Master 8 Essential Data Structures Every Programmer Should Know

Data structures are specialized ways of organizing and storing data that enable efficient operations on the stored information. They are foundational in computer science and software engineering, and are a key topic in technical interviews.

1. Array

An array is a fixed-size structure that holds elements of the same data type, allowing random access via indexed positions. Common operations include traversal, insertion, deletion, search, and update. Arrays serve as the basis for other structures such as array lists, heaps, hash tables, vectors, and matrices, and are used in sorting algorithms like insertion sort, quicksort, bubble sort, and merge sort.

2. Linked List

A linked list is a sequential structure composed of nodes, each containing a key and a pointer to the next node. It supports only sequential access. Variants include singly linked lists, doubly linked lists, and circular linked lists. Core operations are search, insertion (at head, tail, or middle), and deletion (from head, tail, or middle). Applications include symbol table management in compilers and implementing Alt‑Tab functionality.

3. Stack

A stack follows a Last‑In‑First‑Out (LIFO) order. Basic operations are push (insert at top) and pop (remove from top). Additional operations include peek, isEmpty, and isFull. Stacks are used for expression evaluation, function call management in recursion, and implementing priority queues.

4. Queue

A queue follows a First‑In‑First‑Out (FIFO) order. Core operations are enqueue (insert at the rear) and dequeue (remove from the front). Queues are employed for thread management in multithreaded environments and for implementing various queuing systems such as priority queues.

5. Hash Table

A hash table stores key‑value pairs and provides efficient lookup, insertion, and deletion using a hash function that maps keys to indices. Collisions are resolved through techniques such as chaining or open addressing. Applications include database indexing, associative arrays, and implementing set data structures.

6. Tree

A tree is a hierarchical structure where nodes are linked in a parent‑child relationship. Common types include binary search trees, B‑trees, red‑black trees, AVL trees, and n‑ary trees. Binary search trees store keys in sorted order, enabling efficient search, insertion, and deletion.

7. Heap

A heap is a specialized binary tree that satisfies the heap property: in a min‑heap, each parent’s key is less than or equal to its children’s keys; in a max‑heap, each parent’s key is greater than or equal to its children’s keys. Heaps can be represented using arrays and are used to implement priority queues and heap sort.

8. Graph

A graph consists of a set of vertices (nodes) and edges connecting pairs of vertices. Graphs can be directed or undirected. Directed graphs have edges with a direction (from a source vertex to a target vertex), while undirected graphs have bidirectional edges. Applications include modeling social networks, web page linking for search engines, and GPS routing.

References

[1] Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein. Introduction to Algorithms , 3rd Edition.

[2] Wikipedia – List of data structures.

Original article translated from Vijini Mallawaarachchi’s “8 Common Data Structures every Programmer must know”.