What are Dynamic Arrays?

Introduction

Arrays are fixed in size. You must specify the number of elements your array will hold ahead of time.

Converting an array from fixed size to resizing itself is the most common use-case for creating all complex data structures like ArrayList, growable arrays, and many more. Other names of dynamic arrays are mutable arrays, resizable arrays, etc.

A dynamic array resizes or expands when you add more elements than the capacity of the array.

Strengths

There are many advantages to using dynamic arrays, including:

1. Fast lookups (Random access)
2. Array size
3. Cache friendly

1. Fast lookups

Retrieving the element at a given index takes O(1) time, regardless of the array's length.

2. Array size

You can add as many elements as you need. There is no limitation. Dynamic arrays expand to hold them.

3. Cache friendly

Just like arrays, dynamic arrays place items right next to each other in memory, efficiently using caches.

Weaknesses

Some of the disadvantages of using dynamic arrays include:

1. Size doubling appends
2. Costly inserts
3. Costly deletes

1. Size doubling appends

Let us consider an array with a capacity of 5 elements.

But the elements we want to store in this array are more, which means we have to double the size, create a new array, copy the old array elements, and add new elements, which takes O(n) time.

2. Costly inserts

Inserting an element at the end of the array takes O(1) time. But, inserting an element at the start/middle of the array takes O(n) time. Why?

If we want to insert something into an array, first, we have to make space by "scooting over" everything starting at the index we're inserting into, as shown in the image. In the worst case, we're inserting into the 0th index in the array (prepending), so we have to "scoot over" everything in the array.

That's O(n) time.

Inserting an element at the 2nd index and moving the rest of the element right shift each once. The resultant array becomes – { A, B, C, D, E }.

I recommend you read Array insertions and shifting algorithms(link below) with a clear explanation with code snippets and sketches to understand why these inserts are expensive at the start and middle.

3. Costly deletes

Deleting an element at the end of the array takes O(1) time, which is the best case. In computer science, we only care about the worst-case scenarios when working on algorithms. But, when we remove an element from the middle or start of the array, we have to fill the gap by scooting over all the elements after it. This will be O(n) if we consider a case of deleting an element from the 0theindex.

Deleting an element at the 3rdindex and filling the gap by left-shifting.