Overview
What this challenge is about.
Given a target false-positive rate of 1 percent at 200 million keys per shard, compute the optimal bit-array size (m) and hash-function count (k) using the canonical formulas. Implement a Bloom filter in Go using two non-cryptographic hash functions (e.g., murmur3 and xxhash) plus double hashing to derive the rest. Replay 30 days of anonymized origin-miss traces (around 4 billion lookups) and measure: actual false-positive rate, memory footprint per shard, and percent of wasted origin lookups eliminated. Deliver the Go reference implementation, a 6-page sizing memo, a benchmark report, and recommended operational dashboards.
The Brief
What you'll do, and what you'll demonstrate.
Design and benchmark a Bloom-filter front for an edge cache that eliminates at least 90 percent of negative-result origin lookups at a 1 percent false-positive rate.
Earning criteria — what you'll demonstrate
- Derive Bloom filter parameters (m, k) from a target false-positive rate
- Apply Kirsch-Mitzenmacher double hashing to avoid k independent hash computations
- Measure observed-vs-theoretical false-positive rate on real workloads
- Reason about probabilistic data structures as a memory-vs-accuracy trade
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.