollbacks on failure. Best Practices for Healthy Microservices -Design for failure: retries, timeouts, circuit breakers, fallbacks -Make services observable: metrics, logs, traces, business KPIs -Keep services loosely coupled: no shared databases, clear boundaries -Automate everything: CI/CD, testing, deployments, rollbacks -Secure by design: OAuth2, input validation, network isolation Microservices are powerful — but only when backed by strong architecture, observability, and operational maturity. If you want to learn microservices with practical, real-world applications, follow me or DM me. Let’s build systems that scale, recover, and stay maintainable under pressure. #Microservices #SystemDesign #DevOps #Kafka #Observability #DistributedSystems #SoftwareEngineering #Architecture #ProductionReady #EngineeringExcellence

🚀 Microservices Design Patterns: Must-Know for Interviews! Microservices architecture breaks large applications into smaller, independent services. To build scalable, resilient, and maintainable systems, developers rely on well-established design patterns. Here are the most important Microservices Design Patterns every developer should know 👇 🧩 1. Decomposition Patterns Purpose: Break down the monolith into manageable microservices. By Business Capability – Each service handles one business function (e.g., Payments, Orders). By Subdomain (DDD) – Split services based on domain-driven design concepts. 🔗 2. Integration Patterns Purpose: Enable communication between services. Synchronous (REST, gRPC) – Real-time communication. Asynchronous (Messaging/Event Bus) – Using Kafka, RabbitMQ for decoupled, scalable systems. API Gateway Pattern – A single entry point for all client requests; handles routing, authentication, and throttling. 💾 3. Database Patterns Purpose: Manage distributed data effectively. Database per Service – Each microservice has its own database. Shared Database (Anti-pattern) – Avoid unless legacy constraint. Saga Pattern – Handles distributed transactions using events or compensating actions. CQRS (Command Query Responsibility Segregation) – Separate read and write models for performance. 🔁 4. Reliability Patterns Purpose: Build fault-tolerant systems. Circuit Breaker – Prevents cascading failures by stopping calls to a failing service. Retry Pattern – Automatically retries failed requests. Bulkhead Pattern – Isolates failures by limiting resource sharing between services. Timeout Pattern – Defines time limits for service calls to avoid hanging requests. 🧠 5. Observability Patterns Purpose: Enable monitoring and debugging of distributed systems. Centralized Logging – Use ELK Stack (Elasticsearch, Logstash, Kibana). Distributed Tracing – Trace requests across services (e.g., Jaeger, Zipkin). Metrics & Health Checks – Collect service health data (e.g., Prometheus, Grafana). ⚙️ 6. Deployment Patterns Purpose: Manage versioning and updates efficiently. Blue-Green Deployment – Two environments for zero downtime deployment. Canary Release – Gradually roll out new versions to a small subset of users. Sidecar Pattern – Attach helper components (e.g., logging, monitoring) beside main service containers. 🧰 7. Security Patterns Purpose: Protect microservices and data. Token-Based Authentication (JWT, OAuth2) – Secure communication. API Gateway Authentication – Centralized security layer. Service Mesh (e.g., Istio) – Handles service-to-service security and traffic management.

