Forem: Pramod Kumar

Why Senior Engineers Are Quietly Moving Away from Microservices (And Back to Monoliths)

Pramod Kumar — Sun, 05 Apr 2026 14:27:24 +0000

For years, microservices have been the gold standard of modern architecture. Scalable, flexible, independent deployments — what’s not to love?

But something interesting is happening lately…

Senior engineers — the ones who’ve actually lived through production pain — are slowly, quietly moving back toward monoliths.

Let’s talk about why 👇

🧩 1. Microservices Add Massive Complexity

In theory:
👉 Small, independent services
👉 Clear boundaries
👉 Easy scaling

In reality:
❌ Distributed systems problems
❌ Network latency
❌ Debugging across services
❌ Versioning hell

You don’t just write code anymore — you manage communication between systems.

🔍 2. Debugging Becomes a Nightmare

Bug in a monolith:
→ Check logs → fix → done

Bug in microservices:
→ Which service?
→ Which version?
→ Is it network? auth? data mismatch?
→ Trace across 5–10 services

Suddenly, a “simple bug” becomes a full investigation.

⚙️ 3. Operational Overhead Explodes

Microservices require:

CI/CD pipelines for each service
Monitoring & alerting per service
Service discovery
API gateways
Container orchestration

That’s a lot of infrastructure before solving actual business problems.

🧠 4. Cognitive Load is Real

With microservices:

Engineers must understand multiple repos
Different services, patterns, and contracts
System-wide behavior

With a monolith:
✔ One codebase
✔ Easier onboarding
✔ Clearer mental model

Less context-switching = more productivity.

🚀 5. Monoliths Are Not the Enemy

Modern monoliths (a.k.a. modular monoliths) offer:

Clear internal boundaries
Simpler deployment
Easier testing
Faster iteration

You can still scale — just intelligently.

⚖️ 6. Microservices Still Have a Place

They make sense when:
✅ Teams are large and independent
✅ Systems need extreme scalability
✅ Domains are clearly separable

But for most startups and mid-sized teams?

👉 They’re often overkill.

💡 The Real Takeaway

Microservices aren’t bad.

But they’re not a default choice.

Senior engineers are realizing:
👉 Start simple
👉 Scale complexity only when needed
👉 Optimize for developer productivity, not trends

🔥 The industry is maturing.

We’re moving from:
“Microservices everywhere!”
to
“Use the right tool for the job.”

If you’ve worked with both architectures, you know:
👉 Simplicity wins more often than hype.

I’ve broken this down in more detail (with real-world examples) here:
🔗 https://medium.com/@pramod.er90/why-senior-engineers-are-quietly-moving-away-from-microservices-and-back-to-monoliths-d19a97a81da2

softwareengineering #microservices #systemdesign #backend #devcommunity

Why JSON Breaks in AI Pipelines — and the TOON Format I Built to Fix It

Pramod Kumar — Sat, 04 Apr 2026 04:49:13 +0000

Why JSON Breaks in AI Pipelines — and the TOON Format I Built to Fix It

JSON has been the default data format for decades. It’s simple, readable, and works almost everywhere.

But recently, while building AI-driven workflows, I kept running into the same problem:

👉 JSON isn’t designed for meaning. It’s designed for structure.

The Problem I Hit

When working with LLMs and agent-based systems, I noticed:

JSON is too verbose for iterative reasoning
Nested structures become hard to interpret semantically
Relationships between data are implicit, not explicit
Small changes break parsing or require rigid schemas

Example

{
  "task": {
    "name": "generateReport",
    "input": {
      "user": "Alex",
      "role": "developer"
    }
  }
}

This works for machines — but for AI systems trying to reason, it’s noisy.

What I Needed Instead

I wasn’t looking for a “better JSON”.

I needed something that:

Is human-readable at a glance
Represents relationships clearly
Works well with AI parsing + generation
Reduces unnecessary syntax

Introducing TOON (Experimental)

So I started experimenting with a lightweight format I’m calling TOON.

