StarRocks

Overview

StarRocks is the world's fastest open query engine for sub-second analytics on data lakehouses. Version 3.4+ includes native vector indexing for approximate nearest neighbor search, combining analytical and vector workloads in a single system.

Vector Search Features

Index Types

• IVFPQ: Inverted File with Product Quantization for large-scale high-dimensional vectors
• HNSW: Hierarchical Navigable Small World graph-based algorithm
• Both support approximate nearest neighbor search (ANNS)

Capabilities

• Native vector index support (v3.4+)
• High-dimensional vector similarity search
• Join ANN results with dimension tables
• SQL aggregations and window functions over vector results
• Unified analytics and vector search

Key Features

• Sub-Second Analytics: Fast query performance for real-time insights
• MPP Architecture: Massively parallel processing for scalability
• Multi-Dimensional Analytics: Complex analytical queries
• Real-Time Analytics: Fresh data analysis
• Ad-Hoc Queries: Flexible query patterns
• Vector + Analytics: Combine ANN with traditional SQL operations

Architecture

• Shared-nothing cluster architecture
• Native vector indexing in analytical engine
• Built-in converged index for multiple workload types
• Supports data lakehouse architectures

Integration

LangChain

• Native StarRocks vector store integration
• Seamless embedding storage and retrieval
• Python client support

AI Agent Support

• Store embedding vectors in StarRocks tables
• Perform fast KNN or semantic lookups
• SQL-based vector operations

Use Cases

• Data AI Agents: Built-in vector search for agent memory
• Content Retrieval: Semantic search over large datasets
• Recommendation Systems: Vector-based recommendations
• LLM RAG: Retrieval Augmented Generation pipelines
• Hybrid Search: Combine vector similarity with analytical filters

Performance

• Sub-second query response times
• Scales to billions of vectors
• Efficient resource utilization
• Optimized for both OLAP and vector workloads

Production Setup

Production-ready deployment in 6 steps:

1. Cluster deployment
2. Schema design
3. Vector index configuration
4. Data ingestion
5. Query optimization
6. Monitoring and scaling

Linux Foundation Project

StarRocks is a Linux Foundation project, ensuring:

• Open governance
• Community-driven development
• Enterprise adoption
• Long-term sustainability

Comparison to Pure Vector DBs

• Unified system for analytics and vectors
• No need for separate vector database
• SQL familiarity for developers
• Existing analytics infrastructure leveraged

Pricing

Free and open-source under Apache 2.0 license. No licensing costs. Commercial support available through enterprise partners.