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Vector Search with ScyllaDB by Szymon Wasik

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.

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A ScyllaDB Community Vector Search with ScyllaDB Prof. Szymon Wąsik Director of Engineering
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
■ Vector search usage scenarios ■ Internal ScyllaDB architecture ■ Preliminary benchmark results ■ Roadmap Presentation Agenda
Vector search usage scenarios
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
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
RAG: High level workflow LLM Knowledge Augmenting Prompt Answering Answer
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 …
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
Internal ScyllaDB architecture
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;
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
Architecture ScyllaDB Usearch @ Rust HNSW index Vectors Table with objects, features and embeddings Vector index metadata RPC
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
Preliminary benchmark results
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
Preliminary Results - Latency [ms]
Preliminary Results - RPS
Preliminary Results - Index Construction [min]
Roadmap
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
Stay in Touch Szymon Wąsik szymon.wasik@scylladb.com github.com/swasik www.linkedin.com/in/szymon-wasik/