Same data in TOON:

task:
  name -> generateReport
  input:
    user -> Alex
    role -> developer

What’s Different?

1. Less syntax, more signal

No quotes, fewer brackets — easier to scan.

2. Explicit relationships

Using -> makes intent clearer than key-value ambiguity.

3. AI-friendly structure

LLMs tend to:

generate this format more consistently
interpret it with fewer errors

Where This Actually Helped

In my tests (early stage):

Prompt-to-structure conversion became more stable
Parsing required less strict validation
Debugging outputs was faster

But It’s Not Perfect

This is not a “JSON killer”.

TOON currently lacks:

Standardization
Tooling ecosystem
Performance benchmarks at scale

And JSON is still the best choice for:

APIs
Production systems
Interoperability

So Why Explore This?

Because AI systems are changing how we think about data.

We’re moving from:

“How do we structure data for machines?”

to:

“How do we structure data for reasoning systems?”

Full Article Medium Link: https://medium.com/stackademic/farewell-json-hello-toon-the-future-just-arrived-561b74a0059e

This One Spring Boot Change Made My API 10 Faster (It Wasn’t Caching)

Pramod Kumar — Fri, 03 Apr 2026 16:21:07 +0000

Response time dropped from 1200ms → 120ms.

I always thought caching was the biggest performance booster…

Turns out, I was completely overlooking something much simpler.

After reducing my API response size by 80%, the performance improvement was insane — nearly 10× faster responses.

In this article, I break down:

Why large payloads silently kill performance
The exact mistake I was making in Spring Boot
How I fixed it with a simple change
Real before vs after impact

If you're working with Spring Boot APIs, this might save you hours of optimization work.

👉 Read the full article here: https://medium.com/@pramod.er90/this-one-spring-boot-change-made-my-api-10-faster-it-wasnt-caching-8d18bdf196b0?sk=fe6436e07e4acc739bcd1c6e7dbfe501

🚀 Building Production-Ready AI Systems with Spring Boot 4 + JDK 21 (Part 2)

Pramod Kumar — Mon, 30 Mar 2026 15:05:39 +0000

Most AI backends work… until they don’t.

You ship a simple API:

POST /ai → returns response

Everything looks fine — until:

💸 costs explode (same prompts repeated)
🐢 responses feel slow
🚨 bots spam your API
🤖 answers lack real context

In Part 1, we built a scalable backend.

In this post, we turn it into a real AI system using:

🔴 Redis (distributed cache)
🌊 Streaming (SSE)
🚦 Rate limiting
🧠 RAG (context-aware AI)

🔗 Full Detailed Guide (Medium)

👉 Read the full 6-min deep dive with diagrams & full code:
👉 [Add your Medium link here]

🧱 1. Redis — Stop Burning Money

@Cacheable("ai-cache")
public String generate(String prompt) {
    return aiClient.generate(prompt);
}

👉 Shared cache across instances → massive cost reduction

🌊 2. Streaming — Real-Time UX

@GetMapping(value = "/stream", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
public Flux<String> stream(String prompt) {
    return Flux.create(sink -> {
        Thread.startVirtualThread(() -> {
            String response = aiService.generate(prompt);
            for (String token : response.split(" ")) {
                sink.next(token);
            }
            sink.complete();
        });
    });
}

👉 ChatGPT-like experience ⚡

🚦 3. Rate Limiting — Protect Your API

if (bucket.tryConsume(1)) {
    chain.doFilter(request, response);
} else {
    response.setStatus(429);
}

👉 Prevent abuse + control cost

🧠 4. RAG — Context-Aware AI

String prompt = """
Answer using context:
%s

Question: %s
""".formatted(context, question);

👉 AI becomes smarter, not just reactive

🔁 Final Architecture

Client
  ↓
Rate Limiter
  ↓
Service
  ↓
Redis + RAG + Streaming
  ↓
AI Client

🔥 What You Built

✔ Distributed cache
✔ Streaming API
✔ Rate-limited system
✔ Context-aware AI

🏁 Final Thought

Most developers build:

