👨‍💻 Hazriq’s Dev Blog

If you’ve read my previous post on AWS Lambda, you already know how magical it feels to just upload your code and let AWS handle the rest.

But here’s a question most beginners eventually ask:

“If Lambda can already run my code, why do people still use EC2?”

Good question. Let’s break it down in plain English.

–

EC2 — The Always-On Office PC

EC2 is like renting your own computer in the cloud.
You control everything — OS, software, uptime — but you also pay even when you’re not using it.

Perfect for:

• Apps that need to run 24/7.
• Consistent traffic or background services.
• Workloads needing custom setups (special libraries, daemons, etc.).

⚠️ Downside: You manage scaling, patching, and cost.

–

Lambda — The Cloud Vending Machine

Lambda, on the other hand, is event-driven magic.
You just drop in your code, and it runs only when something triggers it.

If you want to understand Lambda in detail (handlers, events, roles, limits, etc.),
check out my earlier post AWS Lambda Basics.

Here, let’s keep things simple.

You pay only when it runs, it scales automatically, and it shuts down when done.
But you give up some control — you can’t run long-lived processes or manage your own environment.

Real-Life Example: SQS → DynamoDB Forwarder

Imagine you have a small data forwarder:
it listens to messages in SQS, processes them, and stores results in DynamoDB.

Let’s see what happens with both EC2 and Lambda

–

EC2 Version

You set up an EC2 instance, install your app, and keep it running 24/7, polling SQS every few seconds.

✅ Pros

• Full control and visibility.
• Works great when messages keep coming in all day.
• You can fine-tune performance (threads, caching, retries).

❌ Cons

• Still running (and billing) even when the queue is empty.
• You manage scaling, logs, and health checks.

Billing vibe: Pay per hour. Idle? Still billed.

–

Lambda Version

You configure SQS as a trigger for your Lambda.
When a message arrives, AWS spins up your function, processes it, and shuts it down.

✅ Pros

• Pay only when messages arrive.
• No servers, no scaling worries.
• Handles bursty traffic automatically.

❌ Cons

• Time-limited execution (max 15 min).
• Cold starts add slight delay.
• Harder to debug long or stateful logic.

Billing vibe: No messages = no cost.

Which One Fits You?

Simple analogy:

• EC2 = rent a car → you maintain it.
• Lambda = GrabCar → you just ride when needed.

–

In real projects, both often coexist:
EC2 runs the main services, while Lambda handles small, event-based tasks.

Start simple — use Lambda for event-driven bits, and bring EC2 in when you need always-on power.
AWS gives you both tools so you can pick what fits the moment.

Understanding how Java handles multiple things at once — and how Quarkus changes the game.

In my Back to Java journey, I’ve been rediscovering parts of the language that I used years ago but never really understood deeply.
Today’s topic is one of the most fundamental — and honestly, one of the most misunderstood: Threads.

Threads are the foundation of concurrency in Java.
And before I dive deeper into Quarkus’ reactive and non-blocking world, I want to understand what happens underneath.

What Are Threads (in general)?

A thread is like a worker inside your program — an independent flow of execution that can run code in parallel with others.

When you run a Java program, it always starts with one main thread:

public class MainExample {
    public static void main(String[] args) {
        System.out.println("Running in: " + Thread.currentThread().getName());
    }
}

Output:

Running in: main

That’s your main worker.
Every additional thread you create runs alongside it.

–

Creating Threads in Java

There are two common ways to create threads:

1. Extend the Thread class

public class MyThread extends Thread {
    public void run() {
        System.out.println("Running in: " + Thread.currentThread().getName());
    }

    public static void main(String[] args) {
        MyThread t1 = new MyThread();
        t1.start(); // Start a new thread
        System.out.println("Main: " + Thread.currentThread().getName());
    }
}

• start() creates a new thread and calls run() asynchronously.
• If you call run() directly, it just runs on the same thread — no concurrency.

2. Use a Runnable (preferred approach)

public class RunnableExample {
    public static void main(String[] args) {
        Runnable task = () -> System.out.println("Running in: " + Thread.currentThread().getName());
        Thread thread = new Thread(task);
        thread.start();
    }
}

Runnable separates the work (the code you want to run) from the worker (the Thread).

–

Threads in Practice: A Quick Hands-On

Let’s simulate doing two things at once — something slow, like making coffee ☕ and toasting bread 🍞.

public class Breakfast {
    public static void main(String[] args) {
        Thread coffee = new Thread(() -> makeCoffee());
        Thread toast = new Thread(() -> toastBread());

        coffee.start();
        toast.start();
    }

    static void makeCoffee() {
        try {
            Thread.sleep(2000);
            System.out.println("☕ Coffee is ready!");
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
    }

    static void toastBread() {
        try {
            Thread.sleep(3000);
            System.out.println("🍞 Bread is toasted!");
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
    }
}

Output (order may vary):

☕ Coffee is ready!
🍞 Bread is toasted!

