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

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

Editorial information provided by DB-Engines
NameHeroic  Xexclude from comparisonHive  Xexclude from comparisonIRONdb  Xexclude from comparisonKinetica  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparison
DescriptionTime Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchdata warehouse software for querying and managing large distributed datasets, built on HadoopA 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 modelTime Series DBMSRelational DBMSTime Series DBMSRelational DBMSRDF store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.23
Rank#251  Overall
#16  Time Series DBMS
Score84.24
Rank#14  Overall
#9  Relational DBMS
Score0.06
Rank#305  Overall
#26  Time Series DBMS
Score0.57
Rank#198  Overall
#96  Relational DBMS
Score0.39
Rank#220  Overall
#10  RDF stores
Websitespotify.github.io/­heroic/­#!/­indexhive.apache.orgwww.irondb.iowww.kinetica.comrdf4j.org
Technical documentationspotify.github.io/­heroic/­#!/­docs/­overviewcwiki.apache.org/­confluence/­display/­Hive/­Homewww.irondb.io/­docswww.kinetica.com/­docsdocs.rdf4j.org
DeveloperSpotifyApache Software Foundation infoinitially developed by FacebookCirconus LLC.KineticaSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.
Initial release20142012201720122004
Current release3.1.2, August 2019V0.10.20, January 20186.0
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoApache Version 2commercialcommercialOpen Source infoEclipse Distribution License (EDL), v1.0.
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaC and C++C, C++Java
Server operating systemsAll OS with a Java VMLinuxLinuxLinux
OS X
Unix
Windows
Data schemeschema-freeyesschema-freeyesyes infoRDF Schemas
Typing infopredefined data types such as float or dateyesyesyes 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 indexesyes infovia Elasticsearchyesnoyesyes
SQL infoSupport of SQLnoSQL-like DML and DDL statementsSQL-like query language (Circonus Analytics Query Language: CAQL)SQL-like DML and DDL statementsno
APIs and other access methodsHQL (Heroic Query Language, a JSON-based language)
HTTP API
JDBC
ODBC
Thrift
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 proceduresnoyes infouser defined functions and integration of map-reduceyes, 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 nodesShardingShardingAutomatic, metric affinity per nodeShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesyesselectable replication factorconfigurable replication factor, datacenter awareMaster-slave replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infoquery execution via MapReducenonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Eventual 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 datayesyesyesyesyes
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 rolesnoAccess rights for users and roles on table levelno
More information provided by the system vendor
HeroicHiveIRONdbKineticaRDF4J 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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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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3rd partiesDremio is like magic for Hive accelerating your analytical queries up to 1,000x.
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More resources
HeroicHiveIRONdbKineticaRDF4J infoformerly known as Sesame
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