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#Webclient Vs Resttemplate Reel by @suhail.builds - 1 Synchronous Communication (Real- Time) & REST API (Most Common) Service A calls Service B using HTTP Usually via Feign Client / RestTemplate / Web C
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@suhail.builds
1 Synchronous Communication (Real- Time) & REST API (Most Common) Service A calls Service B using HTTP Usually via Feign Client / RestTemplate / Web Client in Spring Boot JSON over HTTP Example: Order Service → calls → Payment Service Used when you need immediate response Simple to implement ❌ Tight coupling ❌ If Payment is down → Order fails & gRPC High-performance RPC Uses Protocol Buffers Faster than REST • Used in high-performance systems Low latency ❌ Slightly complex setup 2) Asynchronous Communication (Event-Driven) • Message Broker (Recommended in real systems) Using: Kafka RabbitMQ ActiveMQ Flow: Order Service → publishes event → Kafka Payment Service → consumes event J Loose coupling More scalable Failure tolerant • Best for microservices #microservices #systemdesign #csstudents #coding #developer
#Webclient Vs Resttemplate Reel by @unipro.code - In the simplest way 👇🏻 It's not about one best option, itself about requirements. In a microservices architecture, services don't share databases.
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@unipro.code
In the simplest way 👇🏻 It’s not about one best option, itself about requirements. In a microservices architecture, services don’t share databases. They communicate using APIs, events, or RPC calls. Common ways microservices communicate: 1️⃣ Synchronous (REST / HTTP) 👉 Service A calls Service B and waits for a response Use when: Immediate response is required Example: Order Service → Payment Service 2️⃣ Asynchronous (Message Queue / Events) 👉 Service A sends a message and continues Use when: Reliability & scalability matter Example: Order placed → inventory, payment, notification react later 3️⃣ Event-Driven Communication 👉 Services react to events instead of calling each other directly Use when: Many services depend on the same action Example: “User signed up” → welcome email, notifications, rewards 4️⃣ gRPC (Binary, Fast Calls) 👉 Faster, structured communication between internal services Use when: Services talk frequently and performance matters Example: Pricing service calling discount service hundreds of times per second 📌 Interview tip: Microservices prefer loose coupling, not tight dependencies. Save this for your future interviews! #development #grpc #systemdesign #java #foryou
#Webclient Vs Resttemplate Reel by @priforyou._ - In the simplest way 👇🏻 It's not about one best option, itself about requirements. In a microservices architecture, services don't share databases.
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@priforyou._
In the simplest way 👇🏻 It’s not about one best option, itself about requirements. In a microservices architecture, services don’t share databases. They communicate using APIs, events, or RPC calls. Common ways microservices communicate: 1️⃣ Synchronous (REST / HTTP) 👉 Service A calls Service B and waits for a response Use when: Immediate response is required Example: Order Service → Payment Service 2️⃣ Asynchronous (Message Queue / Events) 👉 Service A sends a message and continues Use when: Reliability & scalability matter Example: Order placed → inventory, payment, notification react later 3️⃣ Event-Driven Communication 👉 Services react to events instead of calling each other directly Use when: Many services depend on the same action Example: “User signed up” → welcome email, notifications, rewards 4️⃣ gRPC (Binary, Fast Calls) 👉 Faster, structured communication between internal services Use when: Services talk frequently and performance matters Example: Pricing service calling discount service hundreds of times per second 📌 Interview tip: Microservices prefer loose coupling, not tight dependencies. Save this for your future interviews! #development #grpc #systemdesign #java #microserivices #softwareengineer #learning #techforgood #fyp #viral #explore #backenddeveloper #interview
#Webclient Vs Resttemplate Reel by @softwarengineering - Read 👇 🔗 If two microservices need to communicate, how do you do it? 1️⃣ Synchronous REST API Service A calls Service B via HTTP. Simple, fast, bu
