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DSA Mastery Series

A data structures and algorithms series taking you from Big-O all the way to advanced structures, in Python and Node.js. Runs as long as the material needs — some topics get one post, others get a full breakdown across several. Every post ships sandbox-tested code and real benchmarks, not just theory.

2 articles Created Jul 2026
DSA Mastery Series

Articles in this Series

2 articles to guide your learning journey

01

Big-O Notation Explained: The Regex That Took Down Cloudflare for 27 Minutes

Big-O notation explained through the real regex that took Cloudflare offline for 27 minutes, plus sandbox-tested benchmarks in Python and Node.js showing exactly how each complexity class behaves.

Arpan SinghArpan Singh Jul 20 8 min read
Big-O Notation Explained: The Regex That Took Down Cloudflare for 27 Minutes
02

Arrays Explained from Memory to Big O (With Real Benchmarks)

A common array bug that silently deletes the wrong items, and what it reveals: how arrays actually work in memory, why insert/delete cost what they cost, and real benchmarks in Python and Node.js to prove it.

Arpan SinghArpan Singh Jul 24 7 min read
Arrays Explained from Memory to Big O (With Real Benchmarks)

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Overview

Total Articles2
CreatedJul 2026
Last UpdatedJul 2026

Contributors

Arpan Singh
Arpan Singh
@arpan

Most DSA content online is written for people cramming before an interview: memorize the pattern, solve a few LeetCode problems, then forget it a week later.

This series is built differently.

Instead of treating DSA as interview trivia, every post focuses on three things:

  • Implementing the data structure or algorithm from scratch in both Python and Node.js

  • Showing where it appears in production systems, not just coding interview questions

  • Backing every complexity claim with actual benchmark results instead of stopping at theoretical Big-O notation

The roadmap

The topics are ordered by dependency, not alphabetically.

We start with complexity analysis, because everything else depends on understanding how algorithms scale. Then we move through linear data structures, searching and sorting, non-linear structures, graphs, and finally the algorithmic techniques that usually cause the most trouble in interviews and system design.

  1. Big-O / Complexity Analysis

  2. Arrays & Strings

  3. Recursion Fundamentals

  4. Linked Lists

  5. Stacks & Queues

  6. Searching (Linear & Binary Search)

  7. Sorting (Merge Sort, Quick Sort, Timsort vs V8's sort())

  8. Hash Maps

  9. Trees I — BST + Traversals

  10. Heaps / Priority Queues

  11. Tries

  12. Graphs I — Representation + BFS/DFS

  13. Graphs II — Dijkstra & Shortest Paths

  14. Greedy Algorithms

  15. Backtracking

  16. Dynamic Programming I — Foundations

  17. Dynamic Programming II — Production Problems

  18. Advanced Structures — Union-Find & Segment Trees

Note: Arrays and Linked Lists may each become two posts (Fundamentals + Patterns), so the final series will probably end up with more than 18 articles.

Every article includes:

  • Python and Node.js implementations

  • Time and space complexity for every operation

  • Benchmark results on real hardware

  • Production use cases

  • Sandbox-tested code you can run yourself