Both tasks run at the same time — this is concurrency in action.
Each task gets its own thread, and the program doesn’t wait for one to finish before starting the other.

–

Thread Pools: Managing Many Tasks

Creating new threads every time can be expensive.
A better way is to reuse threads using an ExecutorService (a thread pool).

import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;

public class ThreadPoolExample {
    public static void main(String[] args) {
        ExecutorService pool = Executors.newFixedThreadPool(3);

        for (int i = 1; i <= 5; i++) {
            int taskId = i;
            pool.submit(() ->
                System.out.println("Running task " + taskId + " on " + Thread.currentThread().getName())
            );
        }

        pool.shutdown();
    }
}

This allows multiple tasks to share a limited number of threads efficiently — something frameworks like Quarkus also do under the hood.

–

Java Threads vs JavaScript Async (Same Goal, Different Path)

Java uses multiple threads to handle concurrency.

JavaScript (and TypeScript) runs on a single thread but uses an event loop to handle async operations (like fetch, setTimeout, etc.).

💡 So both aim to “do many things at once,” but JavaScript uses asynchronous scheduling, while Java can literally run multiple threads in parallel.

–

Threads and Quarkus: Same Idea, Smarter Execution

Quarkus applications still use threads — but they’re used more efficiently.

Traditional Java apps (like Spring or Servlet-based apps) use a thread per request model:

• Every incoming HTTP request gets its own thread.
• If a thread waits for a DB or network call, it’s blocked.

Quarkus, on the other hand, is built for non-blocking I/O:

• Threads don’t sit idle waiting for I/O.
• A small pool of threads can handle thousands of concurrent requests.
• Reactive frameworks (like Mutiny and Vert.x) schedule work when data arrives — instead of holding a thread hostage.

👉 So, threads are still there — Quarkus just uses them smarter.

–

Real-Life Scenario: 1000 Requests Hitting the Same Endpoint

Imagine this common situation:
You’ve built an endpoint /users/profile that calls an external user service (maybe a third-party authentication API).
Sometimes it’s fast (50 ms), sometimes slow (2 s).
Now — 1000 users open the app at once.

–

Traditional Java (Blocking I/O)

@Path("/users")
public class UserResource {

    @GET
    @Path("/profile")
    public String getProfile() {
        // Blocking call to external API
        String user = externalService.getUserData(); 
        return "Profile: " + user;
    }
}

• Each request is assigned its own thread from a pool (say, 200 threads).
• The thread makes the external call and then waits.
• Once all 200 threads are busy, new requests are queued until one is free.

If 1000 requests come in:

• 200 active threads, 800 waiting.
• Threads consume memory even while idle.
• Response time grows as requests queue up.
• CPU usage stays low (most threads are sleeping).

–

Quarkus (Reactive / Non-Blocking I/O)

@Path("/users")
public class ReactiveUserResource {

    @GET
    @Path("/profile")
    public Uni<String> getProfile() {
        return externalService.getUserDataAsync() // returns Uni<String>
            .onItem().transform(user -> "Profile: " + user)
            .onFailure().recoverWithItem("Fallback profile");
    }
}

• The call is non-blocking — it sends the HTTP request and releases the thread.
• The same few event-loop threads handle other requests while waiting for responses.
• When the API responds, the result is processed asynchronously.

If 1000 requests come in:

• The same 8–16 event-loop threads can manage them all.
• No threads sit idle; they’re constantly reused.
• Memory footprint stays low, and response times remain stable even if some APIs are slow.

💬 In short:

Traditional Java: many threads that spend most time waiting.
Quarkus: fewer threads that never wait — they react when data is ready.

–

What This Means in Real Life

• Predictable performance: Even if a third-party API slows down, Quarkus keeps serving others.
• Lower cost: Fewer threads → lower memory, better CPU utilization.
• Massive scalability: A single service can handle thousands of concurrent users.
• Still Java: It’s still built on threads — just orchestrated smarter.

☕ Analogy

Think of a coffee shop:

• Traditional Java: one barista per customer — efficient until the queue grows.
• Quarkus: a few baristas multitasking, preparing drinks while others brew — no one stands idle.

–

Scenarios Where Threads Are Useful

1. Parallel processing: Doing multiple independent computations (e.g., processing files).
2. Background tasks: Logging, cleanup, or asynchronous notifications.
3. Simulations or CPU-bound tasks: Heavy calculations that benefit from multiple cores.
4. Learning async behavior: Understanding how frameworks like Quarkus optimize concurrency.

–

Bringing It All Together

• Threads are workers that help Java do multiple things at once.
• Quarkus still relies on them — but in a reactive, efficient way.
• JavaScript handles concurrency differently — single-threaded but event-driven.
• And understanding these fundamentals helps make sense of why reactive programming feels so different (yet familiar).

