Why typed arrays matter in Typescript - Performance Analysis

Time Complexity: Why typed arrays matter

O(n)

Understanding Time Complexity

When working with large amounts of numbers, how fast your program runs can change a lot depending on the data type you use.

We want to see how using typed arrays affects the speed of processing compared to regular arrays.

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

const size = 1000000;
const regularArray = new Array(size).fill(0);
const typedArray = new Uint8Array(size);

for (let i = 0; i < size; i++) {
  regularArray[i] = i;
  typedArray[i] = i % 256;
}

This code fills both a regular JavaScript array and a typed array with numbers from 0 up to size - 1.

Identify Repeating Operations

• Primary operation: A single loop that runs through all elements to assign values.
• How many times: Exactly size times, once for each element.

How Execution Grows With Input

As the number of elements grows, the time to fill the arrays grows in a straight line.

Input Size (n)	Approx. Operations
10	10 assignments
100	100 assignments
1000	1000 assignments

Pattern observation: Doubling the input doubles the work because each element is handled once.

Final Time Complexity

Time Complexity: O(n)

This means the time to fill the array grows directly with the number of elements.

Common Mistake

[X] Wrong: "Typed arrays always make the code run faster because they are special arrays."

[OK] Correct: Typed arrays help with memory and speed in some cases, but the loop still runs once per element, so time complexity stays the same.

Interview Connect

Understanding how data types affect speed helps you write code that works well with large data, a skill valued in many programming tasks.

Self-Check

"What if we used a nested loop to fill a two-dimensional typed array? How would the time complexity change?"