HelixDB/helix-db

Features

• Distributed Architectures - Provides a distributed graph database with ACID transactions and auto-scaling reader nodes on object storage.
• Object Storage Integration - Persists graph nodes and edges on cloud object storage to enable durable and unlimited scaling.
• Hybrid Search Methods - Combines approximate vector similarity and full-text search to retrieve relevant data for AI tasks.
• ACID Transactional Cores - Operates as an ACID-compliant system to ensure consistency during concurrent reads and writes.
• Knowledge Graphs - Manages interconnected data as a property graph with support for complex recursive traversals.
• Graph Data Modifiers - Creates, updates, and deletes nodes and edges or modifies their properties through batch operations.
• Graph Element Retrieval - Fetches specific nodes and edges using unique identifiers, labels, or conditional predicates.
• Dynamic Query Execution - Enables execution of complex graph logic and recursive queries via an API without redeploying application code.
• Graph Traversal - Navigates from one element to another across one or more hops using directional edges.
• Hybrid Vector and Full-Text Search - Combines approximate vector search and full-text search to retrieve relevant data within a property graph.
• Hybrid Vector-Graph Databases - Persists large volumes of graph and vector information using object storage for cost-effective scalability.
• Hybrid Vector-Keyword Indexing - Combines approximate nearest neighbor search and full-text indexing to locate entry points for graph traversals.
• Knowledge Graph Management - Manages interconnected data as nodes and edges using an object-storage backend to maintain relationships.
• Object-Storage Persistence - Stores massive volumes of graph and vector information in object storage to reduce costs and ensure durability.
• Object Storage Persistence - Persists graph nodes, edges, and properties in object storage to enable durable and unlimited data scaling.
• Graph Storage Engines - Persists nodes and edges on object storage to enable durable and unlimited data scaling for graphs.
• Query Execution Engines - Implements a query execution engine that processes domain-specific language queries at runtime.
• Recursive Graph Queries - Supports advanced logic including recursion and conditional branching to resolve sophisticated graph patterns.
• Serializable Snapshot Isolation - Ensures data consistency during concurrent operations through serializable snapshot isolation.
• Graph DSL Executions - Processes complex graph logic and recursive queries via a domain-specific language without requiring application redeployment.
• Graph Traversal Engines - Navigates multi-hop relationships between nodes using directional edges and conditional predicates.
• Vector Search Engines - Provides a system for storing, indexing, and querying high-dimensional vector embeddings combined with graph data.
• HTTP Data Interfaces - Provides HTTP interfaces for retrieving and modifying graph data using JSON or language payloads.
• Distributed Cluster Setups - Maintains system uptime through a distributed cluster of gateways and nodes to prevent single points of failure.
• Auto-scaling Engines - Dynamically adjusts the number of active reader nodes based on query load to optimize costs.
• Automatic Compute Scaling - Automatically adjusts the number of active reader nodes based on load to optimize performance and cost.
• Distributed Database Clusters - Operates as a high-availability system with auto-scaling reader nodes and gateways to prevent single points of failure.
• Database High Availability - Ensures continuous service availability for the database cluster through automated failover and redundancy.
• Request Gateways - Uses a distributed cluster of stateless gateways to route requests and ensure high availability.
• Database Systems - Graph-vector database for intelligent RAG and AI storage.

Star history