vesoft-inc/nebula
Nebula
Nebula is a distributed graph database designed for storing and querying massive volumes of interconnected vertices and edges across a horizontally scalable cluster. It functions as a Kubernetes-native database and a distributed graph analytics engine, utilizing a Raft-based distributed store to ensure strong consistency and high availability.
The system features an OpenCypher query engine for performing complex graph traversals and pattern matching. It distinguishes itself with a decoupled compute-storage architecture and a shared-nothing distributed design, allowing query processing and data storage to scale independently.
The platform covers a broad range of capabilities, including distributed graph analytics for algorithms like PageRank, full-text search, and property indexing. It provides tools for data ingestion via CSV and real-time synchronization, as well as integration with big data frameworks like Apache Spark and Apache Flink.
Deployment and management are supported through a Kubernetes-native operator, a native command-line interface, and a web-based graph explorer.
Features
- Distributed Graph Storage - Manages massive volumes of interconnected vertices and edges across a horizontally scalable cluster for high availability.
- Distributed Storage Engines - Features a distributed storage engine that manages massive volumes of vertices and edges across a horizontally scalable cluster.
- Cypher Engines - Provides a query engine that executes graph traversals and pattern matching using the OpenCypher standard.
- Graph Pattern Matching - Supports declarative retrieval of complex graph patterns using structural match clauses.
- Log-Structured Merge-Trees - Utilizes a log-structured merge-tree storage engine for high-throughput writes and efficient range scans.
- Data Sharding - Distributes graph data across nodes by hashing vertex IDs to balance load and enable scalability.
- Strongly Consistent Data Stores - Implements a distributed store that ensures strong consistency and high availability across replicas using the Raft consensus protocol.
- Distributed Consensus Stores - Utilizes a Raft-based distributed store to ensure strong consistency and high availability across replicas.
- Distributed Query Processing - Decomposes complex graph traversals into parallel sub-tasks executed concurrently across multiple storage nodes.
- Distributed Sharding Architectures - Implements a sharding mechanism that distributes the graph across nodes based on vertex IDs for horizontal scaling.
- Graph Databases - Provides a distributed graph database designed to store and query massive volumes of interconnected data.
- Decoupled Compute and Storage Scaling - Separates query processing from storage to allow resources to scale independently based on workload.
- Language SDKs - Provides native client libraries in multiple programming languages for programmatic database operations.
- Graph Analytics - Implements distributed graph-native analytical algorithms such as PageRank and community detection on massive datasets.
- Storage-Compute Architectures - Implements an architecture that decouples the query processing layer from the data storage layer for independent scaling.
- Consensus-Based Replication - Configures data replicas across nodes via a consensus protocol to ensure high availability.
- Database Cluster Orchestration - Offers a Kubernetes-native operator to automate the deployment, scaling, and maintenance of database clusters.
- Database Cluster Deployments - Provides a Kubernetes-native operator to automate the deployment and lifecycle management of distributed database clusters.
- Raft Consensus Implementations - Uses the Raft consensus protocol to ensure strong consistency and high availability across replicas.
- Graph Traversal Engines - Provides a distributed graph engine designed to traverse highly connected data in milliseconds for complex workloads.
- Database Node Distribution - Employs a shared-nothing architecture to distribute data and processing across independent nodes.
- OpenCypher Implementations - Implements a query engine compatible with the OpenCypher standard for performing complex graph traversals.
- Kubernetes Operators - Provides a Kubernetes-native operator to automate the deployment and lifecycle management of database clusters.
- Backup and Recovery - Supports the creation of point-in-time snapshots and recovery tools for disaster recovery.
- Apache Spark Connectors - Provides a distributed processing connector for exchanging data between the database and Apache Spark clusters.
- Data Pipeline Connectors - Enables high-volume data exchange between the graph database and distributed frameworks like Apache Spark and Flink.
- Visual Data Explorers - Ships a web-based explorer for composing schemas, importing data, and visually exploring graph relationships.
- Bulk Data Migrations - Enables distributed migration of large volumes of batch or streaming data from external environments using Apache Spark.
- Distributed Computing - Enables the execution of complex graph algorithms on dataframes via a distributed computing engine.
- Full Text Search - Implements specialized indexing for string properties to enable complex phrase and pattern-based text retrieval across the graph.
- CSV Bulk Importers - Provides a utility for reading local CSV files and loading their contents into the graph database.
- Graph Property Indexing - Supports creating exact-match and range indexes on vertex and edge properties to accelerate graph lookups and filtering.
- Real-time Data Synchronization - Supports continuous streaming of changes from external databases for near-instant updates to the graph.
- Simple Path Discovery - Calculates the precise sequence of edges and vertices connecting two specific points in the network.
- Apache Flink Connectors - Provides a specialized stream processing connector for real-time data exchange with Apache Flink.
- Subgraph Extractions - Isolates specific portions of the graph based on criteria to analyze localized sets of relationships.
- Database Command-Line Interfaces - Provides a native CLI for executing graph queries and managing database settings.
- Cloud Native Orchestration - Offers automated deployment and lifecycle management of database clusters using cloud-native orchestration.
- Capacity Scaling - Allows manual addition or removal of meta, graph, and storage nodes to scale cluster capacity.
- Programmatic Graph APIs - Provides native language SDKs allowing applications to programmatically perform data operations on the graph.
- Cluster Health Monitoring - Includes a visualization dashboard for tracking the operational status and health of distributed cluster services.
- Shard Rebalancing - Provides automated processes for redistributing data shards across storage nodes to maintain balanced load and performance.
- Database Systems - Distributed graph database with horizontal scalability.
