Beginner’s Guide to Recursion in Java

Recursion is one of those topics that either clicks immediately, or makes your brain melt a little.

And honestly? That’s normal because at first glance, recursion feels like a method calling itself that should explode your program. But once you understand what’s happening behind the scenes, recursion becomes one of the most elegant tools in your Java toolbox. One that you shouldn't overuse though.

In this guide, we’ll break it all down step by step. You’ll learn what recursion really is, how it works in Java, where it shines, and how to avoid the common traps.

Let’s make it finally click.

Sidenote: If you find that you’re struggling with the topics in this guide, or perhaps feel that you could use some more training, or simply want to build some more impressive projects for your portfolio, then check out my Java programming bootcamp:

Updated for 2025, you'll learn Java programming fundamentals all the way from complete beginner to advanced skills.

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With that out of the way, let’s get into this 5-minute tutorial…

What is recursion?

Recursion is when a method calls itself to solve a problem.

Erm what?

Yeah, that’s the textbook definition, but it doesn’t help much on its own. Especially if you’re trying to wrap your head around why you’d want to do that in the first place.

Here’s a better way to think about it: recursion is about breaking a problem down into smaller pieces that look just like the original problem.

So instead of solving everything at once, you solve a tiny piece, and then let the method call itself to handle the rest. Each recursive call works on a smaller version of the problem, until it’s so small that you can give a direct answer and stop.

For example

A classic real-world example of this idea are Russian nesting dolls.

You open the biggest doll, and inside is another, smaller version. You keep opening until you reach the smallest one, which has nothing inside.

Recursion works the same way in that each method call opens the next one, until it hits the smallest case and starts closing them all back up.

Why do we care about this?

Mainly because recursion is a great way to express problems that have a repeating or self-similar structure. It’s especially handy for things like traversing trees, solving puzzles, or working with problems where each step naturally depends on solving a smaller version of itself.

However, recursion only works well when you know how to stop. That’s where the base case comes in, which we’ll get to in a bit. For now, just remember: recursion is about solving the small piece, and trusting the rest to recursive calls.

And to help you understand it a little easier, let's see how it works in code.

How recursion works in Java (and what’s happening behind the scenes)

To write a recursive method in Java, you need just two things:

• A way for the method to call itself, and
• A condition that tells it when to stop

That’s it. But understanding how that actually plays out in memory is what makes it all click.

For example

Let’s start with a simple structure, and just for fun - let’s honor the great Matthew Mcconaughey:

public void sayAlright(int count) {
	if (count == 0) {
    	return; // base case
	}
	System.out.println("Alright");
	sayAlright(count - 1); // recursive call
}

So what’s happening here?

Well, each time sayAlright() is called, Java sets aside a new block of memory to keep track of that method call. This memory is managed using something called the call stack. A call stack is the list of method calls that Java keeps track of while your program runs.

Here’s what happens step by step if you call sayAlright(3):

1. Java enters the method with count = 3. It prints "Alright", then calls sayAlright(2)
2. That call goes on top of the stack. It prints "Alright", then calls sayAlright(1)
3. Another call goes on top. It prints "Alright", then calls sayAlright(0)

4. Now the base case is hit. sayAlright(0) doesn’t print anything else — it just returns
5. The method on the stack with count = 1 finishes and returns
6. Then count = 2 finishes and returns
7. Finally, the original count = 3 call finishes and returns

Simply put, each method call pauses and waits for the one it triggered to finish. Nothing is truly done until the deepest recursive call hits the base case and starts unwinding the stack. (More on this in a second).

This is the key to understanding recursion: every recursive call creates its own copy of the method’s variables, and Java keeps track of all of them in memory until the base case is reached.

That’s why forgetting a base case or accidentally writing a recursive call that never gets closer to it causes a stack overflow. The call stack keeps growing with no end in sight, and eventually Java says, “That’s enough.”

So while recursion might feel like magic at first, it’s actually very mechanical. Each call is just a snapshot of a method, sitting patiently on the stack, waiting its turn to finish.

The question of course is how do you make sure all of that works correctly? And how do you write recursion that stops at the right time and doesn’t blow up your stack?

That’s where the two golden rules of recursion come in. Break either one, and things fall apart. Follow both, and recursion works like a charm.

So let’s break these down.

The two golden rules of recursion

Every recursive method has to follow two simple rules. Skip either one, and your program won’t work.

Rule #1. Always remember the base case

This is the condition that tells your method when to stop calling itself. Without it, recursion would go on forever. The base case lets your method know it’s done.

For example

In our earlier example, the base case is:

if (count == 0) {
	return;
}

When the count hits 0, the method doesn’t call itself again. Instead, it just returns and starts unwinding the stack.

Rule #2. Remember that the recursive call moves towards the base case

Each time the method calls itself, it must be working on a smaller or simpler version of the original problem: something that gets it closer to the base case.

Otherwise, even if you have a base case, your program will never reach it.

For example

In our example, we do that with:

sayAlright(count - 1);

This makes sure each recursive call is working with a smaller count, heading toward 0. If you forgot the - 1, or accidentally increased it, you’d just spiral further away from the base case and end up with a stack overflow like we mentioned earlier.

TL;DR

If remember nothing else about recursion, remember this:

1. Always have a base case
2. Always move toward it

It’s like a loop with a stop condition and a way to reach it, but just written differently.

Simple examples to get it into your brain

Once you understand the rules, the best way to make recursion stick is by seeing it in action.

So let’s walk through a few beginner-friendly examples that highlight how recursion works and how it solves problems by breaking them down step by step.

