Understanding Big O Notation: The Key to Writing Efficient Code

Whether you're solving problems on LeetCode, cracking system design interviews, or optimizing backend queries, knowing Big O Notation is crucial for writing performant code.

📘 What is Big O Notation?

Big O Notation describes the time or space complexity of an algorithm in terms of input size n. It gives you a high-level understanding of how your algorithm scales.

⏱️ Common Time Complexities

Big O	Name	Example Use Case
O(1)	Constant Time	Accessing array index
O(log n)	Logarithmic	Binary search
O(n)	Linear Time	Single loop through array
O(n log n)	Linearithmic	Merge sort, Quick sort
O(n²)	Quadratic	Nested loops (e.g., bubble sort)
O(2ⁿ)	Exponential	Recursive Fibonacci
O(n!)	Factorial	Brute-force permutations

🎯 Real-Life Examples

O(1) – Constant Time

function getFirstElement(arr) {
  return arr[0];
}

O(n) – Linear Time

function printAll(arr) {
  for (let el of arr) {
    console.log(el);
  }
}

O(n²) – Quadratic Time

function printPairs(arr) {
  for (let i = 0; i < arr.length; i++) {
    for (let j = 0; j < arr.length; j++) {
      console.log(arr[i], arr[j]);
    }
  }
}

📦 Space Complexity

Big O also measures memory usage:

function createArray(n) {
  let arr = [];
  for (let i = 0; i < n; i++) {
    arr.push(i);
  }
  return arr; // O(n) space
}

💡 Pro Tips

Avoid nested loops where possible.
Use hash maps and sets to reduce time complexity.
Recursion can sometimes be optimized using memoization (Dynamic Programming).

🧠 Interview Insight

When you're asked to explain your code, always mention:

Time complexity
Space complexity
Edge case handling

It shows you're thinking like a real engineer, not just a coder.

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Disclaimer

This content has been generated with the assistance of AI. While I strive for accuracy and quality, please verify critical information independently.

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