MariaDB/server
Server
This project is an open source relational database management system and SQL database designed for storing and managing structured data. It functions as a relational database for ensuring consistency and reliability, while also operating as a vector database for storing and querying high-dimensional vector embeddings.
The system incorporates a columnar storage engine to optimize analytical query processing and large-scale data aggregation. It further enables vector similarity search, allowing users to find similar items by querying vector embeddings.
The software covers a broad capability surface including relational data management, analytical query execution, and database telemetry collection for gathering hardware and configuration statistics.
Features
- Relational - Stores and queries structured data using relational models to ensure strict integrity and consistency.
- Columnar Analytics - Executes high-performance analytical queries using columnar storage for efficient aggregations.
- Columnar Storage Engines - Ships a storage format optimized for analytical workloads by organizing data by column.
- Multi-Version Concurrency Control - Uses multi-version concurrency control to enable non-blocking read and write operations.
- Open Source SQL Databases - Provides a community-driven implementation of the relational model for scalable data storage and retrieval.
- Pluggable Storage Engines - Provides an architecture that allows switching between different data persistence backends based on table needs.
- Relational Database Management Systems - Functions as a robust engine that stores structured information in tables and enforces relational constraints.
- Vector Databases - Operates as a system optimized for storing and querying high-dimensional vector embeddings.
- Vector Search - Provides capabilities for finding similar items using mathematical similarity in high-dimensional vector spaces.
- Vector Embedding Indexes - Includes specialized indexes for storing and performing similarity searches on high-dimensional vector data.
- Vector Similarity Search - Implements techniques for identifying nearest neighbors using distance metrics for AI applications.
- Column-Oriented Disk Storage - Organizes data by column on disk to accelerate aggregate calculations and analytical query performance.
- B-Tree - Implements B-Tree structures to optimize disk-based range queries and lookups.
- Write-Ahead Logging - Implements write-ahead logging to record data mutations for durability and crash recovery.
- Thread-Per-Connection Models - Employs a model where each client connection is managed by a dedicated worker thread.
- Database Systems - Community fork of MySQL with enhanced features.
- Server and Infrastructure - Provides a community-developed database server fork.
Star history
How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
