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DBMS > Hive vs. IRONdb vs. Kinetica vs. RDF4J vs. Spark SQL

System Properties Comparison Hive vs. IRONdb vs. Kinetica vs. RDF4J vs. Spark SQL

Editorial information provided by DB-Engines
NameHive  Xexclude from comparisonIRONdb  Xexclude from comparisonKinetica  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonSpark SQL  Xexclude from comparison
Descriptiondata 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.Spark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSTime Series DBMSRelational DBMSRDF storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
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
Score17.13
Rank#35  Overall
#21  Relational DBMS
Websitehive.apache.orgwww.irondb.iowww.kinetica.comrdf4j.orgspark.apache.org/­sql
Technical documentationcwiki.apache.org/­confluence/­display/­Hive/­Homewww.irondb.io/­docswww.kinetica.com/­docsdocs.rdf4j.orgspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software Foundation infoinitially developed by FacebookCirconus LLC.KineticaSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.Apache Software Foundation
Initial release20122017201220042014
Current release3.1.2, August 2019V0.10.20, January 20186.0v2.4.4, September 2019
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercialOpen Source infoEclipse Distribution License (EDL), v1.0.Open Source infoApache 2.0
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 languageJavaC and C++C, C++JavaScala
Server operating systemsAll OS with a Java VMLinuxLinuxLinux
OS X
Unix
Windows
Linux
OS X
Windows
Data schemeyesschema-freeyesyes infoRDF Schemasyes
Typing infopredefined data types such as float or dateyesyes infotext, numeric, histogramsyesyesyes
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 indexesyesnoyesyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query language (Circonus Analytics Query Language: CAQL)SQL-like DML and DDL statementsnoSQL-like DML and DDL statements
APIs and other access methodsJDBC
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
JDBC
ODBC
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
Java
Python
R
Scala
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyes, in Luauser defined functionsyesno
Triggersnonoyes infotriggers when inserted values for one or more columns fall within a specified rangeyesno
Partitioning methods infoMethods for storing different data on different nodesShardingAutomatic, metric affinity per nodeShardingnoneyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorconfigurable replication factor, datacenter awareMaster-slave replicationnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate consistency per node, eventual consistency across nodesImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononoACID infoIsolation support depends on the API usedno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoin-memory storage is supported as wellyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infoGPU vRAM or System RAMno
User concepts infoAccess controlAccess rights for users, groups and rolesnoAccess rights for users and roles on table levelnono
More information provided by the system vendor
HiveIRONdbKineticaRDF4J infoformerly known as SesameSpark SQL
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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More resources
HiveIRONdbKineticaRDF4J infoformerly known as SesameSpark SQL
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