The Importance of Constrained Data Structures

By definition, if a stack is just a constrained version of an array, that means an array can do anything a stack can do. If so, what advantage does a stack provide?

Constrained data structures like the stack (and the queue, which we’ll encounter shortly) are important for several reasons.

First, when we work with a constrained data structure, we can prevent potential bugs. The linting algorithm, for example, only works if we exclusively remove items from the top of the stack. If a programmer inadvertently writes code that removes items from the middle of the array, the algorithm will break down. By using a stack, we’re forced into only removing items from the top, as it’s impossible to get the stack to remove any other item.

Second, data structures like stacks give us a new mental model for tackling problems. The stack, for example, gives us the whole idea of a last-in, first-out process. We can then apply this LIFO mindset to solve all sorts of problems, such as the linter just described.

And once we’re familiar with the stack and its LIFO nature, the code we write using the stack becomes familiar and elegant to other developers who read our code. As soon as someone sees a stack being used within an algorithm, they immediately know that the algorithm is working with a LIFO-based process.

Stack Wrap-Up

Stacks are ideal for processing any data that should be handled last in, first out. The undo function in a word processor, for example, is a great use case for a stack. As the user types, we keep track of each keystroke by pushing the keystroke onto the stack. Then, when the user hits the undo key, we pop the most recent keystroke from the stack and eliminate it from the document. At this point, their next-to-most recent keystroke is now sitting at the top of the stack, ready to be undone if need be.