awesome-repositories.com
Blog
awesome-repositories.com

Découvrez les meilleurs dépôts open-source grâce à notre recherche par IA.

ExplorerRecherches sélectionnéesAlternatives open sourceLogiciels auto-hébergésBlogPlan du site
ProjetÀ proposNotre méthodologiePresseServeur MCP
Mentions légalesConfidentialitéConditions d'utilisation
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·
MariaDB avatar

MariaDB/server

0
View on GitHub↗

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.

Recherche par IA

Explorez plus de dépôts awesome

Décrivez vos besoins en langage naturel — l'IA classe des milliers de projets open source sélectionnés par pertinence.

Start searching with AI
mariadb.org
↗

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.
7,196 stars·1,973 forks·C++·gpl-2.0·12 vues

Historique des stars

Graphique de l'historique des stars pour mariadb/serverGraphique de l'historique des stars pour mariadb/server

Questions fréquentes

Que fait mariadb/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.

Quelles sont les fonctionnalités principales de mariadb/server ?

Les fonctionnalités principales de mariadb/server sont : Relational, Columnar Analytics, Columnar Storage Engines, Multi-Version Concurrency Control, Open Source SQL Databases, Pluggable Storage Engines, Relational Database Management Systems, Vector Databases.

Quelles sont les alternatives open-source à mariadb/server ?

Les alternatives open-source à mariadb/server incluent : lancedb/lancedb — LanceDB is a vector database and columnar data store designed to function as a versioned dataset manager and vector… h2database/h2database — H2 is a JDBC-compliant relational database management system written in Java. It functions as an embeddable SQL… paradedb/paradedb — ParadeDB is a database extension that integrates full-text search, vector database capabilities, and real-time… databendlabs/databend — Databend is a cloud-native data warehouse and OLAP database designed for large-scale analytics. It functions as a… alibaba/zvec — zvec is an embedded vector database engine and indexing library designed for high-dimensional similarity search. It… mysql/mysql-server — MySQL Server is a relational database management system designed to organize and store structured information. It…

Alternatives open source à Server

Projets open source similaires, classés selon le nombre de fonctionnalités partagées avec Server.
  • lancedb/lancedbAvatar de lancedb

    lancedb/lancedb

    9,031Voir sur GitHub↗

    LanceDB is a vector database and columnar data store designed to function as a versioned dataset manager and vector search engine. It serves as a high-performance backend for indexing and retrieving high-dimensional embeddings, providing the foundation for machine learning data pipelines. The system distinguishes itself through a combination of cloud-native object storage and immutable version tracking, allowing for data time-travel and reproducible AI experiments. It integrates hybrid search capabilities, merging dense vector similarity with BM25 full-text search and SQL-like scalar filters

    HTMLapproximate-nearest-neighbor-searchimage-searchnearest-neighbor-search
    Voir sur GitHub↗9,031
  • h2database/h2databaseAvatar de h2database

    h2database/h2database

    4,607Voir sur GitHub↗

    H2 is a JDBC-compliant relational database management system written in Java. It functions as an embeddable SQL database that can run directly within an application process to remove network latency, or as an in-memory database for high-performance volatile storage. It also includes a web-based console for executing SQL commands and administering schemas. The system is characterized by its flexible deployment modes, including a standalone server mode for remote TCP/IP access and a mixed mode for simultaneous local and remote connectivity. It features a dialect emulation layer and compatibilit

    Javadatabasejavajdbc
    Voir sur GitHub↗4,607
  • paradedb/paradedbAvatar de paradedb

    paradedb/paradedb

    8,370Voir sur GitHub↗

    ParadeDB is a database extension that integrates full-text search, vector database capabilities, and real-time analytics directly into a relational engine. It functions as a plugin that adds new storage and query execution capabilities to an existing database architecture. The project distinguishes itself by supporting hybrid search workflows that combine lexical keyword matching with dense and sparse vector similarity in a single query. It utilizes reciprocal rank fusion to merge these ranked result sets and employs logical replication to synchronize data from external instances, removing th

    Rustaggregationsanalyticsbm25
    Voir sur GitHub↗8,370
  • databendlabs/databendAvatar de databendlabs

    databendlabs/databend

    9,351Voir sur GitHub↗

    Databend is a cloud-native data warehouse and OLAP database designed for large-scale analytics. It functions as a SQL-compliant engine and serverless analytics platform that separates compute from storage to allow for independent scaling. The system integrates vector database capabilities, indexing high-dimensional embeddings to enable semantic, hybrid, and full-text searches across massive datasets. It further distinguishes itself through serverless compute management that automatically scales resources based on demand and shuts them down during idle periods. The platform covers a broad set

    Rustaibigdatacloud-native
    Voir sur GitHub↗9,351
Voir les 30 alternatives à Server→