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Brian Baliach

If you’ve been following me, you might recall a harrowing tale of a developer coming to grips with recursion. The aptly titled 'Decoding Recursion: The Concept That Had Me Stumped'. But your cogitation and feedback suggest we dig a tad deeper. Brave ones have emerged requesting a practical walkthrough of recursion. In this sequel, we'll take a hands-on approach to recursion using the call stack/heap.

Let's hoist the mainsail and dive in.

### Under the Hood: What Happens When Functions Run?

So, matcha latte in hand, (or is it an afternoon beer?), let’s unravel what’s going on 'under the hood' when functions are involved. Every function in its operation has an allocated memory space. Avoiding the technical jargon for the sake of our shared sanity, let's visualize this as tiny compartments in your computer's brain.

When a function runs, it's like the computer opens that compartment and dumps everything the function needs inside – the variables, calculations, and that little reminder of your lunch break at 12 pm.

This "compartment" goes by a couple of names in the programming world: Stack and Heap. While they are both for storage, they differ slightly in usage. The stack is used for static memory allocation and the heap for dynamic memory allocation.

### Enter Recursion: A Practical Look

Since we're all here for recursion, let's take a prime example - factorial computation. A factorial is the product of a number (n) and all the integers below it. Remember 5! (factorial) equaling 5x4x3x2x1? Yes, that one.

In code form, a basic recursive function for a factorial would look something like (javascript this time, cause turns out, it has a much larger audience):

```javascript
function factorial(n) {
  if (n === 1) {
    return 1;
  } else {
    return n * factorial(n - 1);
  }
}
```

Now, let's visualize this on the call stack.

Every recursive call will add a layer to the stack. It's like a pile of dishes. The first call (n=5) is the first plate. Then n=4, then 3, 2, and finally 1. After that, the result bubbles up, washing the dirty dishes one by one from the top until you reach the clean plate at the bottom, i.e., the final answer.
Remember that in heaps/stacks, data is added in a first come last serve basis.
Here's a visualization:

| Heap         | Result | Code explanation                                                                        |
|--------------|--------|-----------------------------------------------------------------------------------------|
| n==1         | 1      | base case: (n==1) return 1;                                                             |
| n==2         | 1 * 2  | n is 1 (n * factorial(n-1) which comes from previous call above. result then becomes 2. |
| n==3         | 2 * 3  | n is 2, which again comes from previous call above. the result becomes 6.               |
| n==4         | 6 * 4  | n is 6, which again comes from the call above. the result becomes 24.                   |
| n==5         | 24 * 5 | n is 24, the result becomes 120.                                                        |
| final result | 120    | This is what is finally returned. 120.                                                  |


### Stack Overflow: Recursion's Gotcha!

Stack overflow – it isn't just a popular website, folks; it's recursion's Achilles heel. This happens when our recursion burrows too deep, causing the application to run out of memory space. This is because every OS assigns a running program a fixed memory allocation (i.e. 4MB worth of space). If you exhaust this space, you then might get a stack overflow /segmentation fault error. There goes the stack of dirty dishes tumbling down!

This potential drawback highlights how deep understanding of recursion can help developers optimize for performance.

### Wrapping It Up

There you have it - a code soup stirred with the call stack/heap and topped off with a sprinkle of recursion. Hopefully, this has helped demystify the concept a bit. But who knows? Perhaps our odyssey is just starting - linked lists, binary trees, sorting algorithms - all served with that recursion spice, are waiting!

Remember, fellow devs, recursion is a powerful tool and understanding how it works at every level aids in writing better, cleaner, more intuitive and more efficient code... even if it stumps us occasionally!

As always, sail through your coding voyage, one recursion at a time.



Unraveling Recursion: A Practical Look at the Call Stack and Heap

Microservices might be the heroes of scalable and reliable applications today, but let's shift gears and talk about a concept in the software development landscape that is as mind-bending as it is powerful: recursion.

As someone who enjoys the adrenaline rush of conquering coding challenges, I've come across various concepts that are the bread and butter of computer science. Some felt like meeting an old friend on the first day of school—familiar and comforting. Others were like that one high school math problem that kept me up at night. Enter recursion.

Recursion, to put it simply, is like a mythic serpent that swallows its own tail—a function that munches on itself to get to a result. My initial thought was, "Why suffer through this mind-boggling feat when a good ol’ loop could suffice?" But then, as I chewed over recursion, the mist started to fade.

### Imagine This...
Let's play with recursion's call stack visualization. Suppose a group of kids are playing with a neat stack of blocks. Each block has a unique number. The rule is: a player can only place a block on the stack if it decreases the previous number by one. The game starts with 10 and ends with 1—a stack of decrescendoing integers.

So, what does this have to do with recursion? Each block placement is akin to a function call in recursion. You place one block (function call), then another (another call), and another, till you reach the edge case — in our scenario, the number 1 block. That's when the players start to cheer and pass the stacks backwards, unwinding them while they execute some extra celebration (in our case, processing returns).

### Dividing to Conquer
Remember those divide-and-conquer strategies in ancient warfare history? They work magic in recursion too. A problem must be conquerable by breaking it down into smaller, more manageable versions of itself.  Simply take down towers one by one until the main castle surrenders.

### A Quick Dive Into Code
Let's demonstrate recursion with a problem that even I, a seasoned loop-lover, found elegant in its recursive cloak — a classic problem: calculating the factorial of a number.

The factorial of a number n (denoted as n!) is the product of all positive integers up to n. For instance, the factorial of 4 is 4! which equals 4 * 3 * 2 * 1 = 24.

A looping method would have us running circles. Here's a python example (python because, from my perspective, is closest to English):

```python
def factorial_loop(n):
    result = 1
    for i in range(2, n + 1):
        result *= i
    return result
```

But let's sprinkle the magic dust of recursion:

```python
def factorial_recursive(n):
    if n == 1:
        return 1
    else:
        return n * factorial_recursive(n - 1)
```

This second snippet embraces the soul of recursion. We define an edge case — our base condition that stops the infinite tumble down the rabbit hole (n == 1). The function then keeps calling itself, shrinking the problem down each time (n - 1), till it hits that edge case. And then the unwinding begins, with each call resolving and returning its piece of the puzzle, eventually giving us our answer.

### Why Bother With Recursion?
Sure, recursion might seem like overkill for simple problems that a loop can fix. But consider the elegance of solutions it provides for more complex issues like tree traversals, algorithmic problem solving such as the Towers of Hanoi, or graph searches. Sometimes, recursion is not just a solution; it's the most intuitive one.

### Closing Thoughts
Once I got past the intimidating facade of recursion, I found a beautiful and streamlined way of solving certain kinds of problems. Just like my transition from being a REST advocate to appreciating the swiftness of gRPC, embracing recursion has opened up new pathways in my coding journey.

If you're still feeling the heebie-jeebies about recursion, you're not alone. Take it from someone who was once stumped by the concept: the enigma of recursion is worthy of exploration. With each recursive function we write, we're training our minds to see not just the stack of problems but the stack of solutions waiting beneath. Happy recursing!


Decoding Recursion: The Concept That Had Me Stumped

Showing posts from recursion tag

Unraveling Recursion: A Practical Look at the Call Stack and Heap

Decoding Recursion: The Concept That Had Me Stumped