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DBMS > Hive vs. Hyprcubd vs. IRONdb vs. Kinetica vs. RDF4J

System Properties Comparison Hive vs. Hyprcubd vs. IRONdb vs. Kinetica vs. RDF4J

Editorial information provided by DB-Engines
NameHive  Xexclude from comparisonHyprcubd  Xexclude from comparisonIRONdb  Xexclude from comparisonKinetica  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparison
Descriptiondata warehouse software for querying and managing large distributed datasets, built on HadoopServerless Time Series DBMSA distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicityGPU-accelerated database for real-time analysis of large and streaming datasetsRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.
Primary database modelRelational DBMSTime Series DBMSTime Series DBMSRelational DBMSRDF store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score78.65
Rank#14  Overall
#9  Relational DBMS
Score0.00
Rank#336  Overall
#30  Time Series DBMS
Score0.06
Rank#312  Overall
#27  Time Series DBMS
Score0.61
Rank#197  Overall
#95  Relational DBMS
Score0.48
Rank#215  Overall
#9  RDF stores
Websitehive.apache.orgwww.hyprcubd.comwww.irondb.iowww.kinetica.comrdf4j.org
Technical documentationcwiki.apache.org/­confluence/­display/­Hive/­Homewww.irondb.io/­docswww.kinetica.com/­docsrdf4j.org/­documentation
DeveloperApache Software Foundation infoinitially developed by FacebookHyprcubd, Inc.Circonus LLC.KineticaSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.
Initial release2012201720122004
Current release3.1.2, August 2019V0.10.20, January 20186.0
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercialcommercialOpen Source infoEclipse Distribution License (EDL), v1.0.
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaGoC and C++C, C++Java
Server operating systemsAll OS with a Java VMhostedLinuxLinuxLinux
OS X
Unix
Windows
Data schemeyesyesschema-freeyesyes infoRDF Schemas
Typing infopredefined data types such as float or dateyesyes infotime, int, uint, float, stringyes infotext, numeric, histogramsyesyes
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.nonono
Secondary indexesyesnonoyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query languageSQL-like query language (Circonus Analytics Query Language: CAQL)SQL-like DML and DDL statementsno
APIs and other access methodsJDBC
ODBC
Thrift
gRPC (https)HTTP APIJDBC
ODBC
RESTful HTTP API
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
Supported programming languagesC++
Java
PHP
Python
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
C++
Java
JavaScript (Node.js)
Python
Java
PHP
Python
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenoyes, in Luauser defined functionsyes
Triggersnononoyes infotriggers when inserted values for one or more columns fall within a specified rangeyes
Partitioning methods infoMethods for storing different data on different nodesShardingAutomatic, metric affinity per nodeShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorconfigurable replication factor, datacenter awareMaster-slave replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenononono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual ConsistencyImmediate consistency per node, eventual consistency across nodesImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonononoACID infoIsolation support depends on the API used
Concurrency infoSupport for concurrent manipulation of datayesnoyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes infoin-memory storage is supported as well
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes infoGPU vRAM or System RAM
User concepts infoAccess controlAccess rights for users, groups and rolestoken accessnoAccess rights for users and roles on table levelno
More information provided by the system vendor
HiveHyprcubdIRONdbKineticaRDF4J infoformerly known as Sesame
Specific characteristicsIRONdb is a highly available, distributed Time Series Database. It can support dozens...
» more
Competitive advantagesUnmatched Scalability IRONdb is unique among TSDBs in that it does not use a consensus...
» more
Typical application scenariosReal Systems Monitoring Monitor your systems infrastructure in real time across thousands...
» more
Key customersIRONdb serves the needs of the world's largest Time Series Database customers. One...
» more
Market metricsOnly TSDB capable of scaling to billions of metrics. Only TSDB to scale to dozens...
» more
Licensing and pricing modelsIRONdb is licensed under a subscription model based on the number of active metrics...
» more

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HiveHyprcubdIRONdbKineticaRDF4J infoformerly known as Sesame
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