Controller → AI API

You’re building:
👉 AI systems that scale

🔗 Full Article (Recommended)

👉 Part 1: https://medium.com/stackademic/production-ready-ai-with-spring-boot-4-jdk-21-using-webclient-part-1-b609dda54d8c

👉 Part 2: https://medium.com/@pramod.er90/scalable-ai-systems-with-redis-streaming-rag-spring-boot-4-jdk-21-part-2-983e505b16da

💬 Follow for Part 3: Kafka + Observability + Multi-Tenant AI Systems

💡 20 Java Stream Interview Questions — Every Backend Developer Must Know

Pramod Kumar — Fri, 20 Mar 2026 09:27:41 +0000

Preparing for backend interviews? Java Streams are a must. Here’s a quick snapshot 👇

🔹 What is a Stream? → Processes data functionally
🔹 map() vs flatMap() → transform vs flatten
🔹 filter() → conditional selection
🔹 reduce() → aggregation
🔹 collect() → convert to collection
🔹 Intermediate vs Terminal ops
🔹 findFirst() vs findAny()
🔹 distinct(), sorted()
🔹 Parallel streams (when to use/avoid)
🔹 peek() for debugging
🔹 limit() vs skip()
🔹 Optional in Streams

…and more!

🚀 Clean, concise, and interview-ready.

👉 Full explanations + code examples on Medium (link in comments)

🔖 Save this for quick revision!

Full Medium Link:
https://medium.com/@pramod.er90/20-java-stream-interview-questions-with-solutions-every-backend-developer-must-know-27c2fc7141e4

Java 26 Released 🚀: What’s New, What Matters & Why It’s Faster Than Ever

Pramod Kumar — Wed, 18 Mar 2026 04:30:38 +0000

Java 26 has officially been released (March 2026), and while it may not look like a massive update, it introduces some important improvements under the hood.

From HTTP/3 support to structured concurrency and JVM performance upgrades, this release focuses on making Java faster, cleaner, and more modern.

I’ve written a detailed breakdown with examples 👇
👉 https://medium.com/@pramod.er90/java-26-released-whats-new-what-matters-why-it-s-faster-than-ever-717562987e65

7 Java Stream Mistakes That Quietly Hurt Your Production Code ⚠️

Pramod Kumar — Mon, 16 Mar 2026 10:04:20 +0000

Java Streams are powerful.

But in real production systems, I've seen them quietly introduce performance issues, readability problems, and even bugs.

Here are 7 mistakes Java developers often make with Streams 👇

1️⃣ Using Streams for Simple Iteration

Streams look elegant, but sometimes a simple loop is clearer and faster.

❌

list.stream().forEach(item -> process(item));

✅

for (Item item : list) {
    process(item);
}

Use streams when transforming data, not just looping.

2️⃣ Forgetting Streams Are Lazy

Intermediate operations don't execute until a terminal operation is called.

❌

list.stream()
    .filter(x -> x > 10)
    .map(x -> x * 2);

Nothing runs here.

✅

list.stream()
    .filter(x -> x > 10)
    .map(x -> x * 2)
    .toList();

3️⃣ Blindly Using `parallelStream()`

Parallel streams can:

• increase CPU usage
• cause thread contention
• slow your application

❌

list.parallelStream().forEach(this::process);

Always benchmark before using parallel streams.

4️⃣ Mutating Shared State

Streams should be stateless.

❌

List<Integer> result = new ArrayList<>();
list.stream().forEach(result::add);

✅

List<Integer> result = list.stream().toList();

Side effects inside streams can cause serious concurrency bugs.

5️⃣ Creating Unreadable Stream Chains

When stream pipelines get too long, readability suffers.

list.stream()
    .filter(a -> a.isActive())
    .map(a -> a.getOrders())
    .flatMap(List::stream)
    .filter(o -> o.getPrice() > 100)
    .map(Order::getCustomer)
    .distinct()
    .toList();

Break complex pipelines into smaller steps.

