JanusGraph/janusgraph

Features

• Graph Databases - An open-source graph database that stores and queries highly connected data across a cluster of machines with ACID consistency.
• Graph Batch Analytics - Runs full-graph processing jobs as MapReduce or Spark tasks on a Hadoop cluster for offline computation.
• Pluggable Storage Backends - Decouples the graph engine from the underlying store so any key-value or document database can serve as the persistence layer.
• Distributed Real-Time Traversals - Answering complex traversal queries on huge graphs in milliseconds with horizontally scalable transactional capacity.
• High Availability Architectures - Keeps the graph database accessible without a single point of failure using a masterless architecture.
• Distributed Graph Caches - Cache frequently accessed vertices and edges in memory, letting the cache grow automatically as the cluster expands.
• Elastically Scalable Graph Clusters - A graph database that scales storage and transactional capacity horizontally by adding or removing cluster nodes without downtime.
• Multi-Modal Index Queries - Combines graph traversal with external search indexes (Elasticsearch, Solr, Lucene) for geo, range, and full-text queries.
• Strong Consistency Guarantees - Guarantees strictly consistent reads and writes for graph data when backed by a consistent store.
• Masterless Architectures - Eliminates single points of failure by letting every node handle reads and writes without a central coordinator.
• Storage Scaling - Adds or removes machines from the cluster without downtime to adjust storage capacity.
• Graph Vertex-Centric Indexes - Indexes edges per vertex instead of globally, enabling fast local traversals even when a vertex has millions of incident edges.
• Consistent Hashing - Splits the graph across machines using consistent hashing, minimising data movement when the cluster scales.
• Graph Storage Clusters - Distributes vertices and edges across multiple machines so the graph grows with the cluster size.
• Transactional Graph Engines - Guarantees ACID semantics on single-graph operations and eventual consistency for multi-machine traversals.
• Multi-Criteria Graph Searches - Queries vertices and edges using geo, numeric range, and full-text search across very large graphs.
• Multi-Model Property Graph Stores - A graph database that supports property graph data model with geo, numeric range, and full-text search capabilities.
• Elastic Cache Layers - Distributes frequently accessed vertices and edges across cluster memory, growing the cache automatically as nodes join.
• Interactive Graph Visualizers - Renders graph structures as interactive diagrams through a built-in web tool and multiple third-party integrations.
• Graph OLAP Engines - An engine that runs batch analytics and global graph computations on large datasets using the Hadoop framework.
• Graph Analytics - Running batch processing jobs over the entire graph using Hadoop for offline analysis and computation.
• Vertex-Centric Edge Indexes - Resolve the super-node problem by indexing edges at the vertex level for fast local lookups.
• Backend and Infrastructure - Distributed graph database for large datasets.

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