Data Structures and Algorithms Explained with Java
Your Java code can compile, pass tests, and still fail the senior-engineer check: complexity, data structures, and worst cases.
In this video, we explain algorithms and data structures using Java examples — from Big O notation to the real behavior of Java collections. You’ll see why O(1), O(log n), O(n), and O(n²) lead to very different outcomes as data grows: at 1 million elements, that’s the difference between 1 operation and roughly 10¹².
We cover arrays, bubble sort, quicksort, ArrayList growth, amortized O(1), LinkedList trade-offs, ArrayDeque, HashMap internals, collisions, resizing, and red-black tree behavior.
The goal is not to memorize algorithms. The goal is to review Java code better, choose the right collection for the workload, understand what AI-generated code may miss, and avoid performance assumptions that only fail in production.
Which Java collection surprised you the most in production or interviews? Tell us in the comments.
00:00 — Why Complexity Matters for Developers
01:42 — Big O notation
02:29 — Arrays
03:21 — Bubble Sort
04:18 — Quicksort
05:58 — ArrayList
08:12 — LinkedList
09:16 — ArrayDeque
10:18 — HashMap
12:14 — Red-Black Tree
13:31 — Wrap-Up