DuckDB
An in-memory, open-source, and free analytical database that speaks SQL, heavily based on vectorization. It can store and process vector embeddings using Array and List data types to enable vector search, bridging the gap between data engineering and AI workflows with fast response times.
About this tool
DuckDB is a fast, in-memory, open-source, and free analytical database system that speaks SQL. It is heavily based on vectorization and can store and process vector embeddings using Array and List data types to enable vector search, bridging the gap between data engineering and AI workflows with fast response times. It allows users to query and transform data anywhere using its feature-rich SQL dialect.
Features
- Simple: Easy to install and deploy with zero external dependencies, running in-process or as a single binary.
- Portable: Runs on Linux, macOS, Windows, Android, iOS, and all popular hardware architectures, offering idiomatic client APIs for major programming languages.
- Feature-rich SQL Dialect: Provides a comprehensive SQL dialect capable of reading and writing file formats such as CSV, Parquet, and JSON, from local file systems and remote endpoints like S3 buckets.
- Fast Performance: Delivers blazing speed for analytical queries thanks to its columnar engine, which supports parallel execution and can process larger-than-memory workloads.
- Extensible: Supports third-party features including new data types, functions, file formats, and SQL syntax, with user contributions available as community extensions.
- Free and Open-Source: Available under the permissive MIT License, with its intellectual property held by the DuckDB Foundation.
Installation
DuckDB is seamlessly integrated with major programming languages and can be installed rapidly, often in less than 10 seconds. Supported installation methods include:
- Shell (via curl)
- Python (pip)
- R (install.packages)
- Java (Maven/JDBC)
- Node.js (npm)
- Rust (cargo)
- Go (go get)
Loading more......
Information
Categories
Tags
Similar Products
6 result(s)Apache Arrow is a cross-language development platform for in-memory data that is commonly used to facilitate efficient integration between vector databases and machine learning frameworks. It provides a standardized format for data exchange that is useful for storing and querying high-dimensional vectors in AI applications.
Crate is an open-source distributed SQL database with support for vector data types and vector search, suitable for AI-driven applications.
Valkey is an open-source in-memory key-value data store that supports vector search operations, making it useful for AI and machine learning vector database workloads. It is also a specialized open-source vector database designed for efficient management and retrieval of high-dimensional vector data, offering advanced APIs and optimized storage for AI workloads.
ClickHouse is an open-source column-oriented database that supports vectorized computation and now offers vector search features. Its architecture enables efficient real-time analytics and vector operations, making it a relevant choice for vector database use cases.
Trieve provides an all-in-one infrastructure for vector search, recommendations, retrieval-augmented generation (RAG), and analytics, accessible via API for seamless integration.
ChromaDB (also known as Chroma or chroma-core) is an open-source vector database focused on LLM applications, emphasizing simplicity and in-memory HNSW-based dense vector search. It is suited for prototyping, metadata filtering, and offers a user-friendly interface for building and testing vector search applications, though it currently lacks hybrid and distributed features.