3 dépôts
Libraries and drivers that enable applications to connect to and interact with specific database systems.
Distinguishing note: Focuses on client-side connectivity and protocol compatibility for database systems, distinct from general networking or API client tools.
Explore 3 awesome GitHub repositories matching data & databases · Database Client SDKs. Refine with filters or upvote what's useful.
Dragonfly is a high-performance, multi-model in-memory data store designed to serve as a drop-in replacement for existing database infrastructures. By utilizing a multi-threaded, shared-nothing architecture and a fiber-based concurrency model, it maximizes CPU utilization and minimizes latency for read and write operations. The system supports a wide range of data structures, including strings, hashes, lists, sets, sorted sets, and JSON documents, while maintaining full compatibility with standard industry wire protocols and client libraries. What distinguishes Dragonfly is its focus on effic
Supports standard database client libraries to ensure seamless integration with existing development workflows and protocols.
Jedis is a Java library for connecting to Redis servers to execute commands and manage key-value data structures. It serves as a Java client and connection manager that facilitates the storage and retrieval of high-performance data. The project provides a cluster client for distributing data and requests across multiple nodes to ensure scaling and high availability. It includes a dedicated pub-sub client for real-time messaging through channel subscriptions and a pipelining tool to increase throughput by sending multiple commands in a single network round-trip. The library covers a broad ran
Allows changing the weight of available databases at runtime to determine preferred connections.
Infinity is a distributed vector database and multimodal vector store designed to manage large-scale datasets for retrieval and similarity search. It serves as a backend for large language model applications and retrieval augmented generation pipelines by storing and retrieving dense vectors, sparse vectors, and full-text data. The system functions as a hybrid search engine, combining vector embeddings and full-text search with reranking algorithms to identify the most relevant documents. It supports multimodal data storage, allowing the maintenance of diverse data types including tensors, st
Performs data operations and system management through a Python library for streamlined integration.