Different Types Of Data Structures and their Use

Data structures are like tools in a toolbox 🧰: each tool is designed to do a specific job. This chapter will review some of the more common data structures, and how to decide which data structure to use for which problem, using analogies to make them stick.

Linear Data Structures: The Queue at the Concert 🎤

Linear data structures are collections of data elements arranged sequentially, where each element is connected to its previous and next element. This ensures that the data is always accessed and processed in a linear manner ➡️. Examples include arrays, linked lists, stacks, and queues.

1. Arrays 🗂️

An array is a linear data structure that stores elements in contiguous memory locations, making it easy to access elements using their index 🔢. Each element in an array is of the same data type, and indices start from zero in most programming languages.

Imagine a row of seats at the concert 🎶, numbered and fixed in place.

• Use Case: Quick access to elements by index.

• Example: Keeping a list of registered attendees.

2. Stacks (Last In, First Out) 🍽️

A stack is a linear data structure that follows the Last In, First Out (LIFO) principle, meaning the last element added is the first to be removed.

Think of a stack of plates at a buffet 🥞, the last one added is the first one taken.

• Use Case: Undo operations in text editors or navigating backward in a browser.

3. Queues (First In, First Out) 🛤️

A queue is a linear data structure that follows the First In, First Out (FIFO) principle, meaning elements are removed in the order they were added.

Remember our concert case study? The first fan in line gets the first ticket.

• Use Case: Managing scheduled tasks 🕒

4. Linked Lists 🔗

A linked list is a linear data structure consisting of nodes, where each node contains data and a reference (or pointer) to the next node in the sequence. Unlike arrays, linked lists do not use contiguous memory.

Picture a chain where each link connects to the next ⛓️.

• Use Case: Dynamic memory allocation, where sizes can change during runtime

Non-Linear Data Structures: The Family Tree 🌳

Non-linear data structures branch out like family trees or city maps. Examples include trees, graphs, and hash tables.

1. Trees 🌲

A tree is a hierarchical data structure consisting of nodes, where each node has a value and links to child nodes. The topmost node is called the root, and nodes without children are leaves 🍂.

Imagine a family tree, with parents branching out to children.

• Use Case: Organizing hierarchical data like a file system 🗃️

2. Graphs 🛣️

A graph is a non-linear data structure consisting of vertices (nodes) and edges (connections). It represents relationships or connections between entities. Graphs are widely used in network analysis, social media connections 🌐, routing algorithms 🛤️, and more.

Think of Google Maps, where intersections (nodes) connect via roads (edges).

• Use Case: Social networks, where users are nodes and friendships are edges.

3. Hash Tables 🔑

A hash table (or hash map) is a data structure that stores key-value pairs, allowing efficient data retrieval. It uses a hash function to compute an index (hash code) based on the key, where the corresponding value is stored.

Visualize a key locker 🗄️, where each key is tied to a specific locker.

• Use Case: Fast lookups, like finding contact info from a phone number.

Data structures are like cheat codes in programming — using the right one makes solving problems a breeze 🌬️. Now that you’ve met the big boys 💪, you’re ready to dive deeper into their inner workings!