6️⃣ Using `map()` Instead of `flatMap()`

This is a very common mistake with nested collections.

❌

Stream<List<Order>> orders =
    users.stream().map(User::getOrders);

✅

Stream<Order> orders =
    users.stream().flatMap(user -> user.getOrders().stream());

flatMap() flattens nested streams.

7️⃣ Trying to Reuse a Stream

Streams cannot be reused after a terminal operation.

❌

Stream<Integer> stream = list.stream();
stream.count();
stream.forEach(System.out::println);

✅

list.stream().count();
list.stream().forEach(System.out::println);

💡 Final Thought

Streams are powerful — but readability, maintainability, and performance should always come first.

Clean code > clever code.

📖 I wrote a deeper breakdown with examples here:

👉 Read the full article on Medium: https://medium.com/@pramod.er90/bb6af5e0361f

💬 Curious:

Which Java Stream mistake have you seen the most in production?

Stop Using map() for Nested Collections In Java — Use flatMap() Instead

Pramod Kumar — Fri, 13 Mar 2026 11:56:38 +0000

Most Java developers learn map() pretty quickly when working with Streams.

But things start getting confusing when dealing with nested collections.

For example, imagine a simple backend model:

Customer → Accounts → Transactions

If you try to extract transactions using map(), you might end up with something like:

List<List<List<Transaction>>>

Not exactly helpful.

In this article, I explain how flatMap() solves this problem using practical examples from a banking-style backend system, including:

Flattening nested collections
Filtering high-value debit transactions
Generating transaction statements
Fraud detection example
map() vs flatMap() comparison

Interview questions

If you're working with Java Streams, especially with nested data structures, this might help.

👉 Read the full article here:
https://medium.com/@pramod.er90/stop-using-map-for-nested-collections-use-flatmap-instead-2276dbb4fa78

Curious — what part of Java Streams was hardest for you to understand?

Spring Boot 4.x — A Big Step Forward for Java Developers

Pramod Kumar — Fri, 13 Mar 2026 05:21:52 +0000

🚀 Spring Boot 4.x — A Big Step Forward for Java Developers

The new generation of Spring Boot is pushing Java development further into the cloud-native era.

Here are a few things that stand out for the developer community:

• ⚡ Faster startup and improved performance
• 📦 Better container & Docker support
• 🔍 Improved observability and metrics
• 🧠 Cleaner configuration management
• ☁️ More cloud-native friendly architecture
• 🔧 Improved native image support

One thing I appreciate about Spring Boot is how it continues to remove boilerplate and simplify enterprise Java development.

Instead of spending hours on configuration, developers can focus on building scalable services and APIs.

Curious to hear from the dev community 👨‍💻

👉 What feature of Spring Boot do you use the most in production?

🚫 Stop Creating ObjectMapper Instances in Spring Boot — Let Spring Manage It

Pramod Kumar — Wed, 11 Mar 2026 05:08:30 +0000

Many Spring Boot developers have written this line at least once:

ObjectMapper mapper = new ObjectMapper();

It works… but inside a Spring Boot application, this can silently introduce problems.

Creating ObjectMapper manually bypasses Spring Boot’s Jackson configuration, which means you might lose:

Registered modules like JavaTimeModule
Custom serializers/desterilizes
Global JSON configuration
Consistent serialization across APIs

Spring Boot already provides a fully configured ObjectMapper as a singleton bean, so the better approach is simply to inject it.

@Service
public class JsonService {
    private final ObjectMapper objectMapper;
    public JsonService(ObjectMapper objectMapper) {
        this.objectMapper = objectMapper;
    }
}

This ensures your application uses one consistent JSON configuration everywhere.

Spring Boot also automatically converts objects to JSON in controllers using MappingJackson2HttpMessageConverter, so in many cases you don’t need to use ObjectMapper directly at all.

I wrote a full guide explaining the common mistakes developers make and the correct way to use ObjectMapper in Spring Boot.

