DEV Community: Sai Kumar Bitra

Building Efficient APIs with GraphQL in Java: Faster Responses, Smarter Payloads

Sai Kumar Bitra — Fri, 23 May 2025 00:50:17 +0000

When building modern APIs, especially for frontend-heavy applications, performance and flexibility are critical. REST APIs, while widely adopted, often return either too much or too little data, requiring multiple roundtrips or custom endpoints. That’s where GraphQL shines—allowing clients to query exactly what they need in a single request. In this post, we’ll explore how to build an efficient GraphQL API using Java, why it improves response speed for complex payloads, and walk through a step-by-step demo.

Why GraphQL?

Precise payloads: Clients request only the fields they need.
Reduces over-fetching/under-fetching: Unlike REST, no more generic response objects.
Efficient rendering: Especially useful for UI components that depend on deeply nested data.
Single endpoint, flexible queries: One endpoint, many possibilities.

Tech Stack

Java 17+
Spring Boot 3+
graphql-java (https://www.graphql-java.com/)
GraphQL Spring Boot Starter
Gradle or Maven

Step-by-Step: Create a Sample GraphQL API in Java

1. Set Up Spring Boot Project
Use Spring Initializr with dependencies:

Spring Web
Spring Boot DevTools
Spring Boot GraphQL

Or add to pom.xml (for Maven):

<dependency>
  <groupId>com.graphql-java</groupId>
  <artifactId>graphql-java</artifactId>
  <version>21.0</version>
</dependency>

2. Define Your GraphQL Schema
Create schema.graphqls in src/main/resources/graphql:

type Query {
  bookById(id: ID!): Book
}

type Book {
  id: ID
  title: String
  author: String
  publishedYear: Int
}

3. Create Domain and Data

public class Book {
    private String id;
    private String title;
    private String author;
    private int publishedYear;
    // Constructors, Getters, Setters
}

4. Create a Repository or Service

@Service
public class BookService {
    private final Map<String, Book> bookStore = Map.of(
        "1", new Book("1", "GraphQL in Action", "Sai", 2022),
        "2", new Book("2", "Spring Boot Essentials", "Lee", 2023)
    );

    public Book getBookById(String id) {
        return bookStore.get(id);
    }
}

5. Create GraphQL Query Resolver

@Component
public class BookQueryResolver implements GraphQLQueryResolver {
    private final BookService bookService;

    public BookQueryResolver(BookService bookService) {
        this.bookService = bookService;
    }

    public Book bookById(String id) {
        return bookService.getBookById(id);
    }
}

6. Test with GraphiQL
Run your app and open GraphiQL or Postman:

query {
  bookById(id: "1") {
    title
    author
  }
}

Performance Insights

With GraphQL, clients don’t over-fetch nested or unused attributes, which reduces bandwidth.
Especially beneficial in mobile/web apps where response time is critical.
Backend services are optimized because resolvers run only for requested fields.

Final Thoughts

GraphQL gives you the power to shape responses around UI needs while maintaining backend efficiency. In Java, it integrates cleanly with Spring Boot, and once you get used to schema-first development, it’s incredibly productive.

Supercharging Personalization with Akamai EdgeWorkers: Fast, Smart, and Serverless

Sai Kumar Bitra — Thu, 22 May 2025 03:40:18 +0000

Personalization is at the heart of modern digital experiences. Whether it’s suggesting the right product, showing tailored promotions, or dynamically updating content, users expect fast, relevant interactions across every touchpoint. But here’s the challenge: the closer the personalization logic lives to the backend, the more latency and potential bottlenecks you introduce. That's where Akamai EdgeWorkers change the game.

Edge computing has revolutionized how we think about performance. Instead of relying entirely on origin servers, we can now push business logic and personalization closer to the user—right to the edge.

Why Personalization Needs the Edge

Traditionally, personalized content is generated at runtime by backend services. This often involves API calls, session validation, user segmentation, and rendering logic. While powerful, this approach can:

Increase page load times
Strain backend services under high load
Cause SLA violations when traffic spikes
Delay dynamic rendering of personalized UI components

With Akamai EdgeWorkers, we can offload much of this to the edge. That means faster responses, lower origin load, and improved user experience.

What Are Akamai EdgeWorkers?

Akamai EdgeWorkers are serverless JavaScript functions that run at the edge nodes of Akamai’s content delivery network. Think of them as lightweight, distributed workers that:

Intercept and modify requests/responses
Access cookies, headers, and query parameters
Make edge-side decisions (e.g., segment-based rendering)
Serve or rewrite cached content intelligently

Personalization Use Cases with EdgeWorkers

Here’s how we used EdgeWorkers to improve performance and responsiveness for our personalization engine:

1. User Segmentation at the Edge

Instead of waiting for backend services to identify segments (e.g., "loyal customer", "first-time visitor"), we evaluated cookies and headers directly on the edge to determine user groups. This enabled:

Targeted experiences without backend roundtrips

Reduced personalization latency to under 50ms

2. Personalized Component Injection

We injected pre-rendered personalized modules into cached pages. For example, based on user segment, the EdgeWorker dynamically added a "Recommended for You" section into the HTML response.

