Eight Common Data Structures Every Programmer Should Know

Data structures are specialized ways to organize and store data, enabling efficient operations and are fundamental in computer science and software engineering.

Almost all programs use data structures; understanding them is crucial for software engineering interviews.

1. Array

An array is a fixed-size structure that holds elements of the same type, supports random access via indexed positions.

Array Operations

Traverse, insert, delete, search, update.

Applications of Arrays

Basis for other structures (array list, heap, hash table, vector, matrix) and used in sorting algorithms (insertion sort, quicksort, bubble sort, merge sort).

2. Linked List

A linked list is a sequential structure of nodes, each containing a key and a pointer to the next node; it requires sequential access.

Types: singly linked list, doubly linked list, circular linked list.

Linked List Operations

Search, insert (at head, tail, middle), delete (head, tail, middle).

Applications of Linked Lists

Symbol table management in compilers, Alt‑Tab program switching, etc.

3. Stack

A stack is a LIFO (last‑in‑first‑out) structure used in many programming languages.

Stack Operations

Push, pop, peek, isEmpty, isFull.

Applications of Stacks

Expression evaluation, function call handling in recursion, etc.

4. Queue

A queue is a FIFO (first‑in‑first‑out) structure.

Queue Operations

Enqueue, dequeue.

Applications of Queues

Thread management, priority queue implementations, etc.

5. Hash Table

A hash table stores key‑value pairs and provides efficient lookup via a hash function that maps keys to slots.

Hash Function

Defines h(k) → slot index; m is table size, often a prime number.

Applications of Hash Tables

Database indexing, associative arrays, set implementations.

6. Tree

Trees are hierarchical structures; common variants include binary search trees, B‑trees, red‑black trees, AVL trees, etc.

Binary Search Tree

Each node has a key, left and right child pointers, and a parent pointer; left subtree keys ≤ node key, right subtree keys ≥ node key.

Applications of Trees

Expression parsers, search applications, JVM heap storage, routing, etc.

7. Heap

A heap is a special binary tree where parent nodes are ordered relative to children (min‑heap or max‑heap).

Applications of Heaps

Priority queues, O(log n) queue operations, finding k smallest/largest elements, heap sort.

8. Graph

A graph consists of vertices (nodes) and edges connecting them; can be directed or undirected.

Directed Graph

Edges have direction from a source vertex to a target vertex; may include loops.

Undirected Graph

Edges have no direction; isolated vertices have no connections.

Applications of Graphs

Social network modeling, web page linking and PageRank, GPS routing, etc.

References: Thomas H. Cormen et al., Introduction to Algorithms, 3rd edition; Wikipedia data structure list; original article “8 Common Data Structures every Programmer must know”.