vesoft-inc/nebula

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.

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