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@softwarengineering
Read 👇 🔗 If two microservices need to communicate, how do you do it? 1️⃣ Synchronous REST API Service A calls Service B via HTTP. Simple, fast, but tightly coupled. 2️⃣ gRPC (Service-to-Service) Binary protocol, faster than REST. Great for internal, high-throughput communication. 3️⃣ Asynchronous Messaging Use Kafka / RabbitMQ. Service A emits an event → Service B consumes it later. Highly scalable & loosely coupled. 4️⃣ Event-Driven Architecture Services react to events instead of calling each other directly. Best for large, distributed systems. 5️⃣ API Gateway (External Calls) Clients go through gateway → internal services stay hidden. 6️⃣ Service Discovery Services find each other dynamically (Eureka, Consul, K8s DNS). 7️⃣ Circuit Breakers & Retries Prevents one failing service from crashing others. 8️⃣ Idempotent APIs Retries don’t cause duplicate processing. ⚡ When to use what? Need immediate response? → REST / gRPC Need scale & resilience? → Async events Microservices shouldn’t chat all the time — they should notify and move on. #softwareengineering #systemdesign
#Webclient Vs Resttemplate Reel by @this.tech.girl - This isn't about one right answer. It's about trade-offs in system design 👇 1️⃣ Synchronous Communication (REST / gRPC) Used when you need an immedi
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@this.tech.girl
This isn’t about one right answer. It’s about trade-offs in system design 👇 1️⃣ Synchronous Communication (REST / gRPC) Used when you need an immediate response (request → response). Simple to debug but increases coupling and latency. When to choose: User-facing APIs, low latency paths Tech Stack: REST, gRPC, API Gateway 2️⃣ Asynchronous Communication (Message Queues / Events) Services communicate via events without waiting for replies. Improves scalability and resilience. When to choose: High traffic, background processing, loose coupling Tech Stack: Kafka, RabbitMQ, AWS SQS 3️⃣ Event-Driven Architecture (Pub/Sub) One service publishes events, multiple services consume them independently. Enables extensibility. When to choose: Multiple downstream consumers, analytics, notifications Tech Stack: Kafka, Google Pub/Sub, SNS 4️⃣ Service-to-Service Calls with Retry & Timeout Direct calls but protected with retries, timeouts, and circuit breakers to avoid cascading failures. When to choose: Critical synchronous flows Tech Stack: gRPC, Resilience4j 5️⃣ Database or Cache as Communication (Use Carefully ⚠️) Sharing data indirectly via DB/cache. Works but creates tight coupling. When to choose: Read-heavy or legacy systems Tech Stack: Redis, Shared DBs 💡 How interviewers expect you to decide: Latency needs • Scalability • Failure handling • Coupling • Traffic patterns 📌 Save this for system design interviews 🔁 Share with your backend friends ➕ Follow for more real-world system design answers [microservices communication, system design interview, REST vs gRPC, async messaging, event driven architecture, backend architecture, distributed systems] #systemdesign #microservices #backendengineering #distributedSystems #techreels
#Webclient Vs Resttemplate Reel by @coding_with_deepa - MOST ASKED INTERVIEW QUESTIONS ON MICROSERVICE DESIGN PATTERN ⚡️ Comment "pdf" for interview precise answers to all these questions. Basic ______ 1
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@coding_with_deepa
MOST ASKED INTERVIEW QUESTIONS ON MICROSERVICE DESIGN PATTERN ⚡️ Comment "pdf" for interview precise answers to all these questions. Basic ______ 1️⃣What is the API Gateway Pattern and why is it used in microservices? 2️⃣How would you implement an API Gateway using Spring Cloud Gateway? 3️⃣What problems does API Gateway solve compared to direct client-to-service communication? 4️⃣What is Service Discovery in microservices? 5️⃣Difference between Client-side discovery and Server-side discovery? IMPORTANT ______ 6️⃣What is the Saga Pattern in distributed transactions? 7️⃣Difference between Orchestration Saga and Choreography Saga? 8️⃣When should Saga be preferred over 2 Phase Commit? 9️⃣What is Event Driven Architecture in microservices? 1️⃣0️⃣How would you implement event communication using Apache Kafka? SUPER IMPORTANT ______ 1️⃣1️⃣Why should each microservice have its own database? 1️⃣2️⃣What problems occur when multiple services share the same database? 1️⃣3️⃣What is the CQRS (Command Query Responsibility Segregation) pattern? 1️⃣4️⃣When should CQRS be used in microservices? 1️⃣5️⃣What is the Strangler Fig Pattern used for in microservices migration? Comment "pdf" for rest of the interview questions and precise answers to all these questions. #systemdesign #engineers #developers #softwareengineering #springboot [coding, system design, springboot, genAl developers, software engineer, coders, java]
