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DBMS > IRONdb vs. RDF4J vs. Vertica

System Properties Comparison IRONdb vs. RDF4J vs. Vertica

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Editorial information provided by DB-Engines
NameIRONdb  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonVertica infoVertica Analytics Platform  Xexclude from comparison
DescriptionA 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.Columnar relational DBMS designed to handle modern analytic workloads, enabling fast query performance
Primary database modelTime Series DBMSRDF storeRelational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.05
Rank#314  Overall
#27  Time Series DBMS
Score0.37
Rank#223  Overall
#10  RDF stores
Score22.55
Rank#29  Overall
#17  Relational DBMS
Websitewww.irondb.iordf4j.orgwww.vertica.com
Technical documentationwww.irondb.io/­docsdocs.rdf4j.orgwww.vertica.com/­documentation/­vertica
DeveloperCirconus LLC.Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.Vertica / Micro Focus infoprior to that Hewlett Packard
Initial release201720042005
Current releaseV0.10.20, January 2018Vertica Analytics Platform 9.2.x, March 2019
License infoCommercial or Open SourcecommercialOpen Source infoEclipse Distribution License (EDL), v1.0.commercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenonono infoavailable on Amazon AWS and Microsoft Azure
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC and C++Java
Server operating systemsLinuxLinux
OS X
Unix
Windows
Linux
Data schemeschema-freeyes infoRDF Schemasyes infoUnstructured data can be stored in specific Flex-Tables
Typing infopredefined data types such as float or dateyes 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.noyes
Secondary indexesnoyesno infoThe concept of 'projections' can be used instead of indexes.
SQL infoSupport of SQLSQL-like query language (Circonus Analytics Query Language: CAQL)noyes infonearly full SQL99 without support for indexes and triggers
APIs and other access methodsHTTP APIJava API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
Kafka
Proprietary protocol
RESTful HTTP API
ADO.NET
JDBC
ODBC
Supported programming languages.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++
Java
Perl
Python
R
Server-side scripts infoStored proceduresyes, in Luayesyes
Triggersnoyesno
Partitioning methods infoMethods for storing different data on different nodesAutomatic, metric affinity per nodenoneSharding infoSegmentation, Projection Segmentation, Hash Segmentation
Replication methods infoMethods for redundantly storing data on multiple nodesconfigurable replication factor, datacenter awarenoneMaster-master replication infoCluster level and Object level replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes infowith Vertica Connector for Hadoop
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency per node, eventual consistency across nodesImmediate Consistency
Foreign keys infoReferential integritynoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoIsolation support depends on the API usedACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyes 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.no
User concepts infoAccess controlnonofine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
IRONdbRDF4J infoformerly known as SesameVertica infoVertica Analytics Platform
Specific characteristicsIRONdb is a highly available, distributed Time Series Database. It can support dozens...
» more
Columnar storage, MPP, Hybrid storage model, Aggressive data compression, High availability,...
» more
Competitive advantagesUnmatched Scalability IRONdb is unique among TSDBs in that it does not use a consensus...
» more
Performance at exabyte scale, Deploy anywhere, Open source innovation, Analysis in...
» more
Typical application scenariosReal Systems Monitoring Monitor your systems infrastructure in real time across thousands...
» more
Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more
Key customersIRONdb serves the needs of the world's largest Time Series Database customers. One...
» more
Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» 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
Cost-based models and subscription-based models are available
» more

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More resources
IRONdbRDF4J infoformerly known as SesameVertica infoVertica Analytics Platform
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