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DBMS > Apache IoTDB vs. IRONdb vs. RDF4J vs. Splice Machine vs. TerarkDB

System Properties Comparison Apache IoTDB vs. IRONdb vs. RDF4J vs. Splice Machine vs. TerarkDB

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonIRONdb  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonSplice Machine  Xexclude from comparisonTerarkDB  Xexclude from comparison
IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkA 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.Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and SparkA key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB
Primary database modelTime Series DBMSTime Series DBMSRDF storeRelational DBMSKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score0.74
Rank#222  Overall
#9  RDF stores
Score0.54
Rank#252  Overall
#115  Relational DBMS
Score0.08
Rank#367  Overall
#56  Key-value stores
Websiteiotdb.apache.orgwww.circonus.com/solutions/time-series-database/rdf4j.orgsplicemachine.comgithub.com/­bytedance/­terarkdb
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.circonus.com/irondb/category/getting-startedrdf4j.org/­documentationsplicemachine.com/­how-it-worksbytedance.larkoffice.com/­docs/­doccnZmYFqHBm06BbvYgjsHHcKc
DeveloperApache Software FoundationCirconus LLC.Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.Splice MachineByteDance, originally Terark
Initial release20182017200420142016
Current release1.1.0, April 2023V0.10.20, January 20183.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoEclipse Distribution License (EDL), v1.0.Open Source infoAGPL 3.0, commercial license availablecommercial inforestricted open source version available
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC and C++JavaJavaC++
Server operating systemsAll OS with a Java VM (>= 1.8)LinuxLinux
OS X
Unix
Windows
Linux
OS X
Solaris
Windows
Data schemeyesschema-freeyes infoRDF Schemasyesschema-free
Typing infopredefined data types such as float or dateyesyes infotext, numeric, histogramsyesyesno
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 query languageSQL-like query language (Circonus Analytics Query Language: CAQL)noyesno
APIs and other access methodsJDBC
Native API
HTTP APIJava API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
JDBC
Native Spark Datasource
ODBC
C++ API
Java API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
Java
PHP
Python
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
C++
Java
Server-side scripts infoStored proceduresyesyes, in Luayesyes infoJavano
Triggersyesnoyesyesno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)Automatic, metric affinity per nodenoneShared Nothhing Auto-Sharding, Columnar Partitioningnone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasconfigurable replication factor, datacenter awarenoneMulti-source replication
Source-replica replication
none
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparknonoYes, via Full Spark Integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate consistency per node, eventual consistency across nodesImmediate Consistency
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID infoIsolation support depends on the API usedACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)yes
Durability infoSupport for making data persistentyesyesyes infoin-memory storage is supported as wellyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyesyes
User concepts infoAccess controlyesnonoAccess rights for users, groups and roles according to SQL-standardno

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More resources
Apache IoTDBIRONdbRDF4J infoformerly known as SesameSplice MachineTerarkDB
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