Big O notation provides a framework for analyzing the performance of functions based on how their execution time grows with increasing input size. The article discusses four common categories of Big O notation: constant (O(1)), logarithmic (O(log n)), linear (O(n)), and quadratic (O(n^2)), explaining their implications through examples such as summation, sorting, and searching algorithms. It emphasizes the importance of understanding these complexities to optimize code performance effectively.

The article debunks common myths surrounding GraphQL, clarifying misconceptions about its performance, complexity, and suitability for various applications. It emphasizes the importance of understanding GraphQL's strengths and weaknesses rather than relying on popular assumptions.