3. SLA-Driven Failover and Graceful Degradation

If an origin API failed or was slow, EdgeWorkers served fallback content from cache. This protected SLAs, avoided timeouts, and ensured users always saw something personalized—even if slightly stale.

4. Geo and Device-Aware Personalization

We leveraged edge metadata to adapt content by location or device type without additional backend calls. This was great for local promotions, mobile-specific banners, etc.

Architecture at a Glance

Our typical personalization pipeline looked like this:

1. Request hits Akamai CDN

2. EdgeWorker executes:
- Reads user cookies/headers
- Determines user segment
- Fetches personalized HTML snippet (or uses cached fallback)
- Injects dynamic content into response

3. Response is served to user in <100ms, with minimal or no origin interaction

Tools and Techniques We Used

EdgeKV: To store and retrieve personalization snippets at the edge
Akamai Property Manager: For routing logic and associating EdgeWorkers
JSON-based templates: To modularize component injection logic
Logging/Debugging: Using Akamai CLI and sandbox for local dev

Edge Security and Scale Considerations

As powerful as EdgeWorkers are, security and performance at scale require careful design.

🔐 Handling Customer-Sensitive Data

Avoid caching PII (Personally Identifiable Information) at the edge.
Use EdgeKV for user-specific or segment-safe personalization, not full user profiles.
Always configure cache keys to include personalization dimensions like user ID hash or segment.
Secure cookies with HttpOnly, Secure, and SameSite flags.
Validate input rigorously within EdgeWorkers to prevent injection or misuse.

⚙️ Serving Millions of Users Efficiently

Akamai’s massive edge network ensures user requests are distributed globally, reducing node pressure.
EdgeWorkers are stateless and lightweight—built to scale across millions of executions.
Cohort-level personalization (20–100 user segments) keeps cache variant count manageable.
Use rate-limiting and quota protections to avoid abuse.
EdgeKV read operations return in milliseconds—ideal for personalization.

Impact and Learnings

40-60% reduction in origin traffic for personalized pages
~200ms improvement in time-to-interactive for personalized views
Zero SLA violations during peak traffic events
Developers can test logic locally before deploying globally

But the biggest win? We turned personalization into a speed feature, not a performance liability.

Final Thoughts

Personalization doesn’t have to be slow, heavy, or complex. With Akamai EdgeWorkers, we brought intelligence to the edge, delivering faster, smarter experiences to every user.

If you're exploring ways to optimize your digital platform’s performance, consider what you can move to the edge. You might be surprised how much personalization logic fits there—and how much your users benefit from it.

Let me know your thoughts or questions in the comments. Always happy to talk edge, performance, or personalization!

How AI-Powered Personalization Transformed a Telecom Customer Experience Platform

Sai Kumar Bitra — Thu, 22 May 2025 03:09:33 +0000

In today’s hyper-connected world, customers expect seamless, relevant experiences across every touchpoint. For telecom providers managing millions of users, this expectation poses a significant challenge—especially when legacy systems lack the agility to deliver real-time, personalized engagement. This is where AI-powered personalization steps in.

The Challenge

Explain the problem your team was facing before implementing AI.

Fragmented customer data across systems
Static rules-based recommendations
Delays in content delivery and targeting
Limited cross-channel personalization

How to Build a Modern Personalization Engine

Let’s break down the architecture of a robust AI-powered personalization system:

1. Real-Time Event Collection

Every user interaction—click, search, view, scroll—is captured as an event. Tools like Apache Kafka are used to stream these events in real time.

2. Evolving User Profiles

By combining CRM data, device usage, and behavioral patterns, you can build dynamic profiles that update with every user action. Databases like Apache Cassandra are ideal for this use case.

3. Recommendation Models

Using collaborative filtering, clustering, and deep learning models, you can predict what a user is likely to need or engage with next. Frameworks like TensorFlow and Scikit-learn are popular choices.

4. Decision Engine API

Wrap the logic into a RESTful API that provides real-time recommendations within milliseconds. These microservices are containerized and deployed using Kubernetes.

5. Monitoring & Feedback Loop

Use Prometheus, Grafana, and the ELK stack to monitor model performance, track engagement KPIs, and feed results back into training pipelines.

Real-World Results

Teams implementing AI-powered personalization in telecom have seen:

A 30-40% boost in conversion from personalized offers
Faster decision-making and reduced drop-offs in upgrade journeys
Unified experiences across digital channels
Reduced reliance on manual rule updates

Lessons for Developers

Start small. Pilot AI in one part of the journey before scaling.
Combine models and rules. Business logic still matters.
Prioritize observability. Debugging ML in production requires visibility.
Work cross-functionally. Collaboration between product, data, and engineering is essential.

Final Thoughts

AI is not a magic bullet, but when applied thoughtfully, it can dramatically improve how telecom platforms engage users. Personalization at scale demands more than just a good model—it requires a solid architecture, reliable infrastructure, and a user-centric mindset.

Whether you're working on personalization for telecom, e-commerce, or media, these principles can guide your journey toward building smarter, more adaptive experiences.

Got thoughts or questions? Drop them in the comments—let’s make personalization better, together.