Example #1. Using recursion for a countdown

Let’s start with something dead simple: printing numbers from a starting point down to zero:

public void countdown(int number) {
	if (number < 0) {
    	return;
	}
	System.out.println(number);
	countdown(number - 1);
}

If you call countdown(5), here’s what happens:

• It prints 5, then calls countdown(4)
• Then prints 4, calls countdown(3), and so on
• Once it reaches -1, the base case is hit and the recursion stops

Simple right?

You can clearly see the progression and how each call reduces the problem until it’s small enough to stop.

Is this the best way to do this? Absolutely not, but it does explain you how recursion is working.

Example #2. Using recursion to solve a factorial

Now let’s look at something a bit more mathematical: computing a factorial. The factorial of 5, written as 5!, which is 5 * 4 * 3 * 2 * 1.

This is a textbook example of recursion, because the problem contains a smaller version of itself.

So in code, it would look like this:

public int factorial(int n) {
	if (n == 1) {
    	return 1;
	}
	return n * factorial(n - 1);
}

So factorial(5) becomes:

• 5 * factorial(4)
• 5 * 4 * factorial(3)
• 5 * 4 * 3 * factorial(2)
• 5 * 4 * 3 * 2 * factorial(1)
• 5 * 4 * 3 * 2 * 1, which returns 120

Each call stacks up the multiplication, then unwinds when it hits the base case.

Example #3. Can you solve Fibonacci with recursion? (Kind of)

Another classic example is the Fibonacci sequence, where each number is the sum of the two before it. It looks like a perfect use case for recursion, and it’s often used in interviews to test how well you understand recursion patterns.

However, it’s kind of a trick question:

public int fibonacci(int n) {
	if (n <= 1) {
    	return n;
	}
	return fibonacci(n - 1) + fibonacci(n - 2);
}

What’s happening here?

So fibonacci(5) breaks down into:

• fibonacci(4) + fibonacci(3)
• Then fibonacci(3) + fibonacci(2), and so on...

Technically it works. However, this method is painfully slow.

Why? Because each call branches into two more calls, and most of the work is repeated. This means that just calculating fibonacci(3) happens multiple times. So by the time you hit fibonacci(50), you're deep into tens of thousands of redundant calls!

Not super efficient right?

So what’s the solution?

Well, the problem isn’t recursion as such. It’s how the recursion is structured. Standard recursion doesn’t work well here unless you use a technique called memoization, which stores and reuses results you’ve already calculated.

That’s a bigger topic for another time, and we’ll walk through that fix in a future post so stay tuned.

For now though the key takeaway is this: not all recursion is efficient by default. Recursion is powerful, but only when it’s used with care, and when it avoids doing the same work over and over again.

Speaking of when best to use or not use, here’s some great rules to remember.

When (and when not) to use recursion

Recursion can feel elegant and expressive, but that doesn’t mean it’s always the right tool for the job. Knowing when to use recursion is just as important as knowing how to write it.

When recursion makes sense

Recursion shines when a problem can be broken down into smaller, self-similar pieces. You’re not just repeating steps but you’re solving the same type of problem on a smaller scale. This is why recursion is such a natural fit for:

• Tree structures (like file systems or DOM trees)

Each node may have children, and recursion lets you traverse or search through the entire structure cleanly

• Divide-and-conquer algorithms (like merge sort or quicksort)

You solve smaller subproblems, then combine the results

• Mathematical definitions (like factorial or Fibonacci if optimized)

When a concept is naturally defined in terms of itself, recursion often mirrors that definition nicely in code

• Backtracking problems (like solving mazes, generating permutations, or solving Sudoku)

You try a path, and if it fails, you back up and try the next one. Recursion handles this exploration intuitively

In these situations, recursion tends to make your code shorter, easier to read, and closer to the way you’d describe the solution out loud.

When recursion isn’t the right choice

Despite its elegance, recursion comes with trade-offs. This is because each recursive call adds a new frame to the call stack. Too many frames, and you hit a StackOverflowError. That makes recursion risky for problems with very large input sizes or deeply nested operations unless you take care to limit the depth or convert the logic into an iterative version.

For example:

• Calculating large Fibonacci numbers recursively? Bad idea unless you optimize it
• Traversing millions of records in a flat list? A loop is likely safer and faster
• Anything that could go thousands of calls deep? You’ll want to test it carefully or avoid recursion altogether

Also, if you find yourself writing recursion that’s hard to understand, it might not be worth the trade-off. A readable loop is better than a clever recursive solution that no one else (including future you) can follow.

So here's a good rule of thumb:

• Use recursion when the problem naturally fits recursive thinking and your input size won’t explode the call stack
• Otherwise, a loop might serve you better

What to remember so recursion finally makes sense

So as you can see, recursion doesn’t have to be confusing. Once you understand what’s happening behind the scenes and how each method call stacks up and then unwinds… It starts to feel logical, even elegant.

From here, it’s all about practice. Solving real problems is where it clicks. Stay tuned for a follow up article soon where I help walk you through some common Java problems and how to solve them with recursion.

Until then, just keep the two golden rules in mind: always have a base case, and always move toward it.

P.S.

Remember, if you want to fast-track your Java knowledge and get as much hands-on practice as possible, then check out my Java programming bootcamp:

Updated for 2025, you'll learn Java programming fundamentals all the way from complete beginner to advanced skills.

Better still, you’ll be able to reinforce your learning with over 80 exercises and 18 quizzes. This is the only course you need to go from complete programming beginner to being able to get hired as a Backend Developer!

Plus, once you join, you'll have the opportunity to ask questions in our private Discord community from me, other students and working professionals.