DBMS > AllegroGraph
AllegroGraph System Properties
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|Editorial information provided by DB-Engines|
|Description||High performance, persistent RDF store with additional support for Graph DBMS|
|Primary database model||Document store with version 6.5|
|Secondary database models||Spatial DBMS|
|Current release||7.3.1, December 2022|
|License Commercial or Open Source||commercial Limited community edition free|
|Cloud-based only Only available as a cloud service||no|
|DBaaS offerings (sponsored links) Database as a Service|
Providers of DBaaS offerings, please contact us to be listed.
|Server operating systems||Linux|
|Data scheme||yes RDF schemas|
|Typing predefined data types such as float or date||yes|
|XML support Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.||no bulk load of XML files possible|
|SQL Support of SQL||SPARQL is used as query language|
|APIs and other access methods||RESTful HTTP API|
|Supported programming languages||C#|
|Partitioning methods Methods for storing different data on different nodes||with Federation|
|Replication methods Methods for redundantly storing data on multiple nodes||Multi-source replication|
|MapReduce Offers an API for user-defined Map/Reduce methods||no|
|Consistency concepts Methods to ensure consistency in a distributed system||Immediate Consistency or Eventual Consistency depending on configuration|
|Foreign keys Referential integrity||no|
|Transaction concepts Support to ensure data integrity after non-atomic manipulations of data||ACID|
|Concurrency Support for concurrent manipulation of data||yes|
|Durability Support for making data persistent||yes|
|In-memory capabilities Is there an option to define some or all structures to be held in-memory only.||no|
|User concepts Access control||Users with fine-grained authorization concept, user roles and pluggable authentication|
|More information provided by the system vendor|
One of the most powerful features of AllegroGraph is that it is possible to mix Geospatial, Temporal, Social Network Analytics, and Reasoning, all in the same query (SPARQL or Prolog). An example:
Find whether the most important friend of Sonya (using the SNA centrality statistic) made a payment within a 100 miles of Rotterdam, NY (using geospatial) within the last 10 years (temporal).
This would be impossible in any other graph database, RDF store, document store or Hadoop like solution in such a concise way.
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