Beyond Traditional Database Queries

SQL is powerful, but it has limitations. How do you find "similar" log entries when the exact text differs? How do you search for images that "look like" a reference photo? Traditional keyword matching falls short for these semantic similarity use cases.

Vector search transforms this landscape by converting data into numerical representations that capture semantic meaning.

The Vector Database Integration Story

GreptimeDB v0.10 introduced vector search capabilities through integration with the VSAG vector search library. This isn't just another feature - it's a fundamental shift toward AI-powered observability.

Real-World Vector Search Applications

Consider these scenarios:

• Finding log entries with similar error patterns
• Identifying anomalous system behavior through pattern matching
• Semantic search across documentation and incident reports
• Image similarity for industrial quality control

Technical Implementation Deep Dive

Vector search works by:

1. Converting data to vectors using embedding models
2. Storing vectors alongside traditional structured data
3. Performing similarity calculations (dot product, cosine similarity)
4. Ranking results by semantic relevance

Performance Characteristics

Our vector search benchmarks show:

• 768-dimensional vector storage
• Efficient compression in columnar format
• SQL-native vector functions like vec_dot_product
• Seamless integration with time-series queries

The Competitive Landscape

While specialized vector databases exist, GreptimeDB's unified approach offers advantages:

• Single system for metrics, logs, and vectors
• Familiar SQL interface
• Time-series aware vector operations
• Cost-effective object storage backend

Vector Search vs. Traditional Methods

When searching for "China Sports" content:

• Keyword matching: Limited to exact term occurrences
• Vector search: Finds semantically related content regardless of exact wording
• Combined approach: Best of both worlds with GreptimeDB

Getting Started with Vector Analytics

Implementing vector search capabilities:

1. Choose appropriate embedding models
2. Design vector storage schema
3. Optimize for your query patterns
4. Monitor performance and costs

GreptimeDB's vector integration simplifies this process with built-in functions and optimized storage formats.

Start experimenting with vector search using our demo datasets and tutorials.

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About Greptime

GreptimeDB is an open-source, cloud-native database purpose-built for real-time observability. Built in Rust and optimized for cloud-native environments, it provides unified storage and processing for metrics, logs, and traces—delivering sub-second insights from edge to cloud —at any scale.

• GreptimeDB OSS – The open-sourced database for small to medium-scale observability and IoT use cases, ideal for personal projects or dev/test environments.

• GreptimeDB Enterprise – A robust observability database with enhanced security, high availability, and enterprise-grade support.

• GreptimeCloud – A fully managed, serverless DBaaS with elastic scaling and zero operational overhead. Built for teams that need speed, flexibility, and ease of use out of the box.

🚀 We’re open to contributors—get started with issues labeled good first issue and connect with our community.

⭐ GitHub | 🌐 Website | 📚 Docs

💬 Slack | 🐦 Twitter | 💼 LinkedIn

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