Complete Guide to Data Structures and Algorithms (DSA)
Data structures are the building blocks of efficient software. This guide covers every data structure available on DataStructure360 with explanations, time complexities, and real-world use cases.
Linear Data Structures
Arrays store items in contiguous memory with O(1) access by index. They are the most fundamental data structure. Linked lists connect nodes via pointers, allowing O(1) insertion at the head without shifting elements. Stacks follow last-in-first-out (LIFO) order and are used for undo history, expression parsing, and DFS. Queues follow first-in-first-out (FIFO) order and power BFS, task scheduling, and buffering.
Tree-Based Data Structures
Binary search trees (BST) keep values sorted for O(log n) search, insert, and delete on average. AVL trees are self-balancing BSTs that guarantee O(log n) height through rotations. Heaps are complete binary trees that maintain the min or max at the root in O(log n) and back priority queues. Tries (prefix trees) store strings letter by letter for O(L) lookup and power autocomplete and spell checking.
Hash-Based and Graph Data Structures
Hash tables provide O(1) average lookup using a hash function and handle collisions via chaining. Graphs model networks and relationships. BFS finds shortest paths in unweighted graphs; DFS explores all reachable nodes.
How to choose the right data structure
| Need | Best choice | Why |
|---|---|---|
| Fast lookup by key | Hash table | O(1) average |
| Sorted data with range queries | BST / AVL tree | O(log n) and in-order traversal |
| Priority processing | Heap | O(log n) insert and extract |
| LIFO / undo | Stack | O(1) push and pop |
| FIFO / scheduling | Queue | O(1) enqueue and dequeue |
| Prefix search / autocomplete | Trie | O(L) per word |
| Network / relationships | Graph | BFS / DFS in O(V + E) |
Start visualizing
Pick any data structure above and watch it work step by step. Every operation is animated with its Big O complexity explained.