#Webclient Vs Resttemplate Reel by @c_forcoding - Read 👇 🔗 If two microservices need to communicate, how do you do it? 1️⃣ Synchronous REST API Service A calls Service B via HTTP. Simple, fast, bu
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@c_forcoding
Read 👇 🔗 If two microservices need to communicate, how do you do it? 1️⃣ Synchronous REST API Service A calls Service B via HTTP. Simple, fast, but tightly coupled. 2️⃣ gRPC (Service-to-Service) Binary protocol, faster than REST. Great for internal, high-throughput communication. 3️⃣ Asynchronous Messaging Use Kafka / RabbitMQ. Service A emits an event → Service B consumes it later. Highly scalable & loosely coupled. 4️⃣ Event-Driven Architecture Services react to events instead of calling each other directly. Best for large, distributed systems. 5️⃣ API Gateway (External Calls) Clients go through gateway → internal services stay hidden. 6️⃣ Service Discovery Services find each other dynamically (Eureka, Consul, K8s DNS). 7️⃣ Circuit Breakers & Retries Prevents one failing service from crashing others. 8️⃣ Idempotent APIs Retries don’t cause duplicate processing. ⚡ When to use what? Need immediate response? → REST / gRPC Need scale & resilience? → Async events Microservices shouldn’t chat all the time — they should notify and move on. #softwareengineering #systemdesign
#Webclient Vs Resttemplate Reel by @the.he24 - ​1️⃣ Caching: Don't do the same work twice! Store frequently accessed data in fast memory (RAM) so your server responds instantly. ⚡ ​2️⃣ Rate Limite
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@the.he24
​1️⃣ Caching: Don't do the same work twice! Store frequently accessed data in fast memory (RAM) so your server responds instantly. ⚡ ​2️⃣ Rate Limiter: The bouncer of your API. It limits how many requests a user can make in a specific time to prevent server crashes (and spam!). 🛑 ​3️⃣ Monolith vs. Microservices: One giant codebase where everything is connected (Monolith) 🆚 many small, independent services talking to each other (Microservices). 🏗️ ​4️⃣ API vs. Webhooks: ​API: You asking the server, "Do you have new data?" (Polling). ​Webhooks: The server telling you, "Hey! Here is the new data!" (Real-time). 📲 ​5️⃣ API Gateway: The "Receptionist." It sits in front of your backend services to handle routing, security, and authentication before requests get inside. 🛡️ ​6️⃣ Proxy: The middleman. It sits between the client and the internet to provide anonymity, security, or filtering. 🕵️‍♂️ ​7️⃣ Sharding: When your database gets too big, you split it into smaller, faster chunks called "shards" (Horizontal Scaling). 🧩 ​8️⃣ CDN (Content Delivery Network): A network of servers around the world. It delivers images and videos to users from the server geographically closest to them. 🌍 ​🔥 BONUS CONCEPTS: ​9️⃣ Load Balancing: Imagine a traffic cop. It distributes incoming traffic evenly across multiple servers so no single server gets overwhelmed. ⚖️ ​🔟 Database Indexing: Think of the index at the back of a textbook. It helps the database find specific data instantly without reading every single row. 📖 ​💾 Save this for your next System Design interview! Which one do you use the most? Let me know in the comments! 👇 #developerlife #backenddev #softwareengineering #thehelmetengineer #systemdesign
#Webclient Vs Resttemplate Reel by @codewith_sushant - To design a microservice that needs data from 3 other services without tight coupling, I would follow these principles: 1️⃣ API Composition / Aggrega
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@codewith_sushant
