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DBMS > AllegroGraph vs. Datomic vs. Google Cloud Datastore vs. Hive

System Properties Comparison AllegroGraph vs. Datomic vs. Google Cloud Datastore vs. Hive

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Editorial information provided by DB-Engines
NameAllegroGraph  Xexclude from comparisonDatomic  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHive  Xexclude from comparison
DescriptionHigh performance, persistent RDF store with additional support for Graph DBMSDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platformdata warehouse software for querying and managing large distributed datasets, built on Hadoop
Primary database modelDocument store infowith version 6.5
Graph DBMS
RDF store
Relational DBMSDocument storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.07
Rank#166  Overall
#28  Document stores
#14  Graph DBMS
#7  RDF stores
Score2.92
Rank#105  Overall
#51  Relational DBMS
Score5.51
Rank#70  Overall
#11  Document stores
Score70.27
Rank#15  Overall
#10  Relational DBMS
Websiteallegrograph.comwww.datomic.comcloud.google.com/­datastorehive.apache.org
Technical documentationfranz.com/­agraph/­support/­documentation/­current/­agraph-introduction.htmldocs.datomic.comcloud.google.com/­datastore/­docscwiki.apache.org/­confluence/­display/­Hive/­Home
DeveloperFranz Inc.CognitectGoogleApache Software Foundation infoinitially developed by Facebook
Initial release2004201220082012
Current release7.0.0, April 20200.9.5951, July 20193.1.2, August 2019
License infoCommercial or Open Sourcecommercial infoLimited community edition freecommercial infolimited edition freecommercialOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, ClojureJava
Server operating systemsLinux
OS X
Windows
All OS with a Java VMhostedAll OS with a Java VM
Data schemeyes infoRDF schemasyesschema-freeyes
Typing infopredefined data types such as float or dateyesyesyes, details hereyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.no infobulk load of XML files possiblenono
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSPARQL is used as query languagenoSQL-like query language (GQL)SQL-like DML and DDL statements
APIs and other access methodsRESTful HTTP API
SPARQL
RESTful HTTP APIgRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
Thrift
Supported programming languagesC#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala
Clojure
Java
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++
Java
PHP
Python
Server-side scripts infoStored proceduresyes infoJavaScript or Common Lispyes infoTransaction Functionsusing Google App Engineyes infouser defined functions and integration of map-reduce
TriggersyesBy using transaction functionsCallbacks using the Google Apps Engineno
Partitioning methods infoMethods for storing different data on different nodeswith Federationnone infoBut extensive use of caching in the application peersShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
none infoBut extensive use of caching in the application peersMulti-source replication using Paxosselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes infousing Google Cloud Dataflowyes infoquery execution via MapReduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Eventual Consistency
Foreign keys infoReferential integritynonoyes infovia ReferenceProperties or Ancestor pathsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes inforecommended only for testing and developmentno
User concepts infoAccess controlUsers with fine-grained authorization concept, user roles and pluggable authenticationnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users, groups and roles
More information provided by the system vendor
AllegroGraphDatomicGoogle Cloud DatastoreHive
Specific characteristicsKnowledge Graph Platform Leader FedShard - Designed for Entity-Event Knowledge Graph...
» more
Competitive advantagesAllegroGraph is uniquely suited to support adhoc queries through SPARQL, Prolog and...
» more
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