–

Closing

Before learning Quarkus’ reactive style, I used to think “threads” were just a low-level concept from the old Java days.
But understanding how they work — and how Quarkus builds on top of them — makes the reactive magic feel more logical than mysterious.

Revisiting the basics — understanding floating-point numbers in Java.

In my ongoing Back to Java series, I’ve been rediscovering parts of the language that I used years ago but never really thought deeply about.
Today’s topic: double vs float — two simple types that can quietly cause big differences in performance and precision.

What Are float and double?

Both are floating-point data types used for storing decimal numbers.

The key differences are in their size, precision, and use cases.

So, double literally means double-precision floating point — twice the size and precision of float.

Double Is the Default for Decimals in Java

One thing that often surprises new Java developers:

In Java, all decimal literals are treated as double by default.

That’s why you can write this:

double pi = 3.14159; // ✅ works fine

But this won’t compile:

float price = 19.99; // ❌ error: possible lossy conversion from double to float

You have to explicitly mark it as a float:

float price = 19.99f; // ✅ correct

Java does this to favor precision over size — it assumes you want a double unless you tell it otherwise.

–

When to Use Which

✅ Use float when:

• Memory is limited (e.g., large arrays, 3D graphics, sensor data).
• You don’t need extreme precision (6–7 digits is enough).

✅ Use double when:

• Precision matters (scientific calculations, analytics, or financial models).
• You want the default — most math operations and decimal constants in Java use double.

💡 Rule of thumb:

• Use double by default. Use float only when you know you need to save space.

–

Precision Pitfalls

Floating-point numbers can’t always represent decimals exactly.

double a = 0.1;
double b = 0.2;
System.out.println(a + b); // prints 0.30000000000000004

Why? Binary floating-point can’t represent 0.1 precisely — it’s a repeating fraction in base 2. This tiny difference often doesn’t matter, but it can accumulate in calculations.

–

A Quick Look at BigDecimal

When you need exact decimal precision, especially in financial or accounting systems, you should use BigDecimal — a class from the java.math package.

import java.math.BigDecimal;

BigDecimal price = new BigDecimal("19.99");
BigDecimal quantity = new BigDecimal("3");
BigDecimal total = price.multiply(quantity);

System.out.println(total); // prints 59.97 exactly

• BigDecimal stores numbers as unscaled integers + scale (not binary floating point).
• It avoids rounding errors that can happen with float or double.
• Downside: it’s slower and uses more memory, but you get perfect accuracy.

👉 That’s why it’s commonly used in banking, billing, and currency calculations.

–

For years, I used double by habit — it worked, so I never questioned it. But learning about precision again reminded me that choosing the right type is part of writing reliable code.

Sometimes you need speed (float), sometimes you need safety (BigDecimal), and most of the time, double is that balanced middle ground — it’s even the default for decimals in Java.

–

When I was asked to test the performance of a new API endpoint, I quickly realized: I had never done load or latency testing before. I knew performance mattered, but I didn’t know how to measure it, what numbers to care about, or how to even start.

This post is my attempt to write down what I learned. I’ll explain the terms in plain English, the different types of tests, and show a simple example of how to run your first load test. If you’ve ever seen words like p95 latency or throughput and felt lost—this one is for you.

Key Terms Explained

• Latency: simply how long a request takes. If a user clicks “Save Preference” and the response comes back in 180 ms, that’s the latency for that request.

Why not average?

Let’s say 9 requests are really fast (~100 ms), but 1 request is very slow (~900 ms).

• Average latency looks like ~200 ms (which hides the slow request).
• p50 (median) means “50% of requests are faster than this” (maybe ~120 ms).
• p95 means “95% of requests are faster than this” (maybe ~200 ms).
• p99 means “99% of requests are faster than this” (maybe ~900 ms).

Users notice the slow ones (the “tail”), so we measure p95 and p99 in addition to averages.

• Throughput (RPS/QPS): Requests per second. How many requests your service can process in a given time.

• Concurrency: How many requests are being processed at once.

• Errors: Non-2xx responses (500, 502) or timeouts. Even a 1% error rate is a red flag in production.

–

Types of Performance Tests

• Smoke Test – A tiny test (1 request per second for 1 min) just to check if the endpoint works.
• Baseline Test – Light load to capture normal latency under “calm” conditions.
• Load Test – Run the system at expected traffic (say 100 RPS) for 10–15 minutes. Does it still meet your latency/error targets?
• Stress Test – Push past expected traffic until it breaks. This tells you where the limits are.
• Spike Test – Jump suddenly from low → high traffic. Can the system autoscale?
• Soak Test – Run for hours at moderate load. Useful to find memory leaks or slow drifts.

Each one answers a different question.

Strategy: How to Run Your First Test

1. Define success first.

• Example: “At 100 RPS, p95 ≤ 300 ms, error rate ≤ 0.1%.”

2. Start small.

• Run a smoke test: 1 VU (virtual user), 1 request per second.

3. Ramp up gradually.

• Increase RPS step by step until you reach your target.

