DEV Community: Arkaprava Dhar

Building Scalable Communication Between Microservices Using REST and gRPC via API Gateway

Arkaprava Dhar — Sat, 16 Aug 2025 07:13:00 +0000

When building microservices, one of the biggest challenges is figuring out how these independent services should talk to each other. Each service has its own responsibilities, but they often need to share data or trigger actions in other services. Choosing the right communication style makes a huge difference in terms of performance, scalability, and maintainability.

Two popular ways to enable communication between services are REST and gRPC. And when combined with an API Gateway, they form a flexible and powerful communication setup for any microservice architecture.

REST for External Communication

REST is the most common way services expose APIs to the outside world. It’s simple, human-readable, and widely supported. When clients (like mobile apps, browsers, or third-party integrations) need to interact with our system, REST makes sense.

For example, if a user-facing service exposes endpoints for authentication, profile management, or product listings, REST works perfectly. It uses JSON over HTTP, making it easy for developers and client applications to consume.

The key idea here is:

External communication = REST.
It provides a consistent, developer-friendly interface that works seamlessly across platforms.

gRPC for Internal Communication

While REST is great externally, it can sometimes be inefficient for service-to-service communication inside the system. That’s where gRPC shines.

gRPC uses HTTP/2 under the hood, supports streaming, and relies on Protocol Buffers for data serialization. This makes communication much faster and more efficient compared to REST, especially when microservices are constantly exchanging structured data.

For example, if your User Service needs to fetch data from the Authentication Service or your Order Service needs real-time updates from the Inventory Service, gRPC is the better option.

The key idea here is:

Internal communication = gRPC.
It ensures low latency, better performance, and stronger type safety for inter-service calls.

The Role of the API Gateway

An API Gateway acts as the single entry point to the system. Clients don’t need to know about individual services, their internal communication patterns, or even their locations. Instead, the gateway handles all requests and routes them to the correct service.

Here’s why this matters:

Hides complexity: Clients interact with one endpoint instead of multiple services.

Manages security: The gateway can handle authentication, rate limiting, and request validation.

Protocol translation: The gateway can expose REST APIs externally while communicating with internal services using gRPC. This way, you get the best of both worlds.

Think of the API Gateway as a receptionist in a big office. Instead of every visitor trying to find the right person themselves, they just talk to the receptionist, who knows exactly where to route them.

Why This Combination Works Well

REST is universal, easy to understand, and perfect for client-facing APIs.

gRPC is optimized, strongly typed, and ideal for internal microservice communication.

The API Gateway bridges both worlds, making the system easier to maintain, secure, and scale.

By adopting this hybrid approach, you’re not locked into a single communication style. You can provide a friendly REST interface to the outside while ensuring efficient, high-performance communication inside.

Final Thoughts

This REST + gRPC + API Gateway setup is becoming the standard for modern microservice architectures, especially when scalability and efficiency are top priorities. Personally, I’m using this exact approach in MediBridge, a project I’m currently working on, to ensure smooth communication between services while keeping the system both robust and developer-friendly.

Automating GitHub Code Reviews with AI agent

Arkaprava Dhar — Wed, 02 Apr 2025 07:08:10 +0000

Introduction

Code reviews are essential for maintaining high-quality software, but they can be time-consuming. What if I could automate code reviews using AI? In this article, I'll walk through building an AI-powered Code Review Agent using LangGraph and integrating it with GitHub Actions. This agent will analyze pull requests, identify issues, and provide feedback—all automatically!

Why Use an AI Agent for Code Reviews?

AI agents are designed to process information, make decisions, and take actions autonomously. For this project, I leveraged an AI agent to automate code reviews because:

It Mimics a Human Reviewer: Unlike simple static analysis tools, the AI can provide detailed feedback that considers the context of the code changes.
It Automates a Repetitive Task: Developers often spend hours reviewing pull requests manually. The AI agent reduces this burden by handling the initial review process.
It Provides Intelligent Suggestions: The agent uses GPT-4o to analyze code, detect potential issues, and suggest improvements in natural language.
It Integrates Seamlessly with GitHub: The AI is triggered automatically when a pull request is opened or updated, ensuring timely feedback.

By using an AI agent, I created a system that streamlines the review process, enhances code quality, and allows developers to focus on more complex aspects of their projects.

Why This Project Matters

Solving Real-World Challenges

Traditional code reviews can be:

Time-Consuming: Developers spend significant time manually reviewing pull requests.
Inconsistent: Different reviewers may have different coding standards.
Limited in Scope: Human reviewers may miss security vulnerabilities or best practices.

This AI-powered code review agent eliminates these problems by:

Providing Instant Feedback: Developers get automated review comments as soon as they create a PR.
Ensuring Consistency: AI applies the same standards across all reviews.
Enhancing Security: Automated static analysis helps detect vulnerabilities early.

Why It’s Innovative

Unlike basic linters or rule-based static analyzers, this AI-powered reviewer:

Understands Code Semantics: It leverages GPT-4o to analyze code logic, not just syntax.
Offers Contextual Feedback: Instead of just pointing out issues, it suggests improvements based on best practices.
Adapts to Projects: It can be fine-tuned for different coding styles and guidelines.

