Loading…
Greedy Layer Traversal (HNSW-style) is a hard coding interview question covering vector search, graphs, machine learning — the kind asked at FAANG (Facebook/Meta, Apple, Amazon, Netflix, Google) and AI-first companies like Anthropic, OpenAI, and Nvidia.
Implement the greedy search algorithm used in Hierarchical Navigable Small World (HNSW) graphs -- the index behind Pinecone, Weaviate, Qdrant, and pgvector.
Given: a graph where each node has a list of neighbor node IDs, node vectors, a query vector, and an entry point node.
Greedy search: 1. Start at entry point 2. Find the neighbor with highest cosine similarity to query 3. If any neighbor is more similar than current node, move there 4. Repeat until no neighbor improves similarity 5. Return the current node as the approximate nearest neighbor
Solution walkthrough on Pro
Upgrade to Pro to unlock the guided hints, the step-by-step walkthrough, solutions in Python, C++, Java, and Go, and complexity analysis.
What's inside this guide
Fully unlocked, no account needed — solutions, walkthroughs and follow-up answers included.
Or start the Study Plan — a week-by-week order for your loop, and week 1 is entirely free.
A full interview loop is more than one round. These are the others, each with what is on the site for it.