4. Measure carefully.

• Look at p50/p95/p99 latency, errors, throughput.

5. Observe your system.

• Is CPU near 100%?
• Are DB connections maxed?
• Is an external service slow?

6. Document the results.

• Write down what you tested, the numbers, and what you learned.

–

What to Look For in Results

• Good signs
• p95 stable across the run
• Errors < 0.1%
• CPU and DB usage below ~70%
• Warning signs
• p95/p99 climbing while p50 stays flat → system under strain
• Errors/timeouts creeping in
• DB or external services throttling

Wrap-Up

When I started, terms like p95 and throughput felt intimidating. But once I ran my first smoke test, it clicked: latency is just “how long it takes,” and load testing is just “seeing if it still works when many requests come in.”

The important part is to:

• Learn the basic terms (p95, RPS, errors).
• Run small tests first.
• Build up to realistic load.
• Watch how your system behaves, not just the test numbers.

If you’ve never done load testing before, I encourage you to try a 5-minute k6 script on your own API. It’s eye-opening to see how your service behaves under pressure.

In my previous post, I introduced Quarkus and touched briefly on reactive programming. We saw how non-blocking code allows threads to do more work instead of waiting around.

This time, let’s go deeper — into Mutiny, Quarkus’ reactive programming library — and see how its chaining style makes reactive code both powerful and readable.

–

Traditional Java frameworks are blocking: one request = one thread. If the thread waits on I/O (DB, API, file), it’s stuck.

Reactive programming is non-blocking: the thread is freed while waiting, and picks up the result asynchronously. This makes applications more scalable and efficient, especially in the cloud.

That’s the foundation. Now let’s talk about Mutiny.

What is Mutiny?

Mutiny is the reactive programming API in Quarkus. It’s built on top of Vert.x but designed to be developer-friendly.

Its two core types are:

• Uni<T> → a promise of one item in the future (like JavaScript’s Promise<T>).
• Multi<T> → a stream of many items over time.

But the real magic of Mutiny is in its fluent chaining style.

Example: Calling an External API with Mutiny

Imagine you’re building a service that fetches user info from an external API (which might be slow). Instead of blocking while waiting, we’ll use Mutiny + chaining.

import io.smallrye.mutiny.Uni;
import jakarta.inject.Inject;
import jakarta.ws.rs.GET;
import jakarta.ws.rs.Path;
import org.eclipse.microprofile.rest.client.inject.RestClient;

@Path("/users")
public class UserResource {

    @Inject
    @RestClient
    ExternalUserService externalUserService; // REST client interface

    @GET
    public Uni<String> getUserData() {
        return externalUserService.fetchUser()  // Uni<User>
            .onItem().transform(user -> {
                // Step 1: uppercase the name
                String upperName = user.getName().toUpperCase();

                // Step 2: return formatted string
                return "Hello " + upperName + " with id " + user.getId();
            })
            .onFailure().recoverWithItem("Fallback user");
    }
}

Alternative: Split Into Multiple Steps

Some developers prefer breaking the chain into smaller, clearer transformations:

return externalUserService.fetchUser()
    // Step 1: uppercase the name
    .onItem().transform(user -> new User(user.getId(), user.getName().toUpperCase()))
    // Step 2: format the message
    .onItem().transform(user -> "Hello " + user.getName() + " with id " + user.getId())
    // Step 3: fallback if something fails
    .onFailure().recoverWithItem("Fallback user");

Both versions are valid — it depends if you want compact or step-by-step clarity.

–

Without Mutiny: Blocking Example

If we weren’t using Mutiny, the call to fetchUser() would be blocking:

@Path("/users-blocking")
public class BlockingUserResource {

    @GET
    public String getUserData() {
        // Simulate a slow external API call
        User user = externalUserService.fetchUserBlocking(); // blocks thread
        String upperName = user.getName().toUpperCase();
        return "Hello " + upperName + " with id " + user.getId();
    }
}

In this case:

• The thread waits until fetchUserBlocking() returns.
• While waiting, the thread does nothing else.
• If 100 requests arrive at once → you need 100 threads just sitting idle, each waiting for its response.
• This quickly becomes heavy, especially in microservices where memory and threads are limited.

With Mutiny, the call returns immediately as a Uni<User>:

• The thread is released right away and can handle another request.
• When the external API responds, Quarkus resumes the pipeline and finishes processing.
• If 100 requests arrive at once → you still only need a small pool of threads, since none of them sit idle waiting.
• This means the service can scale much more efficiently with the same resources.

–

Common Mutiny Operators (Beyond transform)

Mutiny has a rich set of operators to handle different scenarios. Some useful ones:

• onItem() – work with the item if it arrives.
  • .transform(x -> ...) → transform the result.
  • .invoke(x -> ...) → side-effect (like logging) without changing the result.
• onFailure() – handle errors.
  • .recoverWithItem("fallback") → return a default value.
  • .retry().atMost(3) → retry the operation up to 3 times.
• onCompletion() – run something once the pipeline is finished (success or failure).
• Multi operators – streaming equivalents, e.g. .map(), .filter(), .select().first(n).
• combine() – merge results from multiple Unis.

