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Manticoresearch is a high-performance search engine and database designed for indexing and retrieving large datasets. It functions as a full-text search engine, a vector search database, and a SQL-based search database, providing a distributed search cluster architecture.
The main features of manticoresoftware/manticoresearch are: Full-Text Search Engines, Full-Text Search Indexes, Distributed Search Engines, Distributed Sharding Architectures, Search-Oriented SQL Engines, Full Text Search, High Availability Architectures, Elasticsearch Compatible Engines.
Projects with overlapping indexed features include: lancedb/lancedb — LanceDB is a vector database and columnar data store designed to function as a versioned dataset manager and vector… redis/go-redis — This project is a feature-rich Go client library designed for interacting with Redis. It serves as a comprehensive… tporadowski/redis — Redis is a high-performance in-memory key-value store that functions as a distributed cache, message broker, and NoSQL… redis/redisinsight — RedisInsight is a graphical user interface and management tool for browsing, analyzing, and administering Redis… apache/kvrocks — Kvrocks is a distributed key-value store and Redis-compatible NoSQL database. It utilizes a RocksDB storage engine to… ravendb/ravendb — RavenDB is a multi-model NoSQL document database designed for high-performance, ACID-compliant data storage. It…
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
This project is a feature-rich Go client library designed for interacting with Redis. It serves as a comprehensive interface for managing remote data stores, enabling developers to execute standard database commands, handle complex data structures, and perform asynchronous operations within Go applications. The library distinguishes itself through its support for advanced Redis capabilities, including connection pooling, pipelining, and transactional integrity. It provides specialized primitives for managing distributed clusters, including automated topology updates and request routing to sha
Redis is a high-performance in-memory key-value store that functions as a distributed cache, message broker, and NoSQL database. It provides sub-millisecond read and write access to data stored in RAM and can operate as a vector database for indexing high-dimensional embeddings. The system supports a wide range of data storage and synchronization primitives, including the management of strings, hashes, lists, sets, and JSON documents. It enables real-time data operations through atomic transactions, hybrid persistence using snapshots and append-only logs, and high-availability configurations
RedisInsight is a graphical user interface and management tool for browsing, analyzing, and administering Redis databases. It provides a visual environment for exploring key-value data structures, managing database instances, and performing data analysis across different operating systems and deployments. The tool distinguishes itself by providing dedicated visual managers for complex operations, including a vector database manager for configuring embeddings and similarity searches, a query workbench for executing raw commands and Lua scripts, and a performance monitoring dashboard for tracki