Tech Stack

LangGraph: For building the AI workflow.
OpenAI GPT-4o (via Azure): To perform the actual code analysis.
GitHub Actions: To trigger the agent on PRs.
Python: For scripting and automation.

How I Used LangGraph

LangGraph is a graph-based framework built on LangChain that helps structure AI workflows. I used it to:

Create a Multi-Step Workflow: The AI goes through several stages—fetching PR details, analyzing the code, running static analysis, and generating review comments.
Define Nodes: Each step is encapsulated in a node, making the system modular.
Manage State: Using LangGraph’s state management, I ensure each step has access to relevant data.

Automating PR Reviews

Now, whenever a PR is created or updated, my GitHub Action triggers the AI agent, and comments appear in the PR.

Conclusion

By combining LangGraph with AI, I created an automated, modular, and scalable code review agent. This can be extended further by adding custom linting checks, security analysis, or even interactive feedback loops.

What do you think? Would you use an AI-powered code reviewer in your projects? Let me know in the comments! 🚀

Building a Patient Management System with Spring Boot: My Learning Journey

Arkaprava Dhar — Tue, 25 Mar 2025 12:44:17 +0000

Introduction

I am currently diving into Spring Boot and building a Patient Management System as a hands-on learning project. My goal is to understand microservices architecture while applying industry best practices to create clean, maintainable, and scalable code. Through this project, I am exploring different technologies, patterns, and tools to enhance my development skills. In this post, I’ll share my progress on the patient-service, detailing how I structured it, the features implemented, and my roadmap for the next stages of development.

Setting Up the Patient Service

As I learn and build, I have structured the patient-service to follow a modular and maintainable design pattern. It consists of the following core components:

Controllers: These handle API requests, process incoming data, and return appropriate responses.
Services: Contain the business logic, ensuring separation of concerns from the controllers.
Entities: Represent the database models, defining patient attributes such as name, age, contact details, and medical history.
DTOs (Data Transfer Objects): Used to transfer data between layers, ensuring security and efficiency by exposing only necessary fields.
Repositories: Manage database interactions using Spring Data JPA, allowing for seamless CRUD operations.
Exception Handling: Provides a centralized mechanism for handling errors gracefully, improving debugging and user experience.

Implementing CRUD Operations

At this stage, the patient-service supports basic CRUD operations, enabling the following functionalities:

Creating a new patient record: Users can add patient details, which are stored securely in the database.
Viewing patient details: Retrieve patient information using unique identifiers.
Updating patient information: Modify existing records to reflect changes in medical data, contact details, or other attributes.
Deleting patient records: Remove patients from the system when necessary, ensuring proper data management.

Learning Docker and Deploying the Application

To make deployment easier and simulate real-world scenarios, I have been learning Docker and its role in containerizing applications. As part of this process:

I created a Dockerfile for the patient-service to package the application with its dependencies.
I am using a PostgreSQL Docker image to serve as the database, ensuring consistency across development and production environments.
I have set up docker-compose to orchestrate both the application and database, making it easier to test and debug locally.

This hands-on experience with Docker is helping me understand containerization concepts, deployment strategies, and the benefits of using Docker in a microservices architecture.

Next Steps in My Learning Journey

As I continue building this system, I plan to enhance it further with additional features and services:

Authentication Service: I will implement authentication and authorization using Spring Security and JWT, ensuring secure access to patient data.
Other Microservices: I plan to explore and develop complementary services such as appointment management and billing, expanding the system's functionality.
Frontend Development: To provide a user-friendly interface, I will build a frontend using React or Next.js, improving the overall user experience.
gRPC for Microservices Communication: Instead of traditional REST-based inter-service communication, I will experiment with gRPC, which offers high performance and efficient data exchange between microservices.

Conclusion

This project is providing me with invaluable hands-on experience in Spring Boot, Docker, PostgreSQL, and microservices architecture. As I continue to learn and refine my skills, I will document my progress and share key insights along the way.

How I Spent My Winter Learning Blockchain

Arkaprava Dhar — Sat, 25 Jan 2025 07:29:39 +0000

This past December, I dedicated my winter vacation to diving into the world of blockchain technology. My main goal was to learn enough to contribute to a project my club was working on for an upcoming event. Here’s a little about my journey, what I learned, and what we built!

What I Learned

I started with the basics of blockchain technology, including fundamental terms, hashing algorithms, and how blockchains achieve decentralization. Then, I explored two of the most popular blockchains:

Bitcoin: Understanding its origin, features, and how mining works to validate transactions.
Ethereum: Learning about its history, smart contracts, and how it split into Ethereum and Ethereum Classic.

As we decided to use Ethereum for our project, I spent time learning Solidity, the language used for writing smart contracts. We didn’t directly deploy on the Ethereum mainnet, though. Instead, we used Ganache for local blockchain deployment to simulate the Ethereum environment. This allowed us to develop and test our contracts efficiently without worrying about gas fees on the mainnet.