Uni.combine().all().unis(api1.call(), api2.call())
    .asTuple()
    .onItem().transform(tuple -> tuple.getItem1() + " & " + tuple.getItem2());

–

Why Mutiny’s Chaining Matters

• Readable → async pipelines look like synchronous code.
• Composable → add/remove steps easily without rewriting everything.
• Declarative → you describe what should happen, not how.
• Error handling inline → .onFailure().recoverWithItem() instead of try/catch gymnastics.

Compared to raw Java CompletableFuture or even RxJava/Reactor, Mutiny feels lighter and easier to follow.

Where to Use Reactive + Mutiny

Reactive code shines in:

• High-concurrency APIs → e.g., chat apps, booking systems, trading platforms.
• Streaming/event-driven systems → Kafka, AMQP, live data.
• Serverless apps → quick startup, minimal resource use.
• Cloud-native microservices → scaling up/down efficiently.

But if you’re writing a small monolithic app, blocking may still be simpler and good enough.

Trade-offs to Keep in Mind

• Learning curve → async code requires a shift in thinking.
• Debugging → stack traces are harder to follow.
• Overhead → reactive isn’t “free”; don’t use it unless concurrency/scalability matter.

–

Quarkus + Mutiny turns reactive programming from a “scary async monster” into something that feels natural and even elegant.

For me, the fluent chaining style is the deal-breaker — it makes reactive code look like a narrative, not a puzzle.

Back to Java, Now with Quarkus

After years of writing mostly in JavaScript and Python, I recently joined a company that relies on Java with Quarkus. Coming back to Java, I quickly realized Quarkus isn’t just “another framework”—it’s Java re-imagined for today’s cloud-native world.

What is Quarkus?

Quarkus is a Kubernetes-native Java framework built for modern apps. It’s optimized for:

• Cloud (runs smoothly on Kubernetes, serverless, containers)
• Performance (fast boot time, low memory)
• Developer experience (hot reload, unified config, reactive support)

It’s often described as “Supersonic Subatomic Java.”

What’s the Difference?

Compared to traditional Java frameworks (like Spring Boot or Jakarta EE):

• Startup time: Quarkus apps start in milliseconds, not seconds.
• Memory footprint: Uses less RAM—great for microservices in containers.
• Native compilation: Works with GraalVM to compile Java into native binaries.
• Reactive by design: Built to handle modern async workloads.

Reactive Programming in Quarkus

One thing you’ll hear often in the Quarkus world is reactive programming.

At a high level:

• Traditional Java apps are usually blocking → one request = one thread. If that thread is waiting for a database or network response, it just sits idle until the result comes back.
• Reactive apps are non-blocking → threads don’t get stuck. Instead, when an I/O call is made (like fetching from a DB or API), the thread is freed to do other work. When the result is ready, the app picks it back up asynchronously.

Think of it like this:

• Blocking (restaurant analogy): A waiter takes your order, then just stands at the kitchen until your food is ready. They can’t serve anyone else.
• Non-blocking (reactive): The waiter takes your order, gives it to the kitchen, and immediately goes to serve another table. When your food is ready, they bring it over. Same waiter, more customers served.

Blocking vs Non-blocking in Quarkus

Blocking Example:

@Path("/blocking")
public class BlockingResource {

    @GET
    public String getData() throws InterruptedException {
        // Simulate slow service
        Thread.sleep(2000);
        return "Blocking response after 2s";
    }
}

• Each request holds a thread for 2 seconds.
• If 100 users hit this at once, you need 100 threads just waiting.

Non-blocking Example with Mutiny:

import io.smallrye.mutiny.Uni;
import java.time.Duration;

@Path("/non-blocking")
public class NonBlockingResource {

    @GET
    public Uni<String> getData() {
        // Simulate async response
        return Uni.createFrom()
            .item("Non-blocking response after 2s")
            .onItem().delayIt().by(Duration.ofSeconds(2));
    }
}

• The thread is released immediately.
• Quarkus will resume the request once the result is ready, without hogging threads.
• Much more scalable in high-concurrency environments.

👉 In short: Reactive = Non-blocking = More scalable and efficient in modern distributed systems.

💡 Note on Mutiny Quarkus doesn’t invent its own reactive system from scratch. Instead, it builds on Vert.x (a popular reactive toolkit for the JVM) and introduces Mutiny as a friendly API for developers.

• Uni<T> → like a Promise of a single item in the future.
• Multi<T> → like a stream of multiple items over time.

So when you see Uni or Multi in Quarkus code, that’s Mutiny helping you handle non-blocking results in a clean, developer-friendly way.

When Should Developers Consider Quarkus?

