Your Code is Fast… But Still Crashes (Space Complexity Explained)

Your code runs fast…
But suddenly crashes when input grows

Why?

Because of Space Complexity

What is Space Complexity?

Space complexity measures how much memory your algorithm uses as input grows.

It includes:

• Input memory
• Extra memory (auxiliary space)

Common Space Complexities

• O(1) – Constant space
• O(n) – Linear space
• O(n²) – Huge memory usage

Examples
O(1) Space
int sum = 0;
for (int i = 0; i < n; i++) sum += arr[i];

Only a few variables used

O(n) Space
int[] copy = new int[arr.length];
for (int i = 0; i < arr.length; i++) copy[i] = arr[i];

Creates a new array

O(n²) Space
int[][] matrix = new int[n][n];

Memory usage grows very fast

Hidden Danger: Recursion

Recursive functions also use stack memory.

Example:
Fibonacci recursion → O(n) space

Reality Check

Fast code ≠ Efficient code

If memory usage is high →
Your app can crash or lag badly

For More: https://www.quipoin.com/tutorial/data-structure-with-java/space-complexity