5 Most Fundamental Data Structures That All Programmers Should Master

Data structures are an essential part of programming and are used to store, organize, and access data in an efficient manner. They allow us to manipulate data in various ways, such as inserting and deleting elements, searching for specific items, and sorting data.

There are many different data structures available, each with its own strengths and weaknesses. It is important for a programmer to be familiar with a variety of data structures and to choose the one that is most appropriate for a given task.

In this blog post, we will be introducing five data structures that are fundamental to programming: arrays, linked lists, stacks, queues, and trees.

Essential Data Structures: What it is, How it works, and Common Use Cases

Let’s take a look at some important data structures and how they work, as well as the common use cases where these structures are used to solve problems in programming.

The Array

An array is a data structure that stores a fixed-size sequential collection of elements of the same type. It is one of the simplest and most widely used data structures in computer science.

An array is created by specifying the size of the array and the type of elements it will hold. Once an array is created, it cannot be resized. Arrays are stored in contiguous blocks of memory, and each element in the array is accessed using an index, which is an integer that represents the position of the element within the array. The index of the first element in the array is always 0, and the index of the last element is one less than the size of the array.

One of the main advantages of using an array is that it allows for fast access to elements using their indices. This makes it well-suited for tasks that involve random access to elements, such as looking up an element by its index or iterating over the elements of the array in a specific order.

There are many common use cases for arrays, including:

Storing a list of elements that need to be accessed sequentially, such as the elements of a list or the elements of a string.
Implementing data structures that require fast element access, such as stacks and queues.
Storing the data for a two-dimensional grid, such as a matrix or a table.
Holding a large number of elements that are used for temporary storage or as intermediate results in a computation.

The Linked List

A linked list is a linear data structure that consists of a group of nodes, where each node stores a reference to an element and a reference to the next node in the list. The nodes are connected in a linear fashion, forming a chain. The first node in the list is called the head, and the last node is called the tail.

Linked lists are useful when we need to store a large number of elements but do not need random access to any particular element. This is because, unlike an array, we cannot access an element in a linked list using an index. Instead, we must traverse the list from the head, following the references from one node to the next, until we reach the desired element.

There are two types of linked lists: singly linked lists and doubly linked lists. In a singly linked list, each node only stores a reference to the next node in the list. In a doubly linked list, each node stores a reference to both the next and the previous node in the list.

One of the main advantages of linked lists is their flexibility. Elements can be easily inserted or removed from the list without the need to shift other elements. This makes linked lists particularly useful for implementing dynamic data structures such as stacks and queues.

Another advantage of linked lists is that they can be easily extended to implement more complex data structures, such as trees and graphs.

Some common use cases for linked lists include:

Implementing dynamic data structures such as stacks and queues.
Storing data that needs to be frequently inserted or removed, such as in a cache.
Implementing data structures that require frequent element insertion or deletion, such as in a text editor.
Storing data that needs to be sorted and accessed sequentially, such as in a merge sort algorithm.

Stacks

A stack is a linear data structure that follows the last-in, first-out (LIFO) principle. This means that the last element added to the stack is the first one to be removed.

A stack is implemented using an array or a linked list. It has two main operations: push and pop. The push operation adds an element to the top of the stack, and the pop operation removes an element from the top of the stack.

Stacks are useful when we need to store data in a temporary buffer, such as when we are implementing a function that calls itself recursively or when we need to reverse the order of elements.

Some common use cases for stacks include:

Implementing function calls in a programming language.
Undo and redo operations in a text editor.
Balancing parentheses in an expression.
Reversing the order of elements in a list.

Hash Tables

A hash table is a data structure that stores key-value pairs and allows for efficient insertion, deletion, and lookup of values based on their corresponding keys.

A hash table is implemented using an array of linked lists or an array of arrays. The array is called the hash table, and the linked lists or arrays are called the buckets.

To insert a key-value pair into a hash table, we first compute a hash function on the key to determine which bucket the key-value pair should be stored in. The hash function maps the key to an integer, which is used as an index into the array. The key-value pair is then added to the linked list or array at the corresponding index.

To look up the value for a given key, we again use the hash function to determine which bucket the key belongs in and then search the linked list or array for the key-value pair.

One of the main advantages of hash tables is their fast lookup and insertion times. On average, these operations take O(1) time, making hash tables a good choice for storing and accessing data in large datasets.

Some common use cases for hash tables include:

Implementing a dictionary or map data structure.
Storing and looking up data in a database.
Caching frequently accessed data.
Implementing an autocomplete feature.

Trees

A tree is a hierarchical data structure that consists of nodes arranged in a tree-like structure. Each node in a tree has a value and may have one or more children, which are other nodes in the tree. The node at the top of the tree is called the root, and the nodes that do not have any children are called leaf nodes.

Trees are often used to store data that has a hierarchical structure, such as the files and directories on a computer. In this case, the root node represents the top-level directory, and the leaf nodes represent individual files.

Trees have a number of properties that make them useful for various tasks:

Trees can be searched efficiently, allowing us to find a particular value in the tree in O(log n) time, where n is the number of nodes in the tree.
Trees can be sorted efficiently, allowing us to sort a large dataset in O(n log n) time.
Trees can be used to implement efficient data structures such as maps and sets.

There are many different types of trees, including binary trees, balanced trees, and B-trees. Each type of tree has its own characteristics and is suited for different tasks.

Some common use cases for trees include:

Storing and searching data in a hierarchical structure, such as the files and directories on a computer.
Implementing efficient data structures such as maps and sets.
Implementing algorithms for sorting and searching data.

Few Final Words

At last, data structures are an essential part of programming and are used to store, organize, and access data in an efficient manner. There are many different data structures available, each with its own strengths and weaknesses. It is important for a programmer to be familiar with a variety of data structures and to choose the one that is most appropriate for a given task.

We encourage software developers to use these essential data structures to improve the performance and efficiency of their code. By understanding the characteristics and capabilities of each data structure, developers can choose the one that is most appropriate for the task at hand and write more efficient and effective code.