To design a microservice that needs data from 3 other services without tight coupling, I would follow these principles: 1️⃣ API Composition / Aggregator Pattern Introduce an orchestrator or aggregator service that calls the 3 dependent services. The consumer service interacts with only one API, not all 3 services directly. This keeps the core service clean and loosely coupled. 2️⃣ Asynchronous Communication (Event-Driven) Use events (Kafka/RabbitMQ) where possible instead of synchronous REST calls. Each service publishes domain events. The consuming service maintains a local read model built from events. Reduces runtime dependency and improves resilience. 3️⃣ Avoid Shared Databases Each service owns its data. Data is shared only via APIs or events, never direct DB access. Prevents schema-level coupling. 4️⃣ Use Contracts, Not Implementations Interact using well-defined DTOs and API contracts. Version APIs to avoid breaking changes. Never rely on internal fields or logic of other services. 5️⃣ Resilience Patterns Implement Circuit Breaker, Retry, Timeout, and Fallback (Resilience4j). Prevent cascading failures if one service is down. 6️⃣ Cache Where Appropriate Cache frequently used data at the aggregator layer. Reduces repeated calls and dependency pressure. 7️⃣ Domain-Driven Design (DDD) Fetch only data relevant to the bounded context. Avoid pulling full objects—use minimal required attributes. ✅ Summary I avoid tight coupling by using an API Aggregator or event-driven design, strict service boundaries, contract-based communication, and resilience patterns, ensuring services remain independent, scalable, and fault tolerant. Do Follow @codewith_sushant for more tech tips. #tech #java #microservices #interview #coder
#Webclient Vs Resttemplate Reel by @thetechinterview - This is usually a race condition caused by retries. Imagine a payment request hits your API. The payment succeeds. But the response never reaches th
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@thetechinterview
This is usually a race condition caused by retries. Imagine a payment request hits your API. The payment succeeds. But the response never reaches the client (timeout, network issue, etc). From the client’s perspective, the request failed. So it retries. Now the server processes the same operation again, and the user gets charged twice. The fix is usually idempotency. Each request includes a unique idempotency key, and the server stores the result the first time it runs. If the same request arrives again, it simply returns the original response instead of executing the operation again. That’s why most payment APIs require idempotency keys. It protects against retries, race conditions, and network failures. And it’s a really common interview discussion point.
#Webclient Vs Resttemplate Reel by @an.engineers.quest - Is your system lagging because of slow queries or blocking calls? ⚡️ Choosing between Synchronous and Asynchronous communication is one of the most cr
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@an.engineers.quest
Is your system lagging because of slow queries or blocking calls? ⚡️ Choosing between Synchronous and Asynchronous communication is one of the most critical decisions in system design. While Sync offers simplicity and immediate feedback, Async unlocks the scalability needed for high-traffic microservices. The Breakdown: 🔹 Sync: Best for real-time operations where “now” is the only option. Think logins and payments. 🔹 Async: The hero of high-throughput. Use it for background jobs, notifications, and decoupling services to prevent “the waiting game.” Stop guessing why your latency is spiking and start looking at how your services talk to each other. Hashtags #systemdesign #microservices #backenddevelopment #softwarearchitecture #scalability
#Webclient Vs Resttemplate Reel by @muniker.codes - What does it mean for an API to be stateless? ➡️ It means the server does not remember anything about previous requests. Every request brings all the
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@muniker.codes
What does it mean for an API to be stateless? ➡️ It means the server does not remember anything about previous requests. Every request brings all the context needed, like auth, resource IDs, and what action to take. ➡️ Because the server does not rely on prior interactions, requests can be handled by any instance, which simplifies horizontal scaling, improves fault isolation, and reduces coordination overhead. ➡️ In real systems, state lives in durable stores or client tokens rather than in server memory. #softwareengineer #cs #dev #tech

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