# Apache Cassandra Vector Search

> Distributed NoSQL database with vector search capabilities via Storage-Attached Indexes (SAI) in Cassandra 5.0+. Uses Lucene HNSW for approximate nearest neighbor search. This is an OSS database under Apache 2.0 license.

- **URL:** https://cassandra.apache.org/
- **Category:** Multi Model & Hybrid Databases
- **Tags:** Open Source, Distributed, nosql
- **Featured:** yes
- **Updated:** 2026-03-06 18:05
- **Canonical page:** https://vectordb.ever.works/items/apache-cassandra-vector-search

## Details

## Overview

Apache Cassandra 5.0 introduces native vector search capabilities through Storage-Attached Indexes (SAI), enabling AI and machine learning applications to leverage Cassandra's distributed architecture for semantic search at scale.

## Vector Search Implementation

### Storage-Attached Indexes (SAI)
- Highly-scalable, globally-distributed indexes
- Column-level indexing for vector data type
- First validation of SAI extensibility
- Leverages Lucene's HNSW library

### Technical Foundation
- **CEP-7**: Storage Attached Index (SAI)
- **CEP-30**: Approximate Nearest Neighbor via SAI + Lucene
- **HNSW Algorithm**: Best ANN algorithm for Java
- Native integration with Cassandra architecture

## Key Features

- **New Vector CQL Type**: Native vector<float, N> data type for embeddings
- **ANN Operator**: New "ANN OF" operator for approximate nearest neighbor searches
- **Similarity Functions**: cosine_similarity, dot_product_similarity, euclidean_similarity
- **Distributed**: Leverages Cassandra's proven distributed architecture
- **Scalable**: Horizontal scaling across clusters

## CQL Syntax

```sql
-- Create table with vector column
CREATE TABLE embeddings (
  id uuid PRIMARY KEY,
  vector vector<float, 1536>,
  metadata text
);

-- Create SAI index on vector column
CREATE CUSTOM INDEX ON embeddings(vector) USING 'StorageAttachedIndex';

-- Query with ANN
SELECT * FROM embeddings 
ORDER BY vector ANN OF [0.1, 0.2, ...] 
LIMIT 10;
```

## Use Cases

- Recommendation systems at scale
- Semantic search over distributed data
- Image similarity search
- AI-driven workloads requiring high availability
- Multi-datacenter AI applications
- Real-time personalization

## Advantages

### Distributed Architecture
- Multi-datacenter replication
- Linear scalability
- High availability
- No single point of failure

### Proven Technology
- Battle-tested distributed database
- Used by major enterprises
- Consistent performance at scale
- Strong operational tooling

## Cassandra 5.0 Enhancements

- Vector Search via SAI
- Improved indexing capabilities
- Better performance characteristics
- Enhanced CQL functionality

## Managed Services

Multiple managed options available:
- **Astra DB**: DataStax managed Cassandra with vector search
- **Instaclustr**: Managed Cassandra 5.0 with vector support
- **Self-managed**: Deploy on your infrastructure

## Integration

- CQL drivers for all major languages
- Compatible with Cassandra ecosystem tools
- Works with existing Cassandra applications
- LangChain and LlamaIndex support

## Performance

- Optimized for distributed vector search
- Leverages HNSW for efficiency
- Scales horizontally
- Consistent latency across clusters

## Data Modeling

Comprehensive guidance for:
- Vector column design
- Partitioning strategies
- Index configuration
- Query optimization

## Comparison to Purpose-Built Vector DBs

### Advantages
- Proven distributed architecture
- Multi-datacenter support
- Rich data model beyond vectors
- Operational maturity

### Trade-offs
- More complex setup
- Operational overhead
- Learning curve for Cassandra

## Pricing

Free and open-source under Apache 2.0 license. No licensing costs for self-hosted deployments. Managed services have usage-based pricing.

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