8 basic data structures every programmer should know

Products mentioned
Improve your skills

If you want to become a programmer, you should master the basic data structures. It will help you stand out from the crowd and make your resume shine with one in-demand skill.

In this guide, I’ll share the eight most common data structures that you should know. But before that, let’s start from the basics!

Related: Top 5 things to know before applying for web developer jobs

What is a data structure?

A data structure is a specific method to store and organize data in a computer to be easily used to efficiently perform operations on it.

When data is unstructured, it is not organized or doesn’t have a defined data model.

Then, it is not suitable for the analysis or operations. Unstructured data is a very common problem. It is estimated that 80% of the world’s data is unstructured.

Most organizations collect data and store it in an unorganized way that is not effective in making data easy to use.

There are different types (forms) of data structure, and each type could be effective for some operations but not for others.

So it is the programmer’s job to understand which data structure is suitable to analyze the data efficiently so it can be used to solve the problems or achieve the goal.

Data structures are the fundamentals of software engineering and computer science and are being used in most software systems.

Editor’s note: One of the best ways to impress clients is with a web professional certificate from GoDaddy Academy. Taught online by industry experts, the video-based certifications end with a live project-based assessment.

Most common types of data structure

Now let us discuss eight of the most common data structures.

1.   Arrays

The first in our list of basic data structures is one of the simplest data structures. An array is a fixed-size structure that stores multiple items of the same kind of data sequentially.

An array contains the same but fixed-size data type elements (also known as variables). That’s why you can’t change the array’s size. In an array index, each item starts with “0.”

Example of an array
Source of all tables in this article

An array has elements inside each container and lined up small containers in a sequence.

As arrays are fixed in size, it is not possible to insert or delete an element to an array.

To add an element, you need to create a new array with increased size (+1), then copy the existing elements and add a new element to it.

They are mostly used as a structure to build more complicated data structures and for sorting algorithms.

2. Linked lists

A linked list is a linear data structure where items are arranged in linear order and linked (connected) to each other. That’s why you cannot access random data; you need to access data only in order (sequentially).

Diagram illustrating linked lists

In a linked list, the first element in the list is known as “Head,” and the last item is known as “Tail.”

Each item is called a “node,” and each contains a pointer and a key in the linked list. The Pointer takes you to the next Node known as “next.”

You can traverse each item from head to tail (in forward direction) by creating a single linked list. In the same way, a double-linked list can traverse in both directions; from head to tail (forward direction) and from tail to head (backward direction).

They are used in switching between programs for symbol table management.

Related: How to grow your design business into an agency

3. Stacks

Next on our list of basic data structures is Stack. Stack is also a linear order structure, but it works in a LIFO (Last in First Out) order. That’s why it is also known as LIFO structure. It means the last-placed element can be accessed first.

Example of stack structure

You can push (add a new) or pop (delete) an element on/from the top of the elements, like a stack of plates in the real world.

Stacks are mostly used in recursion programming to implement function calls and mathematical expressions for parsing and evaluations.

4. Queues

Queues are the same as Stack structure, but they don’t follow the LIFO model. A queue follows the FIFO (First in First Out) model. This means the first element can be accessed first.

Diagram illustrating queue structure

A line of people entering the building is a great example. The first person (starting the line) will enter the building before anyone else, and the person standing in the last of the line will enter in the last.

In the same way, you can add a new element (enqueue) at the end of the structure and dequeue (delete) an element from the starting of the structure.

They are mostly used in multithreading to manage threads as well as to execute priority queuing systems.

5. Hash tables

The hash table data structure connects each value with a key and stores them.

You can efficiently lookup values by using a key.


From a group of similar objects, you can easily find a specific object.

Example of hash table

To understand the Hash table, you can take it as a student ID (Key) that a college assigns to students. All the details related to a student can be easily found by using the student ID.

To map any size of data set to one fixed size, the hash table uses the hash function. The values returned by a hash function are known as hash values.

They are mostly used to create associate arrays, database indexes, and a “set.”

6. Trees

Another basic data structure is a Tree. In the tree structure, data is linked together as in the linked list but organized hierarchically, just like the visual representation of a person’s family tree.

Tree basic data structure

There are various types of trees like:

  • Binary search tree (BST)
  • Red-black tree
  • B tree, treap
  • Splay tree
  • N-ary tree
  • AVL tree

And each type is suited for certain applications.

For example, the BST data structure stores values (data) in sorted order. In a BST, every node comprises the following attributes:

  • Key is the stored value in the node
  • Left is the Pointer to the left child node
  • Right is the point to the right child node
  • P is the Pointer to the parent node

BST structure is widely used in different types of search operations, and other types of tree structures are used to create expression solvers and in wireless networking.

7. Heaps

A heap is a specific type of binary tree where the parent nodes are compared to their child nodes, and values are arranged in the nodes accordingly.

A heap can be represented as an array or a binary tree as you can see in the images below:

Binary Tree Representation

Binary tree representation

Array Representation

Tree array representation

There are two types of heaps:

  1. Min heap, where the parent’s key is equal or less than the keys of its children.
  2. Max heap, where the parent’s key is greater than the keys of its children.

Heaps are widely used to find the largest and smallest values in an array and to create priority queues in algorithms.

8. Graphs

A graph is a non-linear and abstract data structure that consists of a fixed (finite) set of nodes or vertices and is connected by a set of edges. Edges are the arcs or lines that simply connect nodes in the graph.

Graph diagram

Graphs are great for solving real-world problems, as well as representations of digital networks. They’re also used for the representations of the networks like circuit networks.

Final words about basic data structures

We have explored the eight most common data structures in this guide that every programmer should know. And once you have a basic understanding of data structures, you can even practice creating new ones.


Got a few minutes? (Probably not.)

Fumbling for login credentials, running endless updates, explaining product purchases… No thanks. We built the Hub from GoDaddy Pro to save you an average three hours per month for every client site you maintain.

Sign up for Free