When developing applications in Java, it’s common to overlook the performance implications of certain code structures until they start impacting real users. Big O notation—expressed as O(1), O(n), O(n^2), etc.—is a powerful tool for predicting how your code’s performance will change as input size grows. Understanding these complexities isn't just for academics; it's essential for writing scalable, efficient applications. In this article, we’ll dive into the technical details and nuances of O(1), O(n), and O(n^2) complexities, using Java code examples to illustrate their impact.