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DBMS > GridDB vs. HBase vs. RDF4J vs. VictoriaMetrics

System Properties Comparison GridDB vs. HBase vs. RDF4J vs. VictoriaMetrics

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Editorial information provided by DB-Engines
NameGridDB  Xexclude from comparisonHBase  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonVictoriaMetrics  Xexclude from comparison
DescriptionScalable in-memory time series database optimized for IoT and Big DataWide-column store based on Apache Hadoop and on concepts of BigTableRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.A fast, cost-effective and scalable Time Series DBMS and monitoring solution
Primary database modelTime Series DBMSWide column storeRDF storeTime Series DBMS
Secondary database modelsKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.09
Rank#120  Overall
#10  Time Series DBMS
Score27.97
Rank#26  Overall
#2  Wide column stores
Score0.74
Rank#222  Overall
#9  RDF stores
Score1.23
Rank#172  Overall
#15  Time Series DBMS
Websitegriddb.nethbase.apache.orgrdf4j.orgvictoriametrics.com
Technical documentationdocs.griddb.nethbase.apache.org/­book.htmlrdf4j.org/­documentationdocs.victoriametrics.com
github.com/­VictoriaMetrics/­VictoriaMetrics/­wiki
DeveloperToshiba CorporationApache Software Foundation infoApache top-level project, originally developed by PowersetSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.VictoriaMetrics
Initial release2013200820042018
Current release5.1, August 20222.3.4, January 2021v1.91, May 2023
License infoCommercial or Open SourceOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache version 2Open Source infoEclipse Distribution License (EDL), v1.0.Open Source infoApache Version 2.0
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 languageC++JavaJavaGo
Server operating systemsLinuxLinux
Unix
Windows infousing Cygwin
Linux
OS X
Unix
Windows
FreeBSD
Linux
macOS
OpenBSD
Data schemeyesschema-free, schema definition possibleyes infoRDF Schemas
Typing infopredefined data types such as float or dateyes infonumerical, string, blob, geometry, boolean, timestampoptions to bring your own types, AVROyes
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 indexesyesnoyes
SQL infoSupport of SQLSQL92, SQL-like TQL (Toshiba Query Language)nonono
APIs and other access methodsJDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
Java API
RESTful HTTP API
Thrift
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write
Supported programming languagesC
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C
C#
C++
Groovy
Java
PHP
Python
Scala
Java
PHP
Python
Server-side scripts infoStored proceduresnoyes infoCoprocessors in Javayesno
Triggersyesyesyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationMulti-source replication
Source-replica replication
noneSynchronous replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency within container, eventual consistency across containersImmediate Consistency or Eventual ConsistencyEventual Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID at container levelSingle row ACID (across millions of columns)ACID infoIsolation support depends on the API usedno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes 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.yesyesno
User concepts infoAccess controlAccess rights for users can be defined per databaseAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACno
More information provided by the system vendor
GridDBHBaseRDF4J infoformerly known as SesameVictoriaMetrics
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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More resources
GridDBHBaseRDF4J infoformerly known as SesameVictoriaMetrics
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