Table 3 Graph databases
From: Modeling temporal aspects of sensor data for MongoDB NoSQL database
Name | Data model | Scalability | Description | Who uses it |
|---|---|---|---|---|
neo4j from Neo Technology [52] | Flexible network structure of nodes; data stored in: edges, nodes, or attributes; neo4django: an Object Graph Mapper [84]; custom data types | No direct sharding but cache [82], no replication and persistency | Most popular high performance [85], ACID, monitoring:Neo4j Metrics; query methods: Cypher, SparQL, nativeJavaAPI, JRuby | 42talents, ActiveState, Cisco Securus, Apptium, BISTel [52] |
AllegroGraph from http://franz.com | Triplestore, resource description framework (RDF) and graph database | Data replication and synchronization; Partitioning with Federation | Linked data format; brings semantic Web to Twitter; Common Lisp: dialect of Lisp; eventual consistency; ACID | Stanford, IBM, Ford, Novartis, AT and T, Siemens, NASA, US Census |
ArangoDB from ArangoDB GmbH [80] | Native multi-data models: key/value, document, and graph data to be stored together and queried with a common language [86] | Synchronous replication, tripple store sharding | Most popular having open source license; ACID-compliant for the master; eventualy consistent [87]; annotation query language (AQL) for RDF | DemonWare, Douglas, Craneware, ictual, mobility, egress |
OrientDB from OrientDB Ltd [81] | Multi-data models: graph and document database; custom data types | Multi-master replication; supports sharding | Highly available; SQL using pattern matching to support MapReduce, eventual consistency; ACID; Schema-less, Schema mix | Progress, UltrDNS proteus, Enel Flux Gtech, NIH |