You don’t always need Quarkus. Here are scenarios where it makes sense:

• ✅ Microservices – You’re building many small services that need to be fast, lightweight, and cloud-friendly.
• ✅ Containers & Kubernetes – Your apps are deployed in Docker/K8s and you want to reduce memory costs.
• ✅ Serverless – Functions that need to start fast and consume minimal resources.
• ✅ Event-driven / Reactive systems – You’re working with Kafka, messaging, or need to handle high concurrency.
• ✅ Cloud cost optimization – Running many services at scale and every MB of memory counts.

On the other hand:

• If you’re running a monolithic enterprise app on a stable server, traditional Java frameworks may be simpler.
• If your team is heavily invested in another ecosystem (e.g., Spring), migration cost could outweigh the benefit.

Benefits at a Glance:

• 🚀 Fast: Startup in milliseconds.
• 🐇 Lightweight: Minimal memory usage.
• 🐳 Container-native: Tailored for Docker/Kubernetes.
• 🔌 Reactive-ready: Async handling out of the box.
• 🔥 Fun to dev: Hot reload + clear config = better DX.

Java vs Quarkus: A Quick Comparison

For me, Quarkus feels like Java reborn for the cloud era. It keeps the strengths of Java (ecosystem, type safety, mature libraries) but strips away the heavyweight feel.

In the previous post, we looked at the difference between long (a primitive) and Long (its wrapper class). That was just one example — but in fact, every primitive type in Java has a wrapper class.

So in this post, let’s zoom out and cover the bigger picture:

• What are primitive data types?
• What are wrapper classes?
• How are they initialized?
• What are the benefits, differences, and when should you use one over the other?

By the end, you’ll have a simple rule of thumb that will save you from confusion: stick to primitives by default, use wrappers only when you need object features.

–

1. Primitive Data Types

Primitives are the basic building blocks of data in Java. They are not objects and store their values directly in memory (usually on the stack).

Java provides 8 primitive types:

• byte, short, int, long (integers)
• float, double (floating-point numbers)
• char (character)
• boolean (true/false)

Example:

int number = 10;
boolean isActive = true;

They are fast, memory-efficient, and always hold an actual value.

2. Wrapper Classes

For every primitive, Java provides a corresponding wrapper class in the java.lang package. These are objects that “wrap” a primitive inside a class. • Byte, Short, Integer, Long • Float, Double • Character • Boolean

Integer numberObj = Integer.valueOf(10);
Boolean isActiveObj = Boolean.TRUE;

Wrappers are essential when:

• Working with Collections (e.g., List<Integer> instead of List<int>).
• You need to represent null (absence of a value).
• You want to use utility methods (like parsing strings into numbers).

2.5 Initializing Primitives vs Wrappers

Primitive Initialization

• Direct and straightforward.
• Local variables must be initialized before use.
• Class fields get a default value (int → 0, boolean → false).

int x = 10;        // explicit initialization
boolean flag;      // flag must be assigned before use

Wrapper Initialization

• Wrappers are objects, so they can be null.
• Default value for wrapper fields is null.
• Different ways to initialize:

Integer a = new Integer(10);     // old (not recommended)
Integer b = Integer.valueOf(10); // preferred
Integer c = 10;   // autoboxing (simplest)

Similar but Different

• int x = 0; → raw value stored directly.
• Integer y = 0; → an object reference pointing to an Integer.

So while syntax can look similar, the memory model and behavior are not the same.

3. Key Differences

4. Autoboxing & Unboxing

Java makes conversion between primitives and wrappers seamless.

• Autoboxing: primitive → wrapper
• Unboxing: wrapper → primitive

Integer obj = 5;   // autoboxing
int num = obj;     // unboxing

This is convenient, but can introduce performance overhead if overused.

5. Benefits of Wrapper Classes

• Collections & Generics: You can’t store int in a List, but you can store Integer.

List<Integer> numbers = new ArrayList<>();
numbers.add(5);

• Utility Methods:

int parsed = Integer.parseInt("123");

• Null Handling: Sometimes you need null to represent “no value”.

6. When to Use

• Primitives → default choice. Use them when performance matters (loops, counters, math).
• Wrappers → when you need:
  • Collections
  • Nullability
  • Utility methods

6.5 Rule of Thumb

• Default to primitives – they are faster, memory-friendly, and straightforward.
• Use wrappers only when necessary, such as:
  • You need to store them in Collections / Generics.
  • You need to represent null (e.g., database values).
  • You want to leverage utility methods (Integer.parseInt, Boolean.valueOf, etc.).

👉 In short: always use primitive unless there’s a clear reason to use the wrapper.

–

⚡ Bonus: What About String?

If you’re wondering where String fits in — it’s not a primitive, nor a wrapper. String is a regular class in java.lang, but Java gives it special treatment so it behaves almost like a primitive in many cases.

For example:

String name = "Hazriq";

looks as simple as assigning an int or boolean. But under the hood, String is an object, immutable, and stored differently from primitives.