🚀 10 Essential Microservices Design Patterns Every Developer Should Know Building microservices isn’t just about splitting a monolith — it’s about designing smart, scalable, and resilient systems 🔥 Here are 10 patterns that every backend or cloud engineer should master 👇 🧩 1️⃣ API Gateway Pattern 🛡️ Acts as a single entry point for all client requests. 🔀 Handles routing, authentication, rate-limiting, and aggregation. 💡 Think of it as your system’s traffic controller. ⚡ 2️⃣ Circuit Breaker Pattern 🚧 Stops cascading failures when a service is down. 💬 Returns fallback responses automatically. 💡 Keeps your system resilient under heavy load. 🔗 3️⃣ Saga Pattern 🌀 Manages distributed transactions with local compensations. 📊 Ensures data consistency across multiple microservices. 💡 Because rollback in distributed systems isn’t that simple! 💾 4️⃣ Database per Service Pattern 🗃️ Each microservice has its own database — no sharing! 🔒 Keeps services loosely coupled and independently deployable. 💡 Own your data, own your domain. 📣 5️⃣ Event-Driven Pattern 📬 Microservices communicate asynchronously using events. ⚙️ Enables decoupled and reactive systems. 💡 Perfect for scalability and real-time updates. 🧠 6️⃣ CQRS (Command Query Responsibility Segregation) 📊 Separates write (commands) and read (queries) models. 🚀 Improves performance and simplifies complex data operations. 💡 Ideal for large-scale apps and reporting systems. 🧱 7️⃣ Sidecar Pattern 🔍 Deploys helper components (logging, monitoring, proxy) alongside main services. 🧩 Often used with service meshes like Istio or Linkerd. 💡 Because every hero needs a sidekick. 🦸♂️ 🧭 8️⃣ Service Discovery Pattern 🔍 Helps microservices automatically find and connect to each other. ⚙️ Works with tools like Eureka, Consul, or Kubernetes DNS. 💡 No hardcoded URLs — pure flexibility. 📡 9️⃣ Strangler Fig Pattern 🌿 Gradually replaces parts of a monolith with microservices. ♻️ Routes specific requests to new services while keeping old ones alive. 💡 A smooth path to modernization. 🧰 🔟 Bulkhead Pattern 🚢 Isolates resources (like threads or connections) per service or pool. 🛡️ Prevents one failing component from sinking the entire system. 💡 Think of it as watertight compartments in a ship. ✨ In short: 🛡️ API Gateway ⚡ Circuit Breaker 🔗 Saga 💾 Database per Service 📣 Event-Driven 🧠 CQRS 🧱 Sidecar 🧭 Service Discovery 🌿 Strangler Fig 🚢 Bulkhead 💬 Which of these patterns have you implemented in your projects? Let’s discuss below 👇 #Java #JavaDeveloper #BackendDeveloper #JavaTips #RemoteJobs #SoftwareEngineer #SoftwareEngineering #JavaCoding #SpringBoot #RESTAPI #JavaDevelopment #BackendEngineering #RESTAPI #GraphQL #SpringSecurity #Microservices #JWT #OAuth2 #Microservices

𝗚𝗜𝗧𝗢𝗣𝗦-𝗗𝗥𝗜𝗩𝗘𝗡 𝗢𝗕𝗦𝗘𝗥𝗩𝗔𝗕𝗜𝗟𝗜𝗧𝗬: 𝗔 𝗠𝗢𝗗𝗘𝗥𝗡 𝗔𝗣𝗣𝗥𝗢𝗔𝗖𝗛 𝗧𝗢 𝗠𝗔𝗡𝗔𝗚𝗜𝗡𝗚 𝗬𝗢𝗨𝗥 𝗢𝗣𝗘𝗡𝗦𝗛𝗜𝗙𝗧 𝗢𝗕𝗦𝗘𝗥𝗩𝗔𝗕𝗜𝗟𝗜𝗧𝗬 𝗦𝗧𝗔𝗖𝗞 In today's cloud-native world, observability isn't just a nice-to-have—it's the nervous system of your applications. But here's the challenge: deploying and managing a complete observability stack can be complex, error-prone, and difficult to reproduce across environments. 𝗧𝗛𝗘 𝗖𝗛𝗔𝗟𝗟𝗘𝗡𝗚𝗘 Modern microservices architectures generate massive amounts of data: metrics, logs, traces, and network flows. Organizations need to: • 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 data from hundreds or thousands of services • 𝗦𝘁𝗼𝗿𝗲 it efficiently and cost-effectively • 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 it in meaningful ways • 𝗔𝗹𝗲𝗿𝘁 when things go wrong • 𝗖𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗲 across different data types to troubleshoot issues 𝗘𝗡𝗧𝗘𝗥 𝗚𝗜𝗧𝗢𝗣𝗦 GitOps helps us by treating infrastructure configuration as code stored in Git. 