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DSA Notes

Data structures and algorithms, with real-world analogies, where you've probably already used them, and working JS.

22 topics · Practice problems → · Dictionary →

Fundamentals

How to describe an algorithm's growth, not its speed.

Linear Structures

Contiguous, indexed slots — instant access, expensive middle inserts.

A chain of nodes — cheap inserts anywhere, no random access.

Last In, First Out — only the top is reachable.

First In, First Out — the checkout-line structure.

Push and pop from both ends — a stack and a queue in one.

Hash-Based Structures

Key → value storage with ~O(1) average lookup.

A HashMap that only remembers keys — 'have I seen this?' in O(1).

Trees & Heaps

A hierarchy — one root, any number of children per node.

Binary Search Tree

A tree with an ordering rule — smaller left, bigger right.

Heap (Priority Queue)

A tree where the smallest (or biggest) item is always on top.

Trie (Prefix Tree)

A tree built for strings — shared prefixes, fast prefix search.

Graphs

Nodes and the connections between them — no strict hierarchy.

The two ways to walk a graph or tree — level by level, or all the way down first.

Dijkstra's Algorithm

Shortest path in a weighted graph — always expand the closest node.

Searching & Sorting

Find a value in a sorted array by halving the search space each step.

Split, sort, merge — guaranteed O(n log n), and stable.

Pick a pivot, partition around it, recurse — fast in-place sorting.

Techniques & Patterns

Walk two positions through data instead of nesting loops.

Keep a moving window over the data instead of recomputing from scratch.

Kadane's Algorithm

Maximum sum of a contiguous subarray, in one O(n) pass.

Dynamic Programming

Dynamic Programming

Cache overlapping subproblem results so you never recompute them.