This is why you can do things like:

int length = name.length();   // methods available

So:

• Primitives = raw values
• Wrappers = object versions of primitives
• String = class, not a primitive, but commonly treated as a “basic type” in day-to-day Java

7. Best Practices & Gotchas

• Avoid unnecessary boxing/unboxing in performance-critical code.
• Be careful comparing wrappers:

Integer a = 1000;
Integer b = 1000;

System.out.println(a == b);      // false (different objects)
System.out.println(a.equals(b)); // true

• Remember: wrapper classes and String are immutable. Once created, their value never changes — any “modification” actually creates a new object. (We’ll explore immutability in depth in a future post.)

–

Primitives are simple and fast, wrappers are flexible and object-friendly, and String is a special class that feels primitive but isn’t.

Understanding when to use each is one of those small but important skills that makes you write cleaner and more efficient Java code.

Recently, I got a PR review comment that made me pause. It was about something I thought I already knew well: choosing between long and Long in Java.

And honestly, it hit differently because of how my new company approaches engineering.

In my previous company, the priority was speed. We had the luxury of pushing features straight to production quickly. Optimization, memory efficiency, and cost tuning weren’t the main focus. The mission was simple: deliver fast, and move on.

But in my new company, the approach is different. We take more time to build the right way — thinking about memory, cost, long-term maintainability, and performance.

For someone like me with 8 years of experience, this shift has been an eye-opener. It’s one thing to “make it work.” It’s another thing entirely to “make it work well.”

Which brings me back to… long vs Long.

Primitive vs Wrapper: A Quick Refresher

Java is a bit different from languages like Python or JavaScript. It has two “flavors” of types:

• Primitives: raw values like int, long, boolean.
• Wrapper Classes: object versions of these primitives: Integer, Long, Boolean.

This distinction often feels academic at first, but it has real consequences in how your program behaves.

So what’s the actual difference?

• long:

  • A primitive 64-bit value.
  • Default value: 0.
  • Lightweight and memory efficient.
  • Cannot be null.

• Long:

  • A wrapper class around long.
  • Default value (when uninitialized in an object): null.
  • Heavier — since it’s an object, it lives on the heap.
  • Can be used in places where only objects are allowed (like List<Long>).

Autoboxing and Unboxing

One of the reasons developers sometimes overlook the difference between long and Long is because Java silently converts between them. This feature is called autoboxing and unboxing.

• Autoboxing: automatically converting a primitive (long) into its wrapper (Long).
• Unboxing: automatically converting a wrapper (Long) back into its primitive (long).

This allows you to write code that looks simple:

Long a = 5L;   // autoboxing: primitive long -> Long object
long b = a;    // unboxing: Long object -> primitive long

Without autoboxing, you’d have to do this manually:

Long a = Long.valueOf(5L);   // boxing
long b = a.longValue();      // unboxing

Pretty verbose, right? That’s why Java added this feature in Java 5 — to make our lives easier.

The convenience comes with a trade-off:

• Performance cost: Each conversion creates extra instructions, and sometimes even new objects. In a loop that runs millions of times, those hidden allocations can hurt performance.
• Null safety: If you try to unbox a Long that’s actually null, you’ll get a NullPointerException. For example:

This is why being deliberate about whether you use long or Long matters.

In short:

• Autoboxing and unboxing make your code cleaner.
• But they also hide potential pitfalls in performance and null handling.

When to use which

Here’s a practical rule of thumb I picked up from the review:

• Use long when you need raw performance, don’t care about null, and the value is always expected to be present.
• Use Long when you need:
  • Nullability (e.g., a database field that may not be set).
  • To work with Generics or Collections (List<Long> won’t work with primitives).

Closing

This wasn’t just a lesson about long vs Long. It was a reminder that context matters. In a fast-moving environment, you might get away with just shipping things, but in an environment where optimization, cost, and maintainability matter, these small details make a big difference.

For me, this was an eye-opener even after 8 years in software development. The fundamentals are still powerful, and sometimes revisiting them is the best way to level up.

The next time you see long vs Long, pause for a moment. Is null a possibility? Do you need collection support? Or do you just need a fast, simple number?

That little decision can make your codebase more consistent, more efficient, and less bug-prone.

If you’ve been using Amazon DynamoDB for a while, you’ve probably noticed something: not all your data gets the same attention. Some of it is “hot” — frequently accessed, constantly updated. But a lot of it is “cold” — just sitting there, costing you money every month.

What if you could store that cold data somewhere cheaper without changing your code or losing availability?

That’s exactly what DynamoDB Standard-IA (Infrequent Access) is for. In this post, we’ll break down what it is, how it works, how it can save you money, and when it might not be the best idea.

