thinkaurelius/titan

Features

• Distributed Graph Engines - Functions as a distributed engine designed to execute graph algorithms and deep traversals at scale.
• Distributed Graph Storage - Functions as a scalable storage layer for managing massive volumes of vertices and edges across multi-machine clusters.
• Transactional Storage Layers - Implements a persistence layer that combines atomic transaction support with low-level distributed storage management.
• Graph Computation - Provides a framework for executing large-scale graph processing jobs and deep traversals across a distributed cluster.
• Parallel Query Execution - Implements the decomposition of complex graph traversals into fragments for concurrent execution across distributed cluster nodes.
• Data Partitioning - Splits large graphs across multiple servers using vertex or edge cuts to balance system load.
• Vertex-Cut Partitioning - Utilizes vertex-cut data partitioning to distribute the graph across a cluster and balance processing loads.
• Vertex-Cut Partitioning - Utilizes vertex-cut data partitioning to balance processing loads and optimize performance across a multi-machine cluster.
• Graph Schema Definition - Provides a system for defining vertex labels and property types independently of physical indexing to support global re-indexing.
• Graph Databases - Provides a scalable database designed to store and query massive datasets as interconnected nodes and edges.
• Pluggable Storage Backends - Provides a modular architecture that allows swapping different storage engines for graph data persistence.
• Graph Querying - Implements a distributed query engine capable of deep traversals and complex analytic queries for massive interconnected datasets.
• Graph Analytics - Executes complex processing jobs and deep traversals across billions of vertices to find patterns in big data.
• Large-Scale Dataset Management - Employs a high-scalability architecture capable of managing billions of vertices and edges across distributed clusters.
• Interconnected Data Retrieval - Provides specialized retrieval mechanisms to extract nodes and their relationships for analyzing complex entity connections.
• Transactional Graph Stores - Ensures consistency and integrity across distributed operations using a write-ahead log and recovery process.
• Vertex-Centric Edge Indexes - Maintains sorted property indexes tied to individual vertices to optimize local traversals and neighbor lookups.
• Write-Ahead Logging - Ensures transactional atomicity and data consistency by recording mutations to a persistent write-ahead log before storage.
• Transactional Graph Engines - Ensures data consistency and integrity across distributed graph operations using transactional storage models.
• Storage Backend Configurators - Provides a pluggable storage backend that decouples the graph engine from physical persistence layers.
• Graph Partitioning Utilities - Balances cluster load by segmenting large graph structures into smaller subgraphs using vertex or edge cuts.
• List Property Storage - Supports a set-cardinality property model allowing a single vertex or edge property to store multiple values simultaneously.
• Logical Schema Mapping - Provides a global schema mapping that separates logical graph definitions from physical index structures to facilitate re-indexing.
• Set-Cardinality Property Models - Implements a set-cardinality property model allowing single properties to store multiple values simultaneously.
• Multi-Model Graph Indexes - Supports global, vertex-centric, geographic, numeric, and full-text indexing across multiple pluggable backend stores.
• Graph Traversal Engines - Implements a specialized engine that accelerates graph-global and vertex-centric index lookups using targeted traversal strategies.
• Database Engines - Distributed graph database for large-scale data.
• Database Systems - Graph database for large-scale distributed environments.
• Databases and Storage - Scalable graph database for large-scale datasets.

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