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DBMS > AllegroGraph vs. Datomic vs. Hive vs. Spark SQL

System Properties Comparison AllegroGraph vs. Datomic vs. Hive vs. Spark SQL

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Editorial information provided by DB-Engines
NameAllegroGraph  Xexclude from comparisonDatomic  Xexclude from comparisonHive  Xexclude from comparisonSpark SQL  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 durabilitydata warehouse software for querying and managing large distributed datasets, built on HadoopSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelDocument store infowith version 6.5
Graph DBMS
RDF store
Relational DBMSRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.04
Rank#165  Overall
#26  Document stores
#14  Graph DBMS
#7  RDF stores
Score2.94
Rank#101  Overall
#50  Relational DBMS
Score70.26
Rank#15  Overall
#10  Relational DBMS
Score19.21
Rank#33  Overall
#20  Relational DBMS
Websiteallegrograph.comwww.datomic.comhive.apache.orgspark.apache.org/­sql
Technical documentationfranz.com/­agraph/­support/­documentation/­current/­agraph-introduction.htmldocs.datomic.comcwiki.apache.org/­confluence/­display/­Hive/­Homespark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperFranz Inc.CognitectApache Software Foundation infoinitially developed by FacebookApache Software Foundation
Initial release2004201220122014
Current release7.0.0, April 20200.9.5951, July 20193.1.2, August 20193.0.1, October 2020
License infoCommercial or Open Sourcecommercial infoLimited community edition freecommercial infolimited edition freeOpen Source infoApache Version 2Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, ClojureJavaScala
Server operating systemsLinux
OS X
Windows
All OS with a Java VMAll OS with a Java VMLinux
OS X
Windows
Data schemeyes infoRDF schemasyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyes
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 indexesyesyesyesno
SQL infoSupport of SQLSPARQL is used as query languagenoSQL-like DML and DDL statementsSQL-like DML and DDL statements
APIs and other access methodsRESTful HTTP API
SPARQL
RESTful HTTP APIJDBC
ODBC
Thrift
JDBC
ODBC
Supported programming languagesC#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala
Clojure
Java
C++
Java
PHP
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresyes infoJavaScript or Common Lispyes infoTransaction Functionsyes infouser defined functions and integration of map-reduceno
TriggersyesBy using transaction functionsnono
Partitioning methods infoMethods for storing different data on different nodeswith Federationnone infoBut extensive use of caching in the application peersShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
none infoBut extensive use of caching in the application peersselectable replication factornone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes infoquery execution via MapReduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnono
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 rolesno
More information provided by the system vendor
AllegroGraphDatomicHiveSpark SQL
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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7 October 2020

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5 October 2020

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