Overview
What this challenge is about.
Receive the current Python function (nested loop over (recipe_title, ingredient_list) pairs), the 400k-row dataset (CSV), and 20 representative queries. Step 1: write up the current Big-O analysis and measure baseline latency on all 20 queries. Step 2: propose two redesigns with Big-O analysis and a one-paragraph trade-off note each. Step 3: implement the better choice (hash-map, inverted index, or trie depending on the query mix) and re-measure. Deliver the analysis memo, the implementation in a single .py or .go file, a benchmark table, and a 1-page note recommending which redesign to ship.
The Brief
What you'll do, and what you'll demonstrate.
Diagnose why a recipe search is 22x slower than it was 2 years ago and ship a redesign that returns results in under 50ms on the existing dataset.
Earning criteria — what you'll demonstrate
- Read existing code and identify its asymptotic complexity
- Choose the right data structure (hash, trie, inverted index) for a query pattern
- Implement an indexed search from scratch without a library
- Benchmark honestly with a representative query mix
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Career mappings coming soon.