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DBMS > HBase vs. Hive vs. IRONdb vs. RDF4J

System Properties Comparison HBase vs. Hive vs. IRONdb vs. RDF4J

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Editorial information provided by DB-Engines
NameHBase  Xexclude from comparisonHive  Xexclude from comparisonIRONdb  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparison
DescriptionWide-column store based on Apache Hadoop and on concepts of BigTabledata 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 simplicityRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.
Primary database modelWide column storeRelational DBMSTime Series DBMSRDF store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score49.11
Rank#22  Overall
#2  Wide column stores
Score75.29
Rank#15  Overall
#10  Relational DBMS
Score0.06
Rank#317  Overall
#28  Time Series DBMS
Score0.50
Rank#220  Overall
#9  RDF stores
Websitehbase.apache.orghive.apache.orgwww.irondb.iordf4j.org
Technical documentationhbase.apache.orgcwiki.apache.org/­confluence/­display/­Hive/­Homewww.irondb.io/­docsrdf4j.org/­documentation
DeveloperApache Software Foundation infoApache top-level project, originally developed by PowersetApache Software Foundation infoinitially developed by FacebookCirconus LLC.Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.
Initial release2008201220172004
Current release2.2.0, June 20193.1.2, August 2019V0.10.20, January 2018
License infoCommercial or Open SourceOpen Source infoApache version 2Open Source infoApache Version 2commercialOpen Source infoEclipse Distribution License (EDL), v1.0.
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 languageJavaJavaC and C++Java
Server operating systemsLinux
Unix
Windows infousing Cygwin
All OS with a Java VMLinuxLinux
OS X
Unix
Windows
Data schemeschema-free, schema definition possibleyesschema-freeyes infoRDF Schemas
Typing infopredefined data types such as float or dateoptions to bring your own types, AVROyesyes infotext, numeric, histogramsyes
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.nono
Secondary indexesnoyesnoyes
SQL infoSupport of SQLnoSQL-like DML and DDL statementsSQL-like query language (Circonus Analytics Query Language: CAQL)no
APIs and other access methodsJava API
RESTful HTTP API
Thrift
JDBC
ODBC
Thrift
HTTP APIJava API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
Supported programming languagesC
C#
C++
Groovy
Java
PHP
Python
Scala
C++
Java
PHP
Python
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
Java
PHP
Python
Server-side scripts infoStored proceduresyes infoCoprocessors in Javayes infouser defined functions and integration of map-reduceyes, in Luayes
Triggersyesnonoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingAutomatic, metric affinity per nodenone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
selectable replication factorconfigurable replication factor, datacenter awarenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyes infoquery execution via MapReducenono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyEventual ConsistencyImmediate consistency per node, eventual consistency across nodes
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataSingle row ACID (across millions of columns)nonoACID infoIsolation support depends on the API used
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes 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.yesno
User concepts infoAccess controlAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACAccess rights for users, groups and rolesnono
More information provided by the system vendor
HBaseHiveIRONdbRDF4J 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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HBaseHiveIRONdbRDF4J infoformerly known as Sesame
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