Vector search is an essential element of contemporary machine learning pipelines and AI tools. This talk will share preliminary results on the forthcoming vector storage and search features in ScyllaDB. By leveraging Scylla's scalability and USearch library's performance, we have designed a system with exceptional query latency and throughput. The talk will cover vector search use cases, our roadmap, and a comparison of our initial implementation with other vector databases.

1. A ScyllaDB Community
Vector Search with ScyllaDB
Prof. Szymon Wąsik
Director of Engineering

2. Szymon Wąsik
■ 2007-2018: Research work on discrete optimization
and modeling in bioinformatics
■ 2018-2024: Software Engineering at Google, working
on auto-scaling and analytical infrastructure
■ Currently:
■ Engineering Director at ScyllaDB
■ Professor at Merito University Poznań, Poland

3. ■ Vector search usage scenarios
■ Internal ScyllaDB architecture
■ Preliminary benchmark results
■ Roadmap
Presentation Agenda

4. Vector search usage
scenarios

5. Vector search applications
■ Searching objects that can be represented as a vector:
■ Images search and recognition
■ Music and video search
■ Text and document search, including semantic analysis
■ Genetic sequences
■ Analyzing data, including:
■ Facial recognition
■ Medical imaging
■ Sentiment analysis
■ Code similarity detection

6. Example workflow: RAG
■ Retrieval-augmented generation
■ Method for providing new knowledge for the model
■ Quick to integrate, cheap and small
■ Explainable and always up to date information

7. RAG: High level workflow
LLM
Knowledge
Augmenting
Prompt Answering Answer

8. RAG: Augmenting prompt
System Message:
You are a helpful AI assistant. Read
documents, summarize, and answer user
message.
User Question:
[Prompt]
Context:
1) Document Title: [Title 1]
Excerpt: [Text 1]
…
Instructions to the Assistant:
1. Use only the Context above.
…
Now, please provide the best possible
answer to the user’s question.
Knowledge
Augmenting
Prompt …

9. RAG: Encoding the knowledge
Documents
Tokenize to
chunks
LLM
Encoder
Encode
Embeddings Scylla
Vector DB
Tokenize
LLM
Encoder
Encode
Embedding
Search top
K
Prompt
Knowledge
Storing
Knowledge
Retrieving
Knowledge

10. Internal ScyllaDB architecture

11. Requirements
■ Compatibility with Cassandra CQL syntax
■ Vector type:
■ Vector index:
■ Vector queries:
ALTER TABLE cycling.comments_vs ADD comment_vector VECTOR <FLOAT, 5>
CREATE INDEX IF NOT EXISTS ann_index ON vsearch.com(item_vector)
USING 'usearch'
WITH OPTIONS = { 'similarity_function': 'DOT_PRODUCT' };
SELECT * FROM cycling.comments_vs
ORDER BY comment_vector ANN OF [0.15, 0.1, 0.1, 0.35, 0.55] LIMIT 3;

12. Utilizing USearch library
■ Open source library for vector similarity search
■ Embeddings are stored in an HNSW index
■ Written in c++ for speed and safety
■ Leverages SIMD to speed up distance computations
■ 10x faster than FAISS

13. Architecture
ScyllaDB
Usearch
@ Rust
HNSW
index
Vectors
Table with
objects,
features and
embeddings
Vector
index
metadata
RPC

14. Pros
■ Synergy of USearch speed and
Scylla’s powers:
■ Replication
■ Cloud deployment
■ Backups
■ Makes easy to replace the indexing
technology
■ Allows adding hardware
acceleration
Cons
■ Makes deployment more difficult
■ Use Scylla Cloud!
■ Creates reliability challenges
■ Use Scylla Cloud!
■ Duplicates data
■ But increases performance
■ Adds networking overhead
■ But we still win on latency

15. Preliminary benchmark results

16. Test environment
■ Framework: qdrant vector search benchmark
■ Single test case - glove-100-angular:
■ 1.2M vectors
■ 100 dimensions
■ Single precision baseline: 78% (Cassandra’s out of the box)
■ Azure:
■ D2s v3 VM for client
■ D8s v3 VM for Scylla + usearch
■ Single node deployment
■ Splitting VCPUs between Scylla and usearch

17. Preliminary Results - Latency [ms]

18. Preliminary Results - RPS

19. Preliminary Results - Index Construction [min]

20. Roadmap

21. Roadmap
master
(now)
Vector type support
Storing and getting vector type
data is already merged, to be
included in 2025.2
Drivers-side support
Extensive benchmarks
Support in most popular drivers.
Performance benchmarks and
fixes for different levels of
expected precision and cluster
deployments
Q2
Vector search with USearch
Searching top K most similar
vectors with USearch fully
integrated with ScyllaDB
2025.3
(Q3)
Cloud integration
Possibility to create the vector
search infrastructure managed
automatically by Scylla Cloud
Q3/Q4

22. Stay in Touch
Szymon Wąsik
szymon.wasik@scylladb.com
github.com/swasik
www.linkedin.com/in/szymon-wasik/