Aim of the subject or programmin data stucture is to make data orderly in order to fetch data fast as possible. Data when applied in real programming will be Scheduler in OS, indexing in mySQL, network routing in routers, algorithms in File System, etc
Data Structures are ways of organizing and storing data so it can be accessed, modified, and processed efficiently. They’re fundamental in computer science, algorithms, and system design.
Below is a clear, structured overview, from basics to advanced.
- Fixed-size, contiguous memory
- Fast access by index: O(1)
- Slow insert/delete in the middle: O(n)
Use case: lookup tables, matrices
- Nodes connected by pointers
- Dynamic size
- Slow access: O(n)
Types
- Singly linked list
- Doubly linked list
- Circular linked list
Use case: frequent insertions/deletions
- Last In, First Out
- Operations:
push,pop
Use case
- Function calls
- Undo/Redo
- Expression evaluation
- First In, First Out
Variants
- Simple queue
- Circular queue
- Priority queue
- Deque (double-ended queue)
Use case: task scheduling, buffering
Hierarchical structure (parent–child)
Common types
- Binary Tree
- Binary Search Tree (BST)
- AVL Tree (self-balancing)
- Red-Black Tree
- Heap (Min/Max)
Use case
- File systems
- Databases
- Expression parsing
- Nodes (vertices) + edges
- Directed / Undirected
- Weighted / Unweighted
Representations
- Adjacency list
- Adjacency matrix
Use case
- Social networks
- Routing algorithms
- Knowledge graphs (e.g., Neo4j 😉)
- Key–value pairs
- Average lookup: O(1)
Collision handling
- Chaining
- Open addressing
Use case
- Caches
- Indexing
- Symbol tables
- Efficient string search
- Prefix-based lookup
Use case
- Autocomplete
- Spell checkers
- Range queries (sum, min, max)
Use case
- Competitive programming
- Time-series data
- Tracks connected components
Use case
- Network connectivity
- Kruskal’s MST algorithm
- Probabilistic membership test
- Space-efficient, false positives allowed
Use case
- Caching systems
- Databases
| Data Structure | Access | Search | Insert | Delete |
|---|---|---|---|---|
| Array | O(1) | O(n) | O(n) | O(n) |
| Linked List | O(n) | O(n) | O(1) | O(1) |
| Stack | O(n) | O(n) | O(1) | O(1) |
| Queue | O(n) | O(n) | O(1) | O(1) |
| Hash Table | — | O(1) | O(1) | O(1) |
| BST (balanced) | O(log n) | O(log n) | O(log n) | O(log n) |
Ask yourself:
- Do I need fast lookup? → Hash Table
- Do I need ordering? → Tree
- Do I need hierarchy? → Tree
- Do I need relationships? → Graph
- Do I need simple LIFO/FIFO? → Stack / Queue
If you want, I can:
- Explain data structures with diagrams
- Compare data structures vs algorithms
- Show Python / Java / C++ implementations
- Tailor explanations for interview prep or system design
- Connect data structures to Neo4j / graph DB design
Just tell me 👍