Day 2: Indexing and Performance on Qdrant - Vector Database

HNSW Indexing Fundamentals

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Day 2

HNSW Indexing Fundamentals

At this point, you’ve learned how vector search retrieves the nearest vectors to a query using cosine similarity, dot product, or Euclidean distance. How does this work at scale?

Why Vector Search Needs Indexing

The Vector Search Challenge

You might wonder if Qdrant calculates the distance to every single vector in your collection for each query. This method, known as brute force search, technically works but with millions or billions of vectors this is too slow per query.

Combining Vector Search and Filtering

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Day 2

Combining Vector Search and Filtering

We’ve talked about how Qdrant uses the HNSW graph to efficiently search dense vectors. But in real-world applications, you’ll often want to constrain your search using filters. This creates unique challenges for graph traversal that Qdrant solves elegantly.

The Challenge: Filters Break Graph Connectivity

Consider retrieving items from an online store collection where you only want to show laptops priced under $1,000. That price information, along with the category ’laptop’, isn’t part of the vector - it lives in the payload.

Demo: HNSW Performance Tuning

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Day 2

Demo: HNSW Performance Tuning

Learn how to improve vector search speed with HNSW tuning and payload indexing on a real 100K dataset.

Follow along in Colab:

What You’ll Do

Yesterday you learned the theory behind HNSW indexing. Today you’ll see it in action on a 100,000-vector dataset, measuring performance differences and applying optimization strategies that work in production.

You’ll learn to:

Optimize bulk upload speed with strategic HNSW configuration
Measure the performance impact of payload indexes
Tune HNSW params
Compare full-scan vs. HNSW search performance

The Performance Challenge

Working with 100K high-dimensional vectors (1536 dimensions from OpenAI’s text-embedding-3-large) presents real performance challenges:

Project: HNSW Performance Benchmarking

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Day 2

Project: HNSW Performance Benchmarking

Now that you’ve seen how HNSW parameters and payload indexes affect performance with the DBpedia dataset, it’s time to optimize for your own domain and use case.

Your Mission

Build on your Day 1 search engine by adding performance optimization. You’ll discover which HNSW settings work best for your specific data and queries, and measure the real impact of payload indexing.

Estimated Time: 90 minutes