DSA Complexity means how much time and space (memory) a Data Structure or Algorithm (DSA) needs to solve a problem.

💡 Imagine this:

You're solving a puzzle.

• Time Complexity = How long it takes you to solve it.

• Space Complexity = How much table space (memory) you need while solving it.

📦 Types of Complexity:

1. Time Complexity

   • How fast an algorithm runs as the input gets bigger.

   • Example:

     • If you check each number in a list of 10 elements, it takes 10 steps.

     • For 100 elements, it takes 100 steps.

     • This is called O(n) — "linear time".

2. Space Complexity

   • How much extra memory the algorithm uses while running.

   • Example:

     • If your code stores 5 numbers no matter how big the input is, the space is fixed → O(1)

🔢 Common Time Complexities:

Complexity Meaning Example

O(1) Constant time Accessing one item from an array

O(log n) Logarithmic Binary search

O(n) Linear time Loop through an array

O(n²) Quadratic Nested loops (e.g., bubble sort)