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.