Introduction

Let’s start with a simple idea. Imagine your computer’s memory is a big, messy closet. You have clothes everywhere! It’s hard to find your favorite shirt.

A data structure is a way to organize that closet. You could use shelves, drawers, or hangers. Each method has good and bad points. A stack is like a stack of T-shirts (you can only get the top one), while an array is like a row of shoe boxes.
An algorithm is the method you use to work with your organized clothes. It’s the set of steps you follow to “add a new pair of socks” or “find your blue jacket.”

Different data structures are good at different jobs. Choosing the right one is a huge part of writing fast and efficient software.

A Quick Look at Common Structures

Here is a quick look at a few common data structures and their main features.

Data Structure	How It Works (Simple View)	Good For	Bad For
`Array`	A row of numbered boxes.	Getting an item if you know its number (index).	Searching, adding/removing in the middle, or growing.
`Ordered Array`	A row of mailboxes, sorted by name.	Much faster to search than a normal array (you can guess where to look).	Very slow to add or remove items (you have to shift everything); fixed size.
`Stack`	A pile of items; you only touch the top one.	“Undo” features. (Last-In, First-Out)	Getting an item from the bottom or middle.
`Queue`	A line of items; you add to the back, take from the front.	Managing tasks or requests. (First-In, First-Out)	Getting an item from the middle.
`Linked List`	A chain of items, each pointing to the next.	Adding or removing items in the middle.	Getting an item (you must follow the chain).
`Binary Tree`	A family tree (each person can have two children).	Very fast to search, add, and delete items if the tree is balanced.	Can become unbalanced (like a tall, skinny tree), making it slow.
`Red-Black Tree`	A “self-balancing” family tree.	Always stays balanced, so searching, adding, and deleting are always fast.	Much more complex to build and understand.
`2-3-4 Tree`	A family tree where a parent can have 2, 3, or 4 children.	Always balanced; works very well for storing huge amounts of data (like on a disk).	Even more complex than a red-black tree.
`Hash Table`	A large coat checkroom where your ticket number tells you exactly which hook your coat is on.	Extremely fast to find, add, or remove an item if you know its key (the “ticket number”).	Can use a lot of memory; slow if you don’t know the key; slow to delete.
`Heap`	A company’s organization chart.	Very fast to find the most important item (like the CEO, or the highest/lowest number).	Slow to access any other items in the chart.
`Graph`	A map of cities and the roads connecting them.	Perfect for modeling networks and relationships (like a social network).	Some tasks, like “find the shortest path,” can be slow and very complex.