HelixDB/helix-db
Helix Db
Helix DB is a distributed graph database and knowledge graph platform that persists nodes and edges on object storage for durable and unlimited scaling. It operates as an ACID-compliant system, ensuring data consistency through serializable snapshot isolation during concurrent operations.
The project distinguishes itself by combining a vector search engine and a property graph, utilizing hybrid vector and full-text search to locate entry points for graph traversals. It enables dynamic graph querying through a domain-specific language, allowing complex logic and recursive queries to be executed via an API without redeploying application code.
The system provides high availability through a distributed cluster of gateways and reader nodes that scale automatically based on load. Its broader capabilities include graph data mutation, multi-hop relationship traversal, and query output shaping with filtering and pagination.
A command-line interface is provided for cluster management and project bootstrapping.
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.
- Databases & Data - Graph-vector database optimized for AI and RAG applications.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