𝗧𝗵𝗲 𝗯𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗮𝗿𝗲 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝘃𝗲: • 𝗦𝗶𝗻𝗴𝗹𝗲 𝗦𝗼𝘂𝗿𝗰𝗲 𝗼𝗳 𝗧𝗿𝘂𝘁𝗵: Git becomes the authoritative source for your entire observability stack • 𝗔𝘂𝗱𝗶𝘁 𝗧𝗿𝗮𝗶𝗹: Every change is tracked, reviewed, and versioned • 𝗥𝗲𝗽𝗿𝗼𝗱𝘂𝗰𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Spin up identical observability stacks in any environment • 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Teams can review infrastructure changes through familiar pull request workflows • 𝗥𝗼𝗹𝗹𝗯𝗮𝗰𝗸: Made a mistake? Git revert is your friend 𝗪𝗛𝗔𝗧 𝗧𝗛𝗜𝗦 𝗪𝗢𝗥𝗞𝗦𝗛𝗢𝗣 𝗗𝗘𝗠𝗢𝗡𝗦𝗧𝗥𝗔𝗧𝗘𝗦 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗖𝗼𝘃𝗲𝗿𝗮𝗴𝗲: • 𝗟𝗼𝗴𝗴𝗶𝗻𝗴 with Loki for centralized log aggregation • 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗧𝗿𝗮𝗰𝗶𝗻𝗴 with Tempo and OpenTelemetry • 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 with Prometheus and user workload monitoring • 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 with Grafana dashboards • 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 using eBPF technology • 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗠𝗲𝘀𝗵 with Istio for microservices observability • 𝗔𝗹𝗲𝗿𝘁𝗶𝗻𝗴 with AlertManager integration 𝗚𝗶𝘁𝗢𝗽𝘀-𝗡𝗮𝘁𝗶𝘃𝗲 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁: Everything is deployed using ArgoCD with the "App of Apps" pattern. This modular approach means you can: • Deploy components independently • Update individual services without affecting others • Track the health and sync status of each component • Roll back problematic changes with confidence 𝗚𝗘𝗧𝗧𝗜𝗡𝗚 𝗦𝗧𝗔𝗥𝗧𝗘𝗗 𝗥𝗲𝗮𝗱𝘆 𝘁𝗼 𝗱𝗶𝘃𝗲 𝗱𝗲𝗲𝗽𝗲𝗿? Check out the full repository at [https://lnkd.in/eXyfgHVy) 𝗙𝗼𝘂𝗻𝗱 𝘁𝗵𝗶𝘀 𝘂𝘀𝗲𝗳𝘂𝗹? Share it with your team and give the repository a star to help others discover it. --- #𝑂𝑝𝑒𝑛𝑆ℎ𝑖𝑓𝑡 #𝐺𝑖𝑡𝑂𝑝𝑠 #𝑂𝑏𝑠𝑒𝑟𝑣𝑎𝑏𝑖𝑙𝑖𝑡𝑦 #𝐶𝑙𝑜𝑢𝑑𝑁𝑎𝑡𝑖𝑣𝑒 #𝐷𝑒𝑣𝑂𝑝𝑠 #𝐾𝑢𝑏𝑒𝑟𝑛𝑒𝑡𝑒𝑠 #𝑆𝑅𝐸 #𝑃𝑙𝑎𝑡𝑓𝑜𝑟𝑚𝐸𝑛𝑔𝑖𝑛𝑒𝑒𝑟𝑖𝑛𝑔 #𝑀𝑖𝑐𝑟𝑜𝑠𝑒𝑟𝑣𝑖𝑐𝑒𝑠

Microservices Design Patterns — essential architectural solutions for building scalable and maintainable distributed systems. Here’s a brief explanation of each pattern shown: ⸻ 1. API Gateway • Acts as a single entry point for all client requests. • Routes requests to appropriate microservices, handles authentication, rate limiting, and load balancing. • Example: Netflix Zuul, Spring Cloud Gateway, or AWS API Gateway. ⸻ 2. Message Broker • Enables asynchronous communication between microservices using a publish-subscribe or queue-based model. • Helps decouple services and ensures reliability in communication. • Example: Kafka, RabbitMQ, AWS SQS. ⸻ 3. Database per Service • Each microservice owns its database schema to ensure loose coupling and independence. • Avoids cross-service data dependencies but may need data synchronization mechanisms. ⸻ 4. Event-Driven Architecture • Microservices communicate through events, improving scalability and responsiveness. • Promotes reactive systems — when one service changes state, others react via event notifications. ⸻ 5. CQRS (Command Query Responsibility Segregation) • Separates read and write operations into different models for better performance and scalability. • Ideal for complex business domains with high read/write loads. ⸻ 6. Event Sourcing • Instead of storing the latest state, stores all changes as a series of events. • Allows easy auditing, debugging, and rebuilding of system state at any point in time. ⸻ 7. Backend for Frontend (BFF) • Provides a dedicated backend layer for each frontend (web, mobile, etc.). • Optimizes APIs for frontend needs, reducing over-fetching or under-fetching of data. ⸻ 8. Boundary Service • Defines clear boundaries between internal systems and external clients or third-party APIs. • Improves system security and isolates domain logic.