Data Structures Explained
Master fundamental data structures and their trade-offs for time and space. Choose the right structure to fit your access patterns.
📚 Core Structures
- Arrays/Lists: O(1) index, O(n) insert/delete mid
- Stacks/Queues: O(1) push/pop and enqueue/dequeue
- Hash Tables: expected O(1) search/insert/delete
- Trees (BST, AVL, Red-Black): O(log n) operations
- Heaps: O(log n) insert, O(log n) remove-min/max
❓ Frequently Asked Questions
1) Hash collisions?
Handled via chaining or open addressing.
Handled via chaining or open addressing.
2) When to prefer trees over hashes?
Ordered traversals, range queries, worst-case guarantees.
Ordered traversals, range queries, worst-case guarantees.
3) Priority queues?
Heaps implement efficient priority queues.
Heaps implement efficient priority queues.
4) Cache locality?
Arrays often outperform lists due to contiguous memory.
Arrays often outperform lists due to contiguous memory.
5) Immutable structures?
Persistent data structures enable efficient snapshots.
Persistent data structures enable efficient snapshots.