👉 Read the full article:
https://medium.com/write-a-catalyst/stop-creating-objectmapper-instances-in-spring-boot-let-spring-manage-it-21b038252c6b

🚨 Why Your Spring Boot API Takes 3 Seconds to Respond (And How We Reduced It to 200 ms)

Pramod Kumar — Mon, 09 Mar 2026 14:58:46 +0000

Before optimization: ~3000 ms
After optimization: ~200 ms

Our Spring Boot API suddenly started taking 3 seconds to respond in production.

But in development it was only 150–200 ms.

After profiling the system we discovered the issue wasn't one big problem.

It was several small performance bottlenecks working together:

N+1 Hibernate queries
missing database indexes
blocking controller operations
excessive logging
large JSON responses

After fixing them step by step, the response time dropped from 3 seconds to around 200 ms.

Here are the exact improvements that made the difference.

👉 Originally published on Medium. Republished here for the Dev.to community:
https://medium.com/@pramod.er90/why-your-spring-boot-api-takes-3-seconds-to-respond-and-how-to-fix-it-160f37a4aaa7

Hey

Pramod Kumar — Mon, 09 Mar 2026 09:13:57 +0000

Hello Everyone,

How are you doing today?

Forem: Pramod Kumar

Why Senior Engineers Are Quietly Moving Away from Microservices (And Back to Monoliths)

🧩 1. Microservices Add Massive Complexity

🔍 2. Debugging Becomes a Nightmare

⚙️ 3. Operational Overhead Explodes

🧠 4. Cognitive Load is Real

🚀 5. Monoliths Are Not the Enemy

⚖️ 6. Microservices Still Have a Place

💡 The Real Takeaway

softwareengineering #microservices #systemdesign #backend #devcommunity

Why JSON Breaks in AI Pipelines — and the TOON Format I Built to Fix It

Why JSON Breaks in AI Pipelines — and the TOON Format I Built to Fix It

The Problem I Hit

Example

What I Needed Instead

Introducing TOON (Experimental)

Same data in TOON:

What’s Different?

1. Less syntax, more signal

2. Explicit relationships

3. AI-friendly structure

Where This Actually Helped

But It’s Not Perfect

So Why Explore This?

This One Spring Boot Change Made My API 10 Faster (It Wasn’t Caching)

🚀 Building Production-Ready AI Systems with Spring Boot 4 + JDK 21 (Part 2)

🔗 Full Detailed Guide (Medium)

🧱 1. Redis — Stop Burning Money

🌊 2. Streaming — Real-Time UX

🚦 3. Rate Limiting — Protect Your API

🧠 4. RAG — Context-Aware AI

🔁 Final Architecture

🔥 What You Built

🏁 Final Thought

🔗 Full Article (Recommended)

💡 **20 Java Stream Interview Questions — Every Backend Developer Must Know**

Java 26 Released 🚀: What’s New, What Matters & Why It’s Faster Than Ever

7 Java Stream Mistakes That Quietly Hurt Your Production Code ⚠️

1️⃣ Using Streams for Simple Iteration

2️⃣ Forgetting Streams Are Lazy

3️⃣ Blindly Using parallelStream()

4️⃣ Mutating Shared State

5️⃣ Creating Unreadable Stream Chains

6️⃣ Using map() Instead of flatMap()

7️⃣ Trying to Reuse a Stream

👉 Read the full article on Medium: https://medium.com/@pramod.er90/bb6af5e0361f

Stop Using map() for Nested Collections In Java — Use flatMap() Instead

Spring Boot 4.x — A Big Step Forward for Java Developers

🚫 Stop Creating ObjectMapper Instances in Spring Boot — Let Spring Manage It

🚨 Why Your Spring Boot API Takes 3 Seconds to Respond (And How We Reduced It to 200 ms)

Hey

💡 20 Java Stream Interview Questions — Every Backend Developer Must Know

3️⃣ Blindly Using `parallelStream()`

6️⃣ Using `map()` Instead of `flatMap()`