How is a data structure's time complexity expressed?

Time complexity of a data structure is fundamentally expressed using big O notation. This notation provides a mathematical framework to describe the efficiency of algorithms in relation to the size of the input data, focusing on how the runtime or space requirements grow as the input size increases.

Big O notation helps classify algorithms based on their worst-case scenario performance and allows for comparing the efficiency of different algorithms regardless of the machine or specific implementation details. By expressing time complexity in such terms, developers and computer scientists can ascertain which algorithms will be most suitable for various applications, especially as data scales.

In contrast, measuring time complexity in milliseconds or kilobytes would be specific to certain implementations and environments, failing to abstractly communicate the algorithm's performance characteristics. Expressing time complexity as a percentage of total execution time would also be misleading, as it does not standardize the measure across different inputs or contexts.