We also used Truffle, a development framework, for compiling, deploying, and managing our smart contracts. It simplified our workflow significantly and helped us integrate the blockchain components seamlessly with our application.

Additionally, we incorporated IPFS (InterPlanetary File System) for decentralized storage of NFT assets like spaceship skins and backgrounds. Learning how IPFS ensures distributed, immutable storage added a unique layer to the project.

Why I Learned Blockchain

My club organized an event that featured a dedicated blockchain section. I felt this was the perfect opportunity to learn about blockchain and take part in this section. The idea of exploring a completely new technology excited me, so I decided to dive deep into it with my team during the month of December.

What We Built: SpaceWars

Our project was SpaceWars, a retro-inspired space shooter game integrated with blockchain technology. Here's what makes it special:

NFT Integration: Players can mint their own spaceship skins and background environments as NFTs.
Marketplace: Players can sell, buy, and trade their NFTs within the game.
In-Game Use: Custom NFTs aren’t just collectibles—they’re fully functional in the game, adding a layer of personalization and ownership.

This was my first hands-on experience with blockchain development, and it was an incredible learning opportunity. From writing smart contracts in Solidity to deploying them locally with Ganache and Truffle, and integrating IPFS for decentralized storage, I gained practical skills I’m excited to build upon.

Final Thoughts

Learning blockchain was a steep but rewarding curve. By the end of December, not only did I gain a strong understanding of blockchain fundamentals, but I also got to apply my knowledge in a fun and creative way. Seeing SpaceWars come to life with my team was a highlight of my winter vacation.

I can’t wait to explore more about this technology and its possibilities. Have you ever worked on a blockchain project or dabbled in this field? Let me know in the comments! 🚀

Revolutionizing Agentic Applications with CopilotKit

Arkaprava Dhar — Sun, 19 Jan 2025 14:32:01 +0000

In the world of intelligent applications, creating agents that can autonomously handle complex workflows and provide meaningful interactions is a key challenge. Enter CopilotKit, a toolkit designed to simplify the development of agentic applications, and LangGraph, a tool that structures workflows into easily manageable graphs.

This article explores how these tools can empower developers to build adaptive and intelligent agents across a variety of domains.

What is CopilotKit?

CopilotKit is a robust framework for building AI-driven assistants and applications. It offers an array of pre-built integrations and features, allowing developers to focus on application logic rather than reinventing the wheel. Here’s what makes CopilotKit stand out:

Rapid Development: Reduce development time with pre-configured components and intuitive APIs.
Extensibility: Customize and extend functionality to suit specific needs and domains.
Versatility: Build applications that range from conversational interfaces to autonomous systems.

Why Use CopilotKit for Agentic Applications?

Agentic applications often require dynamic, context-aware interactions. CopilotKit is well-suited for this, offering:

1. Enhanced Workflow Management

With CopilotKit’s seamless integration with LangGraph, you can design state graphs that represent workflows. This simplifies the creation of agents that can handle complex decision-making.

2. Dynamic Context Handling

Agents can store and manage user context, enabling them to provide personalized and relevant responses.

3. Customizable User Interfaces

CopilotKit supports integration with frontend libraries, allowing you to design highly tailored user experiences.

4. Scalable Architecture

Whether your agent is for a small group or a global audience, CopilotKit ensures scalability and reliability.

5. Real-Time Interactions

From processing user inputs to fetching external data, CopilotKit allows agents to operate in real time with minimal latency.

Steps to Build an Agentic Application

Define the Application Goals

Determine what problem your agent will solve. For instance:

Automating customer support.
Assisting with project management.
Providing personalized recommendations in e-commerce.

Design Workflows with LangGraph

LangGraph helps you model workflows as directed graphs, making it easier to manage states and transitions. For example:

Define entry points for user queries.
Specify conditions for transitions based on user input.
Map outputs to desired actions or responses.

Leverage CopilotKit’s Features

Integrate CopilotKit to:

Connect LangGraph workflows to APIs.
Manage conversations and session data.
Deploy agents across web, mobile, or other platforms.

Optimize User Engagement

Focus on delivering value to the end user. Add features like:

Intelligent decision-making based on user data.
Multi-modal interactions (e.g., text, voice, or visual inputs).
Real-time insights and analytics.

Test and Iterate

Deploy your agent in a controlled environment, gather user feedback, and iterate on workflows and functionalities to enhance performance.

Benefits of Combining LangGraph and CopilotKit

Simplified Workflow Design: LangGraph’s visual graph approach complements CopilotKit’s extensible architecture, making development intuitive.
Accelerated Development: Spend less time on infrastructure and more on application logic.
Improved User Experience: Build agents that are smarter, faster, and more engaging.
Scalability and Flexibility: Design agents that grow with your needs, regardless of domain or audience size.

Final Thoughts

Building agentic applications doesn’t have to be a daunting task. With tools like CopilotKit and LangGraph, developers can create intelligent, adaptive agents that deliver exceptional value. Whether you’re automating workflows, building chat-based assistants, or developing autonomous systems, these tools provide the foundation for success.

Are you ready to transform your application ideas into reality? Share your journey or ask questions in the comments below! 🚀