Data Structures & Algorithms

A practical framework for choosing data structures and algorithms by operations, constraints, complexity, and memory/locality.

Contiguous indexed storage, constant-time random access, resizing, insertion/deletion costs, and cache locality.

Hashing, buckets, collisions, load factor, resizing, and expected versus worst-case lookup complexity.

Singly/doubly linked lists, insertion/deletion, traversal, pointer techniques, and locality trade-offs.

FIFO queues, dequeues, circular buffers, bounded queues, and priority-queue distinctions.

Uniqueness collections implemented with hashing, balanced trees, bitmaps, or specialized structures.

LIFO storage, push/pop/peek operations, recursion, parsing, monotonic stacks, and implementation choices.

Rooted trees, binary trees, BSTs, balanced trees, traversals, heaps, tries, and B-tree families.

A self-balancing binary search tree with color invariants that keep height logarithmic.

Probabilistic ordered structure with multiple forward-pointer levels and expected logarithmic search/update.