Recap on previous post:

• AWS: DynamoDB & DAX Cost Factors
• AWS: DynamoDB Basics

DynamoDB Table Classes

DynamoDB offers different table classes to optimize costs based on your access patterns:

1. Standard – For data you access frequently.
   • Designed for low-latency access any time.
   • Best for your main, active application data.
   • The default table class when you create a new DynamoDB table.
   • Suitable for most workloads. Provides high availability and durability.
2. Standard-IA (Infrequent Access) – For data you rarely read or write.
   • Designed for data that is not accessed often but needs to be available when needed.
   • Offers lower storage costs compared to Standard.
   • Higher retrieval costs, so it’s best for data that you access less than once a month.

Both table classes work exactly the same way from a developer’s point of view:

• Same APIs
• Same queries
• Same AWS Console experience

The only difference? How AWS stores it behind the scenes and how much you pay.

What is Standard-IA?

Standard-IA is a table class designed for data that you access infrequently. It’s like a storage locker for your cold data — it’s still there when you need it, but it costs less to keep it around.

Think of it like moving your old books to a basement shelf:

• They’re still yours.
• You can still get them any time.
• But they’re not taking up expensive prime shelf space.

How can it save you money?

The main savings come from storage pricing:

That’s about 60% cheaper for storage.

Example: If you have 100 GB of archived order history:

• Standard = ~$25/month
• Standard-IA = ~$10/month

💡 That’s $15/month saved — or $180/year — just for one table.

The Catch

But it’s not all sunshine and rainbows. There are some important trade-offs to consider:

• Retrieval Costs – Around $0.01 per GB each time you read data from IA.
• Minimum 30-Day Storage Billing – You pay for at least 30 days even if you delete earlier.
• Not for Hot Data – If accessed often, retrieval fees can eat up savings.
• Whole-Table Setting – You can’t mix Standard and IA in one table.

Best Practice before Switching

• Check Access Patterns — Use CloudWatch metrics to see how often the table is read.
• Move Predictable Cold Data — Avoid sudden spikes in retrieval.
• Test on a Smaller Table First — See if retrieval costs are low enough to justify the switch.
• Combine With TTL — Automatically delete expired data to save more.

DynamoDB Standard-IA is like a budget-friendly storage locker for data you still need but rarely touch. It can cut storage costs by more than half — but only if you choose the right workloads.

Rule of thumb: If it’s predictable, cold, and still worth keeping — IA is your friend.

We discussed about DynamoDB in previous post, but let’s dive deeper into the cost factors associated with DynamoDB and its accelerator, DAX (DynamoDB Accelerator).

Recap

• Amazon DynamoDB (DDB) is AWS’s fully managed NoSQL database service, designed for applications that require consistent performance at any scale.

• Amazon DynamoDB Accelerator (DAX) is an in-memory caching service for DynamoDB. Think of it as a turbocharger — it reduces read latency from milliseconds to microseconds by storing frequently accessed data in memory.

Together, DDB and DAX can significantly improve application performance — but they also come with different cost models you’ll want to understand before adopting.

When to Use DAX?

DAX is particularly useful when:

• Your workload has high read traffic with repeated queries for the same items.
• You want microsecond read latency for real-time user experience.
• You aim to offload read traffic from DynamoDB to reduce provisioned read capacity usage.

Example: A database for AI model training, where the same training data is accessed repeatedly.

Skip DAX when:

• Your workload is write-heavy with low read repetition.
• Your queries are strongly consistent (DAX only supports eventually consistent reads).
• Your access patterns are highly dynamic and unpredictable — the cache hit rate might be low.

Understanding DynamoDB Costs

DynamoDB costs come from three main areas:

• Reading Data
  • Imagine a reading allowance — every time you check a page from a book, it uses part of your allowance.
  • You can either pay per read (On-Demand) or buy a monthly “reading subscription” (Provisioned) if you know your usual usage.
• Writing Data
  • Adding or updating books also uses an allowance — think of it as your “writing subscription” or “per-write” payment.
• Storing Data
  • This is your bookshelf space.
  • Regular storage (Standard) is always ready but costs more.
  • Cheaper storage (Standard-IA) is for books you rarely read, but you’ll pay a small fee each time you take one.

Extras You Might Pay For:

• Backups — like taking daily photos of your bookshelf.
• Copies in other regions — like having the same library in multiple cities.

Understanding DAX Costs

5. DAX Costs DAX pricing is per node-hour, depending on node type:

• Smallest node (dax.t3.small) is the cheapest, suitable for dev/test.
• Larger nodes (dax.r5.large, etc.) cost more but handle higher throughput.
• DAX clusters require at least 3 nodes for fault tolerance in production.

Note: DAX charges are separate from DynamoDB — even if your reads come from the cache.

Cost Comparison

If your cache hit rate is low, DAX might increase costs without much benefit.

Final Thoughts

• Use DAX if you have heavy, repeated reads and need lightning-fast results.
• Use Standard-IA storage for rarely accessed data — but don’t forget the retrieval cost.
• Always measure first: monitor read/write usage and cache hit rates before committing.