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.
