so far , we’ve been focusing on time complexity how can we analyze the runtime of an algorithm as the size of the inputs increases ?

We can also use big O notation to analyze space complexity: how much additional memory do we need to allocate in order to run the code in our algorithm

What about the inputs?

• Sometimes you’ll hear the term auxiliary space complexity to refer to space required by the algorithm, not including space taken by the inputs.
• Unless otherwise noted, when we talk about space complexity, technically we’ll be talking about auxiliary space complexity.

Space Complexity in JS

• Most primitives (boolean , numbers , undefined, null) are constant space.
• Strings require O(n) space ( where n is the string length)
• Reference types are generally O(n), where n is the length (for arrays) or the number of keys (for objects).

Example

function sum(arr){
	let total = 0;
	for (let i = 0; i < arr.length;i++){
		total += arr[i];
	}
	return total;
}
time complexity - O(n)
space complexity- O(1) 

function double(arr){
	let newArr = [];
	for(let i = 0; i < arr.length; i++){
		newArr.push(2 * arr[i]);
	}
	return newArr;
}
space complexity - O(n)